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Posted to commits@hive.apache.org by kg...@apache.org on 2019/02/04 18:39:35 UTC
[hive] branch master updated: HIVE-21143: Add rewrite rules to
open/close Between operators (Zoltan Haindrich reviewed by Jesus Camacho
Rodriguez)
This is an automated email from the ASF dual-hosted git repository.
kgyrtkirk pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/hive.git
The following commit(s) were added to refs/heads/master by this push:
new c8eb03a HIVE-21143: Add rewrite rules to open/close Between operators (Zoltan Haindrich reviewed by Jesus Camacho Rodriguez)
c8eb03a is described below
commit c8eb03affa2533f4827cf6497e7c9873bc9520a7
Author: Zoltan Haindrich <ki...@rxd.hu>
AuthorDate: Mon Feb 4 19:38:52 2019 +0100
HIVE-21143: Add rewrite rules to open/close Between operators (Zoltan Haindrich reviewed by Jesus Camacho Rodriguez)
Signed-off-by: Zoltan Haindrich <ki...@rxd.hu>
---
.../rules/HivePointLookupOptimizerRule.java | 312 +++++++-
.../calcite/translator/RexNodeConverter.java | 18 +
ql/src/test/queries/clientpositive/udf_between.q | 29 +
.../clientpositive/correlationoptimizer8.q.out | 162 ++--
.../clientpositive/druid/druidmini_test_ts.q.out | 13 +-
.../results/clientpositive/druid_timeseries.q.out | 2 +-
.../results/clientpositive/filter_numeric.q.out | 2 +-
.../results/clientpositive/infer_join_preds.q.out | 4 +-
ql/src/test/results/clientpositive/join34.q.out | 55 +-
ql/src/test/results/clientpositive/join35.q.out | 65 +-
ql/src/test/results/clientpositive/join45.q.out | 2 +-
ql/src/test/results/clientpositive/join47.q.out | 2 +-
.../llap/materialized_view_rewrite_ssb.q.out | 12 +-
.../llap/materialized_view_rewrite_ssb_2.q.out | 12 +-
.../clientpositive/llap/subquery_scalar.q.out | 156 ++--
.../llap/vector_between_columns.q.out | 4 +-
.../clientpositive/llap/vector_interval_2.q.out | 32 +-
ql/src/test/results/clientpositive/mapjoin47.q.out | 2 +-
.../partition_wise_fileformat2.q.out | 8 +-
ql/src/test/results/clientpositive/pcr.q.out | 4 -
.../clientpositive/perf/spark/query13.q.out | 204 +++--
.../clientpositive/perf/spark/query21.q.out | 8 +-
.../clientpositive/perf/spark/query34.q.out | 4 +-
.../clientpositive/perf/spark/query48.q.out | 134 ++--
.../clientpositive/perf/spark/query54.q.out | 538 ++++++-------
.../clientpositive/perf/spark/query85.q.out | 335 ++++----
.../clientpositive/perf/tez/cbo_query13.q.out | 42 +-
.../clientpositive/perf/tez/cbo_query21.q.out | 2 +-
.../clientpositive/perf/tez/cbo_query34.q.out | 2 +-
.../clientpositive/perf/tez/cbo_query48.q.out | 36 +-
.../clientpositive/perf/tez/cbo_query54.q.out | 131 ++--
.../clientpositive/perf/tez/cbo_query64.q.out | 198 +++--
.../clientpositive/perf/tez/cbo_query85.q.out | 57 +-
.../perf/tez/constraints/cbo_query13.q.out | 34 +-
.../perf/tez/constraints/cbo_query21.q.out | 2 +-
.../perf/tez/constraints/cbo_query34.q.out | 2 +-
.../perf/tez/constraints/cbo_query48.q.out | 22 +-
.../perf/tez/constraints/cbo_query54.q.out | 134 ++--
.../perf/tez/constraints/cbo_query64.q.out | 4 +-
.../perf/tez/constraints/cbo_query85.q.out | 45 +-
.../perf/tez/constraints/query13.q.out | 102 +--
.../perf/tez/constraints/query21.q.out | 6 +-
.../perf/tez/constraints/query34.q.out | 2 +-
.../perf/tez/constraints/query48.q.out | 88 +--
.../perf/tez/constraints/query54.q.out | 528 ++++++-------
.../perf/tez/constraints/query64.q.out | 422 +++++-----
.../perf/tez/constraints/query85.q.out | 232 +++---
.../results/clientpositive/perf/tez/query13.q.out | 190 +++--
.../results/clientpositive/perf/tez/query21.q.out | 6 +-
.../results/clientpositive/perf/tez/query34.q.out | 2 +-
.../results/clientpositive/perf/tez/query48.q.out | 160 ++--
.../results/clientpositive/perf/tez/query54.q.out | 520 +++++++------
.../results/clientpositive/perf/tez/query64.q.out | 854 +++++++++++----------
.../results/clientpositive/perf/tez/query85.q.out | 278 ++++---
.../clientpositive/rand_partitionpruner3.q.out | 12 +-
.../results/clientpositive/select_unquote_or.q.out | 10 +-
.../results/clientpositive/smb_mapjoin_47.q.out | 8 +-
.../test/results/clientpositive/spark/join34.q.out | 47 +-
.../test/results/clientpositive/spark/join35.q.out | 57 +-
ql/src/test/results/clientpositive/spark/pcr.q.out | 4 -
.../clientpositive/spark/subquery_scalar.q.out | 132 ++--
.../test/results/clientpositive/udf_between.q.out | 178 ++++-
.../clientpositive/vector_between_columns.q.out | 8 +-
63 files changed, 3523 insertions(+), 3153 deletions(-)
diff --git a/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/rules/HivePointLookupOptimizerRule.java b/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/rules/HivePointLookupOptimizerRule.java
index 9a525ad..e231b1d 100644
--- a/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/rules/HivePointLookupOptimizerRule.java
+++ b/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/rules/HivePointLookupOptimizerRule.java
@@ -22,6 +22,10 @@ import java.util.Collection;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
+import java.util.IdentityHashMap;
+import java.util.Iterator;
+import java.util.LinkedHashMap;
+import java.util.LinkedHashSet;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
@@ -44,6 +48,7 @@ import org.apache.calcite.rex.RexUtil;
import org.apache.calcite.sql.SqlKind;
import org.apache.calcite.sql.fun.SqlStdOperatorTable;
import org.apache.hadoop.hive.ql.optimizer.calcite.HiveCalciteUtil;
+import org.apache.hadoop.hive.ql.optimizer.calcite.reloperators.HiveBetween;
import org.apache.hadoop.hive.ql.optimizer.calcite.reloperators.HiveIn;
import org.apache.hadoop.hive.ql.parse.SemanticException;
import org.slf4j.Logger;
@@ -59,13 +64,19 @@ import com.google.common.collect.Multimaps;
import com.google.common.collect.Sets;
/**
- * This optimization attempts to identify and close expanded INs.
+ * This optimization attempts to identify and close expanded INs and BETWEENs
*
* Basically:
* <pre>
* (c) IN ( v1, v2, ...) <=> c1=v1 || c1=v2 || ...
* </pre>
* If c is struct; then c=v1 is a group of anded equations.
+ *
+ * Similarily
+ * <pre>
+ * v1 <= c1 and c1 <= v2
+ * <pre>
+ * is rewritten to <p>c1 between v1 and v2</p>
*/
public abstract class HivePointLookupOptimizerRule extends RelOptRule {
@@ -174,9 +185,271 @@ public abstract class HivePointLookupOptimizerRule extends RelOptRule {
// 2. We merge IN expressions
RexMergeInClause mergeInClause = new RexMergeInClause(rexBuilder);
newCondition = mergeInClause.apply(newCondition);
+
+ // 3. Close BETWEEN expressions if possible
+ RexTranformIntoBetween t = new RexTranformIntoBetween(rexBuilder);
+ newCondition = t.apply(newCondition);
return newCondition;
}
+ /**
+ * Transforms inequality candidates into [NOT] BETWEEN calls.
+ *
+ */
+ protected static class RexTranformIntoBetween extends RexShuttle {
+ private final RexBuilder rexBuilder;
+
+ static class DiGraph<V, E> {
+ static class Edge<V, E> {
+ final Node<V, E> s;
+ final Node<V, E> t;
+ final E e;
+
+ public Edge(Node<V, E> s, Node<V, E> t, E e) {
+ this.s = s;
+ this.t = t;
+ this.e = e;
+ }
+ }
+
+ static class Node<V, E> {
+ final Set<Edge<V, E>> edges;
+ final V v;
+
+ public Node(V v) {
+ edges = new LinkedHashSet<>();
+ this.v = v;
+ }
+
+ public void addEdge(Edge<V, E> edge) {
+ edges.add(edge);
+ }
+
+ public E removeEdge(V s, V t) {
+ Iterator<Edge<V, E>> it = edges.iterator();
+ while (it.hasNext()) {
+ Edge<V, E> edge = it.next();
+ if (edge.s.v.equals(s) && edge.t.v.equals(t)) {
+ it.remove();
+ return edge.e;
+ }
+ }
+ return null;
+ }
+
+ }
+
+ private final Map<V, Node<V, E>> nodes;
+
+ public DiGraph() {
+ nodes = new LinkedHashMap<>();
+ }
+
+ public void putEdgeValue(V s, V t, E e) {
+ Node<V, E> nodeS = nodeOf(s);
+ Node<V, E> nodeT = nodeOf(t);
+ Edge<V, E> edge = new Edge<V, E>(nodeS, nodeT, e);
+ nodeS.addEdge(edge);
+ nodeT.addEdge(edge);
+ }
+
+ private Node<V, E> nodeOf(V s) {
+ Node<V, E> node = nodes.get(s);
+ if (node == null) {
+ nodes.put(s, node = new Node<V, E>(s));
+ }
+ return node;
+ }
+
+ public Set<V> nodes() {
+ return nodes.keySet();
+ }
+
+ public Set<V> predecessors(V n) {
+ Set<V> ret = new LinkedHashSet<>();
+ Node<V, E> node = nodes.get(n);
+ if (node == null) {
+ return ret;
+ }
+
+ for (Edge<V, E> edge : node.edges) {
+ if (edge.t.v.equals(n)) {
+ ret.add(edge.s.v);
+ }
+ }
+ return ret;
+ }
+
+ public Set<V> successors(V n) {
+ Set<V> ret = new LinkedHashSet<>();
+ Node<V, E> node = nodes.get(n);
+ if (node == null) {
+ return ret;
+ }
+
+ for (Edge<V, E> edge : node.edges) {
+ if (edge.s.v.equals(n)) {
+ ret.add(edge.t.v);
+ }
+ }
+ return ret;
+ }
+
+ public E removeEdge(V s, V t) {
+ nodeOf(s).removeEdge(s, t);
+ return nodeOf(t).removeEdge(s, t);
+ }
+
+ }
+
+ RexTranformIntoBetween(RexBuilder rexBuilder) {
+ this.rexBuilder = rexBuilder;
+ }
+
+ @Override
+ public RexNode visitCall(RexCall inputCall) {
+ RexNode node = super.visitCall(inputCall);
+ if (node instanceof RexCall) {
+ RexCall call = (RexCall) node;
+ switch (call.getKind()) {
+ case AND:
+ return processComparisions(call, SqlKind.LESS_THAN_OR_EQUAL, false);
+ case OR:
+ return processComparisions(call, SqlKind.GREATER_THAN, true);
+ default:
+ break;
+ }
+ }
+ return node;
+ }
+
+ /**
+ * Represents a replacement candidate.
+ */
+ static class BetweenCandidate {
+
+ private final RexNode newNode;
+ private final RexNode[] oldNodes;
+ // keeps track if this candidate was already used during replacement
+ private boolean used;
+
+ public BetweenCandidate(RexNode newNode, RexNode... oldNodes) {
+ this.newNode = newNode;
+ this.oldNodes = oldNodes;
+ }
+ }
+
+ private RexNode processComparisions(RexCall call, SqlKind forwardEdge, boolean invert) {
+ DiGraph<RexNodeRef, RexCall> g =
+ buildComparisionGraph(call.getOperands(), forwardEdge);
+ Map<RexNode, BetweenCandidate> replacedNodes = new IdentityHashMap<>();
+ for (RexNodeRef n : g.nodes()) {
+ Set<RexNodeRef> pred = g.predecessors(n);
+ Set<RexNodeRef> succ = g.successors(n);
+ if (pred.size() > 0 && succ.size() > 0) {
+ RexNodeRef p = pred.iterator().next();
+ RexNodeRef s = succ.iterator().next();
+
+ RexNode between = rexBuilder.makeCall(HiveBetween.INSTANCE,
+ rexBuilder.makeLiteral(invert), n.node, p.node, s.node);
+ BetweenCandidate bc = new BetweenCandidate(
+ between,
+ g.removeEdge(p, n),
+ g.removeEdge(n, s));
+
+ for (RexNode node : bc.oldNodes) {
+ replacedNodes.put(node, bc);
+ }
+ }
+ }
+ if (replacedNodes.isEmpty()) {
+ // no effect
+ return call;
+ }
+ List<RexNode> newOperands = new ArrayList<>();
+ for (RexNode o : call.getOperands()) {
+ BetweenCandidate candidate = replacedNodes.get(o);
+ if (candidate == null) {
+ newOperands.add(o);
+ } else {
+ if (!candidate.used) {
+ newOperands.add(candidate.newNode);
+ candidate.used = true;
+ }
+ }
+ }
+
+ if (newOperands.size() == 1) {
+ return newOperands.get(0);
+ } else {
+ return rexBuilder.makeCall(call.getOperator(), newOperands);
+ }
+ }
+
+ /**
+ * Builds a graph of the given comparision type.
+ *
+ * The graph edges are annotated with the RexNodes representing the comparision.
+ */
+ private DiGraph<RexNodeRef, RexCall> buildComparisionGraph(List<RexNode> operands, SqlKind cmpForward) {
+ DiGraph<RexNodeRef, RexCall> g = new DiGraph<>();
+ for (RexNode node : operands) {
+ if(!(node instanceof RexCall) ) {
+ continue;
+ }
+ RexCall rexCall = (RexCall) node;
+ SqlKind kind = rexCall.getKind();
+ if (kind == cmpForward) {
+ RexNode opA = rexCall.getOperands().get(0);
+ RexNode opB = rexCall.getOperands().get(1);
+ g.putEdgeValue(new RexNodeRef(opA), new RexNodeRef(opB), rexCall);
+ } else if (kind == cmpForward.reverse()) {
+ RexNode opA = rexCall.getOperands().get(1);
+ RexNode opB = rexCall.getOperands().get(0);
+ g.putEdgeValue(new RexNodeRef(opA), new RexNodeRef(opB), rexCall);
+ }
+ }
+ return g;
+ }
+ }
+
+ /**
+ * This class just wraps around a RexNode enables equals/hashCode based on toString.
+ *
+ * After CALCITE-2632 this might not be needed anymore */
+ static class RexNodeRef {
+
+ public static Comparator<RexNodeRef> COMPARATOR =
+ (RexNodeRef o1, RexNodeRef o2) -> o1.node.toString().compareTo(o2.node.toString());
+ private RexNode node;
+
+ public RexNodeRef(RexNode node) {
+ this.node = node;
+ }
+
+ public RexNode getRexNode() {
+ return node;
+ }
+
+ @Override
+ public int hashCode() {
+ return node.toString().hashCode();
+ }
+
+ @Override
+ public boolean equals(Object o) {
+ if (o instanceof RexNodeRef) {
+ RexNodeRef otherRef = (RexNodeRef) o;
+ return node.toString().equals(otherRef.node.toString());
+ }
+ return false;
+ }
+
+ @Override
+ public String toString() {
+ return "ref for:" + node.toString();
+ }
+ }
/**
* Transforms OR clauses into IN clauses, when possible.
@@ -215,43 +488,6 @@ public abstract class HivePointLookupOptimizerRule extends RelOptRule {
}
/**
- * This class just wraps around a RexNode enables equals/hashCode based on toString.
- *
- * After CALCITE-2632 this might not be needed anymore */
- static class RexNodeRef {
-
- public static Comparator<RexNodeRef> COMPARATOR =
- (RexNodeRef o1, RexNodeRef o2) -> o1.node.toString().compareTo(o2.node.toString());
- private RexNode node;
-
- public RexNodeRef(RexNode node) {
- this.node = node;
- }
-
- public RexNode getRexNode() {
- return node;
- }
-
- @Override
- public int hashCode() {
- return node.toString().hashCode();
- }
-
- @Override
- public boolean equals(Object o) {
- if (o instanceof RexNodeRef) {
- RexNodeRef otherRef = (RexNodeRef) o;
- return node.toString().equals(otherRef.node.toString());
- }
- return false;
- }
-
- @Override
- public String toString() {
- return "ref for:" + node.toString();
- }
- }
- /**
* Represents a contraint.
*
* Example: a=1
diff --git a/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/translator/RexNodeConverter.java b/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/translator/RexNodeConverter.java
index a920e40..d15c710 100644
--- a/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/translator/RexNodeConverter.java
+++ b/ql/src/java/org/apache/hadoop/hive/ql/optimizer/calcite/translator/RexNodeConverter.java
@@ -35,6 +35,7 @@ import org.apache.calcite.rex.RexCall;
import org.apache.calcite.rex.RexNode;
import org.apache.calcite.rex.RexSubQuery;
import org.apache.calcite.rex.RexUtil;
+import org.apache.calcite.sql.SqlBinaryOperator;
import org.apache.calcite.sql.SqlCollation;
import org.apache.calcite.sql.SqlIntervalQualifier;
import org.apache.calcite.sql.SqlKind;
@@ -368,6 +369,23 @@ public class RexNodeConverter {
childRexNodeLst = adjustCaseBranchTypes(childRexNodeLst, retType);
} else if (calciteOp == HiveToDateSqlOperator.INSTANCE) {
childRexNodeLst = rewriteToDateChildren(childRexNodeLst);
+ } else if (calciteOp.getKind() == SqlKind.BETWEEN) {
+ assert childRexNodeLst.get(0).isAlwaysTrue() || childRexNodeLst.get(0).isAlwaysFalse();
+ boolean invert = childRexNodeLst.get(0).isAlwaysTrue();
+ SqlBinaryOperator cmpOp;
+ if (invert) {
+ calciteOp = SqlStdOperatorTable.OR;
+ cmpOp = SqlStdOperatorTable.GREATER_THAN;
+ } else {
+ calciteOp = SqlStdOperatorTable.AND;
+ cmpOp = SqlStdOperatorTable.LESS_THAN_OR_EQUAL;
+ }
+ RexNode op = childRexNodeLst.get(1);
+ RexNode rangeL = childRexNodeLst.get(2);
+ RexNode rangeH = childRexNodeLst.get(3);
+ childRexNodeLst.clear();
+ childRexNodeLst.add(cluster.getRexBuilder().makeCall(cmpOp, rangeL, op));
+ childRexNodeLst.add(cluster.getRexBuilder().makeCall(cmpOp, op, rangeH));
}
expr = cluster.getRexBuilder().makeCall(retType, calciteOp, childRexNodeLst);
} else {
diff --git a/ql/src/test/queries/clientpositive/udf_between.q b/ql/src/test/queries/clientpositive/udf_between.q
index 9ee3352..2544366 100644
--- a/ql/src/test/queries/clientpositive/udf_between.q
+++ b/ql/src/test/queries/clientpositive/udf_between.q
@@ -16,3 +16,32 @@ SELECT * FROM src where 'b' between 'a' AND 'c' LIMIT 1;
explain SELECT * FROM src where 2 between 2 AND '3' LIMIT 1;
SELECT * FROM src where 2 between 2 AND '3' LIMIT 1;
+
+
+create table t(i int);
+insert into t values (0),(1),(2),(3),(4),(5),(6),(7),(8),(9),(10),(11);
+
+SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10;
+
+explain
+SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10;
+
+SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10;
+
+explain
+SELECT * FROM t where i between 6 and 7
+ or i between 9 and 10;
+
+SELECT * FROM t where i between 6 and 7
+ or i between 9 and 10;
+
+explain
+SELECT * FROM t where i not between 6 and 7
+ and i not between 9 and 10;
+
+SELECT * FROM t where i not between 6 and 7
+ and i not between 9 and 10;
+
diff --git a/ql/src/test/results/clientpositive/correlationoptimizer8.q.out b/ql/src/test/results/clientpositive/correlationoptimizer8.q.out
index 9ad55cd..14784bf 100644
--- a/ql/src/test/results/clientpositive/correlationoptimizer8.q.out
+++ b/ql/src/test/results/clientpositive/correlationoptimizer8.q.out
@@ -34,22 +34,22 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (UDFToDouble(key) < 20.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 43500 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (UDFToDouble(key) < 20.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 14442 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 12876 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
Execution mode: vectorized
Reduce Operator Tree:
@@ -58,7 +58,7 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
table:
@@ -71,38 +71,38 @@ STAGE PLANS:
Map Operator Tree:
TableScan
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
TableScan
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
TableScan
alias: x
- filterExpr: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
+ filterExpr: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
Statistics: Num rows: 25 Data size: 4375 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: string)
Reduce Operator Tree:
Join Operator
@@ -112,14 +112,14 @@ STAGE PLANS:
0 _col0 (type: string)
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: bigint)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
@@ -130,22 +130,22 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x1
- filterExpr: (UDFToDouble(key) > 100.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 43500 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (UDFToDouble(key) > 100.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 14442 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 12876 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
Execution mode: vectorized
Reduce Operator Tree:
@@ -154,7 +154,7 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
table:
@@ -237,72 +237,72 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (UDFToDouble(key) < 20.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 43500 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (UDFToDouble(key) < 20.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 14442 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 12876 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
TableScan
alias: x1
- filterExpr: (UDFToDouble(key) > 100.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 43500 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (UDFToDouble(key) > 100.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 14442 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 12876 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
TableScan
alias: x
- filterExpr: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
+ filterExpr: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
Statistics: Num rows: 25 Data size: 4375 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: string)
Reduce Operator Tree:
Demux Operator
- Statistics: Num rows: 182 Data size: 18570 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 171 Data size: 18085 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count(VALUE._col0)
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Mux Operator
- Statistics: Num rows: 348 Data size: 34340 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 319 Data size: 32145 Basic stats: COMPLETE Column stats: COMPLETE
Join Operator
condition map:
Inner Join 0 to 1
@@ -310,14 +310,14 @@ STAGE PLANS:
0 _col0 (type: string)
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: bigint)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
@@ -327,11 +327,11 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Mux Operator
- Statistics: Num rows: 348 Data size: 34340 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 319 Data size: 32145 Basic stats: COMPLETE Column stats: COMPLETE
Join Operator
condition map:
Inner Join 0 to 1
@@ -339,20 +339,20 @@ STAGE PLANS:
0 _col0 (type: string)
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: bigint)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Mux Operator
- Statistics: Num rows: 348 Data size: 34340 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 319 Data size: 32145 Basic stats: COMPLETE Column stats: COMPLETE
Join Operator
condition map:
Inner Join 0 to 1
@@ -360,14 +360,14 @@ STAGE PLANS:
0 _col0 (type: string)
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: bigint)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
- Statistics: Num rows: 382 Data size: 37774 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 350 Data size: 35359 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
@@ -890,22 +890,22 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (UDFToDouble(key) < 20.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 43500 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (UDFToDouble(key) < 20.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 14442 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 12876 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
Execution mode: vectorized
Reduce Operator Tree:
@@ -914,7 +914,7 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
table:
@@ -927,38 +927,38 @@ STAGE PLANS:
Map Operator Tree:
TableScan
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
TableScan
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: bigint)
TableScan
alias: x
- filterExpr: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
+ filterExpr: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
Statistics: Num rows: 25 Data size: 4375 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col1 (type: string)
Reduce Operator Tree:
Join Operator
@@ -968,14 +968,14 @@ STAGE PLANS:
0 _col0 (type: string)
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: bigint)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
@@ -986,22 +986,22 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x1
- filterExpr: (UDFToDouble(key) > 100.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (UDFToDouble(key) > 100.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 29548 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 26344 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string), value (type: string)
mode: hash
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 83 Data size: 15438 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 13764 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string), _col1 (type: string)
sort order: ++
Map-reduce partition columns: _col0 (type: string), _col1 (type: string)
- Statistics: Num rows: 83 Data size: 15438 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 13764 Basic stats: COMPLETE Column stats: COMPLETE
value expressions: _col2 (type: bigint)
Execution mode: vectorized
Reduce Operator Tree:
@@ -1010,11 +1010,11 @@ STAGE PLANS:
keys: KEY._col0 (type: string), KEY._col1 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 83 Data size: 15438 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 13764 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col0 (type: string), _col2 (type: bigint)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
table:
diff --git a/ql/src/test/results/clientpositive/druid/druidmini_test_ts.q.out b/ql/src/test/results/clientpositive/druid/druidmini_test_ts.q.out
index 9c412d9..d2b3acc 100644
--- a/ql/src/test/results/clientpositive/druid/druidmini_test_ts.q.out
+++ b/ql/src/test/results/clientpositive/druid/druidmini_test_ts.q.out
@@ -406,15 +406,18 @@ STAGE PLANS:
Processor Tree:
TableScan
alias: druid_table_alltypesorc
+ filterExpr: (vc BETWEEN TIMESTAMPLOCALTZ'2010-01-01 00:00:00.0 US/Pacific' AND TIMESTAMPLOCALTZ'2011-01-01 00:00:00.0 US/Pacific' or vc BETWEEN TIMESTAMPLOCALTZ'2012-01-01 00:00:00.0 US/Pacific' AND TIMESTAMPLOCALTZ'2013-01-01 00:00:00.0 US/Pacific') (type: boolean)
properties:
druid.fieldNames vc
druid.fieldTypes timestamp with local time zone
- druid.query.json {"queryType":"scan","dataSource":"default.druid_table_alltypesorc","intervals":["2010-01-01T08:00:00.000Z/2011-01-01T08:00:00.001Z","2012-01-01T08:00:00.000Z/2013-01-01T08:00:00.001Z"],"virtualColumns":[{"type":"expression","name":"vc","expression":"\"__time\"","outputType":"LONG"}],"columns":["vc"],"resultFormat":"compactedList"}
+ druid.query.json {"queryType":"scan","dataSource":"default.druid_table_alltypesorc","intervals":["1900-01-01T00:00:00.000Z/2013-01-01T08:00:00.001Z"],"virtualColumns":[{"type":"expression","name":"vc","expression":"\"__time\"","outputType":"LONG"}],"columns":["vc"],"resultFormat":"compactedList"}
druid.query.type scan
- Select Operator
- expressions: vc (type: timestamp with local time zone)
- outputColumnNames: _col0
- ListSink
+ Filter Operator
+ predicate: (vc BETWEEN TIMESTAMPLOCALTZ'2010-01-01 00:00:00.0 US/Pacific' AND TIMESTAMPLOCALTZ'2011-01-01 00:00:00.0 US/Pacific' or vc BETWEEN TIMESTAMPLOCALTZ'2012-01-01 00:00:00.0 US/Pacific' AND TIMESTAMPLOCALTZ'2013-01-01 00:00:00.0 US/Pacific') (type: boolean)
+ Select Operator
+ expressions: vc (type: timestamp with local time zone)
+ outputColumnNames: _col0
+ ListSink
PREHOOK: query: EXPLAIN
SELECT `__time`
diff --git a/ql/src/test/results/clientpositive/druid_timeseries.q.out b/ql/src/test/results/clientpositive/druid_timeseries.q.out
index fd69771..90f7860 100644
--- a/ql/src/test/results/clientpositive/druid_timeseries.q.out
+++ b/ql/src/test/results/clientpositive/druid_timeseries.q.out
@@ -31,7 +31,7 @@ STAGE PLANS:
properties:
druid.fieldNames $f0
druid.fieldTypes bigint
- druid.query.json {"queryType":"timeseries","dataSource":"wikipedia","descending":false,"granularity":"all","filter":{"type":"or","fields":[{"type":"and","fields":[{"type":"bound","dimension":"__time","lower":"2010-01-01T00:00:00.000Z","lowerStrict":false,"ordering":"lexicographic","extractionFn":{"type":"timeFormat","format":"yyyy-MM-dd'T'HH:mm:ss.SSS'Z'","timeZone":"UTC"}},{"type":"bound","dimension":"__time","upper":"2012-03-01T00:00:00.000Z","upperStrict":false,"ordering": [...]
+ druid.query.json {"queryType":"timeseries","dataSource":"wikipedia","descending":false,"granularity":"all","filter":{"type":"or","fields":[{"type":"bound","dimension":"__time","lower":"2010-01-01T00:00:00.000Z","lowerStrict":false,"upper":"2012-03-01T00:00:00.000Z","upperStrict":false,"ordering":"lexicographic","extractionFn":{"type":"timeFormat","format":"yyyy-MM-dd'T'HH:mm:ss.SSS'Z'","timeZone":"UTC"}},{"type":"bound","dimension":"added","upper":"0.0","upperStrict":false,"o [...]
druid.query.type timeseries
Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
Select Operator
diff --git a/ql/src/test/results/clientpositive/filter_numeric.q.out b/ql/src/test/results/clientpositive/filter_numeric.q.out
index 74ce762..0bd100f 100644
--- a/ql/src/test/results/clientpositive/filter_numeric.q.out
+++ b/ql/src/test/results/clientpositive/filter_numeric.q.out
@@ -1664,7 +1664,7 @@ STAGE PLANS:
Processor Tree:
TableScan
alias: partint
- filterExpr: (not hr BETWEEN 12 AND 14) (type: boolean)
+ filterExpr: hr NOT BETWEEN 12 AND 14 (type: boolean)
Statistics: Num rows: 500 Data size: 91000 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string), hr (type: int)
diff --git a/ql/src/test/results/clientpositive/infer_join_preds.q.out b/ql/src/test/results/clientpositive/infer_join_preds.q.out
index 8b0e4eb..0afdd02 100644
--- a/ql/src/test/results/clientpositive/infer_join_preds.q.out
+++ b/ql/src/test/results/clientpositive/infer_join_preds.q.out
@@ -667,10 +667,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: src
- filterExpr: (4.0D BETWEEN UDFToDouble(key) AND UDFToDouble(value) and key is not null and value is not null) (type: boolean)
+ filterExpr: 4.0D BETWEEN UDFToDouble(key) AND UDFToDouble(value) (type: boolean)
Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (4.0D BETWEEN UDFToDouble(key) AND UDFToDouble(value) and key is not null and value is not null) (type: boolean)
+ predicate: 4.0D BETWEEN UDFToDouble(key) AND UDFToDouble(value) (type: boolean)
Statistics: Num rows: 55 Data size: 9790 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
diff --git a/ql/src/test/results/clientpositive/join34.q.out b/ql/src/test/results/clientpositive/join34.q.out
index f5a280f..19f21c8 100644
--- a/ql/src/test/results/clientpositive/join34.q.out
+++ b/ql/src/test/results/clientpositive/join34.q.out
@@ -32,17 +32,6 @@ POSTHOOK: type: QUERY
POSTHOOK: Input: default@src
POSTHOOK: Input: default@src1
POSTHOOK: Output: default@dest_j1_n1
-OPTIMIZED SQL: SELECT `t5`.`key`, `t5`.`value`, `t3`.`value` AS `value1`
-FROM (SELECT `key`, `value`
-FROM `default`.`src`
-WHERE `key` < 20
-UNION ALL
-SELECT `key`, `value`
-FROM `default`.`src`
-WHERE `key` > 100) AS `t3`
-INNER JOIN (SELECT `key`, `value`
-FROM `default`.`src1`
-WHERE (`key` < 20 OR `key` > 100) AND `key` IS NOT NULL) AS `t5` ON `t3`.`key` = `t5`.`key`
STAGE DEPENDENCIES:
Stage-7 is a root stage
Stage-6 depends on stages: Stage-7
@@ -61,17 +50,17 @@ STAGE PLANS:
$hdt$_1:x
TableScan
alias: x
- filterExpr: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
+ filterExpr: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
Statistics: Num rows: 25 Data size: 4375 Basic stats: COMPLETE Column stats: COMPLETE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
HashTable Sink Operator
keys:
0 _col0 (type: string)
@@ -83,19 +72,19 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (UDFToDouble(key) < 20.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) < 20.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 29548 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 26344 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 166 Data size: 29548 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 26344 Basic stats: COMPLETE Column stats: COMPLETE
Union
- Statistics: Num rows: 332 Data size: 59096 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 296 Data size: 52688 Basic stats: COMPLETE Column stats: COMPLETE
Map Join Operator
condition map:
Inner Join 0 to 1
@@ -104,17 +93,17 @@ STAGE PLANS:
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
Position of Big Table: 0
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: string)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
GlobalTableId: 1
#### A masked pattern was here ####
NumFilesPerFileSink: 1
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
#### A masked pattern was here ####
table:
input format: org.apache.hadoop.mapred.TextInputFormat
@@ -145,7 +134,7 @@ STAGE PLANS:
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string)
outputColumnNames: key, value, val2
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: compute_stats(key, 'hll'), compute_stats(value, 'hll'), compute_stats(val2, 'hll')
mode: hash
@@ -171,19 +160,19 @@ STAGE PLANS:
MultiFileSpray: false
TableScan
alias: x1
- filterExpr: (UDFToDouble(key) > 100.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) > 100.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 29548 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 26344 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 166 Data size: 29548 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 26344 Basic stats: COMPLETE Column stats: COMPLETE
Union
- Statistics: Num rows: 332 Data size: 59096 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 296 Data size: 52688 Basic stats: COMPLETE Column stats: COMPLETE
Map Join Operator
condition map:
Inner Join 0 to 1
@@ -192,17 +181,17 @@ STAGE PLANS:
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
Position of Big Table: 0
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: string)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
GlobalTableId: 1
#### A masked pattern was here ####
NumFilesPerFileSink: 1
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
#### A masked pattern was here ####
table:
input format: org.apache.hadoop.mapred.TextInputFormat
@@ -233,7 +222,7 @@ STAGE PLANS:
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: string)
outputColumnNames: key, value, val2
- Statistics: Num rows: 32 Data size: 8512 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 12236 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: compute_stats(key, 'hll'), compute_stats(value, 'hll'), compute_stats(val2, 'hll')
mode: hash
diff --git a/ql/src/test/results/clientpositive/join35.q.out b/ql/src/test/results/clientpositive/join35.q.out
index 2277821..1597278 100644
--- a/ql/src/test/results/clientpositive/join35.q.out
+++ b/ql/src/test/results/clientpositive/join35.q.out
@@ -32,19 +32,6 @@ POSTHOOK: type: QUERY
POSTHOOK: Input: default@src
POSTHOOK: Input: default@src1
POSTHOOK: Output: default@dest_j1_n24
-OPTIMIZED SQL: SELECT `t5`.`key`, `t5`.`value`, `t3`.`$f1` AS `cnt`
-FROM (SELECT `key`, COUNT(*) AS `$f1`
-FROM `default`.`src`
-WHERE `key` < 20
-GROUP BY `key`
-UNION ALL
-SELECT `key`, COUNT(*) AS `$f1`
-FROM `default`.`src`
-WHERE `key` > 100
-GROUP BY `key`) AS `t3`
-INNER JOIN (SELECT `key`, `value`
-FROM `default`.`src1`
-WHERE (`key` < 20 OR `key` > 100) AND `key` IS NOT NULL) AS `t5` ON `t3`.`key` = `t5`.`key`
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-8 depends on stages: Stage-1, Stage-5
@@ -60,25 +47,25 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (UDFToDouble(key) < 20.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 43500 Basic stats: COMPLETE Column stats: COMPLETE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) < 20.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 14442 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 12876 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
tag: -1
value expressions: _col1 (type: bigint)
auto parallelism: false
@@ -143,7 +130,7 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
GlobalTableId: 0
@@ -173,17 +160,17 @@ STAGE PLANS:
$hdt$_1:x
TableScan
alias: x
- filterExpr: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
+ filterExpr: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
Statistics: Num rows: 25 Data size: 4375 Basic stats: COMPLETE Column stats: COMPLETE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 16 Data size: 2800 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 23 Data size: 4025 Basic stats: COMPLETE Column stats: COMPLETE
HashTable Sink Operator
keys:
0 _col0 (type: string)
@@ -196,7 +183,7 @@ STAGE PLANS:
TableScan
GatherStats: false
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Map Join Operator
condition map:
Inner Join 0 to 1
@@ -205,17 +192,17 @@ STAGE PLANS:
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
Position of Big Table: 0
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), UDFToInteger(_col1) (type: int)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 32 Data size: 5728 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8234 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
GlobalTableId: 1
#### A masked pattern was here ####
NumFilesPerFileSink: 1
- Statistics: Num rows: 32 Data size: 5728 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8234 Basic stats: COMPLETE Column stats: COMPLETE
#### A masked pattern was here ####
table:
input format: org.apache.hadoop.mapred.TextInputFormat
@@ -246,7 +233,7 @@ STAGE PLANS:
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: int)
outputColumnNames: key, value, val2
- Statistics: Num rows: 32 Data size: 5728 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8234 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: compute_stats(key, 'hll'), compute_stats(value, 'hll'), compute_stats(val2, 'hll')
mode: hash
@@ -273,7 +260,7 @@ STAGE PLANS:
TableScan
GatherStats: false
Union
- Statistics: Num rows: 166 Data size: 15770 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 148 Data size: 14060 Basic stats: COMPLETE Column stats: COMPLETE
Map Join Operator
condition map:
Inner Join 0 to 1
@@ -282,17 +269,17 @@ STAGE PLANS:
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
Position of Big Table: 0
- Statistics: Num rows: 32 Data size: 5856 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8418 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), UDFToInteger(_col1) (type: int)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 32 Data size: 5728 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8234 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
GlobalTableId: 1
#### A masked pattern was here ####
NumFilesPerFileSink: 1
- Statistics: Num rows: 32 Data size: 5728 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8234 Basic stats: COMPLETE Column stats: COMPLETE
#### A masked pattern was here ####
table:
input format: org.apache.hadoop.mapred.TextInputFormat
@@ -323,7 +310,7 @@ STAGE PLANS:
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col2 (type: int)
outputColumnNames: key, value, val2
- Statistics: Num rows: 32 Data size: 5728 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 46 Data size: 8234 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: compute_stats(key, 'hll'), compute_stats(value, 'hll'), compute_stats(val2, 'hll')
mode: hash
@@ -561,25 +548,25 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x1
- filterExpr: (UDFToDouble(key) > 100.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 43500 Basic stats: COMPLETE Column stats: COMPLETE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) > 100.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 14442 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 12876 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
tag: -1
value expressions: _col1 (type: bigint)
auto parallelism: false
@@ -644,7 +631,7 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 7885 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 74 Data size: 7030 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
GlobalTableId: 0
diff --git a/ql/src/test/results/clientpositive/join45.q.out b/ql/src/test/results/clientpositive/join45.q.out
index 4821340..fd639b9 100644
--- a/ql/src/test/results/clientpositive/join45.q.out
+++ b/ql/src/test/results/clientpositive/join45.q.out
@@ -506,7 +506,7 @@ STAGE PLANS:
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
Statistics: Num rows: 12500 Data size: 4612500 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (((_col2 + _col5) <= 102.0D) and ((_col2 + _col5) >= 100.0D)) (type: boolean)
+ predicate: (_col2 + _col5) BETWEEN 100.0D AND 102.0D (type: boolean)
Statistics: Num rows: 1388 Data size: 512172 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col3 (type: string), _col4 (type: string)
diff --git a/ql/src/test/results/clientpositive/join47.q.out b/ql/src/test/results/clientpositive/join47.q.out
index 8102c54..e9b6be4 100644
--- a/ql/src/test/results/clientpositive/join47.q.out
+++ b/ql/src/test/results/clientpositive/join47.q.out
@@ -502,7 +502,7 @@ STAGE PLANS:
0
1
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
- residual filter predicates: {((_col2 + _col5) >= 100.0D)} {((_col2 + _col5) <= 102.0D)}
+ residual filter predicates: {(_col2 + _col5) BETWEEN 100.0D AND 102.0D}
Statistics: Num rows: 1388 Data size: 512172 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col3 (type: string), _col4 (type: string)
diff --git a/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb.q.out b/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb.q.out
index d98a9b6..421e9fd 100644
--- a/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb.q.out
+++ b/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb.q.out
@@ -1300,10 +1300,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: default.ssb_mv_n0
- filterExpr: ((d_year >= 1992) and (c_region = 'ASIA') and (s_region = 'ASIA') and (d_year <= 1997)) (type: boolean)
+ filterExpr: ((c_region = 'ASIA') and (s_region = 'ASIA') and d_year BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 348 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: ((c_region = 'ASIA') and (d_year <= 1997) and (d_year >= 1992) and (s_region = 'ASIA')) (type: boolean)
+ predicate: ((c_region = 'ASIA') and (s_region = 'ASIA') and d_year BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 348 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: c_nation (type: string), d_year (type: int), s_nation (type: string), lo_revenue (type: double)
@@ -1425,10 +1425,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: default.ssb_mv_n0
- filterExpr: ((d_year >= 1992) and (c_nation = 'UNITED STATES') and (s_nation = 'UNITED STATES') and (d_year <= 1997)) (type: boolean)
+ filterExpr: ((c_nation = 'UNITED STATES') and (s_nation = 'UNITED STATES') and d_year BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 348 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: ((c_nation = 'UNITED STATES') and (d_year <= 1997) and (d_year >= 1992) and (s_nation = 'UNITED STATES')) (type: boolean)
+ predicate: ((c_nation = 'UNITED STATES') and (s_nation = 'UNITED STATES') and d_year BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 348 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: c_city (type: string), d_year (type: int), s_city (type: string), lo_revenue (type: double)
@@ -1550,10 +1550,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: default.ssb_mv_n0
- filterExpr: ((c_city) IN ('UNITED KI1', 'UNITED KI5') and (s_city) IN ('UNITED KI1', 'UNITED KI5') and (d_year >= 1992) and (d_year <= 1997)) (type: boolean)
+ filterExpr: ((c_city) IN ('UNITED KI1', 'UNITED KI5') and (s_city) IN ('UNITED KI1', 'UNITED KI5') and d_year BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 180 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: ((c_city) IN ('UNITED KI1', 'UNITED KI5') and (d_year <= 1997) and (d_year >= 1992) and (s_city) IN ('UNITED KI1', 'UNITED KI5')) (type: boolean)
+ predicate: ((c_city) IN ('UNITED KI1', 'UNITED KI5') and (s_city) IN ('UNITED KI1', 'UNITED KI5') and d_year BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 180 Basic stats: COMPLETE Column stats: COMPLETE
Group By Operator
aggregations: sum(lo_revenue)
diff --git a/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb_2.q.out b/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb_2.q.out
index abefa7e..643c79f 100644
--- a/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb_2.q.out
+++ b/ql/src/test/results/clientpositive/llap/materialized_view_rewrite_ssb_2.q.out
@@ -1302,10 +1302,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: default.ssb_mv
- filterExpr: ((UDFToInteger(d_year) >= 1992) and (c_region = 'ASIA') and (s_region = 'ASIA') and (UDFToInteger(d_year) <= 1997)) (type: boolean)
+ filterExpr: ((c_region = 'ASIA') and (s_region = 'ASIA') and UDFToInteger(d_year) BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 428 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: ((UDFToInteger(d_year) <= 1997) and (UDFToInteger(d_year) >= 1992) and (c_region = 'ASIA') and (s_region = 'ASIA')) (type: boolean)
+ predicate: ((c_region = 'ASIA') and (s_region = 'ASIA') and UDFToInteger(d_year) BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 428 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: c_nation (type: string), s_nation (type: string), UDFToInteger(d_year) (type: int), lo_revenue (type: double)
@@ -1427,10 +1427,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: default.ssb_mv
- filterExpr: ((UDFToInteger(d_year) >= 1992) and (c_nation = 'UNITED STATES') and (s_nation = 'UNITED STATES') and (UDFToInteger(d_year) <= 1997)) (type: boolean)
+ filterExpr: ((c_nation = 'UNITED STATES') and (s_nation = 'UNITED STATES') and UDFToInteger(d_year) BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 428 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: ((UDFToInteger(d_year) <= 1997) and (UDFToInteger(d_year) >= 1992) and (c_nation = 'UNITED STATES') and (s_nation = 'UNITED STATES')) (type: boolean)
+ predicate: ((c_nation = 'UNITED STATES') and (s_nation = 'UNITED STATES') and UDFToInteger(d_year) BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 428 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: c_city (type: string), s_city (type: string), UDFToInteger(d_year) (type: int), lo_revenue (type: double)
@@ -1552,10 +1552,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: default.ssb_mv
- filterExpr: ((c_city) IN ('UNITED KI1', 'UNITED KI5') and (s_city) IN ('UNITED KI1', 'UNITED KI5') and (UDFToInteger(d_year) >= 1992) and (UDFToInteger(d_year) <= 1997)) (type: boolean)
+ filterExpr: ((c_city) IN ('UNITED KI1', 'UNITED KI5') and (s_city) IN ('UNITED KI1', 'UNITED KI5') and UDFToInteger(d_year) BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 260 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: ((UDFToInteger(d_year) <= 1997) and (UDFToInteger(d_year) >= 1992) and (c_city) IN ('UNITED KI1', 'UNITED KI5') and (s_city) IN ('UNITED KI1', 'UNITED KI5')) (type: boolean)
+ predicate: ((c_city) IN ('UNITED KI1', 'UNITED KI5') and (s_city) IN ('UNITED KI1', 'UNITED KI5') and UDFToInteger(d_year) BETWEEN 1992 AND 1997) (type: boolean)
Statistics: Num rows: 1 Data size: 260 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: c_city (type: string), s_city (type: string), UDFToInteger(d_year) (type: int), lo_revenue (type: double)
diff --git a/ql/src/test/results/clientpositive/llap/subquery_scalar.q.out b/ql/src/test/results/clientpositive/llap/subquery_scalar.q.out
index bf9030a..024aa9a 100644
--- a/ql/src/test/results/clientpositive/llap/subquery_scalar.q.out
+++ b/ql/src/test/results/clientpositive/llap/subquery_scalar.q.out
@@ -842,8 +842,8 @@ POSTHOOK: Input: default@part_null_n0
85768 almond antique chartreuse lavender yellow Manufacturer#1 Brand#12 LARGE BRUSHED STEEL 34 SM BAG 1753.76 refull
86428 almond aquamarine burnished black steel Manufacturer#1 Brand#12 STANDARD ANODIZED STEEL 28 WRAP BAG 1414.42 arefully
90681 almond antique chartreuse khaki white Manufacturer#3 Brand#31 MEDIUM BURNISHED TIN 17 SM CASE 1671.68 are slyly after the sl
-Warning: Shuffle Join MERGEJOIN[23][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
-Warning: Shuffle Join MERGEJOIN[24][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
+Warning: Shuffle Join MERGEJOIN[30][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
+Warning: Shuffle Join MERGEJOIN[31][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
PREHOOK: query: explain select * from part where p_size between (select min(p_size) from part) and (select avg(p_size) from part)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
@@ -861,8 +861,8 @@ STAGE PLANS:
Tez
#### A masked pattern was here ####
Edges:
- Reducer 2 <- Map 1 (CUSTOM_SIMPLE_EDGE), Reducer 5 (CUSTOM_SIMPLE_EDGE)
- Reducer 3 <- Reducer 2 (CUSTOM_SIMPLE_EDGE), Reducer 6 (CUSTOM_SIMPLE_EDGE)
+ Reducer 2 <- Map 1 (XPROD_EDGE), Reducer 5 (XPROD_EDGE)
+ Reducer 3 <- Reducer 2 (XPROD_EDGE), Reducer 6 (XPROD_EDGE)
Reducer 5 <- Map 4 (CUSTOM_SIMPLE_EDGE)
Reducer 6 <- Map 4 (CUSTOM_SIMPLE_EDGE)
#### A masked pattern was here ####
@@ -873,13 +873,13 @@ STAGE PLANS:
alias: part
Statistics: Num rows: 26 Data size: 16094 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
- expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
- Statistics: Num rows: 26 Data size: 16094 Basic stats: COMPLETE Column stats: COMPLETE
+ expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string), UDFToDouble(p_size) (type: double)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
+ Statistics: Num rows: 26 Data size: 16302 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
sort order:
- Statistics: Num rows: 26 Data size: 16094 Basic stats: COMPLETE Column stats: COMPLETE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
+ Statistics: Num rows: 26 Data size: 16302 Basic stats: COMPLETE Column stats: COMPLETE
+ value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string), _col9 (type: double)
Execution mode: vectorized, llap
LLAP IO: no inputs
Map 4
@@ -916,41 +916,40 @@ STAGE PLANS:
Reduce Operator Tree:
Merge Join Operator
condition map:
- Left Outer Join 0 to 1
+ Inner Join 0 to 1
keys:
0
1
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
- Statistics: Num rows: 26 Data size: 16198 Basic stats: COMPLETE Column stats: COMPLETE
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
+ residual filter predicates: {(_col10 <= _col9)}
+ Statistics: Num rows: 8 Data size: 5080 Basic stats: COMPLETE Column stats: COMPLETE
Reduce Output Operator
sort order:
- Statistics: Num rows: 26 Data size: 16198 Basic stats: COMPLETE Column stats: COMPLETE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string), _col9 (type: int)
+ Statistics: Num rows: 8 Data size: 5080 Basic stats: COMPLETE Column stats: COMPLETE
+ value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string), _col9 (type: double)
Reducer 3
Execution mode: llap
Reduce Operator Tree:
Merge Join Operator
condition map:
- Left Outer Join 0 to 1
+ Inner Join 0 to 1
keys:
0
1
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
- Statistics: Num rows: 26 Data size: 16406 Basic stats: COMPLETE Column stats: COMPLETE
- Filter Operator
- predicate: UDFToDouble(_col5) BETWEEN UDFToDouble(_col9) AND _col10 (type: boolean)
- Statistics: Num rows: 2 Data size: 1262 Basic stats: COMPLETE Column stats: COMPLETE
- Select Operator
- expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col11
+ residual filter predicates: {(_col9 <= _col11)}
+ Statistics: Num rows: 2 Data size: 1270 Basic stats: COMPLETE Column stats: COMPLETE
+ Select Operator
+ expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
+ Statistics: Num rows: 2 Data size: 1238 Basic stats: COMPLETE Column stats: COMPLETE
+ File Output Operator
+ compressed: false
Statistics: Num rows: 2 Data size: 1238 Basic stats: COMPLETE Column stats: COMPLETE
- File Output Operator
- compressed: false
- Statistics: Num rows: 2 Data size: 1238 Basic stats: COMPLETE Column stats: COMPLETE
- table:
- input format: org.apache.hadoop.mapred.SequenceFileInputFormat
- output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
- serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
+ table:
+ input format: org.apache.hadoop.mapred.SequenceFileInputFormat
+ output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
+ serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Reducer 5
Execution mode: vectorized, llap
Reduce Operator Tree:
@@ -959,10 +958,14 @@ STAGE PLANS:
mode: mergepartial
outputColumnNames: _col0
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: COMPLETE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: COMPLETE
- value expressions: _col0 (type: int)
+ Select Operator
+ expressions: UDFToDouble(_col0) (type: double)
+ outputColumnNames: _col0
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE
+ value expressions: _col0 (type: double)
Reducer 6
Execution mode: vectorized, llap
Reduce Operator Tree:
@@ -986,8 +989,8 @@ STAGE PLANS:
Processor Tree:
ListSink
-Warning: Shuffle Join MERGEJOIN[23][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
-Warning: Shuffle Join MERGEJOIN[24][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
+Warning: Shuffle Join MERGEJOIN[30][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
+Warning: Shuffle Join MERGEJOIN[31][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
PREHOOK: query: select * from part where p_size between (select min(p_size) from part) and (select avg(p_size) from part)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
@@ -1801,7 +1804,7 @@ POSTHOOK: Input: default@part
POSTHOOK: Input: default@part_null_n0
#### A masked pattern was here ####
192697 almond antique blue firebrick mint Manufacturer#5 Brand#52 MEDIUM BURNISHED TIN 31 LG DRUM 1789.69 ickly ir
-Warning: Shuffle Join MERGEJOIN[32][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
+Warning: Shuffle Join MERGEJOIN[44][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
PREHOOK: query: explain select * from part where p_size BETWEEN (select min(p_size) from part_null_n0 where part_null_n0.p_type = part.p_type) AND (select max(p_size) from part_null_n0)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
@@ -1822,7 +1825,7 @@ STAGE PLANS:
#### A masked pattern was here ####
Edges:
Reducer 2 <- Map 1 (SIMPLE_EDGE), Reducer 5 (ONE_TO_ONE_EDGE)
- Reducer 3 <- Reducer 2 (CUSTOM_SIMPLE_EDGE), Reducer 7 (CUSTOM_SIMPLE_EDGE)
+ Reducer 3 <- Reducer 2 (XPROD_EDGE), Reducer 7 (XPROD_EDGE)
Reducer 5 <- Map 4 (SIMPLE_EDGE)
Reducer 7 <- Map 6 (CUSTOM_SIMPLE_EDGE)
#### A masked pattern was here ####
@@ -1831,17 +1834,21 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: part
+ filterExpr: p_type is not null (type: boolean)
Statistics: Num rows: 26 Data size: 16094 Basic stats: COMPLETE Column stats: COMPLETE
- Select Operator
- expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
+ Filter Operator
+ predicate: p_type is not null (type: boolean)
Statistics: Num rows: 26 Data size: 16094 Basic stats: COMPLETE Column stats: COMPLETE
- Reduce Output Operator
- key expressions: _col4 (type: string)
- sort order: +
- Map-reduce partition columns: _col4 (type: string)
+ Select Operator
+ expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
Statistics: Num rows: 26 Data size: 16094 Basic stats: COMPLETE Column stats: COMPLETE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
+ Reduce Output Operator
+ key expressions: _col4 (type: string)
+ sort order: +
+ Map-reduce partition columns: _col4 (type: string)
+ Statistics: Num rows: 26 Data size: 16094 Basic stats: COMPLETE Column stats: COMPLETE
+ value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
Execution mode: vectorized, llap
LLAP IO: no inputs
Map 4
@@ -1892,41 +1899,40 @@ STAGE PLANS:
Reduce Operator Tree:
Merge Join Operator
condition map:
- Left Outer Join 0 to 1
+ Inner Join 0 to 1
keys:
0 _col4 (type: string)
- 1 _col2 (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
- Statistics: Num rows: 28 Data size: 17703 Basic stats: COMPLETE Column stats: NONE
+ 1 _col1 (type: string)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
+ residual filter predicates: {(_col9 <= _col5)}
+ Statistics: Num rows: 9 Data size: 5690 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
sort order:
- Statistics: Num rows: 28 Data size: 17703 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string), _col9 (type: int), _col10 (type: boolean)
+ Statistics: Num rows: 9 Data size: 5690 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
Reducer 3
Execution mode: llap
Reduce Operator Tree:
Merge Join Operator
condition map:
- Left Outer Join 0 to 1
+ Inner Join 0 to 1
keys:
0
1
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col12
- Statistics: Num rows: 28 Data size: 17955 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: CASE WHEN (_col10 is null) THEN (_col5 BETWEEN null AND _col12) ELSE (_col5 BETWEEN _col9 AND _col12) END (type: boolean)
- Statistics: Num rows: 14 Data size: 8977 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
- Statistics: Num rows: 14 Data size: 8977 Basic stats: COMPLETE Column stats: NONE
- File Output Operator
- compressed: false
- Statistics: Num rows: 14 Data size: 8977 Basic stats: COMPLETE Column stats: NONE
- table:
- input format: org.apache.hadoop.mapred.SequenceFileInputFormat
- output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
- serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col11
+ residual filter predicates: {(_col5 <= _col11)}
+ Statistics: Num rows: 3 Data size: 1923 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
+ Statistics: Num rows: 3 Data size: 1923 Basic stats: COMPLETE Column stats: NONE
+ File Output Operator
+ compressed: false
+ Statistics: Num rows: 3 Data size: 1923 Basic stats: COMPLETE Column stats: NONE
+ table:
+ input format: org.apache.hadoop.mapred.SequenceFileInputFormat
+ output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
+ serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Reducer 5
Execution mode: vectorized, llap
Reduce Operator Tree:
@@ -1937,15 +1943,15 @@ STAGE PLANS:
outputColumnNames: _col0, _col1
Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE
Select Operator
- expressions: _col1 (type: int), true (type: boolean), _col0 (type: string)
- outputColumnNames: _col0, _col1, _col2
+ expressions: _col1 (type: int), _col0 (type: string)
+ outputColumnNames: _col0, _col1
Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
- key expressions: _col2 (type: string)
+ key expressions: _col1 (type: string)
sort order: +
- Map-reduce partition columns: _col2 (type: string)
+ Map-reduce partition columns: _col1 (type: string)
Statistics: Num rows: 1 Data size: 188 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col1 (type: boolean)
+ value expressions: _col0 (type: int)
Reducer 7
Execution mode: vectorized, llap
Reduce Operator Tree:
@@ -1965,7 +1971,7 @@ STAGE PLANS:
Processor Tree:
ListSink
-Warning: Shuffle Join MERGEJOIN[32][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
+Warning: Shuffle Join MERGEJOIN[44][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
PREHOOK: query: select * from part where p_size BETWEEN (select min(p_size) from part_null_n0 where part_null_n0.p_type = part.p_type) AND (select max(p_size) from part_null_n0)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
diff --git a/ql/src/test/results/clientpositive/llap/vector_between_columns.q.out b/ql/src/test/results/clientpositive/llap/vector_between_columns.q.out
index a91a36b..40f8e35 100644
--- a/ql/src/test/results/clientpositive/llap/vector_between_columns.q.out
+++ b/ql/src/test/results/clientpositive/llap/vector_between_columns.q.out
@@ -192,7 +192,7 @@ STAGE PLANS:
outputColumnNames: _col0, _col1, _col2, _col3, _col4
Statistics: Num rows: 36 Data size: 708 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
- expressions: _col0 (type: int), _col2 (type: int), _col1 (type: int), _col3 (type: smallint), CASE WHEN (_col1 BETWEEN _col4 AND _col4) THEN ('Ok') ELSE ('NoOk') END (type: string)
+ expressions: _col0 (type: int), _col2 (type: int), _col1 (type: int), _col3 (type: smallint), CASE WHEN (_col4 BETWEEN _col1 AND _col1) THEN ('Ok') ELSE ('NoOk') END (type: string)
outputColumnNames: _col0, _col1, _col2, _col3, _col4
Statistics: Num rows: 36 Data size: 7192 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
@@ -370,7 +370,7 @@ STAGE PLANS:
outputColumnNames: _col0, _col1, _col2, _col3, _col4
Statistics: Num rows: 36 Data size: 708 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: _col1 BETWEEN _col4 AND _col4 (type: boolean)
+ predicate: _col4 BETWEEN _col1 AND _col1 (type: boolean)
Statistics: Num rows: 4 Data size: 80 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col0 (type: int), _col2 (type: int), _col1 (type: int), _col3 (type: smallint)
diff --git a/ql/src/test/results/clientpositive/llap/vector_interval_2.q.out b/ql/src/test/results/clientpositive/llap/vector_interval_2.q.out
index 3b2b7ba..28da504 100644
--- a/ql/src/test/results/clientpositive/llap/vector_interval_2.q.out
+++ b/ql/src/test/results/clientpositive/llap/vector_interval_2.q.out
@@ -1344,7 +1344,7 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: vector_interval_2
- filterExpr: ((DATE'2002-03-01' = (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and (DATE'2002-03-01' <= (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and (DATE'2002-03-01' >= (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and ((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) = DATE'2002-03-01') and ((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) <= DATE'2002-03-01') and ((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) >= DATE'2002-03-01') and (dt <> (dt + CAST( str1 AS INTERVAL Y [...]
+ filterExpr: ((DATE'2002-03-01' = (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and DATE'2002-03-01' BETWEEN (dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) AND (dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) and ((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) = DATE'2002-03-01') and (dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) BETWEEN DATE'2002-03-01' AND DATE'2002-03-01' and (dt <> (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and (DATE'2002-03-01' = (dt + INTERVAL'1-2')) and [...]
Statistics: Num rows: 2 Data size: 366 Basic stats: COMPLETE Column stats: COMPLETE
TableScan Vectorization:
native: true
@@ -1352,8 +1352,8 @@ STAGE PLANS:
Filter Vectorization:
className: VectorFilterOperator
native: true
- predicateExpression: FilterExprAndExpr(children: FilterDateScalarEqualDateColumn(val 11747, col 8:date)(children: DateColAddIntervalYearMonthColumn(col 1:date, col 7:interval_year_month)(children: CastStringToIntervalYearMonth(col 2:string) -> 7:interval_year_month) -> 8:date), FilterDateScalarLessEqualDateColumn(val 11747, col 10:date)(children: DateColAddIntervalYearMonthColumn(col 1:date, col 9:interval_year_month)(children: CastStringToIntervalYearMonth(col 2: [...]
- predicate: (((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) <= DATE'2002-03-01') and ((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) = DATE'2002-03-01') and ((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) >= DATE'2002-03-01') and ((dt + INTERVAL'1-2') <= DATE'2002-03-01') and ((dt + INTERVAL'1-2') = DATE'2002-03-01') and ((dt + INTERVAL'1-2') >= DATE'2002-03-01') and (DATE'2002-03-01' <= (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and (DATE'2002-03-01' <= (dt + INTERVAL'1 [...]
+ predicateExpression: FilterExprAndExpr(children: FilterDateScalarEqualDateColumn(val 11747, col 8:date)(children: DateColAddIntervalYearMonthColumn(col 1:date, col 7:interval_year_month)(children: CastStringToIntervalYearMonth(col 2:string) -> 7:interval_year_month) -> 8:date), SelectColumnIsTrue(col 13:boolean)(children: VectorUDFAdaptor(DATE'2002-03-01' BETWEEN (dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) AND (dt + CAST( str1 AS INTERVAL YEAR TO MONTH)))(childre [...]
+ predicate: (((dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) = DATE'2002-03-01') and ((dt + INTERVAL'1-2') = DATE'2002-03-01') and (DATE'2002-03-01' = (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and (DATE'2002-03-01' = (dt + INTERVAL'1-2')) and (dt + CAST( str1 AS INTERVAL YEAR TO MONTH)) BETWEEN DATE'2002-03-01' AND DATE'2002-03-01' and (dt + INTERVAL'1-2') BETWEEN DATE'2002-03-01' AND DATE'2002-03-01' and (dt <> (dt + CAST( str1 AS INTERVAL YEAR TO MONTH))) and (dt < [...]
Statistics: Num rows: 1 Data size: 183 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: ts (type: timestamp)
@@ -1380,7 +1380,7 @@ STAGE PLANS:
featureSupportInUse: [DECIMAL_64]
inputFileFormats: org.apache.hadoop.hive.ql.io.orc.OrcInputFormat
allNative: true
- usesVectorUDFAdaptor: false
+ usesVectorUDFAdaptor: true
vectorized: true
Reducer 2
Execution mode: vectorized, llap
@@ -1533,7 +1533,7 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: vector_interval_2
- filterExpr: ((TIMESTAMP'2002-03-01 01:02:03' = (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-03-01 01:02:03' <= (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-03-01 01:02:03' >= (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-04-01 01:02:03' <> (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-02-01 01:02:03' < (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-04-01 01:02:03' > (ts + INTERVAL'1-2')) and ((ts + INTERVAL'1-2') = TIMESTAMP'2002-03-01 01:02:03') and ((ts + INTERVAL'1-2') >= TIMESTAMP'200 [...]
+ filterExpr: ((TIMESTAMP'2002-03-01 01:02:03' = (ts + INTERVAL'1-2')) and TIMESTAMP'2002-03-01 01:02:03' BETWEEN (ts + INTERVAL'1-2') AND (ts + INTERVAL'1-2') and (TIMESTAMP'2002-04-01 01:02:03' <> (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-02-01 01:02:03' < (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-04-01 01:02:03' > (ts + INTERVAL'1-2')) and ((ts + INTERVAL'1-2') = TIMESTAMP'2002-03-01 01:02:03') and (ts + INTERVAL'1-2') BETWEEN TIMESTAMP'2002-03-01 01:02:03' AND TIMESTA [...]
Statistics: Num rows: 2 Data size: 80 Basic stats: COMPLETE Column stats: COMPLETE
TableScan Vectorization:
native: true
@@ -1541,8 +1541,8 @@ STAGE PLANS:
Filter Vectorization:
className: VectorFilterOperator
native: true
- predicateExpression: FilterExprAndExpr(children: FilterTimestampScalarEqualTimestampColumn(val 2002-03-01 01:02:03, col 7:timestamp)(children: TimestampColAddIntervalYearMonthScalar(col 0:timestamp, val 1-2) -> 7:timestamp), FilterTimestampScalarLessEqualTimestampColumn(val 2002-03-01 01:02:03, col 8:timestamp)(children: TimestampColAddIntervalYearMonthScalar(col 0:timestamp, val 1-2) -> 8:timestamp), FilterTimestampScalarGreaterEqualTimestampColumn(val 2002-03-01 [...]
- predicate: (((ts + INTERVAL'1-2') < TIMESTAMP'2002-04-01 01:02:03') and ((ts + INTERVAL'1-2') <= TIMESTAMP'2002-03-01 01:02:03') and ((ts + INTERVAL'1-2') <> TIMESTAMP'2002-04-01 01:02:03') and ((ts + INTERVAL'1-2') = TIMESTAMP'2002-03-01 01:02:03') and ((ts + INTERVAL'1-2') > TIMESTAMP'2002-02-01 01:02:03') and ((ts + INTERVAL'1-2') >= TIMESTAMP'2002-03-01 01:02:03') and (TIMESTAMP'2002-02-01 01:02:03' < (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-03-01 01:02:03' <= (t [...]
+ predicateExpression: FilterExprAndExpr(children: FilterTimestampScalarEqualTimestampColumn(val 2002-03-01 01:02:03, col 7:timestamp)(children: TimestampColAddIntervalYearMonthScalar(col 0:timestamp, val 1-2) -> 7:timestamp), SelectColumnIsTrue(col 10:boolean)(children: VectorUDFAdaptor(TIMESTAMP'2002-03-01 01:02:03' BETWEEN (ts + INTERVAL'1-2') AND (ts + INTERVAL'1-2'))(children: TimestampColAddIntervalYearMonthScalar(col 0:timestamp, val 1-2) -> 8:timestamp, Time [...]
+ predicate: (((ts + INTERVAL'1-2') < TIMESTAMP'2002-04-01 01:02:03') and ((ts + INTERVAL'1-2') <> TIMESTAMP'2002-04-01 01:02:03') and ((ts + INTERVAL'1-2') = TIMESTAMP'2002-03-01 01:02:03') and ((ts + INTERVAL'1-2') > TIMESTAMP'2002-02-01 01:02:03') and (TIMESTAMP'2002-02-01 01:02:03' < (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-03-01 01:02:03' = (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-04-01 01:02:03' <> (ts + INTERVAL'1-2')) and (TIMESTAMP'2002-04-01 01:02:03' > (ts [...]
Statistics: Num rows: 1 Data size: 40 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: ts (type: timestamp)
@@ -1569,7 +1569,7 @@ STAGE PLANS:
featureSupportInUse: [DECIMAL_64]
inputFileFormats: org.apache.hadoop.hive.ql.io.orc.OrcInputFormat
allNative: true
- usesVectorUDFAdaptor: false
+ usesVectorUDFAdaptor: true
vectorized: true
Reducer 2
Execution mode: vectorized, llap
@@ -1732,7 +1732,7 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: vector_interval_2
- filterExpr: ((TIMESTAMP'2001-01-01 01:02:03' = (dt + INTERVAL'0 01:02:03.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <> (dt + INTERVAL'0 01:02:04.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <= (dt + INTERVAL'0 01:02:03.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' < (dt + INTERVAL'0 01:02:04.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' >= (dt - INTERVAL'0 01:02:03.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' > (dt - INTERVAL'0 01:02:04.000000000')) [...]
+ filterExpr: ((TIMESTAMP'2001-01-01 01:02:03' = (dt + INTERVAL'0 01:02:03.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <> (dt + INTERVAL'0 01:02:04.000000000')) and TIMESTAMP'2001-01-01 01:02:03' BETWEEN (dt - INTERVAL'0 01:02:03.000000000') AND (dt + INTERVAL'0 01:02:03.000000000') and (TIMESTAMP'2001-01-01 01:02:03' < (dt + INTERVAL'0 01:02:04.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' > (dt - INTERVAL'0 01:02:04.000000000')) and ((dt + INTERVAL'0 01:02:03.00 [...]
Statistics: Num rows: 2 Data size: 192 Basic stats: COMPLETE Column stats: COMPLETE
TableScan Vectorization:
native: true
@@ -1740,8 +1740,8 @@ STAGE PLANS:
Filter Vectorization:
className: VectorFilterOperator
native: true
- predicateExpression: FilterExprAndExpr(children: FilterTimestampScalarEqualTimestampColumn(val 2001-01-01 01:02:03, col 7:timestamp)(children: DateColAddIntervalDayTimeScalar(col 1:date, val 0 01:02:03.000000000) -> 7:timestamp), FilterTimestampScalarNotEqualTimestampColumn(val 2001-01-01 01:02:03, col 8:timestamp)(children: DateColAddIntervalDayTimeScalar(col 1:date, val 0 01:02:04.000000000) -> 8:timestamp), FilterTimestampScalarLessEqualTimestampColumn(val 2001 [...]
- predicate: (((dt + INTERVAL'0 01:02:03.000000000') = TIMESTAMP'2001-01-01 01:02:03') and ((dt + INTERVAL'0 01:02:03.000000000') >= TIMESTAMP'2001-01-01 01:02:03') and ((dt + INTERVAL'0 01:02:04.000000000') <> TIMESTAMP'2001-01-01 01:02:03') and ((dt + INTERVAL'0 01:02:04.000000000') > TIMESTAMP'2001-01-01 01:02:03') and ((dt - INTERVAL'0 01:02:03.000000000') <= TIMESTAMP'2001-01-01 01:02:03') and ((dt - INTERVAL'0 01:02:04.000000000') < TIMESTAMP'2001-01-01 01:02:03') [...]
+ predicateExpression: FilterExprAndExpr(children: FilterTimestampScalarEqualTimestampColumn(val 2001-01-01 01:02:03, col 7:timestamp)(children: DateColAddIntervalDayTimeScalar(col 1:date, val 0 01:02:03.000000000) -> 7:timestamp), FilterTimestampScalarNotEqualTimestampColumn(val 2001-01-01 01:02:03, col 8:timestamp)(children: DateColAddIntervalDayTimeScalar(col 1:date, val 0 01:02:04.000000000) -> 8:timestamp), SelectColumnIsTrue(col 11:boolean)(children: VectorUDF [...]
+ predicate: (((dt + INTERVAL'0 01:02:03.000000000') = TIMESTAMP'2001-01-01 01:02:03') and ((dt + INTERVAL'0 01:02:04.000000000') <> TIMESTAMP'2001-01-01 01:02:03') and ((dt + INTERVAL'0 01:02:04.000000000') > TIMESTAMP'2001-01-01 01:02:03') and ((dt - INTERVAL'0 01:02:04.000000000') < TIMESTAMP'2001-01-01 01:02:03') and (TIMESTAMP'2001-01-01 01:02:03' < (dt + INTERVAL'0 01:02:04.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <> (dt + INTERVAL'0 01:02:04.000000000')) [...]
Statistics: Num rows: 1 Data size: 96 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: ts (type: timestamp)
@@ -1768,7 +1768,7 @@ STAGE PLANS:
featureSupportInUse: [DECIMAL_64]
inputFileFormats: org.apache.hadoop.hive.ql.io.orc.OrcInputFormat
allNative: true
- usesVectorUDFAdaptor: false
+ usesVectorUDFAdaptor: true
vectorized: true
Reducer 2
Execution mode: vectorized, llap
@@ -1931,7 +1931,7 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: vector_interval_2
- filterExpr: ((TIMESTAMP'2001-01-01 01:02:03' = (ts + INTERVAL'0 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <> (ts + INTERVAL'1 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <= (ts + INTERVAL'1 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' < (ts + INTERVAL'1 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' >= (ts - INTERVAL'1 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' > (ts - INTERVAL'1 00:00:00.000000000')) [...]
+ filterExpr: ((TIMESTAMP'2001-01-01 01:02:03' = (ts + INTERVAL'0 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <> (ts + INTERVAL'1 00:00:00.000000000')) and TIMESTAMP'2001-01-01 01:02:03' BETWEEN (ts - INTERVAL'1 00:00:00.000000000') AND (ts + INTERVAL'1 00:00:00.000000000') and (TIMESTAMP'2001-01-01 01:02:03' < (ts + INTERVAL'1 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' > (ts - INTERVAL'1 00:00:00.000000000')) and ((ts + INTERVAL'0 00:00:00.00 [...]
Statistics: Num rows: 2 Data size: 80 Basic stats: COMPLETE Column stats: COMPLETE
TableScan Vectorization:
native: true
@@ -1939,8 +1939,8 @@ STAGE PLANS:
Filter Vectorization:
className: VectorFilterOperator
native: true
- predicateExpression: FilterExprAndExpr(children: FilterTimestampScalarEqualTimestampColumn(val 2001-01-01 01:02:03, col 7:timestamp)(children: TimestampColAddIntervalDayTimeScalar(col 0:timestamp, val 0 00:00:00.000000000) -> 7:timestamp), FilterTimestampScalarNotEqualTimestampColumn(val 2001-01-01 01:02:03, col 8:timestamp)(children: TimestampColAddIntervalDayTimeScalar(col 0:timestamp, val 1 00:00:00.000000000) -> 8:timestamp), FilterTimestampScalarLessEqualTime [...]
- predicate: (((ts + INTERVAL'0 00:00:00.000000000') = TIMESTAMP'2001-01-01 01:02:03') and ((ts + INTERVAL'1 00:00:00.000000000') <> TIMESTAMP'2001-01-01 01:02:03') and ((ts + INTERVAL'1 00:00:00.000000000') > TIMESTAMP'2001-01-01 01:02:03') and ((ts + INTERVAL'1 00:00:00.000000000') >= TIMESTAMP'2001-01-01 01:02:03') and ((ts - INTERVAL'1 00:00:00.000000000') < TIMESTAMP'2001-01-01 01:02:03') and ((ts - INTERVAL'1 00:00:00.000000000') <= TIMESTAMP'2001-01-01 01:02:03') [...]
+ predicateExpression: FilterExprAndExpr(children: FilterTimestampScalarEqualTimestampColumn(val 2001-01-01 01:02:03, col 7:timestamp)(children: TimestampColAddIntervalDayTimeScalar(col 0:timestamp, val 0 00:00:00.000000000) -> 7:timestamp), FilterTimestampScalarNotEqualTimestampColumn(val 2001-01-01 01:02:03, col 8:timestamp)(children: TimestampColAddIntervalDayTimeScalar(col 0:timestamp, val 1 00:00:00.000000000) -> 8:timestamp), SelectColumnIsTrue(col 11:boolean) [...]
+ predicate: (((ts + INTERVAL'0 00:00:00.000000000') = TIMESTAMP'2001-01-01 01:02:03') and ((ts + INTERVAL'1 00:00:00.000000000') <> TIMESTAMP'2001-01-01 01:02:03') and ((ts + INTERVAL'1 00:00:00.000000000') > TIMESTAMP'2001-01-01 01:02:03') and ((ts - INTERVAL'1 00:00:00.000000000') < TIMESTAMP'2001-01-01 01:02:03') and (TIMESTAMP'2001-01-01 01:02:03' < (ts + INTERVAL'1 00:00:00.000000000')) and (TIMESTAMP'2001-01-01 01:02:03' <> (ts + INTERVAL'1 00:00:00.000000000')) [...]
Statistics: Num rows: 1 Data size: 40 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: ts (type: timestamp)
@@ -1967,7 +1967,7 @@ STAGE PLANS:
featureSupportInUse: [DECIMAL_64]
inputFileFormats: org.apache.hadoop.hive.ql.io.orc.OrcInputFormat
allNative: true
- usesVectorUDFAdaptor: false
+ usesVectorUDFAdaptor: true
vectorized: true
Reducer 2
Execution mode: vectorized, llap
diff --git a/ql/src/test/results/clientpositive/mapjoin47.q.out b/ql/src/test/results/clientpositive/mapjoin47.q.out
index 2ccce76..9cd3b1e 100644
--- a/ql/src/test/results/clientpositive/mapjoin47.q.out
+++ b/ql/src/test/results/clientpositive/mapjoin47.q.out
@@ -530,7 +530,7 @@ STAGE PLANS:
0
1
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
- residual filter predicates: {((_col2 + _col5) >= 100.0D)} {((_col2 + _col5) <= 102.0D)}
+ residual filter predicates: {(_col2 + _col5) BETWEEN 100.0D AND 102.0D}
Statistics: Num rows: 1388 Data size: 512172 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col0 (type: string), _col1 (type: string), _col3 (type: string), _col4 (type: string)
diff --git a/ql/src/test/results/clientpositive/partition_wise_fileformat2.q.out b/ql/src/test/results/clientpositive/partition_wise_fileformat2.q.out
index a1c5ab3..4421f83 100644
--- a/ql/src/test/results/clientpositive/partition_wise_fileformat2.q.out
+++ b/ql/src/test/results/clientpositive/partition_wise_fileformat2.q.out
@@ -76,10 +76,10 @@ STAGE PLANS:
Processor Tree:
TableScan
alias: partition_test_partitioned
- filterExpr: ((UDFToDouble(dt) >= 100.0D) and (UDFToDouble(dt) <= 102.0D)) (type: boolean)
+ filterExpr: UDFToDouble(dt) BETWEEN 100.0D AND 102.0D (type: boolean)
Statistics: Num rows: 75 Data size: 26925 Basic stats: COMPLETE Column stats: PARTIAL
Filter Operator
- predicate: ((UDFToDouble(dt) <= 102.0D) and (UDFToDouble(dt) >= 100.0D)) (type: boolean)
+ predicate: UDFToDouble(dt) BETWEEN 100.0D AND 102.0D (type: boolean)
Statistics: Num rows: 8 Data size: 2872 Basic stats: COMPLETE Column stats: PARTIAL
Select Operator
expressions: key (type: string), value (type: string), dt (type: string), BLOCK__OFFSET__INSIDE__FILE (type: bigint)
@@ -200,10 +200,10 @@ STAGE PLANS:
Processor Tree:
TableScan
alias: partition_test_partitioned
- filterExpr: ((UDFToDouble(dt) >= 100.0D) and (UDFToDouble(dt) <= 102.0D)) (type: boolean)
+ filterExpr: UDFToDouble(dt) BETWEEN 100.0D AND 102.0D (type: boolean)
Statistics: Num rows: 75 Data size: 26925 Basic stats: COMPLETE Column stats: PARTIAL
Filter Operator
- predicate: ((UDFToDouble(dt) <= 102.0D) and (UDFToDouble(dt) >= 100.0D)) (type: boolean)
+ predicate: UDFToDouble(dt) BETWEEN 100.0D AND 102.0D (type: boolean)
Statistics: Num rows: 8 Data size: 2872 Basic stats: COMPLETE Column stats: PARTIAL
Select Operator
expressions: key (type: string), value (type: string), dt (type: string), BLOCK__OFFSET__INSIDE__FILE (type: bigint)
diff --git a/ql/src/test/results/clientpositive/pcr.q.out b/ql/src/test/results/clientpositive/pcr.q.out
index 660556f..f8fe477 100644
--- a/ql/src/test/results/clientpositive/pcr.q.out
+++ b/ql/src/test/results/clientpositive/pcr.q.out
@@ -3055,10 +3055,6 @@ POSTHOOK: Input: default@pcr_t1@ds=2000-04-09
POSTHOOK: Input: default@pcr_t1@ds=2000-04-10
POSTHOOK: Input: default@pcr_t1@ds=2000-04-11
#### A masked pattern was here ####
-OPTIMIZED SQL: SELECT `key`, `value`, `ds`
-FROM `default`.`pcr_t1`
-WHERE `ds` > '2000-04-08' AND `ds` < '2000-04-11' OR `ds` >= '2000-04-08' AND `ds` <= '2000-04-11' AND `key` = 2
-ORDER BY `key`, `value`, `ds`
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1
diff --git a/ql/src/test/results/clientpositive/perf/spark/query13.q.out b/ql/src/test/results/clientpositive/perf/spark/query13.q.out
index 2b6c19d..b5df0bc 100644
--- a/ql/src/test/results/clientpositive/perf/spark/query13.q.out
+++ b/ql/src/test/results/clientpositive/perf/spark/query13.q.out
@@ -122,7 +122,7 @@ STAGE PLANS:
Spark
#### A masked pattern was here ####
Vertices:
- Map 7
+ Map 10
Map Operator Tree:
TableScan
alias: store
@@ -137,12 +137,12 @@ STAGE PLANS:
Statistics: Num rows: 1704 Data size: 3256276 Basic stats: COMPLETE Column stats: NONE
Spark HashTable Sink Operator
keys:
- 0 _col5 (type: int)
+ 0 _col4 (type: int)
1 _col0 (type: int)
Execution mode: vectorized
Local Work:
Map Reduce Local Work
- Map 8
+ Map 9
Map Operator Tree:
TableScan
alias: household_demographics
@@ -157,7 +157,7 @@ STAGE PLANS:
Statistics: Num rows: 7200 Data size: 770400 Basic stats: COMPLETE Column stats: NONE
Spark HashTable Sink Operator
keys:
- 0 _col3 (type: int)
+ 0 _col2 (type: int)
1 _col0 (type: int)
Execution mode: vectorized
Local Work:
@@ -166,72 +166,52 @@ STAGE PLANS:
Stage: Stage-1
Spark
Edges:
- Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 45), Map 6 (PARTITION-LEVEL SORT, 45)
- Reducer 3 <- Map 9 (PARTITION-LEVEL SORT, 218), Reducer 2 (PARTITION-LEVEL SORT, 218)
- Reducer 4 <- Map 10 (PARTITION-LEVEL SORT, 55), Reducer 3 (PARTITION-LEVEL SORT, 55)
+ Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 398), Map 6 (PARTITION-LEVEL SORT, 398)
+ Reducer 3 <- Map 7 (PARTITION-LEVEL SORT, 596), Reducer 2 (PARTITION-LEVEL SORT, 596)
+ Reducer 4 <- Map 8 (PARTITION-LEVEL SORT, 366), Reducer 3 (PARTITION-LEVEL SORT, 366)
Reducer 5 <- Reducer 4 (GROUP, 1)
#### A masked pattern was here ####
Vertices:
Map 1
Map Operator Tree:
TableScan
- alias: date_dim
- filterExpr: ((d_year = 2001) and d_date_sk is not null) (type: boolean)
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: ((d_year = 2001) and d_date_sk is not null) (type: boolean)
- Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: d_date_sk (type: int)
- outputColumnNames: _col0
- Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
- Execution mode: vectorized
- Map 10
- Map Operator Tree:
- TableScan
- alias: customer_demographics
- filterExpr: ((cd_marital_status) IN ('M', 'D', 'U') and (cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and cd_demo_sk is not null) (type: boolean)
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ alias: store_sales
+ filterExpr: (((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and ((ss_net_profit >= 100) or (ss_net_profit <= 200) or ss_net_profit is not null or (ss_net_profit <= 300) or (ss_net_profit <= 250)) and ss_store_sk is not null and ss_cdemo_sk is not null and ss_hdemo_sk is not null and ss_addr_sk is not null and ss_sold_date_sk is not null) (type: boolean)
+ Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null) (type: boolean)
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ predicate: (((ss_net_profit >= 100) or (ss_net_profit <= 200) or ss_net_profit is not null or (ss_net_profit <= 300) or (ss_net_profit <= 250)) and ((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_hdemo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null) (type: boolean)
+ Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
Select Operator
- expressions: cd_demo_sk (type: int), (cd_marital_status = 'M') (type: boolean), (cd_education_status = '4 yr Degree') (type: boolean), (cd_marital_status = 'D') (type: boolean), (cd_education_status = 'Primary') (type: boolean), (cd_marital_status = 'U') (type: boolean), (cd_education_status = 'Advanced Degree') (type: boolean)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ expressions: ss_sold_date_sk (type: int), ss_cdemo_sk (type: int), ss_hdemo_sk (type: int), ss_addr_sk (type: int), ss_store_sk (type: int), ss_quantity (type: int), ss_ext_sales_price (type: decimal(7,2)), ss_ext_wholesale_cost (type: decimal(7,2)), ss_net_profit BETWEEN 100 AND 200 (type: boolean), ss_net_profit BETWEEN 150 AND 300 (type: boolean), ss_net_profit BETWEEN 50 AND 250 (type: boolean), ss_sales_price BETWEEN 100 AND 150 (type: boolean), ss_sales_price [...]
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13
+ Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: int)
sort order: +
Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean)
+ Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col12 (type: boolean), _col13 (type: boolean)
Execution mode: vectorized
Map 6
Map Operator Tree:
TableScan
- alias: store_sales
- filterExpr: ((ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_net_profit BETWEEN 100 AND 200 or ss_net_profit BETWEEN 150 AND 300 or ss_net_profit BETWEEN 50 AND 250) and ss_store_sk is not null and ss_cdemo_sk is not null and ss_hdemo_sk is not null and ss_addr_sk is not null and ss_sold_date_sk is not null) (type: boolean)
- Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
+ alias: date_dim
+ filterExpr: ((d_year = 2001) and d_date_sk is not null) (type: boolean)
+ Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((ss_net_profit BETWEEN 100 AND 200 or ss_net_profit BETWEEN 150 AND 300 or ss_net_profit BETWEEN 50 AND 250) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_hdemo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null) (type: boolean)
- Statistics: Num rows: 63999513 Data size: 5646055611 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((d_year = 2001) and d_date_sk is not null) (type: boolean)
+ Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
Select Operator
- expressions: ss_sold_date_sk (type: int), ss_cdemo_sk (type: int), ss_hdemo_sk (type: int), ss_addr_sk (type: int), ss_store_sk (type: int), ss_quantity (type: int), ss_ext_sales_price (type: decimal(7,2)), ss_ext_wholesale_cost (type: decimal(7,2)), ss_net_profit BETWEEN 100 AND 200 (type: boolean), ss_net_profit BETWEEN 150 AND 300 (type: boolean), ss_net_profit BETWEEN 50 AND 250 (type: boolean), ss_sales_price BETWEEN 100 AND 150 (type: boolean), ss_sales_price [...]
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13
- Statistics: Num rows: 63999513 Data size: 5646055611 Basic stats: COMPLETE Column stats: NONE
+ expressions: d_date_sk (type: int)
+ outputColumnNames: _col0
+ Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: int)
sort order: +
Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 63999513 Data size: 5646055611 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col12 (type: boolean), _col13 (type: boolean)
+ Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
Execution mode: vectorized
- Map 9
+ Map 7
Map Operator Tree:
TableScan
alias: customer_address
@@ -251,9 +231,27 @@ STAGE PLANS:
Statistics: Num rows: 20000000 Data size: 20297597642 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean)
Execution mode: vectorized
+ Map 8
+ Map Operator Tree:
+ TableScan
+ alias: customer_demographics
+ filterExpr: ((cd_marital_status) IN ('M', 'D', 'U') and (cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and cd_demo_sk is not null) (type: boolean)
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: ((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null) (type: boolean)
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: cd_demo_sk (type: int), (cd_marital_status = 'M') (type: boolean), (cd_education_status = '4 yr Degree') (type: boolean), (cd_marital_status = 'D') (type: boolean), (cd_education_status = 'Primary') (type: boolean), (cd_marital_status = 'U') (type: boolean), (cd_education_status = 'Advanced Degree') (type: boolean)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean)
+ Execution mode: vectorized
Reducer 2
- Local Work:
- Map Reduce Local Work
Reduce Operator Tree:
Join Operator
condition map:
@@ -261,79 +259,77 @@ STAGE PLANS:
keys:
0 _col0 (type: int)
1 _col0 (type: int)
- outputColumnNames: _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14
- Statistics: Num rows: 70399465 Data size: 6210661306 Basic stats: COMPLETE Column stats: NONE
- Map Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col5 (type: int)
- 1 _col0 (type: int)
- outputColumnNames: _col2, _col3, _col4, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14
- input vertices:
- 1 Map 7
- Statistics: Num rows: 77439413 Data size: 6831727584 Basic stats: COMPLETE Column stats: NONE
- Map Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col3 (type: int)
- 1 _col0 (type: int)
- outputColumnNames: _col2, _col4, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col17, _col18
- input vertices:
- 1 Map 8
- Statistics: Num rows: 85183356 Data size: 7514900505 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col4 (type: int)
- sort order: +
- Map-reduce partition columns: _col4 (type: int)
- Statistics: Num rows: 85183356 Data size: 7514900505 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col2 (type: int), _col6 (type: int), _col7 (type: decimal(7,2)), _col8 (type: decimal(7,2)), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col12 (type: boolean), _col13 (type: boolean), _col14 (type: boolean), _col17 (type: boolean), _col18 (type: boolean)
+ outputColumnNames: _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13
+ Statistics: Num rows: 633595212 Data size: 55895953508 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col3 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col3 (type: int)
+ Statistics: Num rows: 633595212 Data size: 55895953508 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col1 (type: int), _col2 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col12 (type: boolean), _col13 (type: boolean)
Reducer 3
Reduce Operator Tree:
Join Operator
condition map:
Inner Join 0 to 1
keys:
- 0 _col4 (type: int)
+ 0 _col3 (type: int)
1 _col0 (type: int)
- outputColumnNames: _col2, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col17, _col18, _col20, _col21, _col22
- Statistics: Num rows: 93701693 Data size: 8266390734 Basic stats: COMPLETE Column stats: NONE
+ outputColumnNames: _col1, _col2, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col16, _col17, _col18
+ Statistics: Num rows: 696954748 Data size: 61485550191 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((_col20 and _col9) or (_col21 and _col10) or (_col22 and _col11)) (type: boolean)
- Statistics: Num rows: 70276269 Data size: 6199792984 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((_col16 and _col8) or (_col17 and _col9) or (_col18 and _col10)) (type: boolean)
+ Statistics: Num rows: 522716061 Data size: 46114162643 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
- key expressions: _col2 (type: int)
+ key expressions: _col1 (type: int)
sort order: +
- Map-reduce partition columns: _col2 (type: int)
- Statistics: Num rows: 70276269 Data size: 6199792984 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col6 (type: int), _col7 (type: decimal(7,2)), _col8 (type: decimal(7,2)), _col12 (type: boolean), _col13 (type: boolean), _col14 (type: boolean), _col17 (type: boolean), _col18 (type: boolean)
+ Map-reduce partition columns: _col1 (type: int)
+ Statistics: Num rows: 522716061 Data size: 46114162643 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col2 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col11 (type: boolean), _col12 (type: boolean), _col13 (type: boolean)
Reducer 4
+ Local Work:
+ Map Reduce Local Work
Reduce Operator Tree:
Join Operator
condition map:
Inner Join 0 to 1
keys:
- 0 _col2 (type: int)
+ 0 _col1 (type: int)
1 _col0 (type: int)
- outputColumnNames: _col6, _col7, _col8, _col12, _col13, _col14, _col17, _col18, _col24, _col25, _col26, _col27, _col28, _col29
- Statistics: Num rows: 77303897 Data size: 6819772430 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: ((_col24 and _col25 and _col12 and _col17) or (_col26 and _col27 and _col13 and _col18) or (_col28 and _col29 and _col14 and _col18)) (type: boolean)
- Statistics: Num rows: 14494479 Data size: 1278707181 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: _col6 (type: int), _col7 (type: decimal(7,2)), _col8 (type: decimal(7,2))
- outputColumnNames: _col6, _col7, _col8
- Statistics: Num rows: 14494479 Data size: 1278707181 Basic stats: COMPLETE Column stats: NONE
- Group By Operator
- aggregations: sum(_col6), count(_col6), sum(_col7), count(_col7), sum(_col8), count(_col8)
- mode: hash
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
- Statistics: Num rows: 1 Data size: 256 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
+ outputColumnNames: _col2, _col4, _col5, _col6, _col7, _col11, _col12, _col13, _col20, _col21, _col22, _col23, _col24, _col25
+ Statistics: Num rows: 574987679 Data size: 50725580006 Basic stats: COMPLETE Column stats: NONE
+ Map Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col2 (type: int)
+ 1 _col0 (type: int)
+ outputColumnNames: _col4, _col5, _col6, _col7, _col11, _col12, _col13, _col20, _col21, _col22, _col23, _col24, _col25, _col27, _col28
+ input vertices:
+ 1 Map 9
+ Statistics: Num rows: 632486460 Data size: 55798139215 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: ((_col20 and _col21 and _col11 and _col27) or (_col22 and _col23 and _col12 and _col28) or (_col24 and _col25 and _col13 and _col28)) (type: boolean)
+ Statistics: Num rows: 118591209 Data size: 10462150904 Basic stats: COMPLETE Column stats: NONE
+ Map Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col4 (type: int)
+ 1 _col0 (type: int)
+ outputColumnNames: _col5, _col6, _col7
+ input vertices:
+ 1 Map 10
+ Statistics: Num rows: 130450332 Data size: 11508366243 Basic stats: COMPLETE Column stats: NONE
+ Group By Operator
+ aggregations: sum(_col5), count(_col5), sum(_col6), count(_col6), sum(_col7), count(_col7)
+ mode: hash
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
Statistics: Num rows: 1 Data size: 256 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: bigint), _col1 (type: bigint), _col2 (type: decimal(17,2)), _col3 (type: bigint), _col4 (type: decimal(17,2)), _col5 (type: bigint)
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 1 Data size: 256 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: bigint), _col1 (type: bigint), _col2 (type: decimal(17,2)), _col3 (type: bigint), _col4 (type: decimal(17,2)), _col5 (type: bigint)
Reducer 5
Execution mode: vectorized
Reduce Operator Tree:
diff --git a/ql/src/test/results/clientpositive/perf/spark/query21.q.out b/ql/src/test/results/clientpositive/perf/spark/query21.q.out
index 6c2efe7..091c3ee 100644
--- a/ql/src/test/results/clientpositive/perf/spark/query21.q.out
+++ b/ql/src/test/results/clientpositive/perf/spark/query21.q.out
@@ -231,12 +231,12 @@ STAGE PLANS:
outputColumnNames: _col0, _col1, _col2, _col3
Statistics: Num rows: 25012152 Data size: 395187961 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: CASE WHEN ((_col2 > 0L)) THEN ((UDFToDouble(_col3) / UDFToDouble(_col2)) BETWEEN 0.666667D AND 1.5D) ELSE (null) END (type: boolean)
- Statistics: Num rows: 12506076 Data size: 197593980 Basic stats: COMPLETE Column stats: NONE
+ predicate: (CASE WHEN ((_col2 > 0L)) THEN (((UDFToDouble(_col3) / UDFToDouble(_col2)) <= 1.5D)) ELSE (null) END and CASE WHEN ((_col2 > 0L)) THEN ((0.666667D <= (UDFToDouble(_col3) / UDFToDouble(_col2)))) ELSE (null) END) (type: boolean)
+ Statistics: Num rows: 6253038 Data size: 98796990 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string), _col1 (type: string)
sort order: ++
- Statistics: Num rows: 12506076 Data size: 197593980 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 6253038 Data size: 98796990 Basic stats: COMPLETE Column stats: NONE
TopN Hash Memory Usage: 0.1
value expressions: _col2 (type: bigint), _col3 (type: bigint)
Reducer 4
@@ -245,7 +245,7 @@ STAGE PLANS:
Select Operator
expressions: KEY.reducesinkkey0 (type: string), KEY.reducesinkkey1 (type: string), VALUE._col0 (type: bigint), VALUE._col1 (type: bigint)
outputColumnNames: _col0, _col1, _col2, _col3
- Statistics: Num rows: 12506076 Data size: 197593980 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 6253038 Data size: 98796990 Basic stats: COMPLETE Column stats: NONE
Limit
Number of rows: 100
Statistics: Num rows: 100 Data size: 1500 Basic stats: COMPLETE Column stats: NONE
diff --git a/ql/src/test/results/clientpositive/perf/spark/query34.q.out b/ql/src/test/results/clientpositive/perf/spark/query34.q.out
index 1dd58b3..517f352 100644
--- a/ql/src/test/results/clientpositive/perf/spark/query34.q.out
+++ b/ql/src/test/results/clientpositive/perf/spark/query34.q.out
@@ -153,10 +153,10 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: date_dim
- filterExpr: ((d_year) IN (2000, 2001, 2002) and (d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and d_date_sk is not null) (type: boolean)
+ filterExpr: ((d_year) IN (2000, 2001, 2002) and ((d_dom >= 1) or (d_dom <= 3) or (d_dom >= 25) or (d_dom <= 28)) and (d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and d_date_sk is not null) (type: boolean)
Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and (d_year) IN (2000, 2001, 2002) and d_date_sk is not null) (type: boolean)
+ predicate: (((d_dom >= 1) or (d_dom <= 3) or (d_dom >= 25) or (d_dom <= 28)) and (d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and (d_year) IN (2000, 2001, 2002) and d_date_sk is not null) (type: boolean)
Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: d_date_sk (type: int)
diff --git a/ql/src/test/results/clientpositive/perf/spark/query48.q.out b/ql/src/test/results/clientpositive/perf/spark/query48.q.out
index 024d205..3c99f4f 100644
--- a/ql/src/test/results/clientpositive/perf/spark/query48.q.out
+++ b/ql/src/test/results/clientpositive/perf/spark/query48.q.out
@@ -150,7 +150,7 @@ STAGE PLANS:
Spark
#### A masked pattern was here ####
Vertices:
- Map 8
+ Map 9
Map Operator Tree:
TableScan
alias: store
@@ -165,7 +165,7 @@ STAGE PLANS:
Statistics: Num rows: 1704 Data size: 3256276 Basic stats: COMPLETE Column stats: NONE
Spark HashTable Sink Operator
keys:
- 0 _col4 (type: int)
+ 0 _col3 (type: int)
1 _col0 (type: int)
Execution mode: vectorized
Local Work:
@@ -174,52 +174,33 @@ STAGE PLANS:
Stage: Stage-1
Spark
Edges:
- Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 46), Map 6 (PARTITION-LEVEL SORT, 46)
- Reducer 3 <- Map 7 (PARTITION-LEVEL SORT, 49), Reducer 2 (PARTITION-LEVEL SORT, 49)
- Reducer 4 <- Map 9 (PARTITION-LEVEL SORT, 218), Reducer 3 (PARTITION-LEVEL SORT, 218)
+ Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 133), Map 6 (PARTITION-LEVEL SORT, 133)
+ Reducer 3 <- Map 7 (PARTITION-LEVEL SORT, 147), Reducer 2 (PARTITION-LEVEL SORT, 147)
+ Reducer 4 <- Map 8 (PARTITION-LEVEL SORT, 319), Reducer 3 (PARTITION-LEVEL SORT, 319)
Reducer 5 <- Reducer 4 (GROUP, 1)
#### A masked pattern was here ####
Vertices:
Map 1
Map Operator Tree:
TableScan
- alias: customer_demographics
- filterExpr: ((cd_marital_status = 'M') and (cd_education_status = '4 yr Degree') and cd_demo_sk is not null) (type: boolean)
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: ((cd_education_status = '4 yr Degree') and (cd_marital_status = 'M') and cd_demo_sk is not null) (type: boolean)
- Statistics: Num rows: 465450 Data size: 179296539 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: cd_demo_sk (type: int)
- outputColumnNames: _col0
- Statistics: Num rows: 465450 Data size: 179296539 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 465450 Data size: 179296539 Basic stats: COMPLETE Column stats: NONE
- Execution mode: vectorized
- Map 6
- Map Operator Tree:
- TableScan
alias: store_sales
- filterExpr: ((ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_net_profit BETWEEN 0 AND 2000 or ss_net_profit BETWEEN 150 AND 3000 or ss_net_profit BETWEEN 50 AND 25000) and ss_store_sk is not null and ss_cdemo_sk is not null and ss_addr_sk is not null and ss_sold_date_sk is not null) (type: boolean)
+ filterExpr: (((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and ((ss_net_profit >= 0) or (ss_net_profit <= 2000) or ss_net_profit is not null or (ss_net_profit <= 3000) or (ss_net_profit <= 25000)) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and ss_store_sk is not null and ss_cdemo_sk is not null and ss_addr_sk is not null and ss_sold_da [...]
Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((ss_net_profit BETWEEN 0 AND 2000 or ss_net_profit BETWEEN 150 AND 3000 or ss_net_profit BETWEEN 50 AND 25000) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null) (type: boolean)
- Statistics: Num rows: 63999513 Data size: 5646055611 Basic stats: COMPLETE Column stats: NONE
+ predicate: (((ss_net_profit >= 0) or (ss_net_profit <= 2000) or ss_net_profit is not null or (ss_net_profit <= 3000) or (ss_net_profit <= 25000)) and ((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_sold_date_sk is not null and ss_st [...]
+ Statistics: Num rows: 191998545 Data size: 16938167362 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: ss_sold_date_sk (type: int), ss_cdemo_sk (type: int), ss_addr_sk (type: int), ss_store_sk (type: int), ss_quantity (type: int), ss_net_profit BETWEEN 0 AND 2000 (type: boolean), ss_net_profit BETWEEN 150 AND 3000 (type: boolean), ss_net_profit BETWEEN 50 AND 25000 (type: boolean)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7
- Statistics: Num rows: 63999513 Data size: 5646055611 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 191998545 Data size: 16938167362 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
- key expressions: _col1 (type: int)
+ key expressions: _col0 (type: int)
sort order: +
- Map-reduce partition columns: _col1 (type: int)
- Statistics: Num rows: 63999513 Data size: 5646055611 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean)
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 191998545 Data size: 16938167362 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean)
Execution mode: vectorized
- Map 7
+ Map 6
Map Operator Tree:
TableScan
alias: date_dim
@@ -238,7 +219,26 @@ STAGE PLANS:
Map-reduce partition columns: _col0 (type: int)
Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
Execution mode: vectorized
- Map 9
+ Map 7
+ Map Operator Tree:
+ TableScan
+ alias: customer_demographics
+ filterExpr: ((cd_marital_status = 'M') and (cd_education_status = '4 yr Degree') and cd_demo_sk is not null) (type: boolean)
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: ((cd_education_status = '4 yr Degree') and (cd_marital_status = 'M') and cd_demo_sk is not null) (type: boolean)
+ Statistics: Num rows: 465450 Data size: 179296539 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: cd_demo_sk (type: int)
+ outputColumnNames: _col0
+ Statistics: Num rows: 465450 Data size: 179296539 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 465450 Data size: 179296539 Basic stats: COMPLETE Column stats: NONE
+ Execution mode: vectorized
+ Map 8
Map Operator Tree:
TableScan
alias: customer_address
@@ -265,18 +265,16 @@ STAGE PLANS:
Inner Join 0 to 1
keys:
0 _col0 (type: int)
- 1 _col1 (type: int)
- outputColumnNames: _col1, _col3, _col4, _col5, _col6, _col7, _col8
- Statistics: Num rows: 70399465 Data size: 6210661306 Basic stats: COMPLETE Column stats: NONE
+ 1 _col0 (type: int)
+ outputColumnNames: _col1, _col2, _col3, _col4, _col5, _col6, _col7
+ Statistics: Num rows: 211198404 Data size: 18631984502 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col1 (type: int)
sort order: +
Map-reduce partition columns: _col1 (type: int)
- Statistics: Num rows: 70399465 Data size: 6210661306 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col3 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean)
+ Statistics: Num rows: 211198404 Data size: 18631984502 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean)
Reducer 3
- Local Work:
- Map Reduce Local Work
Reduce Operator Tree:
Join Operator
condition map:
@@ -284,43 +282,41 @@ STAGE PLANS:
keys:
0 _col1 (type: int)
1 _col0 (type: int)
- outputColumnNames: _col3, _col4, _col5, _col6, _col7, _col8
- Statistics: Num rows: 77439413 Data size: 6831727584 Basic stats: COMPLETE Column stats: NONE
- Map Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col4 (type: int)
- 1 _col0 (type: int)
- outputColumnNames: _col3, _col5, _col6, _col7, _col8
- input vertices:
- 1 Map 8
- Statistics: Num rows: 85183356 Data size: 7514900505 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col3 (type: int)
- sort order: +
- Map-reduce partition columns: _col3 (type: int)
- Statistics: Num rows: 85183356 Data size: 7514900505 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col5 (type: int), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean)
+ outputColumnNames: _col2, _col3, _col4, _col5, _col6, _col7
+ Statistics: Num rows: 232318249 Data size: 20495183396 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col2 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col2 (type: int)
+ Statistics: Num rows: 232318249 Data size: 20495183396 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col3 (type: int), _col4 (type: int), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean)
Reducer 4
+ Local Work:
+ Map Reduce Local Work
Reduce Operator Tree:
Join Operator
condition map:
Inner Join 0 to 1
keys:
- 0 _col3 (type: int)
+ 0 _col2 (type: int)
1 _col0 (type: int)
- outputColumnNames: _col5, _col6, _col7, _col8, _col12, _col13, _col14
- Statistics: Num rows: 93701693 Data size: 8266390734 Basic stats: COMPLETE Column stats: NONE
+ outputColumnNames: _col3, _col4, _col5, _col6, _col7, _col11, _col12, _col13
+ Statistics: Num rows: 255550079 Data size: 22544702224 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((_col12 and _col6) or (_col13 and _col7) or (_col14 and _col8)) (type: boolean)
- Statistics: Num rows: 70276269 Data size: 6199792984 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: _col5 (type: int)
- outputColumnNames: _col5
- Statistics: Num rows: 70276269 Data size: 6199792984 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((_col11 and _col5) or (_col12 and _col6) or (_col13 and _col7)) (type: boolean)
+ Statistics: Num rows: 191662557 Data size: 16908526469 Basic stats: COMPLETE Column stats: NONE
+ Map Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col3 (type: int)
+ 1 _col0 (type: int)
+ outputColumnNames: _col4
+ input vertices:
+ 1 Map 9
+ Statistics: Num rows: 210828817 Data size: 18599379519 Basic stats: COMPLETE Column stats: NONE
Group By Operator
- aggregations: sum(_col5)
+ aggregations: sum(_col4)
mode: hash
outputColumnNames: _col0
Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
diff --git a/ql/src/test/results/clientpositive/perf/spark/query54.q.out b/ql/src/test/results/clientpositive/perf/spark/query54.q.out
index 690f1d4..b3c9566 100644
--- a/ql/src/test/results/clientpositive/perf/spark/query54.q.out
+++ b/ql/src/test/results/clientpositive/perf/spark/query54.q.out
@@ -1,7 +1,4 @@
-Warning: Shuffle Join JOIN[111][tables = [$hdt$_1, $hdt$_2]] in Work 'Reducer 3' is a cross product
-Warning: Shuffle Join JOIN[107][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4]] in Work 'Reducer 14' is a cross product
-Warning: Shuffle Join JOIN[114][tables = [$hdt$_1, $hdt$_2, $hdt$_0]] in Work 'Reducer 4' is a cross product
-Warning: Map Join MAPJOIN[144][bigTable=?] in task 'Stage-1:MAPRED' is a cross product
+Warning: Shuffle Join JOIN[107][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4, $hdt$_5, $hdt$_6]] in Work 'Reducer 4' is a cross product
PREHOOK: query: explain
with my_customers as (
select distinct c_customer_sk
@@ -132,86 +129,15 @@ POSTHOOK: Input: default@web_sales
#### A masked pattern was here ####
STAGE DEPENDENCIES:
Stage-2 is a root stage
- Stage-3 depends on stages: Stage-2
- Stage-1 depends on stages: Stage-3
+ Stage-1 depends on stages: Stage-2
Stage-0 depends on stages: Stage-1
STAGE PLANS:
Stage: Stage-2
Spark
- Edges:
- Reducer 29 <- Map 28 (GROUP, 2)
- Reducer 30 <- Reducer 29 (GROUP, 1)
-#### A masked pattern was here ####
- Vertices:
- Map 28
- Map Operator Tree:
- TableScan
- alias: date_dim
- filterExpr: ((d_year = 1999) and (d_moy = 3)) (type: boolean)
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: ((d_moy = 3) and (d_year = 1999)) (type: boolean)
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: (d_month_seq + 1) (type: int)
- outputColumnNames: _col0
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Group By Operator
- keys: _col0 (type: int)
- mode: hash
- outputColumnNames: _col0
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Execution mode: vectorized
- Reducer 29
- Execution mode: vectorized
- Reduce Operator Tree:
- Group By Operator
- keys: KEY._col0 (type: int)
- mode: mergepartial
- outputColumnNames: _col0
- Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
- Group By Operator
- aggregations: count()
- mode: hash
- outputColumnNames: _col0
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: bigint)
- Reducer 30
- Execution mode: vectorized
- Local Work:
- Map Reduce Local Work
- Reduce Operator Tree:
- Group By Operator
- aggregations: count(VALUE._col0)
- mode: mergepartial
- outputColumnNames: _col0
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: (sq_count_check(_col0) <= 1) (type: boolean)
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Spark HashTable Sink Operator
- keys:
- 0
- 1
-
- Stage: Stage-3
- Spark
#### A masked pattern was here ####
Vertices:
- Map 18
+ Map 11
Map Operator Tree:
TableScan
alias: store
@@ -235,52 +161,28 @@ STAGE PLANS:
Stage: Stage-1
Spark
Edges:
- Reducer 10 <- Reducer 9 (GROUP, 1)
- Reducer 12 <- Map 11 (PARTITION-LEVEL SORT, 398), Map 15 (PARTITION-LEVEL SORT, 398)
- Reducer 13 <- Reducer 12 (PARTITION-LEVEL SORT, 772), Reducer 17 (PARTITION-LEVEL SORT, 772)
- Reducer 14 <- Reducer 13 (PARTITION-LEVEL SORT, 1), Reducer 32 (PARTITION-LEVEL SORT, 1)
- Reducer 17 <- Map 16 (PARTITION-LEVEL SORT, 654), Reducer 23 (PARTITION-LEVEL SORT, 654)
- Reducer 2 <- Map 1 (GROUP, 2)
- Reducer 20 <- Map 19 (PARTITION-LEVEL SORT, 458), Map 24 (PARTITION-LEVEL SORT, 458), Map 25 (PARTITION-LEVEL SORT, 458)
- Reducer 21 <- Map 26 (PARTITION-LEVEL SORT, 505), Reducer 20 (PARTITION-LEVEL SORT, 505)
- Reducer 22 <- Map 27 (PARTITION-LEVEL SORT, 1009), Reducer 21 (PARTITION-LEVEL SORT, 1009)
- Reducer 23 <- Reducer 22 (GROUP, 610)
- Reducer 3 <- Reducer 10 (PARTITION-LEVEL SORT, 1), Reducer 2 (PARTITION-LEVEL SORT, 1)
- Reducer 32 <- Map 31 (GROUP, 2)
- Reducer 4 <- Reducer 14 (PARTITION-LEVEL SORT, 1), Reducer 3 (PARTITION-LEVEL SORT, 1)
+ Reducer 10 <- Map 9 (PARTITION-LEVEL SORT, 654), Reducer 16 (PARTITION-LEVEL SORT, 654)
+ Reducer 13 <- Map 12 (PARTITION-LEVEL SORT, 458), Map 17 (PARTITION-LEVEL SORT, 458), Map 18 (PARTITION-LEVEL SORT, 458)
+ Reducer 14 <- Map 19 (PARTITION-LEVEL SORT, 505), Reducer 13 (PARTITION-LEVEL SORT, 505)
+ Reducer 15 <- Map 20 (PARTITION-LEVEL SORT, 1009), Reducer 14 (PARTITION-LEVEL SORT, 1009)
+ Reducer 16 <- Reducer 15 (GROUP, 610)
+ Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 398), Map 8 (PARTITION-LEVEL SORT, 398)
+ Reducer 22 <- Map 21 (GROUP, 2)
+ Reducer 23 <- Reducer 22 (GROUP, 1)
+ Reducer 25 <- Map 24 (GROUP, 2)
+ Reducer 26 <- Reducer 25 (GROUP, 1)
+ Reducer 28 <- Map 24 (GROUP, 2)
+ Reducer 3 <- Reducer 10 (PARTITION-LEVEL SORT, 772), Reducer 2 (PARTITION-LEVEL SORT, 772)
+ Reducer 30 <- Map 21 (GROUP, 2)
+ Reducer 4 <- Reducer 23 (PARTITION-LEVEL SORT, 1), Reducer 26 (PARTITION-LEVEL SORT, 1), Reducer 28 (PARTITION-LEVEL SORT, 1), Reducer 3 (PARTITION-LEVEL SORT, 1), Reducer 30 (PARTITION-LEVEL SORT, 1)
Reducer 5 <- Reducer 4 (GROUP, 1009)
Reducer 6 <- Reducer 5 (GROUP, 1009)
Reducer 7 <- Reducer 6 (SORT, 1)
- Reducer 9 <- Map 1 (GROUP, 2)
#### A masked pattern was here ####
Vertices:
Map 1
Map Operator Tree:
TableScan
- alias: date_dim
- filterExpr: ((d_year = 1999) and (d_moy = 3)) (type: boolean)
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: ((d_moy = 3) and (d_year = 1999)) (type: boolean)
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: (d_month_seq + 3) (type: int)
- outputColumnNames: _col0
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Group By Operator
- keys: _col0 (type: int)
- mode: hash
- outputColumnNames: _col0
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
- Execution mode: vectorized
- Map 11
- Map Operator Tree:
- TableScan
alias: store_sales
filterExpr: (ss_customer_sk is not null and ss_sold_date_sk is not null) (type: boolean)
Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
@@ -298,58 +200,7 @@ STAGE PLANS:
Statistics: Num rows: 575995635 Data size: 50814502088 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: int), _col2 (type: decimal(7,2))
Execution mode: vectorized
- Map 15
- Map Operator Tree:
- TableScan
- alias: date_dim
- filterExpr: d_date_sk is not null (type: boolean)
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: d_date_sk is not null (type: boolean)
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: d_date_sk (type: int), d_month_seq (type: int)
- outputColumnNames: _col0, _col1
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: int)
- Execution mode: vectorized
- Map 16
- Map Operator Tree:
- TableScan
- alias: customer_address
- filterExpr: (ca_address_sk is not null and ca_county is not null and ca_state is not null) (type: boolean)
- Statistics: Num rows: 40000000 Data size: 40595195284 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: (ca_address_sk is not null and ca_county is not null and ca_state is not null) (type: boolean)
- Statistics: Num rows: 40000000 Data size: 40595195284 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: ca_address_sk (type: int), ca_county (type: string), ca_state (type: string)
- outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 40000000 Data size: 40595195284 Basic stats: COMPLETE Column stats: NONE
- Map Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col1 (type: string), _col2 (type: string)
- 1 _col0 (type: string), _col1 (type: string)
- outputColumnNames: _col0
- input vertices:
- 1 Map 18
- Statistics: Num rows: 44000000 Data size: 44654715780 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 44000000 Data size: 44654715780 Basic stats: COMPLETE Column stats: NONE
- Execution mode: vectorized
- Local Work:
- Map Reduce Local Work
- Map 19
+ Map 12
Map Operator Tree:
TableScan
alias: catalog_sales
@@ -369,7 +220,7 @@ STAGE PLANS:
Statistics: Num rows: 431992504 Data size: 58579807164 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: int), _col2 (type: int)
Execution mode: vectorized
- Map 24
+ Map 17
Map Operator Tree:
TableScan
alias: web_sales
@@ -389,7 +240,7 @@ STAGE PLANS:
Statistics: Num rows: 431992504 Data size: 58579807164 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: int), _col2 (type: int)
Execution mode: vectorized
- Map 25
+ Map 18
Map Operator Tree:
TableScan
alias: date_dim
@@ -408,7 +259,7 @@ STAGE PLANS:
Map-reduce partition columns: _col0 (type: int)
Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
Execution mode: vectorized
- Map 26
+ Map 19
Map Operator Tree:
TableScan
alias: item
@@ -427,7 +278,7 @@ STAGE PLANS:
Map-reduce partition columns: _col0 (type: int)
Statistics: Num rows: 115500 Data size: 165890114 Basic stats: COMPLETE Column stats: NONE
Execution mode: vectorized
- Map 27
+ Map 20
Map Operator Tree:
TableScan
alias: customer
@@ -447,7 +298,31 @@ STAGE PLANS:
Statistics: Num rows: 80000000 Data size: 68801615852 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: int)
Execution mode: vectorized
- Map 31
+ Map 21
+ Map Operator Tree:
+ TableScan
+ alias: date_dim
+ filterExpr: ((d_year = 1999) and (d_moy = 3)) (type: boolean)
+ Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: ((d_moy = 3) and (d_year = 1999)) (type: boolean)
+ Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: (d_month_seq + 3) (type: int)
+ outputColumnNames: _col0
+ Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
+ Group By Operator
+ keys: _col0 (type: int)
+ mode: hash
+ outputColumnNames: _col0
+ Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
+ Execution mode: vectorized
+ Map 24
Map Operator Tree:
TableScan
alias: date_dim
@@ -471,83 +346,58 @@ STAGE PLANS:
Map-reduce partition columns: _col0 (type: int)
Statistics: Num rows: 18262 Data size: 20435178 Basic stats: COMPLETE Column stats: NONE
Execution mode: vectorized
- Reducer 10
+ Map 8
+ Map Operator Tree:
+ TableScan
+ alias: date_dim
+ filterExpr: d_date_sk is not null (type: boolean)
+ Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: d_date_sk is not null (type: boolean)
+ Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: d_date_sk (type: int), d_month_seq (type: int)
+ outputColumnNames: _col0, _col1
+ Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col1 (type: int)
+ Execution mode: vectorized
+ Map 9
+ Map Operator Tree:
+ TableScan
+ alias: customer_address
+ filterExpr: (ca_address_sk is not null and ca_county is not null and ca_state is not null) (type: boolean)
+ Statistics: Num rows: 40000000 Data size: 40595195284 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: (ca_address_sk is not null and ca_county is not null and ca_state is not null) (type: boolean)
+ Statistics: Num rows: 40000000 Data size: 40595195284 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: ca_address_sk (type: int), ca_county (type: string), ca_state (type: string)
+ outputColumnNames: _col0, _col1, _col2
+ Statistics: Num rows: 40000000 Data size: 40595195284 Basic stats: COMPLETE Column stats: NONE
+ Map Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col1 (type: string), _col2 (type: string)
+ 1 _col0 (type: string), _col1 (type: string)
+ outputColumnNames: _col0
+ input vertices:
+ 1 Map 11
+ Statistics: Num rows: 44000000 Data size: 44654715780 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 44000000 Data size: 44654715780 Basic stats: COMPLETE Column stats: NONE
Execution mode: vectorized
- Reduce Operator Tree:
- Group By Operator
- aggregations: count(VALUE._col0)
- mode: mergepartial
- outputColumnNames: _col0
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: (sq_count_check(_col0) <= 1) (type: boolean)
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Reducer 12
- Reduce Operator Tree:
- Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col0 (type: int)
- 1 _col0 (type: int)
- outputColumnNames: _col1, _col2, _col4
- Statistics: Num rows: 633595212 Data size: 55895953508 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col1 (type: int)
- sort order: +
- Map-reduce partition columns: _col1 (type: int)
- Statistics: Num rows: 633595212 Data size: 55895953508 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col2 (type: decimal(7,2)), _col4 (type: int)
- Reducer 13
Local Work:
Map Reduce Local Work
- Reduce Operator Tree:
- Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col1 (type: int)
- 1 _col5 (type: int)
- outputColumnNames: _col2, _col4, _col10
- Statistics: Num rows: 696954748 Data size: 61485550191 Basic stats: COMPLETE Column stats: NONE
- Map Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0
- 1
- outputColumnNames: _col2, _col4, _col10
- input vertices:
- 1 Reducer 30
- Statistics: Num rows: 696954748 Data size: 67758142923 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 696954748 Data size: 67758142923 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col2 (type: decimal(7,2)), _col4 (type: int), _col10 (type: int)
- Reducer 14
- Reduce Operator Tree:
- Join Operator
- condition map:
- Left Outer Join 0 to 1
- keys:
- 0
- 1
- outputColumnNames: _col2, _col4, _col10, _col13
- Statistics: Num rows: 6363893803988 Data size: 7746260663496473 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: _col10 (type: int), _col2 (type: decimal(7,2)), _col4 (type: int), _col13 (type: int)
- outputColumnNames: _col0, _col4, _col11, _col13
- Statistics: Num rows: 6363893803988 Data size: 7746260663496473 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 6363893803988 Data size: 7746260663496473 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col4 (type: decimal(7,2)), _col11 (type: int), _col13 (type: int)
- Reducer 17
+ Reducer 10
Reduce Operator Tree:
Join Operator
condition map:
@@ -562,19 +412,7 @@ STAGE PLANS:
sort order: +
Map-reduce partition columns: _col5 (type: int)
Statistics: Num rows: 316240138 Data size: 42883351551 Basic stats: COMPLETE Column stats: NONE
- Reducer 2
- Execution mode: vectorized
- Reduce Operator Tree:
- Group By Operator
- keys: KEY._col0 (type: int)
- mode: mergepartial
- outputColumnNames: _col0
- Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int)
- Reducer 20
+ Reducer 13
Reduce Operator Tree:
Join Operator
condition map:
@@ -590,7 +428,7 @@ STAGE PLANS:
Map-reduce partition columns: _col2 (type: int)
Statistics: Num rows: 475191764 Data size: 64437789277 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: int)
- Reducer 21
+ Reducer 14
Reduce Operator Tree:
Join Operator
condition map:
@@ -605,7 +443,7 @@ STAGE PLANS:
sort order: +
Map-reduce partition columns: _col1 (type: int)
Statistics: Num rows: 522710951 Data size: 70881569741 Basic stats: COMPLETE Column stats: NONE
- Reducer 22
+ Reducer 15
Reduce Operator Tree:
Join Operator
condition map:
@@ -625,7 +463,7 @@ STAGE PLANS:
sort order: ++
Map-reduce partition columns: _col0 (type: int), _col1 (type: int)
Statistics: Num rows: 574982058 Data size: 77969728405 Basic stats: COMPLETE Column stats: NONE
- Reducer 23
+ Reducer 16
Execution mode: vectorized
Reduce Operator Tree:
Group By Operator
@@ -643,24 +481,119 @@ STAGE PLANS:
Map-reduce partition columns: _col1 (type: int)
Statistics: Num rows: 287491029 Data size: 38984864202 Basic stats: COMPLETE Column stats: NONE
value expressions: _col0 (type: int)
- Reducer 3
+ Reducer 2
Reduce Operator Tree:
Join Operator
condition map:
- Right Outer Join 0 to 1
- filter predicates:
- 0
- 1 {true}
+ Inner Join 0 to 1
keys:
- 0
- 1
+ 0 _col0 (type: int)
+ 1 _col0 (type: int)
+ outputColumnNames: _col1, _col2, _col4
+ Statistics: Num rows: 633595212 Data size: 55895953508 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col1 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col1 (type: int)
+ Statistics: Num rows: 633595212 Data size: 55895953508 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col2 (type: decimal(7,2)), _col4 (type: int)
+ Reducer 22
+ Execution mode: vectorized
+ Reduce Operator Tree:
+ Group By Operator
+ keys: KEY._col0 (type: int)
+ mode: mergepartial
+ outputColumnNames: _col0
+ Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
+ Group By Operator
+ aggregations: count()
+ mode: hash
+ outputColumnNames: _col0
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: bigint)
+ Reducer 23
+ Execution mode: vectorized
+ Reduce Operator Tree:
+ Group By Operator
+ aggregations: count(VALUE._col0)
+ mode: mergepartial
+ outputColumnNames: _col0
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: (sq_count_check(_col0) <= 1) (type: boolean)
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Reducer 25
+ Execution mode: vectorized
+ Reduce Operator Tree:
+ Group By Operator
+ keys: KEY._col0 (type: int)
+ mode: mergepartial
+ outputColumnNames: _col0
+ Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
+ Group By Operator
+ aggregations: count()
+ mode: hash
+ outputColumnNames: _col0
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: bigint)
+ Reducer 26
+ Execution mode: vectorized
+ Reduce Operator Tree:
+ Group By Operator
+ aggregations: count(VALUE._col0)
+ mode: mergepartial
outputColumnNames: _col0
- Statistics: Num rows: 9131 Data size: 10299768 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: (sq_count_check(_col0) <= 1) (type: boolean)
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
+ Reducer 28
+ Execution mode: vectorized
+ Reduce Operator Tree:
+ Group By Operator
+ keys: KEY._col0 (type: int)
+ mode: mergepartial
+ outputColumnNames: _col0
+ Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
sort order:
- Statistics: Num rows: 9131 Data size: 10299768 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
value expressions: _col0 (type: int)
- Reducer 32
+ Reducer 3
+ Reduce Operator Tree:
+ Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col1 (type: int)
+ 1 _col5 (type: int)
+ outputColumnNames: _col2, _col4, _col10
+ Statistics: Num rows: 696954748 Data size: 61485550191 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 696954748 Data size: 61485550191 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col2 (type: decimal(7,2)), _col4 (type: int), _col10 (type: int)
+ Reducer 30
Execution mode: vectorized
Reduce Operator Tree:
Group By Operator
@@ -677,34 +610,36 @@ STAGE PLANS:
Join Operator
condition map:
Inner Join 0 to 1
+ Inner Join 0 to 2
+ Inner Join 0 to 3
+ Inner Join 0 to 4
keys:
0
1
- outputColumnNames: _col0, _col2, _col6, _col13, _col15
+ 2
+ 3
+ 4
+ outputColumnNames: _col2, _col4, _col10, _col14, _col15
Statistics: Num rows: 58108714324214428 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: _col2 (type: int), _col6 (type: decimal(7,2)), _col13 (type: int), _col15 (type: int), _col0 (type: int)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4
- Statistics: Num rows: 58108714324214428 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: _col2 BETWEEN _col3 AND _col4 (type: boolean)
+ Filter Operator
+ predicate: ((_col14 <= _col4) and (_col4 <= _col15)) (type: boolean)
+ Statistics: Num rows: 6456523813801603 Data size: 1024819115206086144 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: _col2 (type: decimal(7,2)), _col10 (type: int)
+ outputColumnNames: _col2, _col10
Statistics: Num rows: 6456523813801603 Data size: 1024819115206086144 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: _col0 (type: int), _col1 (type: decimal(7,2))
+ Group By Operator
+ aggregations: sum(_col2)
+ keys: _col10 (type: int)
+ mode: hash
outputColumnNames: _col0, _col1
Statistics: Num rows: 6456523813801603 Data size: 1024819115206086144 Basic stats: COMPLETE Column stats: NONE
- Group By Operator
- aggregations: sum(_col1)
- keys: _col0 (type: int)
- mode: hash
- outputColumnNames: _col0, _col1
+ Reduce Output Operator
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
Statistics: Num rows: 6456523813801603 Data size: 1024819115206086144 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 6456523813801603 Data size: 1024819115206086144 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: decimal(17,2))
+ value expressions: _col1 (type: decimal(17,2))
Reducer 5
Execution mode: vectorized
Reduce Operator Tree:
@@ -766,25 +701,6 @@ STAGE PLANS:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
- Reducer 9
- Execution mode: vectorized
- Reduce Operator Tree:
- Group By Operator
- keys: KEY._col0 (type: int)
- mode: mergepartial
- outputColumnNames: _col0
- Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- Statistics: Num rows: 9131 Data size: 10217589 Basic stats: COMPLETE Column stats: NONE
- Group By Operator
- aggregations: count()
- mode: hash
- outputColumnNames: _col0
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 1 Data size: 8 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: bigint)
Stage: Stage-0
Fetch Operator
diff --git a/ql/src/test/results/clientpositive/perf/spark/query85.q.out b/ql/src/test/results/clientpositive/perf/spark/query85.q.out
index a7bf288..de1bf24 100644
--- a/ql/src/test/results/clientpositive/perf/spark/query85.q.out
+++ b/ql/src/test/results/clientpositive/perf/spark/query85.q.out
@@ -182,7 +182,8 @@ POSTHOOK: Input: default@web_sales
#### A masked pattern was here ####
STAGE DEPENDENCIES:
Stage-2 is a root stage
- Stage-1 depends on stages: Stage-2
+ Stage-3 depends on stages: Stage-2
+ Stage-1 depends on stages: Stage-3
Stage-0 depends on stages: Stage-1
STAGE PLANS:
@@ -190,7 +191,7 @@ STAGE PLANS:
Spark
#### A masked pattern was here ####
Vertices:
- Map 12
+ Map 15
Map Operator Tree:
TableScan
alias: web_page
@@ -205,12 +206,17 @@ STAGE PLANS:
Statistics: Num rows: 4602 Data size: 2696178 Basic stats: COMPLETE Column stats: NONE
Spark HashTable Sink Operator
keys:
- 0 _col10 (type: int)
+ 0 _col2 (type: int)
1 _col0 (type: int)
Execution mode: vectorized
Local Work:
Map Reduce Local Work
- Map 13
+
+ Stage: Stage-3
+ Spark
+#### A masked pattern was here ####
+ Vertices:
+ Map 14
Map Operator Tree:
TableScan
alias: reason
@@ -234,77 +240,38 @@ STAGE PLANS:
Stage: Stage-1
Spark
Edges:
- Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 28), Map 9 (PARTITION-LEVEL SORT, 28)
- Reducer 3 <- Map 10 (PARTITION-LEVEL SORT, 25), Reducer 2 (PARTITION-LEVEL SORT, 25)
- Reducer 4 <- Map 11 (PARTITION-LEVEL SORT, 21), Reducer 3 (PARTITION-LEVEL SORT, 21)
- Reducer 5 <- Map 14 (PARTITION-LEVEL SORT, 186), Reducer 4 (PARTITION-LEVEL SORT, 186)
- Reducer 6 <- Map 15 (PARTITION-LEVEL SORT, 29), Reducer 5 (PARTITION-LEVEL SORT, 29)
- Reducer 7 <- Reducer 6 (GROUP, 10)
- Reducer 8 <- Reducer 7 (SORT, 1)
+ Reducer 10 <- Map 13 (PARTITION-LEVEL SORT, 198), Reducer 9 (PARTITION-LEVEL SORT, 198)
+ Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 154), Map 6 (PARTITION-LEVEL SORT, 154)
+ Reducer 3 <- Reducer 10 (PARTITION-LEVEL SORT, 401), Reducer 2 (PARTITION-LEVEL SORT, 401)
+ Reducer 4 <- Reducer 3 (GROUP, 58)
+ Reducer 5 <- Reducer 4 (SORT, 1)
+ Reducer 8 <- Map 11 (PARTITION-LEVEL SORT, 169), Map 7 (PARTITION-LEVEL SORT, 169)
+ Reducer 9 <- Map 12 (PARTITION-LEVEL SORT, 181), Reducer 8 (PARTITION-LEVEL SORT, 181)
#### A masked pattern was here ####
Vertices:
Map 1
Map Operator Tree:
TableScan
- alias: web_returns
- filterExpr: (wr_item_sk is not null and wr_order_number is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null and wr_refunded_addr_sk is not null and wr_reason_sk is not null) (type: boolean)
- Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: (wr_item_sk is not null and wr_order_number is not null and wr_reason_sk is not null and wr_refunded_addr_sk is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null) (type: boolean)
- Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: wr_item_sk (type: int), wr_refunded_cdemo_sk (type: int), wr_refunded_addr_sk (type: int), wr_returning_cdemo_sk (type: int), wr_reason_sk (type: int), wr_order_number (type: int), wr_fee (type: decimal(7,2)), wr_refunded_cash (type: decimal(7,2))
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7
- Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int), _col5 (type: int)
- sort order: ++
- Map-reduce partition columns: _col0 (type: int), _col5 (type: int)
- Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2))
- Execution mode: vectorized
- Map 10
- Map Operator Tree:
- TableScan
- alias: cd2
- filterExpr: ((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null) (type: boolean)
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ alias: web_sales
+ filterExpr: (((ws_sales_price >= 100) or (ws_sales_price <= 150) or ws_sales_price is not null or (ws_sales_price <= 200)) and ((ws_net_profit >= 100) or (ws_net_profit <= 200) or ws_net_profit is not null or (ws_net_profit <= 300) or (ws_net_profit <= 250)) and ws_item_sk is not null and ws_order_number is not null and ws_web_page_sk is not null and ws_sold_date_sk is not null) (type: boolean)
+ Statistics: Num rows: 144002668 Data size: 19580198212 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null) (type: boolean)
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ predicate: (((ws_net_profit >= 100) or (ws_net_profit <= 200) or ws_net_profit is not null or (ws_net_profit <= 300) or (ws_net_profit <= 250)) and ((ws_sales_price >= 100) or (ws_sales_price <= 150) or ws_sales_price is not null or (ws_sales_price <= 200)) and ws_item_sk is not null and ws_order_number is not null and ws_sold_date_sk is not null and ws_web_page_sk is not null) (type: boolean)
+ Statistics: Num rows: 144002668 Data size: 19580198212 Basic stats: COMPLETE Column stats: NONE
Select Operator
- expressions: cd_demo_sk (type: int), cd_marital_status (type: string), cd_education_status (type: string)
- outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ expressions: ws_sold_date_sk (type: int), ws_item_sk (type: int), ws_web_page_sk (type: int), ws_order_number (type: int), ws_quantity (type: int), ws_net_profit BETWEEN 100 AND 200 (type: boolean), ws_net_profit BETWEEN 150 AND 300 (type: boolean), ws_net_profit BETWEEN 50 AND 250 (type: boolean), ws_sales_price BETWEEN 100 AND 150 (type: boolean), ws_sales_price BETWEEN 50 AND 100 (type: boolean), ws_sales_price BETWEEN 150 AND 200 (type: boolean)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
+ Statistics: Num rows: 144002668 Data size: 19580198212 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: int)
sort order: +
Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: string), _col2 (type: string)
+ Statistics: Num rows: 144002668 Data size: 19580198212 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col1 (type: int), _col2 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean)
Execution mode: vectorized
Map 11
Map Operator Tree:
TableScan
- alias: date_dim
- filterExpr: ((d_year = 1998) and d_date_sk is not null) (type: boolean)
- Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: ((d_year = 1998) and d_date_sk is not null) (type: boolean)
- Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: d_date_sk (type: int)
- outputColumnNames: _col0
- Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col0 (type: int)
- sort order: +
- Map-reduce partition columns: _col0 (type: int)
- Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
- Execution mode: vectorized
- Map 14
- Map Operator Tree:
- TableScan
alias: customer_address
filterExpr: ((ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV') and (ca_country = 'United States') and ca_address_sk is not null) (type: boolean)
Statistics: Num rows: 40000000 Data size: 40595195284 Basic stats: COMPLETE Column stats: NONE
@@ -322,7 +289,27 @@ STAGE PLANS:
Statistics: Num rows: 20000000 Data size: 20297597642 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: boolean), _col2 (type: boolean), _col3 (type: boolean)
Execution mode: vectorized
- Map 15
+ Map 12
+ Map Operator Tree:
+ TableScan
+ alias: cd2
+ filterExpr: ((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null) (type: boolean)
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: ((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null) (type: boolean)
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: cd_demo_sk (type: int), cd_marital_status (type: string), cd_education_status (type: string)
+ outputColumnNames: _col0, _col1, _col2
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col1 (type: string), _col2 (type: string)
+ Execution mode: vectorized
+ Map 13
Map Operator Tree:
TableScan
alias: cd1
@@ -342,59 +329,46 @@ STAGE PLANS:
Statistics: Num rows: 1861800 Data size: 717186159 Basic stats: COMPLETE Column stats: NONE
value expressions: _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean)
Execution mode: vectorized
- Map 9
+ Map 6
Map Operator Tree:
TableScan
- alias: web_sales
- filterExpr: ((ws_sales_price BETWEEN 100 AND 150 or ws_sales_price BETWEEN 50 AND 100 or ws_sales_price BETWEEN 150 AND 200) and (ws_net_profit BETWEEN 100 AND 200 or ws_net_profit BETWEEN 150 AND 300 or ws_net_profit BETWEEN 50 AND 250) and ws_item_sk is not null and ws_order_number is not null and ws_web_page_sk is not null and ws_sold_date_sk is not null) (type: boolean)
- Statistics: Num rows: 144002668 Data size: 19580198212 Basic stats: COMPLETE Column stats: NONE
+ alias: date_dim
+ filterExpr: ((d_year = 1998) and d_date_sk is not null) (type: boolean)
+ Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((ws_net_profit BETWEEN 100 AND 200 or ws_net_profit BETWEEN 150 AND 300 or ws_net_profit BETWEEN 50 AND 250) and (ws_sales_price BETWEEN 100 AND 150 or ws_sales_price BETWEEN 50 AND 100 or ws_sales_price BETWEEN 150 AND 200) and ws_item_sk is not null and ws_order_number is not null and ws_sold_date_sk is not null and ws_web_page_sk is not null) (type: boolean)
- Statistics: Num rows: 16000296 Data size: 2175577518 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((d_year = 1998) and d_date_sk is not null) (type: boolean)
+ Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
Select Operator
- expressions: ws_sold_date_sk (type: int), ws_item_sk (type: int), ws_web_page_sk (type: int), ws_order_number (type: int), ws_quantity (type: int), ws_net_profit BETWEEN 100 AND 200 (type: boolean), ws_net_profit BETWEEN 150 AND 300 (type: boolean), ws_net_profit BETWEEN 50 AND 250 (type: boolean), ws_sales_price BETWEEN 100 AND 150 (type: boolean), ws_sales_price BETWEEN 50 AND 100 (type: boolean), ws_sales_price BETWEEN 150 AND 200 (type: boolean)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
- Statistics: Num rows: 16000296 Data size: 2175577518 Basic stats: COMPLETE Column stats: NONE
+ expressions: d_date_sk (type: int)
+ outputColumnNames: _col0
+ Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
- key expressions: _col1 (type: int), _col3 (type: int)
- sort order: ++
- Map-reduce partition columns: _col1 (type: int), _col3 (type: int)
- Statistics: Num rows: 16000296 Data size: 2175577518 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col2 (type: int), _col4 (type: int), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean)
+ key expressions: _col0 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col0 (type: int)
+ Statistics: Num rows: 36524 Data size: 40870356 Basic stats: COMPLETE Column stats: NONE
Execution mode: vectorized
- Reducer 2
- Reduce Operator Tree:
- Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col0 (type: int), _col5 (type: int)
- 1 _col1 (type: int), _col3 (type: int)
- outputColumnNames: _col1, _col2, _col3, _col4, _col6, _col7, _col8, _col10, _col12, _col13, _col14, _col15, _col16, _col17, _col18
- Statistics: Num rows: 17600325 Data size: 2393135321 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col3 (type: int)
- sort order: +
- Map-reduce partition columns: _col3 (type: int)
- Statistics: Num rows: 17600325 Data size: 2393135321 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: int), _col2 (type: int), _col4 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col8 (type: int), _col10 (type: int), _col12 (type: int), _col13 (type: boolean), _col14 (type: boolean), _col15 (type: boolean), _col16 (type: boolean), _col17 (type: boolean), _col18 (type: boolean)
- Reducer 3
- Reduce Operator Tree:
- Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col3 (type: int)
- 1 _col0 (type: int)
- outputColumnNames: _col1, _col2, _col4, _col6, _col7, _col8, _col10, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col20, _col21
- Statistics: Num rows: 19360357 Data size: 2632448910 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col8 (type: int)
- sort order: +
- Map-reduce partition columns: _col8 (type: int)
- Statistics: Num rows: 19360357 Data size: 2632448910 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: int), _col2 (type: int), _col4 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col10 (type: int), _col12 (type: int), _col13 (type: boolean), _col14 (type: boolean), _col15 (type: boolean), _col16 (type: boolean), _col17 (type: boolean), _col18 (type: boolean), _col20 (type: string), _col21 (type: string)
- Reducer 4
+ Map 7
+ Map Operator Tree:
+ TableScan
+ alias: web_returns
+ filterExpr: (wr_item_sk is not null and wr_order_number is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null and wr_refunded_addr_sk is not null and wr_reason_sk is not null) (type: boolean)
+ Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
+ Filter Operator
+ predicate: (wr_item_sk is not null and wr_order_number is not null and wr_reason_sk is not null and wr_refunded_addr_sk is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null) (type: boolean)
+ Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: wr_item_sk (type: int), wr_refunded_cdemo_sk (type: int), wr_refunded_addr_sk (type: int), wr_returning_cdemo_sk (type: int), wr_reason_sk (type: int), wr_order_number (type: int), wr_fee (type: decimal(7,2)), wr_refunded_cash (type: decimal(7,2))
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7
+ Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col2 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col2 (type: int)
+ Statistics: Num rows: 14398467 Data size: 1325194184 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: int), _col1 (type: int), _col3 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2))
+ Execution mode: vectorized
+ Reducer 10
Local Work:
Map Reduce Local Work
Reduce Operator Tree:
@@ -402,85 +376,84 @@ STAGE PLANS:
condition map:
Inner Join 0 to 1
keys:
- 0 _col8 (type: int)
- 1 _col0 (type: int)
- outputColumnNames: _col1, _col2, _col4, _col6, _col7, _col10, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col20, _col21
- Statistics: Num rows: 21296393 Data size: 2895693863 Basic stats: COMPLETE Column stats: NONE
+ 0 _col1 (type: int), _col13 (type: string), _col14 (type: string)
+ 1 _col0 (type: int), _col1 (type: string), _col2 (type: string)
+ outputColumnNames: _col0, _col4, _col5, _col6, _col7, _col9, _col10, _col11, _col18, _col19, _col20, _col21, _col22, _col23
+ Statistics: Num rows: 26620000 Data size: 27016104217 Basic stats: COMPLETE Column stats: NONE
Map Join Operator
condition map:
Inner Join 0 to 1
keys:
- 0 _col10 (type: int)
+ 0 _col4 (type: int)
1 _col0 (type: int)
- outputColumnNames: _col1, _col2, _col4, _col6, _col7, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col20, _col21
+ outputColumnNames: _col0, _col5, _col6, _col7, _col9, _col10, _col11, _col18, _col19, _col20, _col21, _col22, _col23, _col25
input vertices:
- 1 Map 12
- Statistics: Num rows: 23426032 Data size: 3185263318 Basic stats: COMPLETE Column stats: NONE
- Map Join Operator
- condition map:
- Inner Join 0 to 1
- keys:
- 0 _col4 (type: int)
- 1 _col0 (type: int)
- outputColumnNames: _col1, _col2, _col6, _col7, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col20, _col21, _col25
- input vertices:
- 1 Map 13
- Statistics: Num rows: 25768635 Data size: 3503789725 Basic stats: COMPLETE Column stats: NONE
+ 1 Map 14
+ Statistics: Num rows: 29282000 Data size: 29717715282 Basic stats: COMPLETE Column stats: NONE
+ Select Operator
+ expressions: _col18 (type: boolean), _col19 (type: boolean), _col20 (type: boolean), _col21 (type: boolean), _col22 (type: boolean), _col23 (type: boolean), _col0 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean), _col25 (type: string)
+ outputColumnNames: _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col14, _col15, _col16, _col18, _col19, _col20, _col25
+ Statistics: Num rows: 29282000 Data size: 29717715282 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
- key expressions: _col2 (type: int)
- sort order: +
- Map-reduce partition columns: _col2 (type: int)
- Statistics: Num rows: 25768635 Data size: 3503789725 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col1 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col12 (type: int), _col13 (type: boolean), _col14 (type: boolean), _col15 (type: boolean), _col16 (type: boolean), _col17 (type: boolean), _col18 (type: boolean), _col20 (type: string), _col21 (type: string), _col25 (type: string)
- Reducer 5
+ key expressions: _col9 (type: int), _col14 (type: int)
+ sort order: ++
+ Map-reduce partition columns: _col9 (type: int), _col14 (type: int)
+ Statistics: Num rows: 29282000 Data size: 29717715282 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col3 (type: boolean), _col4 (type: boolean), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col15 (type: decimal(7,2)), _col16 (type: decimal(7,2)), _col18 (type: boolean), _col19 (type: boolean), _col20 (type: boolean), _col25 (type: string)
+ Reducer 2
Reduce Operator Tree:
Join Operator
condition map:
Inner Join 0 to 1
keys:
- 0 _col2 (type: int)
+ 0 _col0 (type: int)
1 _col0 (type: int)
- outputColumnNames: _col1, _col6, _col7, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col20, _col21, _col25, _col27, _col28, _col29
- Statistics: Num rows: 28345499 Data size: 3854168781 Basic stats: COMPLETE Column stats: NONE
- Filter Operator
- predicate: ((_col27 and _col13) or (_col28 and _col14) or (_col29 and _col15)) (type: boolean)
- Statistics: Num rows: 21259122 Data size: 2890626279 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col1 (type: int), _col20 (type: string), _col21 (type: string)
- sort order: +++
- Map-reduce partition columns: _col1 (type: int), _col20 (type: string), _col21 (type: string)
- Statistics: Num rows: 21259122 Data size: 2890626279 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col12 (type: int), _col16 (type: boolean), _col17 (type: boolean), _col18 (type: boolean), _col25 (type: string)
- Reducer 6
+ outputColumnNames: _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
+ Statistics: Num rows: 158402938 Data size: 21538218500 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col1 (type: int), _col3 (type: int)
+ sort order: ++
+ Map-reduce partition columns: _col1 (type: int), _col3 (type: int)
+ Statistics: Num rows: 158402938 Data size: 21538218500 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col2 (type: int), _col4 (type: int), _col5 (type: boolean), _col6 (type: boolean), _col7 (type: boolean), _col8 (type: boolean), _col9 (type: boolean), _col10 (type: boolean)
+ Reducer 3
+ Local Work:
+ Map Reduce Local Work
Reduce Operator Tree:
Join Operator
condition map:
Inner Join 0 to 1
keys:
- 0 _col1 (type: int), _col20 (type: string), _col21 (type: string)
- 1 _col0 (type: int), _col1 (type: string), _col2 (type: string)
- outputColumnNames: _col6, _col7, _col12, _col16, _col17, _col18, _col25, _col33, _col34, _col35, _col36, _col37, _col38
- Statistics: Num rows: 23385034 Data size: 3179688975 Basic stats: COMPLETE Column stats: NONE
+ 0 _col1 (type: int), _col3 (type: int)
+ 1 _col9 (type: int), _col14 (type: int)
+ outputColumnNames: _col2, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col15, _col16, _col17, _col18, _col19, _col20, _col27, _col28, _col30, _col31, _col32, _col37
+ Statistics: Num rows: 174243235 Data size: 23692040863 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: ((_col33 and _col34 and _col16) or (_col35 and _col36 and _col17) or (_col37 and _col38 and _col18)) (type: boolean)
- Statistics: Num rows: 8769387 Data size: 1192383263 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col12 (type: int), _col25 (type: string)
- outputColumnNames: _col6, _col7, _col12, _col25
- Statistics: Num rows: 8769387 Data size: 1192383263 Basic stats: COMPLETE Column stats: NONE
+ predicate: (((_col15 and _col16 and _col8) or (_col17 and _col18 and _col9) or (_col19 and _col20 and _col10)) and ((_col30 and _col5) or (_col31 and _col6) or (_col32 and _col7))) (type: boolean)
+ Statistics: Num rows: 49005909 Data size: 6663386377 Basic stats: COMPLETE Column stats: NONE
+ Map Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col2 (type: int)
+ 1 _col0 (type: int)
+ outputColumnNames: _col4, _col27, _col28, _col37
+ input vertices:
+ 1 Map 15
+ Statistics: Num rows: 53906501 Data size: 7329725173 Basic stats: COMPLETE Column stats: NONE
Group By Operator
- aggregations: sum(_col12), count(_col12), sum(_col7), count(_col7), sum(_col6), count(_col6)
- keys: _col25 (type: string)
+ aggregations: sum(_col4), count(_col4), sum(_col28), count(_col28), sum(_col27), count(_col27)
+ keys: _col37 (type: string)
mode: hash
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
- Statistics: Num rows: 8769387 Data size: 1192383263 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 53906501 Data size: 7329725173 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 8769387 Data size: 1192383263 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 53906501 Data size: 7329725173 Basic stats: COMPLETE Column stats: NONE
value expressions: _col1 (type: bigint), _col2 (type: bigint), _col3 (type: decimal(17,2)), _col4 (type: bigint), _col5 (type: decimal(17,2)), _col6 (type: bigint)
- Reducer 7
+ Reducer 4
Execution mode: vectorized
Reduce Operator Tree:
Group By Operator
@@ -488,23 +461,23 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
- Statistics: Num rows: 4384693 Data size: 596191563 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 26953250 Data size: 3664862518 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: (UDFToDouble(_col1) / _col2) (type: double), (_col3 / _col4) (type: decimal(37,22)), (_col5 / _col6) (type: decimal(37,22)), substr(_col0, 1, 20) (type: string)
outputColumnNames: _col4, _col5, _col6, _col7
- Statistics: Num rows: 4384693 Data size: 596191563 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 26953250 Data size: 3664862518 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col7 (type: string), _col4 (type: double), _col5 (type: decimal(37,22)), _col6 (type: decimal(37,22))
sort order: ++++
- Statistics: Num rows: 4384693 Data size: 596191563 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 26953250 Data size: 3664862518 Basic stats: COMPLETE Column stats: NONE
TopN Hash Memory Usage: 0.1
- Reducer 8
+ Reducer 5
Execution mode: vectorized
Reduce Operator Tree:
Select Operator
expressions: KEY.reducesinkkey0 (type: string), KEY.reducesinkkey1 (type: double), KEY.reducesinkkey2 (type: decimal(37,22)), KEY.reducesinkkey3 (type: decimal(37,22))
outputColumnNames: _col0, _col1, _col2, _col3
- Statistics: Num rows: 4384693 Data size: 596191563 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 26953250 Data size: 3664862518 Basic stats: COMPLETE Column stats: NONE
Limit
Number of rows: 100
Statistics: Num rows: 100 Data size: 13500 Basic stats: COMPLETE Column stats: NONE
@@ -515,6 +488,38 @@ STAGE PLANS:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
+ Reducer 8
+ Reduce Operator Tree:
+ Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col2 (type: int)
+ 1 _col0 (type: int)
+ outputColumnNames: _col0, _col1, _col3, _col4, _col5, _col6, _col7, _col9, _col10, _col11
+ Statistics: Num rows: 22000000 Data size: 22327357890 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col3 (type: int)
+ sort order: +
+ Map-reduce partition columns: _col3 (type: int)
+ Statistics: Num rows: 22000000 Data size: 22327357890 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: int), _col1 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean)
+ Reducer 9
+ Reduce Operator Tree:
+ Join Operator
+ condition map:
+ Inner Join 0 to 1
+ keys:
+ 0 _col3 (type: int)
+ 1 _col0 (type: int)
+ outputColumnNames: _col0, _col1, _col4, _col5, _col6, _col7, _col9, _col10, _col11, _col13, _col14
+ Statistics: Num rows: 24200000 Data size: 24560094211 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ key expressions: _col1 (type: int), _col13 (type: string), _col14 (type: string)
+ sort order: +++
+ Map-reduce partition columns: _col1 (type: int), _col13 (type: string), _col14 (type: string)
+ Statistics: Num rows: 24200000 Data size: 24560094211 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: int), _col4 (type: int), _col5 (type: int), _col6 (type: decimal(7,2)), _col7 (type: decimal(7,2)), _col9 (type: boolean), _col10 (type: boolean), _col11 (type: boolean)
Stage: Stage-0
Fetch Operator
diff --git a/ql/src/test/results/clientpositive/perf/tez/cbo_query13.q.out b/ql/src/test/results/clientpositive/perf/tez/cbo_query13.q.out
index 19f3039..412cc67 100644
--- a/ql/src/test/results/clientpositive/perf/tez/cbo_query13.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/cbo_query13.q.out
@@ -114,28 +114,28 @@ POSTHOOK: Input: default@store_sales
POSTHOOK: Output: hdfs://### HDFS PATH ###
CBO PLAN:
HiveProject($f0=[/(CAST($0):DOUBLE, $1)], $f1=[/($2, $3)], $f2=[/($4, $5)], $f3=[CAST($4):DECIMAL(17, 2)])
- HiveAggregate(group=[{}], agg#0=[sum($21)], agg#1=[count($21)], agg#2=[sum($22)], agg#3=[count($22)], agg#4=[sum($23)], agg#5=[count($23)])
- HiveJoin(condition=[AND(=($0, $17), OR(AND($1, $2, $27, $12), AND($3, $4, $28, $13), AND($5, $6, $29, $13)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], ==[=($2, _UTF-16LE'M')], =2=[=($3, _UTF-16LE'4 yr Degree')], =3=[=($2, _UTF-16LE'D')], =4=[=($3, _UTF-16LE'Primary')], =5=[=($2, _UTF-16LE'U')], =6=[=($3, _UTF-16LE'Advanced Degree')])
- HiveFilter(condition=[AND(IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
- HiveJoin(condition=[AND(=($12, $0), OR(AND($1, $17), AND($2, $18), AND($3, $19)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
- HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
- HiveJoin(condition=[=($7, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(hd_demo_sk=[$0], ==[=($3, 3)], =2=[=($3, 1)])
- HiveFilter(condition=[AND(IN($3, 3, 1), IS NOT NULL($0))])
- HiveTableScan(table=[[default, household_demographics]], table:alias=[household_demographics])
- HiveJoin(condition=[=($0, $6)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(s_store_sk=[$0])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, store]], table:alias=[store])
- HiveJoin(condition=[=($1, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveAggregate(group=[{}], agg#0=[sum($5)], agg#1=[count($5)], agg#2=[sum($6)], agg#3=[count($6)], agg#4=[sum($7)], agg#5=[count($7)])
+ HiveJoin(condition=[=($29, $4)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(=($2, $26), OR(AND($20, $21, $11, $27), AND($22, $23, $12, $28), AND($24, $25, $13, $28)))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($19, $1)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(=($3, $15), OR(AND($16, $8), AND($17, $9), AND($18, $10)))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($0, $14)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ss_sold_date_sk=[$0], ss_cdemo_sk=[$4], ss_hdemo_sk=[$5], ss_addr_sk=[$6], ss_store_sk=[$7], ss_quantity=[$10], ss_ext_sales_price=[$15], ss_ext_wholesale_cost=[$16], BETWEEN=[BETWEEN(false, $22, 100, 200)], BETWEEN9=[BETWEEN(false, $22, 150, 300)], BETWEEN10=[BETWEEN(false, $22, 50, 250)], BETWEEN11=[BETWEEN(false, $13, 100, 150)], BETWEEN12=[BETWEEN(false, $13, 50, 100)], BETWEEN13=[BETWEEN(false, $13, 150, 200)])
+ HiveFilter(condition=[AND(OR(<=(100, $13), <=($13, 150), IS NOT NULL($13), <=($13, 200)), OR(<=(100, $22), <=($22, 200), IS NOT NULL($22), <=($22, 300), <=($22, 250)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($5), IS NOT NULL($6), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[AND(=($6, 2001), IS NOT NULL($0))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject(ss_sold_date_sk=[$0], ss_cdemo_sk=[$4], ss_hdemo_sk=[$5], ss_addr_sk=[$6], ss_store_sk=[$7], ss_quantity=[$10], ss_ext_sales_price=[$15], ss_ext_wholesale_cost=[$16], BETWEEN=[BETWEEN(false, $22, 100, 200)], BETWEEN9=[BETWEEN(false, $22, 150, 300)], BETWEEN10=[BETWEEN(false, $22, 50, 250)], BETWEEN11=[BETWEEN(false, $13, 100, 150)], BETWEEN12=[BETWEEN(false, $13, 50, 100)], BETWEEN13=[BETWEEN(false, $13, 150, 200)])
- HiveFilter(condition=[AND(OR(BETWEEN(false, $13, 100, 150), BETWEEN(false, $13, 50, 100), BETWEEN(false, $13, 150, 200)), OR(BETWEEN(false, $22, 100, 200), BETWEEN(false, $22, 150, 300), BETWEEN(false, $22, 50, 250)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($5), IS NOT NULL($6), IS NOT NULL($0))])
- HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
+ HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
+ HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveProject(cd_demo_sk=[$0], ==[=($2, _UTF-16LE'M')], =2=[=($3, _UTF-16LE'4 yr Degree')], =3=[=($2, _UTF-16LE'D')], =4=[=($3, _UTF-16LE'Primary')], =5=[=($2, _UTF-16LE'U')], =6=[=($3, _UTF-16LE'Advanced Degree')])
+ HiveFilter(condition=[AND(IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
+ HiveProject(hd_demo_sk=[$0], ==[=($3, 3)], =2=[=($3, 1)])
+ HiveFilter(condition=[AND(IN($3, 3, 1), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, household_demographics]], table:alias=[household_demographics])
+ HiveProject(s_store_sk=[$0])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, store]], table:alias=[store])
diff --git a/ql/src/test/results/clientpositive/perf/tez/cbo_query21.q.out b/ql/src/test/results/clientpositive/perf/tez/cbo_query21.q.out
index 3a67503..0365697 100644
--- a/ql/src/test/results/clientpositive/perf/tez/cbo_query21.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/cbo_query21.q.out
@@ -69,7 +69,7 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
CBO PLAN:
HiveSortLimit(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC], fetch=[100])
HiveProject($f0=[$0], $f1=[$1], $f2=[$2], $f3=[$3])
- HiveFilter(condition=[CASE(>($2, 0), BETWEEN(false, /(CAST($3):DOUBLE, CAST($2):DOUBLE), 6.66667E-1, 1.5E0), null)])
+ HiveFilter(condition=[AND(CASE(>($2, 0), <=(6.66667E-1, /(CAST($3):DOUBLE, CAST($2):DOUBLE)), null), CASE(>($2, 0), <=(/(CAST($3):DOUBLE, CAST($2):DOUBLE), 1.5E0), null))])
HiveAggregate(group=[{0, 1}], agg#0=[sum($2)], agg#1=[sum($3)])
HiveProject($f0=[$1], $f1=[$10], $f2=[CASE($7, $5, 0)], $f3=[CASE($8, $5, 0)])
HiveJoin(condition=[=($4, $0)], joinType=[inner], algorithm=[none], cost=[not available])
diff --git a/ql/src/test/results/clientpositive/perf/tez/cbo_query34.q.out b/ql/src/test/results/clientpositive/perf/tez/cbo_query34.q.out
index 9299409..b2a6320 100644
--- a/ql/src/test/results/clientpositive/perf/tez/cbo_query34.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/cbo_query34.q.out
@@ -88,7 +88,7 @@ HiveSortLimit(sort0=[$0], sort1=[$1], sort2=[$2], sort3=[$3], dir0=[ASC], dir1=[
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($5), IS NOT NULL($3))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[AND(IN($6, 2000, 2001, 2002), OR(BETWEEN(false, $9, 1, 3), BETWEEN(false, $9, 25, 28)), IS NOT NULL($0))])
+ HiveFilter(condition=[AND(IN($6, 2000, 2001, 2002), OR(<=(1, $9), <=($9, 3), <=(25, $9), <=($9, 28)), OR(BETWEEN(false, $9, 1, 3), BETWEEN(false, $9, 25, 28)), IS NOT NULL($0))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
HiveProject(hd_demo_sk=[$0])
HiveFilter(condition=[AND(IN($2, _UTF-16LE'>10000', _UTF-16LE'unknown'), >($4, 0), CASE(>($4, 0), >(/(CAST($3):DOUBLE, CAST($4):DOUBLE), 1.2), null), IS NOT NULL($0))])
diff --git a/ql/src/test/results/clientpositive/perf/tez/cbo_query48.q.out b/ql/src/test/results/clientpositive/perf/tez/cbo_query48.q.out
index 079556e..22b051d 100644
--- a/ql/src/test/results/clientpositive/perf/tez/cbo_query48.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/cbo_query48.q.out
@@ -141,24 +141,24 @@ POSTHOOK: Input: default@store
POSTHOOK: Input: default@store_sales
POSTHOOK: Output: hdfs://### HDFS PATH ###
CBO PLAN:
-HiveAggregate(group=[{}], agg#0=[sum($11)])
- HiveJoin(condition=[AND(=($9, $0), OR(AND($1, $12), AND($2, $13), AND($3, $14)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
- HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
- HiveJoin(condition=[=($0, $6)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(s_store_sk=[$0])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, store]], table:alias=[store])
- HiveJoin(condition=[=($2, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[AND(=($6, 1998), IS NOT NULL($0))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveJoin(condition=[=($0, $2)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0])
- HiveFilter(condition=[AND(=($2, _UTF-16LE'M'), =($3, _UTF-16LE'4 yr Degree'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
+HiveAggregate(group=[{}], agg#0=[sum($10)])
+ HiveJoin(condition=[=($0, $9)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(s_store_sk=[$0])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, store]], table:alias=[store])
+ HiveJoin(condition=[AND(=($7, $0), OR(AND($1, $10), AND($2, $11), AND($3, $12)))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
+ HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveJoin(condition=[=($0, $2)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cd_demo_sk=[$0])
+ HiveFilter(condition=[AND(=($2, _UTF-16LE'M'), =($3, _UTF-16LE'4 yr Degree'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
+ HiveJoin(condition=[=($0, $8)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(ss_sold_date_sk=[$0], ss_cdemo_sk=[$4], ss_addr_sk=[$6], ss_store_sk=[$7], ss_quantity=[$10], BETWEEN=[BETWEEN(false, $22, 0, 2000)], BETWEEN6=[BETWEEN(false, $22, 150, 3000)], BETWEEN7=[BETWEEN(false, $22, 50, 25000)])
- HiveFilter(condition=[AND(OR(BETWEEN(false, $13, 100, 150), BETWEEN(false, $13, 50, 100), BETWEEN(false, $13, 150, 200)), OR(BETWEEN(false, $22, 0, 2000), BETWEEN(false, $22, 150, 3000), BETWEEN(false, $22, 50, 25000)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($6), IS NOT NULL($0))])
+ HiveFilter(condition=[AND(OR(<=(100, $13), <=($13, 150), IS NOT NULL($13), <=($13, 200)), OR(<=(0, $22), <=($22, 2000), IS NOT NULL($22), <=($22, 3000), <=($22, 25000)), OR(BETWEEN(false, $13, 100, 150), BETWEEN(false, $13, 50, 100), BETWEEN(false, $13, 150, 200)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($6), IS NOT NULL($0))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
+ HiveProject(d_date_sk=[$0])
+ HiveFilter(condition=[AND(=($6, 1998), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
diff --git a/ql/src/test/results/clientpositive/perf/tez/cbo_query54.q.out b/ql/src/test/results/clientpositive/perf/tez/cbo_query54.q.out
index 50fa078..39b6a6b 100644
--- a/ql/src/test/results/clientpositive/perf/tez/cbo_query54.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/cbo_query54.q.out
@@ -1,7 +1,7 @@
-Warning: Shuffle Join MERGEJOIN[271][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3]] in Stage 'Reducer 4' is a cross product
-Warning: Shuffle Join MERGEJOIN[272][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4]] in Stage 'Reducer 5' is a cross product
-Warning: Shuffle Join MERGEJOIN[270][tables = [$hdt$_1, $hdt$_2]] in Stage 'Reducer 30' is a cross product
-Warning: Shuffle Join MERGEJOIN[273][tables = [$hdt$_1, $hdt$_2, $hdt$_0]] in Stage 'Reducer 6' is a cross product
+Warning: Shuffle Join MERGEJOIN[269][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3]] in Stage 'Reducer 4' is a cross product
+Warning: Shuffle Join MERGEJOIN[270][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4]] in Stage 'Reducer 5' is a cross product
+Warning: Shuffle Join MERGEJOIN[271][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4, $hdt$_5]] in Stage 'Reducer 6' is a cross product
+Warning: Shuffle Join MERGEJOIN[272][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4, $hdt$_5, $hdt$_6]] in Stage 'Reducer 7' is a cross product
PREHOOK: query: explain cbo
with my_customers as (
select distinct c_customer_sk
@@ -135,72 +135,50 @@ HiveSortLimit(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC], fetch=[100])
HiveProject(segment=[$0], num_customers=[$1], segment_base=[*($0, 50)])
HiveAggregate(group=[{0}], agg#0=[count()])
HiveProject(segment=[CAST(/($1, CAST(50):DECIMAL(10, 0))):INTEGER])
- HiveAggregate(group=[{0}], agg#0=[sum($1)])
- HiveFilter(condition=[BETWEEN(false, $2, $3, $4)])
- HiveProject(c_customer_sk=[$0], ss_ext_sales_price=[$4], d_month_seq=[$11], _o__c0=[$13], $f0=[$14])
+ HiveAggregate(group=[{10}], agg#0=[sum($2)])
+ HiveJoin(condition=[<=($4, $15)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[<=($14, $4)], joinType=[inner], algorithm=[none], cost=[not available])
HiveJoin(condition=[true], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject($f0=[$10], $f1=[$11], ss_sold_date_sk=[$0], ss_customer_sk=[$1], ss_ext_sales_price=[$2], ca_address_sk=[$5], ca_county=[$6], ca_state=[$7], s_county=[$8], s_state=[$9], d_date_sk=[$3], d_month_seq=[$4], cnt=[$12], $f00=[$13])
- HiveJoin(condition=[true], joinType=[left], algorithm=[none], cost=[not available])
- HiveJoin(condition=[true], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($10, $1)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ss_sold_date_sk=[$0], ss_customer_sk=[$3], ss_ext_sales_price=[$15])
- HiveFilter(condition=[AND(IS NOT NULL($3), IS NOT NULL($0))])
- HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
- HiveProject(d_date_sk=[$0], d_month_seq=[$3])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject(ca_address_sk=[$0], ca_county=[$1], ca_state=[$2], s_county=[$3], s_state=[$4], c_customer_sk=[$5], c_current_addr_sk=[$6])
- HiveJoin(condition=[=($6, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[AND(=($1, $3), =($2, $4))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], ca_county=[$7], ca_state=[$8])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($8))])
- HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
- HiveProject(s_county=[$23], s_state=[$24])
- HiveFilter(condition=[AND(IS NOT NULL($23), IS NOT NULL($24))])
- HiveTableScan(table=[[default, store]], table:alias=[store])
- HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$1])
- HiveAggregate(group=[{0, 1}])
+ HiveJoin(condition=[true], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($10, $1)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ss_sold_date_sk=[$0], ss_customer_sk=[$3], ss_ext_sales_price=[$15])
+ HiveFilter(condition=[AND(IS NOT NULL($3), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
+ HiveProject(d_date_sk=[$0], d_month_seq=[$3])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(ca_address_sk=[$0], ca_county=[$1], ca_state=[$2], s_county=[$3], s_state=[$4], c_customer_sk=[$5], c_current_addr_sk=[$6])
+ HiveJoin(condition=[=($6, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(=($1, $3), =($2, $4))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ca_address_sk=[$0], ca_county=[$7], ca_state=[$8])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($8))])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveProject(s_county=[$23], s_state=[$24])
+ HiveFilter(condition=[AND(IS NOT NULL($23), IS NOT NULL($24))])
+ HiveTableScan(table=[[default, store]], table:alias=[store])
+ HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$1])
+ HiveAggregate(group=[{0, 1}])
+ HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$4])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($4))])
+ HiveTableScan(table=[[default, customer]], table:alias=[customer])
+ HiveJoin(condition=[=($2, $4)], joinType=[inner], algorithm=[none], cost=[not available])
HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$4])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($4))])
- HiveTableScan(table=[[default, customer]], table:alias=[customer])
- HiveJoin(condition=[=($2, $4)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$1], cs_item_sk=[$2])
- HiveUnion(all=[true])
- HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$3], cs_item_sk=[$15])
- HiveFilter(condition=[AND(IS NOT NULL($15), IS NOT NULL($0), IS NOT NULL($3))])
- HiveTableScan(table=[[default, catalog_sales]], table:alias=[catalog_sales])
- HiveProject(sold_date_sk=[$0], customer_sk=[$4], item_sk=[$3])
- HiveFilter(condition=[AND(IS NOT NULL($3), IS NOT NULL($0), IS NOT NULL($4))])
- HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
- HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[AND(=($8, 3), =($6, 1999), IS NOT NULL($0))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject(i_item_sk=[$0])
- HiveFilter(condition=[AND(=($12, _UTF-16LE'Jewelry'), =($10, _UTF-16LE'consignment'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, item]], table:alias=[item])
- HiveProject(cnt=[$0])
- HiveFilter(condition=[<=(sq_count_check($0), 1)])
- HiveProject(cnt=[$0])
- HiveAggregate(group=[{}], cnt=[COUNT()])
- HiveProject($f0=[$0])
- HiveAggregate(group=[{0}])
- HiveProject($f0=[+($3, 1)])
- HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
+ HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$1], cs_item_sk=[$2])
+ HiveUnion(all=[true])
+ HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$3], cs_item_sk=[$15])
+ HiveFilter(condition=[AND(IS NOT NULL($15), IS NOT NULL($0), IS NOT NULL($3))])
+ HiveTableScan(table=[[default, catalog_sales]], table:alias=[catalog_sales])
+ HiveProject(sold_date_sk=[$0], customer_sk=[$4], item_sk=[$3])
+ HiveFilter(condition=[AND(IS NOT NULL($3), IS NOT NULL($0), IS NOT NULL($4))])
+ HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
+ HiveProject(d_date_sk=[$0])
+ HiveFilter(condition=[AND(=($8, 3), =($6, 1999), IS NOT NULL($0))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject($f0=[$0])
- HiveAggregate(group=[{0}])
- HiveProject($f0=[+($3, 1)])
- HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveJoin(condition=[true], joinType=[right], algorithm=[none], cost=[not available])
- HiveProject($f0=[$0])
- HiveAggregate(group=[{0}])
- HiveProject($f0=[+($3, 3)])
- HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(i_item_sk=[$0])
+ HiveFilter(condition=[AND(=($12, _UTF-16LE'Jewelry'), =($10, _UTF-16LE'consignment'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, item]], table:alias=[item])
HiveProject(cnt=[$0])
HiveFilter(condition=[<=(sq_count_check($0), 1)])
HiveProject(cnt=[$0])
@@ -210,4 +188,23 @@ HiveSortLimit(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC], fetch=[100])
HiveProject($f0=[+($3, 3)])
HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(cnt=[$0])
+ HiveFilter(condition=[<=(sq_count_check($0), 1)])
+ HiveProject(cnt=[$0])
+ HiveAggregate(group=[{}], cnt=[COUNT()])
+ HiveProject($f0=[$0])
+ HiveAggregate(group=[{0}])
+ HiveProject($f0=[+($3, 1)])
+ HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject($f0=[$0])
+ HiveAggregate(group=[{0}])
+ HiveProject($f0=[+($3, 1)])
+ HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject($f0=[$0])
+ HiveAggregate(group=[{0}])
+ HiveProject($f0=[+($3, 3)])
+ HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
diff --git a/ql/src/test/results/clientpositive/perf/tez/cbo_query64.q.out b/ql/src/test/results/clientpositive/perf/tez/cbo_query64.q.out
index 5a5bb79..356778b 100644
--- a/ql/src/test/results/clientpositive/perf/tez/cbo_query64.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/cbo_query64.q.out
@@ -268,62 +268,58 @@ HiveProject(product_name=[$0], store_name=[$1], store_zip=[$2], b_street_number=
HiveProject(product_name=[$0], store_name=[$2], store_zip=[$3], b_street_number=[$4], b_streen_name=[$5], b_city=[$6], b_zip=[$7], c_street_number=[$8], c_street_name=[$9], c_city=[$10], c_zip=[$11], cnt=[$12], s1=[$13], s2=[$14], s3=[$15], s11=[$20], s21=[$21], s31=[$22], cnt1=[$19])
HiveJoin(condition=[AND(AND(AND(=($1, $16), <=($19, $12)), =($2, $17)), =($3, $18))], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject($f0=[$13], $f1=[$12], $f2=[$10], $f3=[$11], $f4=[$6], $f5=[$7], $f6=[$8], $f7=[$9], $f8=[$2], $f9=[$3], $f10=[$4], $f11=[$5], $f15=[$14], $f16=[$15], $f17=[$16], $f18=[$17])
- HiveAggregate(group=[{9, 11, 16, 17, 18, 19, 25, 26, 27, 28, 30, 31, 48, 49}], agg#0=[count()], agg#1=[sum($45)], agg#2=[sum($46)], agg#3=[sum($47)])
- HiveJoin(condition=[AND(<>($1, $21), =($39, $0))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
- HiveJoin(condition=[=($36, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($1, $18)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($3, $13)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($2, $10)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($4, $8)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($5, $6)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(c_customer_sk=[$0], c_current_cdemo_sk=[$2], c_current_hdemo_sk=[$3], c_current_addr_sk=[$4], c_first_shipto_date_sk=[$5], c_first_sales_date_sk=[$6])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($6), IS NOT NULL($5), IS NOT NULL($2), IS NOT NULL($3), IS NOT NULL($4))])
- HiveTableScan(table=[[default, customer]], table:alias=[customer])
+ HiveAggregate(group=[{9, 11, 16, 17, 18, 19, 25, 26, 27, 28, 30, 31, 47, 48}], agg#0=[count()], agg#1=[sum($44)], agg#2=[sum($45)], agg#3=[sum($46)])
+ HiveJoin(condition=[=($33, $51)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(<>($1, $21), =($38, $0))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
+ HiveJoin(condition=[=($35, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($1, $18)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($3, $13)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($2, $10)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($4, $8)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($5, $6)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(c_customer_sk=[$0], c_current_cdemo_sk=[$2], c_current_hdemo_sk=[$3], c_current_addr_sk=[$4], c_first_shipto_date_sk=[$5], c_first_sales_date_sk=[$6])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($6), IS NOT NULL($5), IS NOT NULL($2), IS NOT NULL($3), IS NOT NULL($4))])
+ HiveTableScan(table=[[default, customer]], table:alias=[customer])
+ HiveProject(d_date_sk=[$0], d_year=[$6])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[d2])
HiveProject(d_date_sk=[$0], d_year=[$6])
HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, date_dim]], table:alias=[d2])
- HiveProject(d_date_sk=[$0], d_year=[$6])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, date_dim]], table:alias=[d3])
- HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1], ib_income_band_sk=[$2])
- HiveJoin(condition=[=($1, $2)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($1))])
- HiveTableScan(table=[[default, household_demographics]], table:alias=[hd2])
- HiveProject(ib_income_band_sk=[$0])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, income_band]], table:alias=[ib2])
- HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_address]], table:alias=[ad2])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
- HiveProject(sr_item_sk=[$0], sr_ticket_number=[$1], ca_address_sk=[$2], ca_street_number=[$3], ca_street_name=[$4], ca_city=[$5], ca_zip=[$6], s_store_sk=[$7], s_store_name=[$8], s_zip=[$9], hd_demo_sk=[$10], hd_income_band_sk=[$11], ib_income_band_sk=[$12], p_promo_sk=[$13], ss_sold_date_sk=[$14], ss_item_sk=[$15], ss_customer_sk=[$16], ss_cdemo_sk=[$17], ss_hdemo_sk=[$18], ss_addr_sk=[$19], ss_store_sk=[$20], ss_promo_sk=[$21], ss_ticket_number=[$22], ss_wholesale_cost= [...]
- HiveJoin(condition=[AND(=($15, $0), =($22, $1))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(sr_item_sk=[$2], sr_ticket_number=[$9])
- HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($9))])
- HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns])
- HiveJoin(condition=[=($17, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_address]], table:alias=[ad1])
- HiveJoin(condition=[=($13, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(s_store_sk=[$0], s_store_name=[$5], s_zip=[$25])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($5), IS NOT NULL($25))])
- HiveTableScan(table=[[default, store]], table:alias=[store])
- HiveJoin(condition=[=($8, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[d3])
+ HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1], ib_income_band_sk=[$2])
HiveJoin(condition=[=($1, $2)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1])
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($1))])
- HiveTableScan(table=[[default, household_demographics]], table:alias=[hd1])
+ HiveTableScan(table=[[default, household_demographics]], table:alias=[hd2])
HiveProject(ib_income_band_sk=[$0])
HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, income_band]], table:alias=[ib1])
- HiveProject(p_promo_sk=[$0], ss_sold_date_sk=[$1], ss_item_sk=[$2], ss_customer_sk=[$3], ss_cdemo_sk=[$4], ss_hdemo_sk=[$5], ss_addr_sk=[$6], ss_store_sk=[$7], ss_promo_sk=[$8], ss_ticket_number=[$9], ss_wholesale_cost=[$10], ss_list_price=[$11], ss_coupon_amt=[$12], i_item_sk=[$13], i_product_name=[$14], d_date_sk=[$15], $f0=[$16])
+ HiveTableScan(table=[[default, income_band]], table:alias=[ib2])
+ HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[ad2])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
+ HiveProject(sr_item_sk=[$0], sr_ticket_number=[$1], ca_address_sk=[$2], ca_street_number=[$3], ca_street_name=[$4], ca_city=[$5], ca_zip=[$6], s_store_sk=[$7], s_store_name=[$8], s_zip=[$9], hd_demo_sk=[$10], hd_income_band_sk=[$11], p_promo_sk=[$12], ss_sold_date_sk=[$13], ss_item_sk=[$14], ss_customer_sk=[$15], ss_cdemo_sk=[$16], ss_hdemo_sk=[$17], ss_addr_sk=[$18], ss_store_sk=[$19], ss_promo_sk=[$20], ss_ticket_number=[$21], ss_wholesale_cost=[$22], ss_list_price=[$ [...]
+ HiveJoin(condition=[AND(=($14, $0), =($21, $1))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(sr_item_sk=[$2], sr_ticket_number=[$9])
+ HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($9))])
+ HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns])
+ HiveJoin(condition=[=($16, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[ad1])
+ HiveJoin(condition=[=($12, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(s_store_sk=[$0], s_store_name=[$5], s_zip=[$25])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($5), IS NOT NULL($25))])
+ HiveTableScan(table=[[default, store]], table:alias=[store])
+ HiveJoin(condition=[=($7, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($1))])
+ HiveTableScan(table=[[default, household_demographics]], table:alias=[hd1])
HiveJoin(condition=[=($8, $0)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(p_promo_sk=[$0])
HiveFilter(condition=[IS NOT NULL($0)])
@@ -335,7 +331,7 @@ HiveProject(product_name=[$0], store_name=[$1], store_zip=[$2], b_street_number=
HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($9), IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($3), IS NOT NULL($4), IS NOT NULL($8), IS NOT NULL($5), IS NOT NULL($6))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(i_item_sk=[$0], i_product_name=[$21])
- HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 35, 45), BETWEEN(false, $5, 36, 50), IS NOT NULL($0))])
+ HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 36, 45), IS NOT NULL($0))])
HiveTableScan(table=[[default, item]], table:alias=[item])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[AND(=($6, 2000), IS NOT NULL($0))])
@@ -350,63 +346,62 @@ HiveProject(product_name=[$0], store_name=[$1], store_zip=[$2], b_street_number=
HiveProject(cr_item_sk=[$2], cr_order_number=[$16], +=[+(+($23, $24), $25)])
HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($16))])
HiveTableScan(table=[[default, catalog_returns]], table:alias=[catalog_returns])
- HiveProject($f1=[$12], $f2=[$10], $f3=[$11], $f15=[$14], $f16=[$15], $f17=[$16], $f18=[$17])
- HiveAggregate(group=[{9, 11, 16, 17, 18, 19, 25, 26, 27, 28, 30, 31, 48, 49}], agg#0=[count()], agg#1=[sum($45)], agg#2=[sum($46)], agg#3=[sum($47)])
- HiveJoin(condition=[AND(<>($1, $21), =($39, $0))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
+ HiveProject(ib_income_band_sk=[$0])
HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
- HiveJoin(condition=[=($36, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($1, $18)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($3, $13)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($2, $10)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($4, $8)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($5, $6)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(c_customer_sk=[$0], c_current_cdemo_sk=[$2], c_current_hdemo_sk=[$3], c_current_addr_sk=[$4], c_first_shipto_date_sk=[$5], c_first_sales_date_sk=[$6])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($6), IS NOT NULL($5), IS NOT NULL($2), IS NOT NULL($3), IS NOT NULL($4))])
- HiveTableScan(table=[[default, customer]], table:alias=[customer])
+ HiveTableScan(table=[[default, income_band]], table:alias=[ib1])
+ HiveProject($f1=[$12], $f2=[$10], $f3=[$11], $f15=[$14], $f16=[$15], $f17=[$16], $f18=[$17])
+ HiveAggregate(group=[{9, 11, 16, 17, 18, 19, 25, 26, 27, 28, 30, 31, 47, 48}], agg#0=[count()], agg#1=[sum($44)], agg#2=[sum($45)], agg#3=[sum($46)])
+ HiveJoin(condition=[=($33, $51)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(<>($1, $21), =($38, $0))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
+ HiveJoin(condition=[=($35, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($1, $18)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($3, $13)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($2, $10)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($4, $8)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($5, $6)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(c_customer_sk=[$0], c_current_cdemo_sk=[$2], c_current_hdemo_sk=[$3], c_current_addr_sk=[$4], c_first_shipto_date_sk=[$5], c_first_sales_date_sk=[$6])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($6), IS NOT NULL($5), IS NOT NULL($2), IS NOT NULL($3), IS NOT NULL($4))])
+ HiveTableScan(table=[[default, customer]], table:alias=[customer])
+ HiveProject(d_date_sk=[$0], d_year=[$6])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[d2])
HiveProject(d_date_sk=[$0], d_year=[$6])
HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, date_dim]], table:alias=[d2])
- HiveProject(d_date_sk=[$0], d_year=[$6])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, date_dim]], table:alias=[d3])
- HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1], ib_income_band_sk=[$2])
- HiveJoin(condition=[=($1, $2)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($1))])
- HiveTableScan(table=[[default, household_demographics]], table:alias=[hd2])
- HiveProject(ib_income_band_sk=[$0])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, income_band]], table:alias=[ib2])
- HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_address]], table:alias=[ad2])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
- HiveProject(sr_item_sk=[$0], sr_ticket_number=[$1], ca_address_sk=[$2], ca_street_number=[$3], ca_street_name=[$4], ca_city=[$5], ca_zip=[$6], s_store_sk=[$7], s_store_name=[$8], s_zip=[$9], hd_demo_sk=[$10], hd_income_band_sk=[$11], ib_income_band_sk=[$12], p_promo_sk=[$13], ss_sold_date_sk=[$14], ss_item_sk=[$15], ss_customer_sk=[$16], ss_cdemo_sk=[$17], ss_hdemo_sk=[$18], ss_addr_sk=[$19], ss_store_sk=[$20], ss_promo_sk=[$21], ss_ticket_number=[$22], ss_wholesale_cost= [...]
- HiveJoin(condition=[AND(=($15, $0), =($22, $1))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(sr_item_sk=[$2], sr_ticket_number=[$9])
- HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($9))])
- HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns])
- HiveJoin(condition=[=($17, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, customer_address]], table:alias=[ad1])
- HiveJoin(condition=[=($13, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(s_store_sk=[$0], s_store_name=[$5], s_zip=[$25])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($5), IS NOT NULL($25))])
- HiveTableScan(table=[[default, store]], table:alias=[store])
- HiveJoin(condition=[=($8, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[d3])
+ HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1], ib_income_band_sk=[$2])
HiveJoin(condition=[=($1, $2)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1])
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($1))])
- HiveTableScan(table=[[default, household_demographics]], table:alias=[hd1])
+ HiveTableScan(table=[[default, household_demographics]], table:alias=[hd2])
HiveProject(ib_income_band_sk=[$0])
HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, income_band]], table:alias=[ib1])
- HiveProject(p_promo_sk=[$0], ss_sold_date_sk=[$1], ss_item_sk=[$2], ss_customer_sk=[$3], ss_cdemo_sk=[$4], ss_hdemo_sk=[$5], ss_addr_sk=[$6], ss_store_sk=[$7], ss_promo_sk=[$8], ss_ticket_number=[$9], ss_wholesale_cost=[$10], ss_list_price=[$11], ss_coupon_amt=[$12], i_item_sk=[$13], i_product_name=[$14], d_date_sk=[$15], $f0=[$16])
+ HiveTableScan(table=[[default, income_band]], table:alias=[ib2])
+ HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[ad2])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
+ HiveProject(sr_item_sk=[$0], sr_ticket_number=[$1], ca_address_sk=[$2], ca_street_number=[$3], ca_street_name=[$4], ca_city=[$5], ca_zip=[$6], s_store_sk=[$7], s_store_name=[$8], s_zip=[$9], hd_demo_sk=[$10], hd_income_band_sk=[$11], p_promo_sk=[$12], ss_sold_date_sk=[$13], ss_item_sk=[$14], ss_customer_sk=[$15], ss_cdemo_sk=[$16], ss_hdemo_sk=[$17], ss_addr_sk=[$18], ss_store_sk=[$19], ss_promo_sk=[$20], ss_ticket_number=[$21], ss_wholesale_cost=[$22], ss_list_price=[$ [...]
+ HiveJoin(condition=[AND(=($14, $0), =($21, $1))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(sr_item_sk=[$2], sr_ticket_number=[$9])
+ HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($9))])
+ HiveTableScan(table=[[default, store_returns]], table:alias=[store_returns])
+ HiveJoin(condition=[=($16, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ca_address_sk=[$0], ca_street_number=[$2], ca_street_name=[$3], ca_city=[$6], ca_zip=[$9])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[ad1])
+ HiveJoin(condition=[=($12, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(s_store_sk=[$0], s_store_name=[$5], s_zip=[$25])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($5), IS NOT NULL($25))])
+ HiveTableScan(table=[[default, store]], table:alias=[store])
+ HiveJoin(condition=[=($7, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(hd_demo_sk=[$0], hd_income_band_sk=[$1])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($1))])
+ HiveTableScan(table=[[default, household_demographics]], table:alias=[hd1])
HiveJoin(condition=[=($8, $0)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(p_promo_sk=[$0])
HiveFilter(condition=[IS NOT NULL($0)])
@@ -418,7 +413,7 @@ HiveProject(product_name=[$0], store_name=[$1], store_zip=[$2], b_street_number=
HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($9), IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($3), IS NOT NULL($4), IS NOT NULL($8), IS NOT NULL($5), IS NOT NULL($6))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(i_item_sk=[$0], i_product_name=[$21])
- HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 35, 45), BETWEEN(false, $5, 36, 50), IS NOT NULL($0))])
+ HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 36, 45), IS NOT NULL($0))])
HiveTableScan(table=[[default, item]], table:alias=[item])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[AND(=($6, 2001), IS NOT NULL($0))])
@@ -433,4 +428,7 @@ HiveProject(product_name=[$0], store_name=[$1], store_zip=[$2], b_street_number=
HiveProject(cr_item_sk=[$2], cr_order_number=[$16], +=[+(+($23, $24), $25)])
HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($16))])
HiveTableScan(table=[[default, catalog_returns]], table:alias=[catalog_returns])
+ HiveProject(ib_income_band_sk=[$0])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, income_band]], table:alias=[ib1])
diff --git a/ql/src/test/results/clientpositive/perf/tez/cbo_query85.q.out b/ql/src/test/results/clientpositive/perf/tez/cbo_query85.q.out
index f5a71b4..0963936 100644
--- a/ql/src/test/results/clientpositive/perf/tez/cbo_query85.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/cbo_query85.q.out
@@ -184,36 +184,37 @@ CBO PLAN:
HiveProject(_o__c0=[$0], _o__c1=[$1], _o__c2=[$2], _o__c3=[$3])
HiveSortLimit(sort0=[$7], sort1=[$4], sort2=[$5], sort3=[$6], dir0=[ASC], dir1=[ASC], dir2=[ASC], dir3=[ASC], fetch=[100])
HiveProject(_o__c0=[substr($0, 1, 20)], _o__c1=[/(CAST($1):DOUBLE, $2)], _o__c2=[/($3, $4)], _o__c3=[/($5, $6)], (tok_function avg (tok_table_or_col ws_quantity))=[/(CAST($1):DOUBLE, $2)], (tok_function avg (tok_table_or_col wr_refunded_cash))=[/($3, $4)], (tok_function avg (tok_table_or_col wr_fee))=[/($5, $6)], (tok_function substr (tok_table_or_col r_reason_desc) 1 20)=[substr($0, 1, 20)])
- HiveAggregate(group=[{14}], agg#0=[sum($32)], agg#1=[count($32)], agg#2=[sum($27)], agg#3=[count($27)], agg#4=[sum($26)], agg#5=[count($26)])
- HiveJoin(condition=[AND(AND(AND(=($1, $18), =($2, $19)), =($0, $21)), OR(AND($3, $4, $36), AND($5, $6, $37), AND($7, $8, $38)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3], ==[=($2, _UTF-16LE'M')], =4=[=($3, _UTF-16LE'4 yr Degree')], =5=[=($2, _UTF-16LE'D')], =6=[=($3, _UTF-16LE'Primary')], =7=[=($2, _UTF-16LE'U')], =8=[=($3, _UTF-16LE'Advanced Degree')])
- HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
- HiveJoin(condition=[AND(=($0, $13), OR(AND($1, $24), AND($2, $25), AND($3, $26)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
- HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveAggregate(group=[{37}], agg#0=[sum($4)], agg#1=[count($4)], agg#2=[sum($28)], agg#3=[count($28)], agg#4=[sum($27)], agg#5=[count($27)])
+ HiveJoin(condition=[=($2, $38)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(AND(AND(=($1, $21), =($3, $26)), OR(AND($15, $16, $8), AND($17, $18, $9), AND($19, $20, $10))), OR(AND($30, $5), AND($31, $6), AND($32, $7)))], joinType=[inner], algorithm=[none], cost=[not available])
HiveJoin(condition=[=($0, $11)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(r_reason_sk=[$0], r_reason_desc=[$2])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, reason]], table:alias=[reason])
- HiveJoin(condition=[=($15, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(wp_web_page_sk=[$0])
- HiveFilter(condition=[IS NOT NULL($0)])
- HiveTableScan(table=[[default, web_page]], table:alias=[web_page])
- HiveJoin(condition=[=($12, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[AND(=($6, 1998), IS NOT NULL($0))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveJoin(condition=[=($0, $6)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3])
- HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IS NOT NULL($0))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
- HiveJoin(condition=[AND(=($9, $0), =($11, $5))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ws_sold_date_sk=[$0], ws_item_sk=[$3], ws_web_page_sk=[$12], ws_order_number=[$17], ws_quantity=[$18], BETWEEN=[BETWEEN(false, $33, 100, 200)], BETWEEN6=[BETWEEN(false, $33, 150, 300)], BETWEEN7=[BETWEEN(false, $33, 50, 250)], BETWEEN8=[BETWEEN(false, $21, 100, 150)], BETWEEN9=[BETWEEN(false, $21, 50, 100)], BETWEEN10=[BETWEEN(false, $21, 150, 200)])
+ HiveFilter(condition=[AND(OR(<=(100, $21), <=($21, 150), IS NOT NULL($21), <=($21, 200)), OR(<=(100, $33), <=($33, 200), IS NOT NULL($33), <=($33, 300), <=($33, 250)), IS NOT NULL($3), IS NOT NULL($17), IS NOT NULL($12), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
+ HiveProject(d_date_sk=[$0])
+ HiveFilter(condition=[AND(=($6, 1998), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$1], cd_education_status=[$2], ==[$3], =4=[$4], =5=[$5], =6=[$6], =7=[$7], =8=[$8], wr_item_sk=[$9], wr_refunded_cdemo_sk=[$10], wr_refunded_addr_sk=[$11], wr_returning_cdemo_sk=[$12], wr_reason_sk=[$13], wr_order_number=[$14], wr_fee=[$15], wr_refunded_cash=[$16], ca_address_sk=[$17], IN=[$18], IN2=[$19], IN3=[$20], cd_demo_sk0=[$21], cd_marital_status0=[$22], cd_education_status0=[$23], r_reason_sk=[$24], r_reason_desc=[$25])
+ HiveJoin(condition=[=($24, $13)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(AND(=($1, $22), =($2, $23)), =($0, $10))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3], ==[=($2, _UTF-16LE'M')], =4=[=($3, _UTF-16LE'4 yr Degree')], =5=[=($2, _UTF-16LE'D')], =6=[=($3, _UTF-16LE'Primary')], =7=[=($2, _UTF-16LE'U')], =8=[=($3, _UTF-16LE'Advanced Degree')])
+ HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
+ HiveJoin(condition=[=($12, $3)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($8, $2)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(wr_item_sk=[$2], wr_refunded_cdemo_sk=[$4], wr_refunded_addr_sk=[$6], wr_returning_cdemo_sk=[$8], wr_reason_sk=[$12], wr_order_number=[$13], wr_fee=[$18], wr_refunded_cash=[$20])
HiveFilter(condition=[AND(IS NOT NULL($2), IS NOT NULL($13), IS NOT NULL($4), IS NOT NULL($8), IS NOT NULL($6), IS NOT NULL($12))])
HiveTableScan(table=[[default, web_returns]], table:alias=[web_returns])
- HiveProject(ws_sold_date_sk=[$0], ws_item_sk=[$3], ws_web_page_sk=[$12], ws_order_number=[$17], ws_quantity=[$18], BETWEEN=[BETWEEN(false, $33, 100, 200)], BETWEEN6=[BETWEEN(false, $33, 150, 300)], BETWEEN7=[BETWEEN(false, $33, 50, 250)], BETWEEN8=[BETWEEN(false, $21, 100, 150)], BETWEEN9=[BETWEEN(false, $21, 50, 100)], BETWEEN10=[BETWEEN(false, $21, 150, 200)])
- HiveFilter(condition=[AND(OR(BETWEEN(false, $21, 100, 150), BETWEEN(false, $21, 50, 100), BETWEEN(false, $21, 150, 200)), OR(BETWEEN(false, $33, 100, 200), BETWEEN(false, $33, 150, 300), BETWEEN(false, $33, 50, 250)), IS NOT NULL($3), IS NOT NULL($17), IS NOT NULL($12), IS NOT NULL($0))])
- HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
+ HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
+ HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3])
+ HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
+ HiveProject(r_reason_sk=[$0], r_reason_desc=[$2])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, reason]], table:alias=[reason])
+ HiveProject(wp_web_page_sk=[$0])
+ HiveFilter(condition=[IS NOT NULL($0)])
+ HiveTableScan(table=[[default, web_page]], table:alias=[web_page])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query13.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query13.q.out
index 811a518..a08c5a4 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query13.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query13.q.out
@@ -114,24 +114,24 @@ POSTHOOK: Input: default@store_sales
POSTHOOK: Output: hdfs://### HDFS PATH ###
CBO PLAN:
HiveProject($f0=[/(CAST($0):DOUBLE, $1)], $f1=[/($2, $3)], $f2=[/($4, $5)], $f3=[CAST($4):DECIMAL(17, 2)])
- HiveAggregate(group=[{}], agg#0=[sum($19)], agg#1=[count($19)], agg#2=[sum($20)], agg#3=[count($20)], agg#4=[sum($21)], agg#5=[count($21)])
- HiveJoin(condition=[AND(=($0, $16), OR(AND($1, $2, $25, $12), AND($3, $4, $26, $13), AND($5, $6, $27, $13)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], ==[=($2, _UTF-16LE'M')], =2=[=($3, _UTF-16LE'4 yr Degree')], =3=[=($2, _UTF-16LE'D')], =4=[=($3, _UTF-16LE'Primary')], =5=[=($2, _UTF-16LE'U')], =6=[=($3, _UTF-16LE'Advanced Degree')])
- HiveFilter(condition=[AND(IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
- HiveJoin(condition=[AND(=($11, $0), OR(AND($1, $15), AND($2, $16), AND($3, $17)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
- HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'))])
- HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
- HiveJoin(condition=[=($6, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(hd_demo_sk=[$0], ==[=($3, 3)], =2=[=($3, 1)])
- HiveFilter(condition=[IN($3, 3, 1)])
- HiveTableScan(table=[[default, household_demographics]], table:alias=[household_demographics])
- HiveJoin(condition=[=($1, $0)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveAggregate(group=[{}], agg#0=[sum($11)], agg#1=[count($11)], agg#2=[sum($12)], agg#3=[count($12)], agg#4=[sum($13)], agg#5=[count($13)])
+ HiveJoin(condition=[AND(=($9, $25), OR(AND($1, $2, $17, $26), AND($3, $4, $18, $27), AND($5, $6, $19, $27)))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($0, $8)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cd_demo_sk=[$0], ==[=($2, _UTF-16LE'M')], =2=[=($3, _UTF-16LE'4 yr Degree')], =3=[=($2, _UTF-16LE'D')], =4=[=($3, _UTF-16LE'Primary')], =5=[=($2, _UTF-16LE'U')], =6=[=($3, _UTF-16LE'Advanced Degree')])
+ HiveFilter(condition=[AND(IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'), IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
+ HiveJoin(condition=[AND(=($3, $14), OR(AND($15, $7), AND($16, $8), AND($17, $9)))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($0, $13)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ss_sold_date_sk=[$0], ss_cdemo_sk=[$4], ss_hdemo_sk=[$5], ss_addr_sk=[$6], ss_quantity=[$10], ss_ext_sales_price=[$15], ss_ext_wholesale_cost=[$16], BETWEEN=[BETWEEN(false, $22, 100, 200)], BETWEEN9=[BETWEEN(false, $22, 150, 300)], BETWEEN10=[BETWEEN(false, $22, 50, 250)], BETWEEN11=[BETWEEN(false, $13, 100, 150)], BETWEEN12=[BETWEEN(false, $13, 50, 100)], BETWEEN13=[BETWEEN(false, $13, 150, 200)])
+ HiveFilter(condition=[AND(OR(<=(100, $13), <=($13, 150), IS NOT NULL($13), <=($13, 200)), OR(<=(100, $22), <=($22, 200), IS NOT NULL($22), <=($22, 300), <=($22, 250)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($5), IS NOT NULL($6), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[=($6, 2001)])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject(ss_sold_date_sk=[$0], ss_cdemo_sk=[$4], ss_hdemo_sk=[$5], ss_addr_sk=[$6], ss_quantity=[$10], ss_ext_sales_price=[$15], ss_ext_wholesale_cost=[$16], BETWEEN=[BETWEEN(false, $22, 100, 200)], BETWEEN9=[BETWEEN(false, $22, 150, 300)], BETWEEN10=[BETWEEN(false, $22, 50, 250)], BETWEEN11=[BETWEEN(false, $13, 100, 150)], BETWEEN12=[BETWEEN(false, $13, 50, 100)], BETWEEN13=[BETWEEN(false, $13, 150, 200)])
- HiveFilter(condition=[AND(OR(BETWEEN(false, $13, 100, 150), BETWEEN(false, $13, 50, 100), BETWEEN(false, $13, 150, 200)), OR(BETWEEN(false, $22, 100, 200), BETWEEN(false, $22, 150, 300), BETWEEN(false, $22, 50, 250)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($5), IS NOT NULL($6), IS NOT NULL($0))])
- HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
+ HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
+ HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'))])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveProject(hd_demo_sk=[$0], ==[=($3, 3)], =2=[=($3, 1)])
+ HiveFilter(condition=[IN($3, 3, 1)])
+ HiveTableScan(table=[[default, household_demographics]], table:alias=[household_demographics])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query21.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query21.q.out
index f304762..5e165e8 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query21.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query21.q.out
@@ -69,7 +69,7 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
CBO PLAN:
HiveSortLimit(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC], fetch=[100])
HiveProject($f0=[$0], $f1=[$1], $f2=[$2], $f3=[$3])
- HiveFilter(condition=[CASE(>($2, 0), BETWEEN(false, /(CAST($3):DOUBLE, CAST($2):DOUBLE), 6.66667E-1, 1.5E0), null)])
+ HiveFilter(condition=[AND(CASE(>($2, 0), <=(6.66667E-1, /(CAST($3):DOUBLE, CAST($2):DOUBLE)), null), CASE(>($2, 0), <=(/(CAST($3):DOUBLE, CAST($2):DOUBLE), 1.5E0), null))])
HiveAggregate(group=[{0, 1}], agg#0=[sum($2)], agg#1=[sum($3)])
HiveProject($f0=[$1], $f1=[$10], $f2=[CASE($7, $5, 0)], $f3=[CASE($8, $5, 0)])
HiveJoin(condition=[=($4, $0)], joinType=[inner], algorithm=[none], cost=[not available])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query34.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query34.q.out
index 6e7384e..987f8d3 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query34.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query34.q.out
@@ -87,7 +87,7 @@ HiveSortLimit(sort0=[$0], sort1=[$1], sort2=[$2], sort3=[$3], dir0=[ASC], dir1=[
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($5), IS NOT NULL($3))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[AND(IN($6, 2000, 2001, 2002), OR(BETWEEN(false, $9, 1, 3), BETWEEN(false, $9, 25, 28)))])
+ HiveFilter(condition=[AND(IN($6, 2000, 2001, 2002), OR(<=(1, $9), <=($9, 3), <=(25, $9), <=($9, 28)), OR(BETWEEN(false, $9, 1, 3), BETWEEN(false, $9, 25, 28)))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
HiveProject(hd_demo_sk=[$0])
HiveFilter(condition=[AND(IN($2, _UTF-16LE'>10000', _UTF-16LE'unknown'), >($4, 0), CASE(>($4, 0), >(/(CAST($3):DOUBLE, CAST($4):DOUBLE), 1.2), null))])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query48.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query48.q.out
index 12d5934..3188d82 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query48.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query48.q.out
@@ -141,20 +141,20 @@ POSTHOOK: Input: default@store
POSTHOOK: Input: default@store_sales
POSTHOOK: Output: hdfs://### HDFS PATH ###
CBO PLAN:
-HiveAggregate(group=[{}], agg#0=[sum($9)])
- HiveJoin(condition=[AND(=($8, $0), OR(AND($1, $10), AND($2, $11), AND($3, $12)))], joinType=[inner], algorithm=[none], cost=[not available])
+HiveAggregate(group=[{}], agg#0=[sum($8)])
+ HiveJoin(condition=[AND(=($7, $0), OR(AND($1, $9), AND($2, $10), AND($3, $11)))], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'))])
HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
- HiveJoin(condition=[=($2, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[=($6, 1998)])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveJoin(condition=[=($0, $2)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0])
- HiveFilter(condition=[AND(=($2, _UTF-16LE'M'), =($3, _UTF-16LE'4 yr Degree'))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
+ HiveJoin(condition=[=($0, $2)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cd_demo_sk=[$0])
+ HiveFilter(condition=[AND(=($2, _UTF-16LE'M'), =($3, _UTF-16LE'4 yr Degree'))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[customer_demographics])
+ HiveJoin(condition=[=($0, $7)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(ss_sold_date_sk=[$0], ss_cdemo_sk=[$4], ss_addr_sk=[$6], ss_quantity=[$10], BETWEEN=[BETWEEN(false, $22, 0, 2000)], BETWEEN6=[BETWEEN(false, $22, 150, 3000)], BETWEEN7=[BETWEEN(false, $22, 50, 25000)])
- HiveFilter(condition=[AND(OR(BETWEEN(false, $13, 100, 150), BETWEEN(false, $13, 50, 100), BETWEEN(false, $13, 150, 200)), OR(BETWEEN(false, $22, 0, 2000), BETWEEN(false, $22, 150, 3000), BETWEEN(false, $22, 50, 25000)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($6), IS NOT NULL($0))])
+ HiveFilter(condition=[AND(OR(<=(100, $13), <=($13, 150), IS NOT NULL($13), <=($13, 200)), OR(<=(0, $22), <=($22, 2000), IS NOT NULL($22), <=($22, 3000), <=($22, 25000)), OR(BETWEEN(false, $13, 100, 150), BETWEEN(false, $13, 50, 100), BETWEEN(false, $13, 150, 200)), IS NOT NULL($7), IS NOT NULL($4), IS NOT NULL($6), IS NOT NULL($0))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
+ HiveProject(d_date_sk=[$0])
+ HiveFilter(condition=[=($6, 1998)])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query54.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query54.q.out
index 6af3162..e33203d 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query54.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query54.q.out
@@ -1,7 +1,7 @@
-Warning: Shuffle Join MERGEJOIN[269][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
-Warning: Shuffle Join MERGEJOIN[278][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4, $hdt$_5]] in Stage 'Reducer 6' is a cross product
-Warning: Shuffle Join MERGEJOIN[277][tables = [$hdt$_1, $hdt$_2]] in Stage 'Reducer 15' is a cross product
-Warning: Shuffle Join MERGEJOIN[279][tables = [$hdt$_1, $hdt$_2, $hdt$_0]] in Stage 'Reducer 7' is a cross product
+Warning: Shuffle Join MERGEJOIN[274][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
+Warning: Shuffle Join MERGEJOIN[280][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
+Warning: Shuffle Join MERGEJOIN[279][tables = [$hdt$_4, $hdt$_5]] in Stage 'Reducer 15' is a cross product
+Warning: Shuffle Join MERGEJOIN[282][tables = [$hdt$_4, $hdt$_5, $hdt$_6]] in Stage 'Reducer 16' is a cross product
PREHOOK: query: explain cbo
with my_customers as (
select distinct c_customer_sk
@@ -135,71 +135,14 @@ HiveSortLimit(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC], fetch=[100])
HiveProject(segment=[$0], num_customers=[$1], segment_base=[*($0, 50)])
HiveAggregate(group=[{0}], agg#0=[count()])
HiveProject(segment=[CAST(/($1, CAST(50):DECIMAL(10, 0))):INTEGER])
- HiveAggregate(group=[{0}], agg#0=[sum($1)])
- HiveFilter(condition=[BETWEEN(false, $2, $3, $4)])
- HiveProject(c_customer_sk=[$0], ss_ext_sales_price=[$4], d_month_seq=[$11], _o__c0=[$13], $f0=[$14])
+ HiveAggregate(group=[{5}], agg#0=[sum($2)])
+ HiveJoin(condition=[=($0, $12)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($5, $1)], joinType=[inner], algorithm=[none], cost=[not available])
HiveJoin(condition=[true], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject($f0=[$4], $f1=[$5], ss_sold_date_sk=[$0], ss_customer_sk=[$1], ss_ext_sales_price=[$2], ca_address_sk=[$8], ca_county=[$9], ca_state=[$10], s_county=[$11], s_state=[$12], d_date_sk=[$6], d_month_seq=[$7], cnt=[$3], $f00=[$13])
- HiveJoin(condition=[true], joinType=[left], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($5, $8)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($0, $6)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($4, $1)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[true], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ss_sold_date_sk=[$0], ss_customer_sk=[$3], ss_ext_sales_price=[$15])
- HiveFilter(condition=[AND(IS NOT NULL($3), IS NOT NULL($0))])
- HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
- HiveProject(cnt=[$0])
- HiveFilter(condition=[<=(sq_count_check($0), 1)])
- HiveProject(cnt=[$0])
- HiveAggregate(group=[{}], cnt=[COUNT()])
- HiveProject($f0=[$0])
- HiveAggregate(group=[{0}])
- HiveProject($f0=[+($3, 1)])
- HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$1])
- HiveAggregate(group=[{0, 1}])
- HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$4])
- HiveFilter(condition=[IS NOT NULL($4)])
- HiveTableScan(table=[[default, customer]], table:alias=[customer])
- HiveJoin(condition=[=($2, $4)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$1], cs_item_sk=[$2])
- HiveUnion(all=[true])
- HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$3], cs_item_sk=[$15])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($3))])
- HiveTableScan(table=[[default, catalog_sales]], table:alias=[catalog_sales])
- HiveProject(sold_date_sk=[$0], customer_sk=[$4], item_sk=[$3])
- HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($4))])
- HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
- HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[AND(=($8, 3), =($6, 1999))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject(i_item_sk=[$0])
- HiveFilter(condition=[AND(=($12, _UTF-16LE'Jewelry'), =($10, _UTF-16LE'consignment'))])
- HiveTableScan(table=[[default, item]], table:alias=[item])
- HiveProject(d_date_sk=[$0], d_month_seq=[$3])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveProject(ca_address_sk=[$0], ca_county=[$1], ca_state=[$2], s_county=[$3], s_state=[$4])
- HiveJoin(condition=[AND(=($1, $3), =($2, $4))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], ca_county=[$7], ca_state=[$8])
- HiveFilter(condition=[AND(IS NOT NULL($7), IS NOT NULL($8))])
- HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
- HiveProject(s_county=[$23], s_state=[$24])
- HiveFilter(condition=[AND(IS NOT NULL($23), IS NOT NULL($24))])
- HiveTableScan(table=[[default, store]], table:alias=[store])
- HiveProject($f0=[$0])
- HiveAggregate(group=[{0}])
- HiveProject($f0=[+($3, 1)])
- HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveJoin(condition=[true], joinType=[right], algorithm=[none], cost=[not available])
- HiveProject($f0=[$0])
- HiveAggregate(group=[{0}])
- HiveProject($f0=[+($3, 3)])
- HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveJoin(condition=[true], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ss_sold_date_sk=[$0], ss_customer_sk=[$3], ss_ext_sales_price=[$15])
+ HiveFilter(condition=[AND(IS NOT NULL($3), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(cnt=[$0])
HiveFilter(condition=[<=(sq_count_check($0), 1)])
HiveProject(cnt=[$0])
@@ -209,4 +152,59 @@ HiveSortLimit(sort0=[$0], sort1=[$1], dir0=[ASC], dir1=[ASC], fetch=[100])
HiveProject($f0=[+($3, 3)])
HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(cnt=[$0])
+ HiveFilter(condition=[<=(sq_count_check($0), 1)])
+ HiveProject(cnt=[$0])
+ HiveAggregate(group=[{}], cnt=[COUNT()])
+ HiveProject($f0=[$0])
+ HiveAggregate(group=[{0}])
+ HiveProject($f0=[+($3, 1)])
+ HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$1], ca_address_sk=[$2], ca_county=[$3], ca_state=[$4], s_county=[$5], s_state=[$6])
+ HiveJoin(condition=[=($1, $2)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$1])
+ HiveAggregate(group=[{0, 1}])
+ HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(c_customer_sk=[$0], c_current_addr_sk=[$4])
+ HiveFilter(condition=[IS NOT NULL($4)])
+ HiveTableScan(table=[[default, customer]], table:alias=[customer])
+ HiveJoin(condition=[=($2, $4)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($0, $3)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$1], cs_item_sk=[$2])
+ HiveUnion(all=[true])
+ HiveProject(cs_sold_date_sk=[$0], cs_bill_customer_sk=[$3], cs_item_sk=[$15])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($3))])
+ HiveTableScan(table=[[default, catalog_sales]], table:alias=[catalog_sales])
+ HiveProject(sold_date_sk=[$0], customer_sk=[$4], item_sk=[$3])
+ HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($4))])
+ HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
+ HiveProject(d_date_sk=[$0])
+ HiveFilter(condition=[AND(=($8, 3), =($6, 1999))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(i_item_sk=[$0])
+ HiveFilter(condition=[AND(=($12, _UTF-16LE'Jewelry'), =($10, _UTF-16LE'consignment'))])
+ HiveTableScan(table=[[default, item]], table:alias=[item])
+ HiveJoin(condition=[AND(=($1, $3), =($2, $4))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ca_address_sk=[$0], ca_county=[$7], ca_state=[$8])
+ HiveFilter(condition=[AND(IS NOT NULL($7), IS NOT NULL($8))])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveProject(s_county=[$23], s_state=[$24])
+ HiveFilter(condition=[AND(IS NOT NULL($23), IS NOT NULL($24))])
+ HiveTableScan(table=[[default, store]], table:alias=[store])
+ HiveProject(d_date_sk=[$0], d_month_seq=[$1], $f0=[$2], $f00=[$3])
+ HiveJoin(condition=[<=($1, $3)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[<=($2, $1)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(d_date_sk=[$0], d_month_seq=[$3])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject($f0=[$0])
+ HiveAggregate(group=[{0}])
+ HiveProject($f0=[+($3, 1)])
+ HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject($f0=[$0])
+ HiveAggregate(group=[{0}])
+ HiveProject($f0=[+($3, 3)])
+ HiveFilter(condition=[AND(=($6, 1999), =($8, 3))])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query64.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query64.q.out
index 4261d06..75e69cd 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query64.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query64.q.out
@@ -292,7 +292,7 @@ HiveProject(product_name=[$0], store_name=[$1], store_zip=[$2], b_street_number=
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($3), IS NOT NULL($4), IS NOT NULL($8), IS NOT NULL($5), IS NOT NULL($6))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(i_item_sk=[$0], i_product_name=[$21])
- HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 35, 45), BETWEEN(false, $5, 36, 50))])
+ HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 36, 45))])
HiveTableScan(table=[[default, item]], table:alias=[item])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[=($6, 2000)])
@@ -351,7 +351,7 @@ HiveProject(product_name=[$0], store_name=[$1], store_zip=[$2], b_street_number=
HiveFilter(condition=[AND(IS NOT NULL($0), IS NOT NULL($7), IS NOT NULL($3), IS NOT NULL($4), IS NOT NULL($8), IS NOT NULL($5), IS NOT NULL($6))])
HiveTableScan(table=[[default, store_sales]], table:alias=[store_sales])
HiveProject(i_item_sk=[$0], i_product_name=[$21])
- HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 35, 45), BETWEEN(false, $5, 36, 50))])
+ HiveFilter(condition=[AND(IN($17, _UTF-16LE'maroon', _UTF-16LE'burnished', _UTF-16LE'dim', _UTF-16LE'steel', _UTF-16LE'navajo', _UTF-16LE'chocolate'), BETWEEN(false, $5, 36, 45))])
HiveTableScan(table=[[default, item]], table:alias=[item])
HiveProject(d_date_sk=[$0])
HiveFilter(condition=[=($6, 2001)])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query85.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query85.q.out
index 6471345..91aad93 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query85.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/cbo_query85.q.out
@@ -184,31 +184,32 @@ CBO PLAN:
HiveProject(_o__c0=[$0], _o__c1=[$1], _o__c2=[$2], _o__c3=[$3])
HiveSortLimit(sort0=[$7], sort1=[$4], sort2=[$5], sort3=[$6], dir0=[ASC], dir1=[ASC], dir2=[ASC], dir3=[ASC], fetch=[100])
HiveProject(_o__c0=[substr($0, 1, 20)], _o__c1=[/(CAST($1):DOUBLE, $2)], _o__c2=[/($3, $4)], _o__c3=[/($5, $6)], (tok_function avg (tok_table_or_col ws_quantity))=[/(CAST($1):DOUBLE, $2)], (tok_function avg (tok_table_or_col wr_refunded_cash))=[/($3, $4)], (tok_function avg (tok_table_or_col wr_fee))=[/($5, $6)], (tok_function substr (tok_table_or_col r_reason_desc) 1 20)=[substr($0, 1, 20)])
- HiveAggregate(group=[{1}], agg#0=[sum($30)], agg#1=[count($30)], agg#2=[sum($26)], agg#3=[count($26)], agg#4=[sum($25)], agg#5=[count($25)])
- HiveJoin(condition=[=($0, $23)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveAggregate(group=[{1}], agg#0=[sum($5)], agg#1=[count($5)], agg#2=[sum($29)], agg#3=[count($29)], agg#4=[sum($28)], agg#5=[count($28)])
+ HiveJoin(condition=[=($0, $26)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(r_reason_sk=[$0], r_reason_desc=[$2])
HiveTableScan(table=[[default, reason]], table:alias=[reason])
- HiveJoin(condition=[AND(AND(AND(=($1, $15), =($2, $16)), =($0, $18)), OR(AND($3, $4, $32), AND($5, $6, $33), AND($7, $8, $34)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3], ==[=($2, _UTF-16LE'M')], =4=[=($3, _UTF-16LE'4 yr Degree')], =5=[=($2, _UTF-16LE'D')], =6=[=($3, _UTF-16LE'Primary')], =7=[=($2, _UTF-16LE'U')], =8=[=($3, _UTF-16LE'Advanced Degree')])
- HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
- HiveJoin(condition=[AND(=($0, $10), OR(AND($1, $20), AND($2, $21), AND($3, $22)))], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
- HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'))])
- HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
- HiveJoin(condition=[=($12, $0)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(d_date_sk=[$0])
- HiveFilter(condition=[=($6, 1998)])
- HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
- HiveJoin(condition=[=($0, $6)], joinType=[inner], algorithm=[none], cost=[not available])
- HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3])
- HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'))])
- HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
- HiveJoin(condition=[AND(=($9, $0), =($10, $5))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[AND(AND(AND(=($1, $20), =($2, $25)), OR(AND($14, $15, $7), AND($16, $17, $8), AND($18, $19, $9))), OR(AND($29, $4), AND($30, $5), AND($31, $6)))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($0, $10)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(ws_sold_date_sk=[$0], ws_item_sk=[$3], ws_order_number=[$17], ws_quantity=[$18], BETWEEN=[BETWEEN(false, $33, 100, 200)], BETWEEN6=[BETWEEN(false, $33, 150, 300)], BETWEEN7=[BETWEEN(false, $33, 50, 250)], BETWEEN8=[BETWEEN(false, $21, 100, 150)], BETWEEN9=[BETWEEN(false, $21, 50, 100)], BETWEEN10=[BETWEEN(false, $21, 150, 200)])
+ HiveFilter(condition=[AND(OR(<=(100, $21), <=($21, 150), IS NOT NULL($21), <=($21, 200)), OR(<=(100, $33), <=($33, 200), IS NOT NULL($33), <=($33, 300), <=($33, 250)), IS NOT NULL($12), IS NOT NULL($0))])
+ HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
+ HiveProject(d_date_sk=[$0])
+ HiveFilter(condition=[=($6, 1998)])
+ HiveTableScan(table=[[default, date_dim]], table:alias=[date_dim])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$1], cd_education_status=[$2], ==[$3], =4=[$4], =5=[$5], =6=[$6], =7=[$7], =8=[$8], wr_item_sk=[$9], wr_refunded_cdemo_sk=[$10], wr_refunded_addr_sk=[$11], wr_returning_cdemo_sk=[$12], wr_reason_sk=[$13], wr_order_number=[$14], wr_fee=[$15], wr_refunded_cash=[$16], ca_address_sk=[$17], IN=[$18], IN2=[$19], IN3=[$20], cd_demo_sk0=[$21], cd_marital_status0=[$22], cd_education_status0=[$23])
+ HiveJoin(condition=[AND(AND(=($1, $22), =($2, $23)), =($0, $10))], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3], ==[=($2, _UTF-16LE'M')], =4=[=($3, _UTF-16LE'4 yr Degree')], =5=[=($2, _UTF-16LE'D')], =6=[=($3, _UTF-16LE'Primary')], =7=[=($2, _UTF-16LE'U')], =8=[=($3, _UTF-16LE'Advanced Degree')])
+ HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd1])
+ HiveJoin(condition=[=($12, $3)], joinType=[inner], algorithm=[none], cost=[not available])
+ HiveJoin(condition=[=($8, $2)], joinType=[inner], algorithm=[none], cost=[not available])
HiveProject(wr_item_sk=[$2], wr_refunded_cdemo_sk=[$4], wr_refunded_addr_sk=[$6], wr_returning_cdemo_sk=[$8], wr_reason_sk=[$12], wr_order_number=[$13], wr_fee=[$18], wr_refunded_cash=[$20])
HiveFilter(condition=[AND(IS NOT NULL($4), IS NOT NULL($8), IS NOT NULL($6), IS NOT NULL($12))])
HiveTableScan(table=[[default, web_returns]], table:alias=[web_returns])
- HiveProject(ws_sold_date_sk=[$0], ws_item_sk=[$3], ws_order_number=[$17], ws_quantity=[$18], BETWEEN=[BETWEEN(false, $33, 100, 200)], BETWEEN6=[BETWEEN(false, $33, 150, 300)], BETWEEN7=[BETWEEN(false, $33, 50, 250)], BETWEEN8=[BETWEEN(false, $21, 100, 150)], BETWEEN9=[BETWEEN(false, $21, 50, 100)], BETWEEN10=[BETWEEN(false, $21, 150, 200)])
- HiveFilter(condition=[AND(OR(BETWEEN(false, $21, 100, 150), BETWEEN(false, $21, 50, 100), BETWEEN(false, $21, 150, 200)), OR(BETWEEN(false, $33, 100, 200), BETWEEN(false, $33, 150, 300), BETWEEN(false, $33, 50, 250)), IS NOT NULL($12), IS NOT NULL($0))])
- HiveTableScan(table=[[default, web_sales]], table:alias=[web_sales])
+ HiveProject(ca_address_sk=[$0], IN=[IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM')], IN2=[IN($8, _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN')], IN3=[IN($8, _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV')])
+ HiveFilter(condition=[AND(IN($8, _UTF-16LE'KY', _UTF-16LE'GA', _UTF-16LE'NM', _UTF-16LE'MT', _UTF-16LE'OR', _UTF-16LE'IN', _UTF-16LE'WI', _UTF-16LE'MO', _UTF-16LE'WV'), =($10, _UTF-16LE'United States'))])
+ HiveTableScan(table=[[default, customer_address]], table:alias=[customer_address])
+ HiveProject(cd_demo_sk=[$0], cd_marital_status=[$2], cd_education_status=[$3])
+ HiveFilter(condition=[AND(IN($3, _UTF-16LE'4 yr Degree', _UTF-16LE'Primary', _UTF-16LE'Advanced Degree'), IN($2, _UTF-16LE'M', _UTF-16LE'D', _UTF-16LE'U'))])
+ HiveTableScan(table=[[default, customer_demographics]], table:alias=[cd2])
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query13.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query13.q.out
index ac81e37..616ed6b 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/query13.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query13.q.out
@@ -115,13 +115,13 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 8 <- Reducer 7 (BROADCAST_EDGE)
-Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 8 (SIMPLE_EDGE)
+Map 1 <- Reducer 8 (BROADCAST_EDGE)
+Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 7 (SIMPLE_EDGE)
Reducer 3 <- Map 9 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
Reducer 4 <- Map 10 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
Reducer 5 <- Map 11 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
Reducer 6 <- Reducer 5 (CUSTOM_SIMPLE_EDGE)
-Reducer 7 <- Map 1 (CUSTOM_SIMPLE_EDGE)
+Reducer 8 <- Map 7 (CUSTOM_SIMPLE_EDGE)
Stage-0
Fetch Operator
@@ -136,80 +136,80 @@ Stage-0
<-Reducer 5 [CUSTOM_SIMPLE_EDGE]
PARTITION_ONLY_SHUFFLE [RS_31]
Group By Operator [GBY_30] (rows=1 width=256)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5"],aggregations:["sum(_col5)","count(_col5)","sum(_col6)","count(_col6)","sum(_col7)","count(_col7)"]
- Select Operator [SEL_29] (rows=40950 width=44)
- Output:["_col5","_col6","_col7"]
- Filter Operator [FIL_28] (rows=40950 width=44)
- predicate:((_col22 and _col23 and _col11 and _col15) or (_col24 and _col25 and _col12 and _col16) or (_col26 and _col27 and _col13 and _col16))
- Merge Join Operator [MERGEJOIN_97] (rows=218403 width=44)
- Conds:RS_25._col2=RS_117._col0(Inner),Output:["_col5","_col6","_col7","_col11","_col12","_col13","_col15","_col16","_col22","_col23","_col24","_col25","_col26","_col27"]
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5"],aggregations:["sum(_col4)","count(_col4)","sum(_col5)","count(_col5)","sum(_col6)","count(_col6)"]
+ Select Operator [SEL_29] (rows=368553 width=44)
+ Output:["_col4","_col5","_col6"]
+ Filter Operator [FIL_28] (rows=368553 width=44)
+ predicate:((_col19 and _col20 and _col10 and _col26) or (_col21 and _col22 and _col11 and _col27) or (_col23 and _col24 and _col12 and _col27))
+ Merge Join Operator [MERGEJOIN_97] (rows=1965626 width=44)
+ Conds:RS_25._col2=RS_117._col0(Inner),Output:["_col4","_col5","_col6","_col10","_col11","_col12","_col19","_col20","_col21","_col22","_col23","_col24","_col26","_col27"]
<-Map 11 [SIMPLE_EDGE] vectorized
SHUFFLE [RS_117]
PartitionCols:_col0
- Select Operator [SEL_116] (rows=265971 width=28)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
- Filter Operator [FIL_115] (rows=265971 width=183)
- predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U'))
- TableScan [TS_12] (rows=1861800 width=183)
- default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
+ Select Operator [SEL_116] (rows=1309 width=12)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_115] (rows=1309 width=8)
+ predicate:(hd_dep_count) IN (3, 1)
+ TableScan [TS_12] (rows=7200 width=8)
+ default@household_demographics,household_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_dep_count"]
<-Reducer 4 [SIMPLE_EDGE]
SHUFFLE [RS_25]
PartitionCols:_col2
- Filter Operator [FIL_24] (rows=218403 width=44)
- predicate:((_col18 and _col8) or (_col19 and _col9) or (_col20 and _col10))
- Merge Join Operator [MERGEJOIN_96] (rows=291204 width=44)
- Conds:RS_21._col4=RS_114._col0(Inner),Output:["_col2","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col15","_col16","_col18","_col19","_col20"]
- <-Map 10 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_114]
- PartitionCols:_col0
- Select Operator [SEL_113] (rows=3529412 width=16)
- Output:["_col0","_col1","_col2","_col3"]
- Filter Operator [FIL_112] (rows=3529412 width=187)
- predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV'))
- TableScan [TS_9] (rows=40000000 width=187)
- default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
- <-Reducer 3 [SIMPLE_EDGE]
- SHUFFLE [RS_21]
- PartitionCols:_col4
- Merge Join Operator [MERGEJOIN_95] (rows=3300311 width=104)
- Conds:RS_18._col3=RS_111._col0(Inner),Output:["_col2","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col15","_col16"]
+ Merge Join Operator [MERGEJOIN_96] (rows=10811694 width=36)
+ Conds:RS_22._col1=RS_114._col0(Inner),Output:["_col2","_col4","_col5","_col6","_col10","_col11","_col12","_col19","_col20","_col21","_col22","_col23","_col24"]
+ <-Map 10 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_114]
+ PartitionCols:_col0
+ Select Operator [SEL_113] (rows=265971 width=28)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
+ Filter Operator [FIL_112] (rows=265971 width=183)
+ predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U'))
+ TableScan [TS_9] (rows=1861800 width=183)
+ default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
+ <-Reducer 3 [SIMPLE_EDGE]
+ SHUFFLE [RS_22]
+ PartitionCols:_col1
+ Filter Operator [FIL_21] (rows=10811694 width=36)
+ predicate:((_col15 and _col7) or (_col16 and _col8) or (_col17 and _col9))
+ Merge Join Operator [MERGEJOIN_95] (rows=14415593 width=36)
+ Conds:RS_18._col3=RS_111._col0(Inner),Output:["_col1","_col2","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17"]
<-Map 9 [SIMPLE_EDGE] vectorized
SHUFFLE [RS_111]
PartitionCols:_col0
- Select Operator [SEL_110] (rows=1309 width=12)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_109] (rows=1309 width=8)
- predicate:(hd_dep_count) IN (3, 1)
- TableScan [TS_6] (rows=7200 width=8)
- default@household_demographics,household_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_dep_count"]
+ Select Operator [SEL_110] (rows=3529412 width=16)
+ Output:["_col0","_col1","_col2","_col3"]
+ Filter Operator [FIL_109] (rows=3529412 width=187)
+ predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV'))
+ TableScan [TS_6] (rows=40000000 width=187)
+ default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
<-Reducer 2 [SIMPLE_EDGE]
SHUFFLE [RS_18]
PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_94] (rows=18152968 width=233)
- Conds:RS_100._col0=RS_108._col0(Inner),Output:["_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Map 1 [SIMPLE_EDGE] vectorized
+ Merge Join Operator [MERGEJOIN_94] (rows=163376714 width=233)
+ Conds:RS_108._col0=RS_100._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
+ <-Map 7 [SIMPLE_EDGE] vectorized
PARTITION_ONLY_SHUFFLE [RS_100]
PartitionCols:_col0
Select Operator [SEL_99] (rows=652 width=4)
Output:["_col0"]
Filter Operator [FIL_98] (rows=652 width=8)
predicate:(d_year = 2001)
- TableScan [TS_0] (rows=73049 width=8)
+ TableScan [TS_3] (rows=73049 width=8)
default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
- <-Map 8 [SIMPLE_EDGE] vectorized
+ <-Map 1 [SIMPLE_EDGE] vectorized
SHUFFLE [RS_108]
PartitionCols:_col0
- Select Operator [SEL_107] (rows=50840141 width=257)
+ Select Operator [SEL_107] (rows=457561292 width=257)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
- Filter Operator [FIL_106] (rows=50840141 width=450)
- predicate:((ss_net_profit BETWEEN 100 AND 200 or ss_net_profit BETWEEN 150 AND 300 or ss_net_profit BETWEEN 50 AND 250) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_sold_date_sk BETWEEN DynamicValue(RS_15_date_dim_d_date_sk_min) AND DynamicValue(RS_15_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_15_date_dim_d_date_sk_bloom_filter))) and [...]
- TableScan [TS_3] (rows=575995635 width=450)
+ Filter Operator [FIL_106] (rows=457561292 width=450)
+ predicate:(((ss_net_profit >= 100) or (ss_net_profit <= 200) or ss_net_profit is not null or (ss_net_profit <= 300) or (ss_net_profit <= 250)) and ((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and (ss_sold_date_sk BETWEEN DynamicValue(RS_16_date_dim_d_date_sk_min) AND DynamicValue(RS_16_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_16_date_dim_d_date_sk [...]
+ TableScan [TS_0] (rows=575995635 width=450)
default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_quantity","ss_sales_price","ss_ext_sales_price","ss_ext_wholesale_cost","ss_net_profit"]
- <-Reducer 7 [BROADCAST_EDGE] vectorized
+ <-Reducer 8 [BROADCAST_EDGE] vectorized
BROADCAST [RS_105]
Group By Operator [GBY_104] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 1 [CUSTOM_SIMPLE_EDGE] vectorized
+ <-Map 7 [CUSTOM_SIMPLE_EDGE] vectorized
PARTITION_ONLY_SHUFFLE [RS_103]
Group By Operator [GBY_102] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query21.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query21.q.out
index 67fdc85..7c68a29 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/query21.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query21.q.out
@@ -83,12 +83,12 @@ Stage-0
File Output Operator [FS_91]
Limit [LIM_90] (rows=100 width=216)
Number of rows:100
- Select Operator [SEL_89] (rows=231983 width=216)
+ Select Operator [SEL_89] (rows=115991 width=216)
Output:["_col0","_col1","_col2","_col3"]
<-Reducer 5 [SIMPLE_EDGE] vectorized
SHUFFLE [RS_88]
- Filter Operator [FIL_87] (rows=231983 width=216)
- predicate:CASE WHEN ((_col2 > 0L)) THEN ((UDFToDouble(_col3) / UDFToDouble(_col2)) BETWEEN 0.666667D AND 1.5D) ELSE (null) END
+ Filter Operator [FIL_87] (rows=115991 width=216)
+ predicate:(CASE WHEN ((_col2 > 0L)) THEN (((UDFToDouble(_col3) / UDFToDouble(_col2)) <= 1.5D)) ELSE (null) END and CASE WHEN ((_col2 > 0L)) THEN ((0.666667D <= (UDFToDouble(_col3) / UDFToDouble(_col2)))) ELSE (null) END)
Group By Operator [GBY_86] (rows=463966 width=216)
Output:["_col0","_col1","_col2","_col3"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)"],keys:KEY._col0, KEY._col1
<-Reducer 4 [SIMPLE_EDGE]
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query34.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query34.q.out
index 5b0ded7..e2c3631 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/query34.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query34.q.out
@@ -153,7 +153,7 @@ Stage-0
Select Operator [SEL_103] (rows=595 width=4)
Output:["_col0"]
Filter Operator [FIL_102] (rows=595 width=12)
- predicate:((d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and (d_year) IN (2000, 2001, 2002))
+ predicate:(((d_dom >= 1) or (d_dom <= 3) or (d_dom >= 25) or (d_dom <= 28)) and (d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and (d_year) IN (2000, 2001, 2002))
TableScan [TS_5] (rows=73049 width=12)
default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_dom"]
<-Map 4 [SIMPLE_EDGE] vectorized
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query48.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query48.q.out
index af9d442..93bafab 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/query48.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query48.q.out
@@ -143,12 +143,12 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 6 <- Reducer 8 (BROADCAST_EDGE)
+Map 1 <- Reducer 7 (BROADCAST_EDGE)
Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 6 (SIMPLE_EDGE)
-Reducer 3 <- Map 7 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
+Reducer 3 <- Map 8 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
Reducer 4 <- Map 9 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
Reducer 5 <- Reducer 4 (CUSTOM_SIMPLE_EDGE)
-Reducer 8 <- Map 7 (CUSTOM_SIMPLE_EDGE)
+Reducer 7 <- Map 6 (CUSTOM_SIMPLE_EDGE)
Stage-0
Fetch Operator
@@ -161,13 +161,13 @@ Stage-0
<-Reducer 4 [CUSTOM_SIMPLE_EDGE]
PARTITION_ONLY_SHUFFLE [RS_24]
Group By Operator [GBY_23] (rows=1 width=8)
- Output:["_col0"],aggregations:["sum(_col4)"]
- Select Operator [SEL_22] (rows=20247 width=24)
- Output:["_col4"]
- Filter Operator [FIL_21] (rows=20247 width=24)
- predicate:((_col10 and _col5) or (_col11 and _col6) or (_col12 and _col7))
- Merge Join Operator [MERGEJOIN_73] (rows=26999 width=24)
- Conds:RS_18._col3=RS_90._col0(Inner),Output:["_col4","_col5","_col6","_col7","_col10","_col11","_col12"]
+ Output:["_col0"],aggregations:["sum(_col3)"]
+ Select Operator [SEL_22] (rows=170127 width=24)
+ Output:["_col3"]
+ Filter Operator [FIL_21] (rows=170127 width=24)
+ predicate:((_col10 and _col4) or (_col11 and _col5) or (_col12 and _col6))
+ Merge Join Operator [MERGEJOIN_73] (rows=226838 width=24)
+ Conds:RS_18._col2=RS_90._col0(Inner),Output:["_col3","_col4","_col5","_col6","_col10","_col11","_col12"]
<-Map 9 [SIMPLE_EDGE] vectorized
SHUFFLE [RS_90]
PartitionCols:_col0
@@ -179,50 +179,50 @@ Stage-0
default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
<-Reducer 3 [SIMPLE_EDGE]
SHUFFLE [RS_18]
- PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_72] (rows=305980 width=12)
- Conds:RS_15._col1=RS_79._col0(Inner),Output:["_col3","_col4","_col5","_col6","_col7"]
- <-Map 7 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_79]
+ PartitionCols:_col2
+ Merge Join Operator [MERGEJOIN_72] (rows=2570826 width=12)
+ Conds:RS_15._col1=RS_87._col0(Inner),Output:["_col2","_col3","_col4","_col5","_col6"]
+ <-Map 8 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_87]
PartitionCols:_col0
- Select Operator [SEL_78] (rows=652 width=4)
+ Select Operator [SEL_86] (rows=29552 width=4)
Output:["_col0"]
- Filter Operator [FIL_77] (rows=652 width=8)
- predicate:(d_year = 1998)
- TableScan [TS_6] (rows=73049 width=8)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
+ Filter Operator [FIL_85] (rows=29552 width=183)
+ predicate:((cd_education_status = '4 yr Degree') and (cd_marital_status = 'M'))
+ TableScan [TS_6] (rows=1861800 width=183)
+ default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
<-Reducer 2 [SIMPLE_EDGE]
SHUFFLE [RS_15]
PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_71] (rows=856943 width=12)
- Conds:RS_76._col0=RS_87._col1(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7"]
- <-Map 1 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_76]
+ Merge Join Operator [MERGEJOIN_71] (rows=57024544 width=22)
+ Conds:RS_84._col0=RS_76._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6"]
+ <-Map 6 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_76]
PartitionCols:_col0
- Select Operator [SEL_75] (rows=29552 width=4)
+ Select Operator [SEL_75] (rows=652 width=4)
Output:["_col0"]
- Filter Operator [FIL_74] (rows=29552 width=183)
- predicate:((cd_education_status = '4 yr Degree') and (cd_marital_status = 'M'))
- TableScan [TS_0] (rows=1861800 width=183)
- default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
- <-Map 6 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_87]
- PartitionCols:_col1
- Select Operator [SEL_86] (rows=53235296 width=27)
+ Filter Operator [FIL_74] (rows=652 width=8)
+ predicate:(d_year = 1998)
+ TableScan [TS_3] (rows=73049 width=8)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
+ <-Map 1 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_84]
+ PartitionCols:_col0
+ Select Operator [SEL_83] (rows=159705893 width=27)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
- Filter Operator [FIL_85] (rows=53235296 width=233)
- predicate:((ss_net_profit BETWEEN 0 AND 2000 or ss_net_profit BETWEEN 150 AND 3000 or ss_net_profit BETWEEN 50 AND 25000) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_sold_date_sk BETWEEN DynamicValue(RS_16_date_dim_d_date_sk_min) AND DynamicValue(RS_16_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_16_date_dim_d_date_sk_bloom_filter))) and ss_add [...]
- TableScan [TS_3] (rows=575995635 width=233)
+ Filter Operator [FIL_82] (rows=159705893 width=233)
+ predicate:(((ss_net_profit >= 0) or (ss_net_profit <= 2000) or ss_net_profit is not null or (ss_net_profit <= 3000) or (ss_net_profit <= 25000)) and ((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_sold_date_sk BETWEEN DynamicValue(RS_13_date_dim_d_date_sk_min) AND Dynamic [...]
+ TableScan [TS_0] (rows=575995635 width=233)
default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_cdemo_sk","ss_addr_sk","ss_store_sk","ss_quantity","ss_sales_price","ss_net_profit"]
- <-Reducer 8 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_84]
- Group By Operator [GBY_83] (rows=1 width=12)
+ <-Reducer 7 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_81]
+ Group By Operator [GBY_80] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 7 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_82]
- Group By Operator [GBY_81] (rows=1 width=12)
+ <-Map 6 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_79]
+ Group By Operator [GBY_78] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_80] (rows=652 width=4)
+ Select Operator [SEL_77] (rows=652 width=4)
Output:["_col0"]
- Please refer to the previous Select Operator [SEL_78]
+ Please refer to the previous Select Operator [SEL_75]
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query54.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query54.q.out
index 8d10899..526fa81 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/query54.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query54.q.out
@@ -1,7 +1,7 @@
-Warning: Shuffle Join MERGEJOIN[269][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
-Warning: Shuffle Join MERGEJOIN[278][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4, $hdt$_5]] in Stage 'Reducer 6' is a cross product
-Warning: Shuffle Join MERGEJOIN[277][tables = [$hdt$_1, $hdt$_2]] in Stage 'Reducer 15' is a cross product
-Warning: Shuffle Join MERGEJOIN[279][tables = [$hdt$_1, $hdt$_2, $hdt$_0]] in Stage 'Reducer 7' is a cross product
+Warning: Shuffle Join MERGEJOIN[274][tables = [$hdt$_0, $hdt$_1]] in Stage 'Reducer 2' is a cross product
+Warning: Shuffle Join MERGEJOIN[280][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Stage 'Reducer 3' is a cross product
+Warning: Shuffle Join MERGEJOIN[279][tables = [$hdt$_4, $hdt$_5]] in Stage 'Reducer 15' is a cross product
+Warning: Shuffle Join MERGEJOIN[282][tables = [$hdt$_4, $hdt$_5, $hdt$_6]] in Stage 'Reducer 16' is a cross product
PREHOOK: query: explain
with my_customers as (
select distinct c_customer_sk
@@ -133,277 +133,289 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 18 <- Reducer 26 (BROADCAST_EDGE), Union 19 (CONTAINS)
-Map 24 <- Reducer 26 (BROADCAST_EDGE), Union 19 (CONTAINS)
-Reducer 10 <- Reducer 9 (SIMPLE_EDGE)
-Reducer 12 <- Map 11 (SIMPLE_EDGE)
+Map 22 <- Reducer 30 (BROADCAST_EDGE), Union 23 (CONTAINS)
+Map 28 <- Reducer 30 (BROADCAST_EDGE), Union 23 (CONTAINS)
+Reducer 10 <- Map 9 (SIMPLE_EDGE)
+Reducer 11 <- Reducer 10 (CUSTOM_SIMPLE_EDGE)
+Reducer 12 <- Map 9 (SIMPLE_EDGE)
Reducer 13 <- Reducer 12 (CUSTOM_SIMPLE_EDGE)
-Reducer 14 <- Map 11 (SIMPLE_EDGE)
-Reducer 15 <- Reducer 14 (CUSTOM_SIMPLE_EDGE), Reducer 17 (CUSTOM_SIMPLE_EDGE)
-Reducer 16 <- Map 11 (SIMPLE_EDGE)
-Reducer 17 <- Reducer 16 (CUSTOM_SIMPLE_EDGE)
-Reducer 2 <- Map 1 (CUSTOM_SIMPLE_EDGE), Reducer 13 (CUSTOM_SIMPLE_EDGE)
-Reducer 20 <- Map 25 (SIMPLE_EDGE), Union 19 (SIMPLE_EDGE)
-Reducer 21 <- Map 27 (SIMPLE_EDGE), Reducer 20 (SIMPLE_EDGE)
-Reducer 22 <- Map 28 (SIMPLE_EDGE), Reducer 21 (SIMPLE_EDGE)
-Reducer 23 <- Reducer 22 (SIMPLE_EDGE)
-Reducer 26 <- Map 25 (CUSTOM_SIMPLE_EDGE)
-Reducer 3 <- Reducer 2 (SIMPLE_EDGE), Reducer 23 (SIMPLE_EDGE)
-Reducer 31 <- Map 30 (SIMPLE_EDGE), Map 32 (SIMPLE_EDGE)
-Reducer 4 <- Map 29 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
-Reducer 5 <- Reducer 31 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
-Reducer 6 <- Reducer 12 (CUSTOM_SIMPLE_EDGE), Reducer 5 (CUSTOM_SIMPLE_EDGE)
-Reducer 7 <- Reducer 15 (CUSTOM_SIMPLE_EDGE), Reducer 6 (CUSTOM_SIMPLE_EDGE)
+Reducer 14 <- Map 9 (SIMPLE_EDGE)
+Reducer 15 <- Map 33 (CUSTOM_SIMPLE_EDGE), Reducer 14 (CUSTOM_SIMPLE_EDGE)
+Reducer 16 <- Reducer 15 (CUSTOM_SIMPLE_EDGE), Reducer 17 (CUSTOM_SIMPLE_EDGE)
+Reducer 17 <- Map 9 (SIMPLE_EDGE)
+Reducer 19 <- Map 18 (SIMPLE_EDGE), Map 21 (SIMPLE_EDGE)
+Reducer 2 <- Map 1 (CUSTOM_SIMPLE_EDGE), Reducer 11 (CUSTOM_SIMPLE_EDGE)
+Reducer 20 <- Reducer 19 (SIMPLE_EDGE), Reducer 27 (SIMPLE_EDGE)
+Reducer 24 <- Map 29 (SIMPLE_EDGE), Union 23 (SIMPLE_EDGE)
+Reducer 25 <- Map 31 (SIMPLE_EDGE), Reducer 24 (SIMPLE_EDGE)
+Reducer 26 <- Map 32 (SIMPLE_EDGE), Reducer 25 (SIMPLE_EDGE)
+Reducer 27 <- Reducer 26 (SIMPLE_EDGE)
+Reducer 3 <- Reducer 13 (CUSTOM_SIMPLE_EDGE), Reducer 2 (CUSTOM_SIMPLE_EDGE)
+Reducer 30 <- Map 29 (CUSTOM_SIMPLE_EDGE)
+Reducer 4 <- Reducer 20 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
+Reducer 5 <- Reducer 16 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
+Reducer 6 <- Reducer 5 (SIMPLE_EDGE)
+Reducer 7 <- Reducer 6 (SIMPLE_EDGE)
Reducer 8 <- Reducer 7 (SIMPLE_EDGE)
-Reducer 9 <- Reducer 8 (SIMPLE_EDGE)
Stage-0
Fetch Operator
limit:100
Stage-1
- Reducer 10 vectorized
- File Output Operator [FS_353]
- Limit [LIM_352] (rows=1 width=16)
+ Reducer 8 vectorized
+ File Output Operator [FS_363]
+ Limit [LIM_362] (rows=1 width=16)
Number of rows:100
- Select Operator [SEL_351] (rows=1 width=16)
+ Select Operator [SEL_361] (rows=1 width=16)
Output:["_col0","_col1","_col2"]
- <-Reducer 9 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_350]
- Select Operator [SEL_349] (rows=1 width=16)
+ <-Reducer 7 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_360]
+ Select Operator [SEL_359] (rows=1 width=16)
Output:["_col0","_col1","_col2"]
- Group By Operator [GBY_348] (rows=1 width=12)
+ Group By Operator [GBY_358] (rows=1 width=12)
Output:["_col0","_col1"],aggregations:["count(VALUE._col0)"],keys:KEY._col0
- <-Reducer 8 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_347]
+ <-Reducer 6 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_357]
PartitionCols:_col0
- Group By Operator [GBY_346] (rows=1 width=12)
+ Group By Operator [GBY_356] (rows=1 width=12)
Output:["_col0","_col1"],aggregations:["count()"],keys:_col0
- Select Operator [SEL_345] (rows=1 width=116)
+ Select Operator [SEL_355] (rows=1 width=116)
Output:["_col0"]
- Group By Operator [GBY_344] (rows=1 width=116)
+ Group By Operator [GBY_354] (rows=1 width=116)
Output:["_col0","_col1"],aggregations:["sum(VALUE._col0)"],keys:KEY._col0
- <-Reducer 7 [SIMPLE_EDGE]
+ <-Reducer 5 [SIMPLE_EDGE]
SHUFFLE [RS_118]
PartitionCols:_col0
- Group By Operator [GBY_117] (rows=312 width=116)
- Output:["_col0","_col1"],aggregations:["sum(_col1)"],keys:_col0
- Select Operator [SEL_116] (rows=624257222 width=127)
- Output:["_col0","_col1"]
- Filter Operator [FIL_115] (rows=624257222 width=127)
- predicate:_col2 BETWEEN _col3 AND _col4
- Select Operator [SEL_114] (rows=5618315000 width=127)
- Output:["_col0","_col1","_col2","_col3","_col4"]
- Merge Join Operator [MERGEJOIN_279] (rows=5618315000 width=127)
- Conds:(Inner),Output:["_col0","_col2","_col6","_col13","_col15"]
- <-Reducer 15 [CUSTOM_SIMPLE_EDGE]
- PARTITION_ONLY_SHUFFLE [RS_111]
- Merge Join Operator [MERGEJOIN_277] (rows=25 width=4)
- Conds:(Right Outer),Output:["_col0"]
- <-Reducer 14 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_335]
- Group By Operator [GBY_334] (rows=25 width=4)
- Output:["_col0"],keys:KEY._col0
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_301]
- PartitionCols:_col0
- Group By Operator [GBY_298] (rows=25 width=4)
- Output:["_col0"],keys:_col0
- Select Operator [SEL_295] (rows=50 width=12)
- Output:["_col0"]
- Filter Operator [FIL_293] (rows=50 width=12)
- predicate:((d_moy = 3) and (d_year = 1999))
- TableScan [TS_26] (rows=73049 width=12)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_month_seq","d_year","d_moy"]
- <-Reducer 17 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_343]
- Select Operator [SEL_342] (rows=1 width=8)
- Filter Operator [FIL_341] (rows=1 width=8)
- predicate:(sq_count_check(_col0) <= 1)
- Group By Operator [GBY_340] (rows=1 width=8)
- Output:["_col0"],aggregations:["count(VALUE._col0)"]
- <-Reducer 16 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_339]
- Group By Operator [GBY_338] (rows=1 width=8)
- Output:["_col0"],aggregations:["count()"]
- Select Operator [SEL_337] (rows=25 width=4)
- Group By Operator [GBY_336] (rows=25 width=4)
- Output:["_col0"],keys:KEY._col0
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_302]
+ Group By Operator [GBY_117] (rows=1 width=116)
+ Output:["_col0","_col1"],aggregations:["sum(_col2)"],keys:_col5
+ Merge Join Operator [MERGEJOIN_284] (rows=998811 width=4)
+ Conds:RS_113._col0=RS_114._col0(Inner),Output:["_col2","_col5"]
+ <-Reducer 16 [SIMPLE_EDGE]
+ SHUFFLE [RS_114]
+ PartitionCols:_col0
+ Select Operator [SEL_103] (rows=5072841 width=12)
+ Output:["_col0"]
+ Filter Operator [FIL_102] (rows=5072841 width=12)
+ predicate:(_col1 <= _col3)
+ Merge Join Operator [MERGEJOIN_282] (rows=15218525 width=12)
+ Conds:(Inner),Output:["_col0","_col1","_col3"]
+ <-Reducer 15 [CUSTOM_SIMPLE_EDGE]
+ PARTITION_ONLY_SHUFFLE [RS_99]
+ Filter Operator [FIL_98] (rows=608741 width=12)
+ predicate:(_col2 <= _col1)
+ Merge Join Operator [MERGEJOIN_279] (rows=1826225 width=12)
+ Conds:(Inner),Output:["_col0","_col1","_col2"]
+ <-Map 33 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_351]
+ Select Operator [SEL_350] (rows=73049 width=8)
+ Output:["_col0","_col1"]
+ TableScan [TS_77] (rows=73049 width=8)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_month_seq"]
+ <-Reducer 14 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_349]
+ Group By Operator [GBY_348] (rows=25 width=4)
+ Output:["_col0"],keys:KEY._col0
+ <-Map 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_309]
+ PartitionCols:_col0
+ Group By Operator [GBY_305] (rows=25 width=4)
+ Output:["_col0"],keys:_col0
+ Select Operator [SEL_301] (rows=50 width=12)
+ Output:["_col0"]
+ Filter Operator [FIL_298] (rows=50 width=12)
+ predicate:((d_moy = 3) and (d_year = 1999))
+ TableScan [TS_3] (rows=73049 width=12)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_month_seq","d_year","d_moy"]
+ <-Reducer 17 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_353]
+ Group By Operator [GBY_352] (rows=25 width=4)
+ Output:["_col0"],keys:KEY._col0
+ <-Map 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_310]
+ PartitionCols:_col0
+ Group By Operator [GBY_306] (rows=25 width=4)
+ Output:["_col0"],keys:_col0
+ Select Operator [SEL_302] (rows=50 width=12)
+ Output:["_col0"]
+ Please refer to the previous Filter Operator [FIL_298]
+ <-Reducer 4 [SIMPLE_EDGE]
+ SHUFFLE [RS_113]
+ PartitionCols:_col0
+ Merge Join Operator [MERGEJOIN_283] (rows=8989304 width=4)
+ Conds:RS_110._col1=RS_111._col0(Inner),Output:["_col0","_col2","_col5"]
+ <-Reducer 20 [SIMPLE_EDGE]
+ SHUFFLE [RS_111]
+ PartitionCols:_col0
+ Select Operator [SEL_76] (rows=55046 width=4)
+ Output:["_col0"]
+ Merge Join Operator [MERGEJOIN_281] (rows=55046 width=4)
+ Conds:RS_73._col0=RS_347._col1(Inner),Output:["_col5"]
+ <-Reducer 19 [SIMPLE_EDGE]
+ SHUFFLE [RS_73]
+ PartitionCols:_col0
+ Merge Join Operator [MERGEJOIN_275] (rows=39720279 width=4)
+ Conds:RS_329._col1, _col2=RS_332._col0, _col1(Inner),Output:["_col0"]
+ <-Map 18 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_329]
+ PartitionCols:_col1, _col2
+ Select Operator [SEL_328] (rows=40000000 width=188)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_327] (rows=40000000 width=188)
+ predicate:(ca_county is not null and ca_state is not null)
+ TableScan [TS_33] (rows=40000000 width=188)
+ default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_county","ca_state"]
+ <-Map 21 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_332]
+ PartitionCols:_col0, _col1
+ Select Operator [SEL_331] (rows=1704 width=184)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_330] (rows=1704 width=184)
+ predicate:(s_county is not null and s_state is not null)
+ TableScan [TS_36] (rows=1704 width=184)
+ default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_county","s_state"]
+ <-Reducer 27 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_347]
+ PartitionCols:_col1
+ Select Operator [SEL_346] (rows=55046 width=8)
+ Output:["_col0","_col1"]
+ Group By Operator [GBY_345] (rows=55046 width=8)
+ Output:["_col0","_col1"],keys:KEY._col0, KEY._col1
+ <-Reducer 26 [SIMPLE_EDGE]
+ SHUFFLE [RS_67]
+ PartitionCols:_col0, _col1
+ Group By Operator [GBY_66] (rows=55046 width=8)
+ Output:["_col0","_col1"],keys:_col6, _col5
+ Merge Join Operator [MERGEJOIN_278] (rows=110092 width=8)
+ Conds:RS_62._col1=RS_344._col0(Inner),Output:["_col5","_col6"]
+ <-Map 32 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_344]
+ PartitionCols:_col0
+ Select Operator [SEL_343] (rows=80000000 width=8)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_342] (rows=80000000 width=8)
+ predicate:c_current_addr_sk is not null
+ TableScan [TS_53] (rows=80000000 width=8)
+ default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_addr_sk"]
+ <-Reducer 25 [SIMPLE_EDGE]
+ SHUFFLE [RS_62]
+ PartitionCols:_col1
+ Merge Join Operator [MERGEJOIN_277] (rows=110092 width=0)
+ Conds:RS_59._col2=RS_341._col0(Inner),Output:["_col1"]
+ <-Map 31 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_341]
PartitionCols:_col0
- Group By Operator [GBY_299] (rows=25 width=4)
- Output:["_col0"],keys:_col0
- Select Operator [SEL_296] (rows=50 width=12)
- Output:["_col0"]
- Please refer to the previous Filter Operator [FIL_293]
- <-Reducer 6 [CUSTOM_SIMPLE_EDGE]
- PARTITION_ONLY_SHUFFLE [RS_112]
- Select Operator [SEL_107] (rows=224732600 width=119)
- Output:["_col0","_col4","_col11","_col13"]
- Merge Join Operator [MERGEJOIN_278] (rows=224732600 width=119)
- Conds:(Left Outer),Output:["_col2","_col4","_col7","_col13"]
- <-Reducer 12 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_305]
- Group By Operator [GBY_303] (rows=25 width=4)
- Output:["_col0"],keys:KEY._col0
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_300]
- PartitionCols:_col0
- Group By Operator [GBY_297] (rows=25 width=4)
- Output:["_col0"],keys:_col0
- Select Operator [SEL_294] (rows=50 width=12)
- Output:["_col0"]
- Please refer to the previous Filter Operator [FIL_293]
- <-Reducer 5 [CUSTOM_SIMPLE_EDGE]
- PARTITION_ONLY_SHUFFLE [RS_104]
- Merge Join Operator [MERGEJOIN_276] (rows=8989304 width=8)
- Conds:RS_101._col5=RS_102._col0(Inner),Output:["_col2","_col4","_col7"]
- <-Reducer 31 [SIMPLE_EDGE]
- SHUFFLE [RS_102]
- PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_275] (rows=39720279 width=4)
- Conds:RS_330._col1, _col2=RS_333._col0, _col1(Inner),Output:["_col0"]
- <-Map 30 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_330]
- PartitionCols:_col1, _col2
- Select Operator [SEL_329] (rows=40000000 width=188)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_328] (rows=40000000 width=188)
- predicate:(ca_county is not null and ca_state is not null)
- TableScan [TS_74] (rows=40000000 width=188)
- default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_county","ca_state"]
- <-Map 32 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_333]
- PartitionCols:_col0, _col1
- Select Operator [SEL_332] (rows=1704 width=184)
- Output:["_col0","_col1"]
- Filter Operator [FIL_331] (rows=1704 width=184)
- predicate:(s_county is not null and s_state is not null)
- TableScan [TS_77] (rows=1704 width=184)
- default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_county","s_state"]
- <-Reducer 4 [SIMPLE_EDGE]
- SHUFFLE [RS_101]
- PartitionCols:_col5
- Merge Join Operator [MERGEJOIN_274] (rows=8989304 width=12)
- Conds:RS_98._col0=RS_327._col0(Inner),Output:["_col2","_col4","_col5","_col7"]
- <-Map 29 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_327]
- PartitionCols:_col0
- Select Operator [SEL_326] (rows=73049 width=8)
- Output:["_col0","_col1"]
- TableScan [TS_72] (rows=73049 width=8)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_month_seq"]
- <-Reducer 3 [SIMPLE_EDGE]
- SHUFFLE [RS_98]
- PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_273] (rows=8989304 width=8)
- Conds:RS_95._col1=RS_325._col0(Inner),Output:["_col0","_col2","_col4","_col5"]
- <-Reducer 2 [SIMPLE_EDGE]
- SHUFFLE [RS_95]
- PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_269] (rows=525327388 width=114)
- Conds:(Inner),Output:["_col0","_col1","_col2"]
- <-Map 1 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_292]
- Select Operator [SEL_291] (rows=525327388 width=114)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_290] (rows=525327388 width=114)
- predicate:(ss_customer_sk is not null and ss_sold_date_sk is not null)
- TableScan [TS_23] (rows=575995635 width=114)
- default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_customer_sk","ss_ext_sales_price"]
- <-Reducer 13 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_311]
- Select Operator [SEL_310] (rows=1 width=8)
- Filter Operator [FIL_309] (rows=1 width=8)
- predicate:(sq_count_check(_col0) <= 1)
- Group By Operator [GBY_308] (rows=1 width=8)
- Output:["_col0"],aggregations:["count(VALUE._col0)"]
- <-Reducer 12 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_307]
- Group By Operator [GBY_306] (rows=1 width=8)
- Output:["_col0"],aggregations:["count()"]
- Select Operator [SEL_304] (rows=25 width=4)
- Please refer to the previous Group By Operator [GBY_303]
- <-Reducer 23 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_325]
+ Select Operator [SEL_340] (rows=453 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_339] (rows=453 width=186)
+ predicate:((i_category = 'Jewelry') and (i_class = 'consignment'))
+ TableScan [TS_50] (rows=462000 width=186)
+ default@item,item,Tbl:COMPLETE,Col:COMPLETE,Output:["i_item_sk","i_class","i_category"]
+ <-Reducer 24 [SIMPLE_EDGE]
+ SHUFFLE [RS_59]
+ PartitionCols:_col2
+ Merge Join Operator [MERGEJOIN_276] (rows=11665117 width=7)
+ Conds:Union 23._col0=RS_335._col0(Inner),Output:["_col1","_col2"]
+ <-Map 29 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_335]
+ PartitionCols:_col0
+ Select Operator [SEL_334] (rows=50 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_333] (rows=50 width=12)
+ predicate:((d_moy = 3) and (d_year = 1999))
+ TableScan [TS_47] (rows=73049 width=12)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_moy"]
+ <-Union 23 [SIMPLE_EDGE]
+ <-Map 22 [CONTAINS] vectorized
+ Reduce Output Operator [RS_369]
+ PartitionCols:_col0
+ Select Operator [SEL_368] (rows=285117831 width=11)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_367] (rows=285117831 width=11)
+ predicate:((cs_sold_date_sk BETWEEN DynamicValue(RS_57_date_dim_d_date_sk_min) AND DynamicValue(RS_57_date_dim_d_date_sk_max) and in_bloom_filter(cs_sold_date_sk, DynamicValue(RS_57_date_dim_d_date_sk_bloom_filter))) and cs_bill_customer_sk is not null and cs_sold_date_sk is not null)
+ TableScan [TS_285] (rows=287989836 width=11)
+ Output:["cs_sold_date_sk","cs_bill_customer_sk","cs_item_sk"]
+ <-Reducer 30 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_365]
+ Group By Operator [GBY_364] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 29 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_338]
+ Group By Operator [GBY_337] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_336] (rows=50 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_334]
+ <-Map 28 [CONTAINS] vectorized
+ Reduce Output Operator [RS_372]
+ PartitionCols:_col0
+ Select Operator [SEL_371] (rows=143930993 width=11)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_370] (rows=143930993 width=11)
+ predicate:((ws_sold_date_sk BETWEEN DynamicValue(RS_57_date_dim_d_date_sk_min) AND DynamicValue(RS_57_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_57_date_dim_d_date_sk_bloom_filter))) and ws_bill_customer_sk is not null and ws_sold_date_sk is not null)
+ TableScan [TS_290] (rows=144002668 width=11)
+ Output:["ws_sold_date_sk","ws_item_sk","ws_bill_customer_sk"]
+ <-Reducer 30 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_366]
+ Please refer to the previous Group By Operator [GBY_364]
+ <-Reducer 3 [SIMPLE_EDGE]
+ SHUFFLE [RS_110]
+ PartitionCols:_col1
+ Merge Join Operator [MERGEJOIN_280] (rows=525327388 width=114)
+ Conds:(Inner),Output:["_col0","_col1","_col2"]
+ <-Reducer 13 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_326]
+ Select Operator [SEL_325] (rows=1 width=8)
+ Filter Operator [FIL_324] (rows=1 width=8)
+ predicate:(sq_count_check(_col0) <= 1)
+ Group By Operator [GBY_323] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count(VALUE._col0)"]
+ <-Reducer 12 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_322]
+ Group By Operator [GBY_321] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count()"]
+ Select Operator [SEL_320] (rows=25 width=4)
+ Group By Operator [GBY_319] (rows=25 width=4)
+ Output:["_col0"],keys:KEY._col0
+ <-Map 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_308]
PartitionCols:_col0
- Group By Operator [GBY_324] (rows=55046 width=8)
- Output:["_col0","_col1"],keys:KEY._col0, KEY._col1
- <-Reducer 22 [SIMPLE_EDGE]
- SHUFFLE [RS_69]
- PartitionCols:_col0, _col1
- Group By Operator [GBY_68] (rows=55046 width=8)
- Output:["_col0","_col1"],keys:_col5, _col6
- Merge Join Operator [MERGEJOIN_272] (rows=110092 width=8)
- Conds:RS_64._col1=RS_323._col0(Inner),Output:["_col5","_col6"]
- <-Map 28 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_323]
- PartitionCols:_col0
- Select Operator [SEL_322] (rows=80000000 width=8)
- Output:["_col0","_col1"]
- Filter Operator [FIL_321] (rows=80000000 width=8)
- predicate:c_current_addr_sk is not null
- TableScan [TS_55] (rows=80000000 width=8)
- default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_addr_sk"]
- <-Reducer 21 [SIMPLE_EDGE]
- SHUFFLE [RS_64]
- PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_271] (rows=110092 width=0)
- Conds:RS_61._col2=RS_320._col0(Inner),Output:["_col1"]
- <-Map 27 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_320]
- PartitionCols:_col0
- Select Operator [SEL_319] (rows=453 width=4)
- Output:["_col0"]
- Filter Operator [FIL_318] (rows=453 width=186)
- predicate:((i_category = 'Jewelry') and (i_class = 'consignment'))
- TableScan [TS_52] (rows=462000 width=186)
- default@item,item,Tbl:COMPLETE,Col:COMPLETE,Output:["i_item_sk","i_class","i_category"]
- <-Reducer 20 [SIMPLE_EDGE]
- SHUFFLE [RS_61]
- PartitionCols:_col2
- Merge Join Operator [MERGEJOIN_270] (rows=11665117 width=7)
- Conds:Union 19._col0=RS_314._col0(Inner),Output:["_col1","_col2"]
- <-Map 25 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_314]
- PartitionCols:_col0
- Select Operator [SEL_313] (rows=50 width=4)
- Output:["_col0"]
- Filter Operator [FIL_312] (rows=50 width=12)
- predicate:((d_moy = 3) and (d_year = 1999))
- TableScan [TS_49] (rows=73049 width=12)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_moy"]
- <-Union 19 [SIMPLE_EDGE]
- <-Map 18 [CONTAINS] vectorized
- Reduce Output Operator [RS_359]
- PartitionCols:_col0
- Select Operator [SEL_358] (rows=285117831 width=11)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_357] (rows=285117831 width=11)
- predicate:((cs_sold_date_sk BETWEEN DynamicValue(RS_59_date_dim_d_date_sk_min) AND DynamicValue(RS_59_date_dim_d_date_sk_max) and in_bloom_filter(cs_sold_date_sk, DynamicValue(RS_59_date_dim_d_date_sk_bloom_filter))) and cs_bill_customer_sk is not null and cs_sold_date_sk is not null)
- TableScan [TS_280] (rows=287989836 width=11)
- Output:["cs_sold_date_sk","cs_bill_customer_sk","cs_item_sk"]
- <-Reducer 26 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_355]
- Group By Operator [GBY_354] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 25 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_317]
- Group By Operator [GBY_316] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_315] (rows=50 width=4)
- Output:["_col0"]
- Please refer to the previous Select Operator [SEL_313]
- <-Map 24 [CONTAINS] vectorized
- Reduce Output Operator [RS_362]
- PartitionCols:_col0
- Select Operator [SEL_361] (rows=143930993 width=11)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_360] (rows=143930993 width=11)
- predicate:((ws_sold_date_sk BETWEEN DynamicValue(RS_59_date_dim_d_date_sk_min) AND DynamicValue(RS_59_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_59_date_dim_d_date_sk_bloom_filter))) and ws_bill_customer_sk is not null and ws_sold_date_sk is not null)
- TableScan [TS_285] (rows=144002668 width=11)
- Output:["ws_sold_date_sk","ws_item_sk","ws_bill_customer_sk"]
- <-Reducer 26 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_356]
- Please refer to the previous Group By Operator [GBY_354]
+ Group By Operator [GBY_304] (rows=25 width=4)
+ Output:["_col0"],keys:_col0
+ Select Operator [SEL_300] (rows=50 width=12)
+ Output:["_col0"]
+ Please refer to the previous Filter Operator [FIL_298]
+ <-Reducer 2 [CUSTOM_SIMPLE_EDGE]
+ PARTITION_ONLY_SHUFFLE [RS_107]
+ Merge Join Operator [MERGEJOIN_274] (rows=525327388 width=114)
+ Conds:(Inner),Output:["_col0","_col1","_col2"]
+ <-Map 1 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_297]
+ Select Operator [SEL_296] (rows=525327388 width=114)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_295] (rows=525327388 width=114)
+ predicate:(ss_customer_sk is not null and ss_sold_date_sk is not null)
+ TableScan [TS_0] (rows=575995635 width=114)
+ default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_customer_sk","ss_ext_sales_price"]
+ <-Reducer 11 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_318]
+ Select Operator [SEL_317] (rows=1 width=8)
+ Filter Operator [FIL_316] (rows=1 width=8)
+ predicate:(sq_count_check(_col0) <= 1)
+ Group By Operator [GBY_315] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count(VALUE._col0)"]
+ <-Reducer 10 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_314]
+ Group By Operator [GBY_313] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count()"]
+ Select Operator [SEL_312] (rows=25 width=4)
+ Group By Operator [GBY_311] (rows=25 width=4)
+ Output:["_col0"],keys:KEY._col0
+ <-Map 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_307]
+ PartitionCols:_col0
+ Group By Operator [GBY_303] (rows=25 width=4)
+ Output:["_col0"],keys:_col0
+ Select Operator [SEL_299] (rows=50 width=12)
+ Output:["_col0"]
+ Please refer to the previous Filter Operator [FIL_298]
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query64.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query64.q.out
index 3aee07a..30f9b6b 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/query64.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query64.q.out
@@ -265,22 +265,22 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 1 <- Reducer 20 (BROADCAST_EDGE)
+Map 1 <- Reducer 48 (BROADCAST_EDGE)
Map 38 <- Reducer 20 (BROADCAST_EDGE)
-Map 50 <- Reducer 36 (BROADCAST_EDGE)
-Map 51 <- Reducer 36 (BROADCAST_EDGE)
+Map 52 <- Reducer 49 (BROADCAST_EDGE)
+Map 53 <- Reducer 36 (BROADCAST_EDGE)
Reducer 10 <- Map 47 (SIMPLE_EDGE), Reducer 9 (SIMPLE_EDGE)
Reducer 11 <- Map 47 (SIMPLE_EDGE), Reducer 10 (SIMPLE_EDGE)
-Reducer 12 <- Map 48 (SIMPLE_EDGE), Reducer 11 (SIMPLE_EDGE)
-Reducer 13 <- Map 48 (SIMPLE_EDGE), Reducer 12 (SIMPLE_EDGE)
-Reducer 14 <- Map 49 (SIMPLE_EDGE), Reducer 13 (SIMPLE_EDGE)
-Reducer 15 <- Map 49 (SIMPLE_EDGE), Reducer 14 (SIMPLE_EDGE)
+Reducer 12 <- Map 50 (SIMPLE_EDGE), Reducer 11 (SIMPLE_EDGE)
+Reducer 13 <- Map 50 (SIMPLE_EDGE), Reducer 12 (SIMPLE_EDGE)
+Reducer 14 <- Map 51 (SIMPLE_EDGE), Reducer 13 (SIMPLE_EDGE)
+Reducer 15 <- Map 51 (SIMPLE_EDGE), Reducer 14 (SIMPLE_EDGE)
Reducer 16 <- Reducer 15 (SIMPLE_EDGE)
Reducer 17 <- Reducer 16 (SIMPLE_EDGE), Reducer 35 (SIMPLE_EDGE)
Reducer 18 <- Reducer 17 (SIMPLE_EDGE)
Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 19 (SIMPLE_EDGE)
Reducer 20 <- Map 19 (CUSTOM_SIMPLE_EDGE)
-Reducer 21 <- Map 19 (SIMPLE_EDGE), Map 50 (SIMPLE_EDGE)
+Reducer 21 <- Map 19 (SIMPLE_EDGE), Map 52 (SIMPLE_EDGE)
Reducer 22 <- Map 47 (SIMPLE_EDGE), Reducer 21 (SIMPLE_EDGE)
Reducer 23 <- Map 37 (SIMPLE_EDGE), Reducer 22 (SIMPLE_EDGE)
Reducer 24 <- Reducer 23 (SIMPLE_EDGE), Reducer 43 (ONE_TO_ONE_EDGE)
@@ -291,17 +291,19 @@ Reducer 28 <- Map 46 (SIMPLE_EDGE), Reducer 27 (SIMPLE_EDGE)
Reducer 29 <- Map 47 (SIMPLE_EDGE), Reducer 28 (SIMPLE_EDGE)
Reducer 3 <- Map 47 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
Reducer 30 <- Map 47 (SIMPLE_EDGE), Reducer 29 (SIMPLE_EDGE)
-Reducer 31 <- Map 48 (SIMPLE_EDGE), Reducer 30 (SIMPLE_EDGE)
-Reducer 32 <- Map 48 (SIMPLE_EDGE), Reducer 31 (SIMPLE_EDGE)
-Reducer 33 <- Map 49 (SIMPLE_EDGE), Reducer 32 (SIMPLE_EDGE)
-Reducer 34 <- Map 49 (SIMPLE_EDGE), Reducer 33 (SIMPLE_EDGE)
+Reducer 31 <- Map 50 (SIMPLE_EDGE), Reducer 30 (SIMPLE_EDGE)
+Reducer 32 <- Map 50 (SIMPLE_EDGE), Reducer 31 (SIMPLE_EDGE)
+Reducer 33 <- Map 51 (SIMPLE_EDGE), Reducer 32 (SIMPLE_EDGE)
+Reducer 34 <- Map 51 (SIMPLE_EDGE), Reducer 33 (SIMPLE_EDGE)
Reducer 35 <- Reducer 34 (SIMPLE_EDGE)
Reducer 36 <- Map 19 (CUSTOM_SIMPLE_EDGE)
Reducer 39 <- Map 38 (SIMPLE_EDGE), Map 41 (SIMPLE_EDGE)
Reducer 4 <- Map 37 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
Reducer 40 <- Reducer 39 (SIMPLE_EDGE)
-Reducer 42 <- Map 41 (SIMPLE_EDGE), Map 51 (SIMPLE_EDGE)
+Reducer 42 <- Map 41 (SIMPLE_EDGE), Map 53 (SIMPLE_EDGE)
Reducer 43 <- Reducer 42 (SIMPLE_EDGE)
+Reducer 48 <- Map 47 (CUSTOM_SIMPLE_EDGE)
+Reducer 49 <- Map 47 (CUSTOM_SIMPLE_EDGE)
Reducer 5 <- Reducer 4 (SIMPLE_EDGE), Reducer 40 (ONE_TO_ONE_EDGE)
Reducer 6 <- Map 44 (SIMPLE_EDGE), Reducer 5 (SIMPLE_EDGE)
Reducer 7 <- Map 45 (SIMPLE_EDGE), Reducer 6 (SIMPLE_EDGE)
@@ -313,216 +315,216 @@ Stage-0
limit:-1
Stage-1
Reducer 18 vectorized
- File Output Operator [FS_1061]
- Select Operator [SEL_1060] (rows=1991254249 width=1702)
+ File Output Operator [FS_1069]
+ Select Operator [SEL_1068] (rows=104583667777 width=1702)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19","_col20"]
<-Reducer 17 [SIMPLE_EDGE]
SHUFFLE [RS_197]
- Select Operator [SEL_196] (rows=1991254249 width=1694)
+ Select Operator [SEL_196] (rows=104583667777 width=1694)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18"]
- Filter Operator [FIL_195] (rows=1991254249 width=1694)
+ Filter Operator [FIL_195] (rows=104583667777 width=1694)
predicate:(_col19 <= _col12)
- Merge Join Operator [MERGEJOIN_973] (rows=5973762748 width=1694)
- Conds:RS_1043._col2, _col1, _col3=RS_1059._col1, _col0, _col2(Inner),Output:["_col0","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col19","_col20","_col21","_col22"]
+ Merge Join Operator [MERGEJOIN_973] (rows=313751003333 width=1694)
+ Conds:RS_1050._col2, _col1, _col3=RS_1067._col1, _col0, _col2(Inner),Output:["_col0","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col19","_col20","_col21","_col22"]
<-Reducer 16 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1043]
+ SHUFFLE [RS_1050]
PartitionCols:_col2, _col1, _col3
- Select Operator [SEL_1042] (rows=2364623 width=1354)
+ Select Operator [SEL_1049] (rows=21299858 width=1354)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15"]
- Group By Operator [GBY_1041] (rows=2364623 width=1362)
+ Group By Operator [GBY_1048] (rows=21299858 width=1362)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count(VALUE._col0)","sum(VALUE._col1)","sum(VALUE._col2)","sum(VALUE._col3)"],keys:KEY._col0, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5, KEY._col6, KEY._col7, KEY._col8, KEY._col9, KEY._col10, KEY._col11, KEY._col12, KEY._col13
<-Reducer 15 [SIMPLE_EDGE]
SHUFFLE [RS_93]
PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13
- Group By Operator [GBY_92] (rows=2364623 width=1362)
+ Group By Operator [GBY_92] (rows=21299858 width=1362)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count()","sum(_col8)","sum(_col9)","sum(_col10)"],keys:_col24, _col11, _col25, _col12, _col29, _col31, _col37, _col38, _col39, _col40, _col42, _col43, _col44, _col45
- Merge Join Operator [MERGEJOIN_957] (rows=2364623 width=1155)
- Conds:RS_88._col17=RS_1038._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40","_col42","_col43","_col44","_col45"]
- <-Map 49 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1038]
+ Merge Join Operator [MERGEJOIN_957] (rows=21299858 width=1155)
+ Conds:RS_88._col17=RS_1045._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40","_col42","_col43","_col44","_col45"]
+ <-Map 51 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1045]
PartitionCols:_col0
- Select Operator [SEL_1036] (rows=40000000 width=365)
+ Select Operator [SEL_1043] (rows=40000000 width=365)
Output:["_col0","_col1","_col2","_col3","_col4"]
TableScan [TS_44] (rows=40000000 width=365)
default@customer_address,ad1,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_street_number","ca_street_name","ca_city","ca_zip"]
<-Reducer 14 [SIMPLE_EDGE]
SHUFFLE [RS_88]
PartitionCols:_col17
- Merge Join Operator [MERGEJOIN_956] (rows=2364623 width=798)
- Conds:RS_85._col5=RS_1037._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40"]
- <-Map 49 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1037]
+ Merge Join Operator [MERGEJOIN_956] (rows=21299858 width=798)
+ Conds:RS_85._col5=RS_1044._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40"]
+ <-Map 51 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1044]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1036]
+ Please refer to the previous Select Operator [SEL_1043]
<-Reducer 13 [SIMPLE_EDGE]
SHUFFLE [RS_85]
PartitionCols:_col5
- Filter Operator [FIL_84] (rows=2364623 width=609)
+ Filter Operator [FIL_84] (rows=21299858 width=609)
predicate:(_col33 <> _col35)
- Merge Join Operator [MERGEJOIN_955] (rows=2364623 width=609)
- Conds:RS_81._col15=RS_1033._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col33","_col35"]
- <-Map 48 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1033]
+ Merge Join Operator [MERGEJOIN_955] (rows=21299858 width=609)
+ Conds:RS_81._col15=RS_1040._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col33","_col35"]
+ <-Map 50 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1040]
PartitionCols:_col0
- Select Operator [SEL_1031] (rows=1861800 width=89)
+ Select Operator [SEL_1038] (rows=1861800 width=89)
Output:["_col0","_col1"]
TableScan [TS_40] (rows=1861800 width=89)
default@customer_demographics,cd1,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status"]
<-Reducer 12 [SIMPLE_EDGE]
SHUFFLE [RS_81]
PartitionCols:_col15
- Merge Join Operator [MERGEJOIN_954] (rows=2331651 width=523)
- Conds:RS_78._col3=RS_1032._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31","_col33"]
- <-Map 48 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1032]
+ Merge Join Operator [MERGEJOIN_954] (rows=21002853 width=525)
+ Conds:RS_78._col3=RS_1039._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31","_col33"]
+ <-Map 50 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1039]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1031]
+ Please refer to the previous Select Operator [SEL_1038]
<-Reducer 11 [SIMPLE_EDGE]
SHUFFLE [RS_78]
PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_953] (rows=2299139 width=436)
- Conds:RS_75._col18=RS_996._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31"]
+ Merge Join Operator [MERGEJOIN_953] (rows=20709989 width=438)
+ Conds:RS_75._col18=RS_980._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31"]
<-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_996]
+ PARTITION_ONLY_SHUFFLE [RS_980]
PartitionCols:_col0
- Select Operator [SEL_990] (rows=73049 width=8)
+ Select Operator [SEL_974] (rows=73049 width=8)
Output:["_col0","_col1"]
TableScan [TS_38] (rows=73049 width=8)
default@date_dim,d3,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
<-Reducer 10 [SIMPLE_EDGE]
SHUFFLE [RS_75]
PartitionCols:_col18
- Merge Join Operator [MERGEJOIN_952] (rows=2299139 width=434)
- Conds:RS_72._col19=RS_998._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col24","_col25","_col29"]
+ Merge Join Operator [MERGEJOIN_952] (rows=20709989 width=438)
+ Conds:RS_72._col19=RS_982._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col24","_col25","_col29"]
<-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_998]
+ PARTITION_ONLY_SHUFFLE [RS_982]
PartitionCols:_col0
- Select Operator [SEL_992] (rows=73049 width=8)
+ Select Operator [SEL_976] (rows=73049 width=8)
Output:["_col0","_col1"]
Please refer to the previous TableScan [TS_38]
<-Reducer 9 [SIMPLE_EDGE]
SHUFFLE [RS_72]
PartitionCols:_col19
- Merge Join Operator [MERGEJOIN_951] (rows=2299139 width=432)
- Conds:RS_69._col16=RS_1028._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col19","_col24","_col25"]
+ Merge Join Operator [MERGEJOIN_951] (rows=20709989 width=437)
+ Conds:RS_69._col16=RS_1035._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col19","_col24","_col25"]
<-Map 46 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1028]
+ SHUFFLE [RS_1035]
PartitionCols:_col0
- Select Operator [SEL_1026] (rows=7200 width=4)
+ Select Operator [SEL_1033] (rows=7200 width=4)
Output:["_col0"]
- Filter Operator [FIL_1025] (rows=7200 width=8)
+ Filter Operator [FIL_1032] (rows=7200 width=8)
predicate:hd_income_band_sk is not null
TableScan [TS_30] (rows=7200 width=8)
default@household_demographics,hd1,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_income_band_sk"]
<-Reducer 8 [SIMPLE_EDGE]
SHUFFLE [RS_69]
PartitionCols:_col16
- Merge Join Operator [MERGEJOIN_950] (rows=2299139 width=433)
- Conds:RS_66._col4=RS_1027._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
+ Merge Join Operator [MERGEJOIN_950] (rows=20709989 width=441)
+ Conds:RS_66._col4=RS_1034._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
<-Map 46 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1027]
+ SHUFFLE [RS_1034]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1026]
+ Please refer to the previous Select Operator [SEL_1033]
<-Reducer 7 [SIMPLE_EDGE]
SHUFFLE [RS_66]
PartitionCols:_col4
- Merge Join Operator [MERGEJOIN_949] (rows=2299139 width=435)
- Conds:RS_63._col6=RS_1023._col0(Inner),Output:["_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
+ Merge Join Operator [MERGEJOIN_949] (rows=20709989 width=443)
+ Conds:RS_63._col6=RS_1030._col0(Inner),Output:["_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
<-Map 45 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1023]
+ SHUFFLE [RS_1030]
PartitionCols:_col0
- Select Operator [SEL_1022] (rows=1704 width=181)
+ Select Operator [SEL_1029] (rows=1704 width=181)
Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_1021] (rows=1704 width=181)
+ Filter Operator [FIL_1028] (rows=1704 width=181)
predicate:(s_store_name is not null and s_zip is not null)
TableScan [TS_27] (rows=1704 width=181)
default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk","s_store_name","s_zip"]
<-Reducer 6 [SIMPLE_EDGE]
SHUFFLE [RS_63]
PartitionCols:_col6
- Merge Join Operator [MERGEJOIN_948] (rows=2299139 width=259)
- Conds:RS_60._col1, _col7=RS_1019._col0, _col1(Inner),Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
+ Merge Join Operator [MERGEJOIN_948] (rows=20709989 width=267)
+ Conds:RS_60._col1, _col7=RS_1026._col0, _col1(Inner),Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
<-Map 44 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1019]
+ SHUFFLE [RS_1026]
PartitionCols:_col0, _col1
- Select Operator [SEL_1018] (rows=57591150 width=8)
+ Select Operator [SEL_1025] (rows=57591150 width=8)
Output:["_col0","_col1"]
TableScan [TS_25] (rows=57591150 width=8)
default@store_returns,store_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["sr_item_sk","sr_ticket_number"]
<-Reducer 5 [SIMPLE_EDGE]
SHUFFLE [RS_60]
PartitionCols:_col1, _col7
- Merge Join Operator [MERGEJOIN_947] (rows=1394510 width=123)
- Conds:RS_57._col1=RS_1017._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
+ Merge Join Operator [MERGEJOIN_947] (rows=12561347 width=135)
+ Conds:RS_57._col1=RS_1024._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
<-Reducer 4 [SIMPLE_EDGE]
SHUFFLE [RS_57]
PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_945] (rows=1394510 width=123)
- Conds:RS_54._col2=RS_1006._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
+ Merge Join Operator [MERGEJOIN_945] (rows=12561347 width=135)
+ Conds:RS_54._col2=RS_1011._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
<-Map 37 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1006]
+ SHUFFLE [RS_1011]
PartitionCols:_col0
- Select Operator [SEL_1005] (rows=69376329 width=23)
+ Select Operator [SEL_1010] (rows=69376329 width=23)
Output:["_col0","_col1","_col2","_col3","_col4","_col5"]
- Filter Operator [FIL_1004] (rows=69376329 width=23)
+ Filter Operator [FIL_1009] (rows=69376329 width=23)
predicate:(c_current_addr_sk is not null and c_current_cdemo_sk is not null and c_current_hdemo_sk is not null and c_first_sales_date_sk is not null and c_first_shipto_date_sk is not null)
TableScan [TS_9] (rows=80000000 width=23)
default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_cdemo_sk","c_current_hdemo_sk","c_current_addr_sk","c_first_shipto_date_sk","c_first_sales_date_sk"]
<-Reducer 3 [SIMPLE_EDGE]
SHUFFLE [RS_54]
PartitionCols:_col2
- Merge Join Operator [MERGEJOIN_944] (rows=1608052 width=119)
- Conds:RS_51._col0=RS_1002._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
+ Merge Join Operator [MERGEJOIN_944] (rows=14484878 width=119)
+ Conds:RS_51._col0=RS_986._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
<-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1002]
+ PARTITION_ONLY_SHUFFLE [RS_986]
PartitionCols:_col0
- Select Operator [SEL_997] (rows=652 width=4)
+ Select Operator [SEL_981] (rows=652 width=4)
Output:["_col0"]
- Filter Operator [FIL_991] (rows=652 width=8)
+ Filter Operator [FIL_975] (rows=652 width=8)
predicate:(d_year = 2000)
Please refer to the previous TableScan [TS_38]
<-Reducer 2 [SIMPLE_EDGE]
SHUFFLE [RS_51]
PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_943] (rows=4503592 width=119)
- Conds:RS_989._col1=RS_976._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
+ Merge Join Operator [MERGEJOIN_943] (rows=40567099 width=312)
+ Conds:RS_998._col1=RS_1001._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
<-Map 19 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_976]
+ PARTITION_ONLY_SHUFFLE [RS_1001]
PartitionCols:_col0
- Select Operator [SEL_975] (rows=518 width=111)
+ Select Operator [SEL_1000] (rows=4666 width=111)
Output:["_col0","_col1"]
- Filter Operator [FIL_974] (rows=518 width=312)
- predicate:((i_color) IN ('maroon', 'burnished', 'dim', 'steel', 'navajo', 'chocolate') and i_current_price BETWEEN 35 AND 45 and i_current_price BETWEEN 36 AND 50)
+ Filter Operator [FIL_999] (rows=4666 width=311)
+ predicate:((i_color) IN ('maroon', 'burnished', 'dim', 'steel', 'navajo', 'chocolate') and i_current_price BETWEEN 36 AND 45)
TableScan [TS_3] (rows=462000 width=311)
default@item,item,Tbl:COMPLETE,Col:COMPLETE,Output:["i_item_sk","i_current_price","i_color","i_product_name"]
<-Map 1 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_989]
+ SHUFFLE [RS_998]
PartitionCols:_col1
- Select Operator [SEL_988] (rows=417313408 width=351)
+ Select Operator [SEL_997] (rows=417313408 width=351)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10"]
- Filter Operator [FIL_987] (rows=417313408 width=355)
- predicate:((ss_item_sk BETWEEN DynamicValue(RS_49_item_i_item_sk_min) AND DynamicValue(RS_49_item_i_item_sk_max) and in_bloom_filter(ss_item_sk, DynamicValue(RS_49_item_i_item_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null)
+ Filter Operator [FIL_996] (rows=417313408 width=355)
+ predicate:((ss_sold_date_sk BETWEEN DynamicValue(RS_52_d1_d_date_sk_min) AND DynamicValue(RS_52_d1_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_52_d1_d_date_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null)
TableScan [TS_0] (rows=575995635 width=355)
default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_item_sk","ss_customer_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_promo_sk","ss_ticket_number","ss_wholesale_cost","ss_list_price","ss_coupon_amt"]
- <-Reducer 20 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_985]
- Group By Operator [GBY_984] (rows=1 width=12)
+ <-Reducer 48 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_995]
+ Group By Operator [GBY_994] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 19 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_982]
- Group By Operator [GBY_980] (rows=1 width=12)
+ <-Map 47 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_992]
+ Group By Operator [GBY_990] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_977] (rows=518 width=4)
+ Select Operator [SEL_987] (rows=652 width=4)
Output:["_col0"]
- Please refer to the previous Select Operator [SEL_975]
+ Please refer to the previous Select Operator [SEL_981]
<-Reducer 40 [ONE_TO_ONE_EDGE] vectorized
- FORWARD [RS_1017]
+ FORWARD [RS_1024]
PartitionCols:_col0
- Select Operator [SEL_1016] (rows=13257 width=4)
+ Select Operator [SEL_1023] (rows=13257 width=4)
Output:["_col0"]
- Filter Operator [FIL_1015] (rows=13257 width=228)
+ Filter Operator [FIL_1022] (rows=13257 width=228)
predicate:(_col1 > (2 * _col2))
- Group By Operator [GBY_1014] (rows=39773 width=228)
+ Group By Operator [GBY_1021] (rows=39773 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)"],keys:KEY._col0
<-Reducer 39 [SIMPLE_EDGE]
SHUFFLE [RS_21]
@@ -530,195 +532,203 @@ Stage-0
Group By Operator [GBY_20] (rows=6482999 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(_col2)","sum(_col5)"],keys:_col0
Merge Join Operator [MERGEJOIN_946] (rows=183085709 width=227)
- Conds:RS_1010._col0, _col1=RS_1012._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
+ Conds:RS_1017._col0, _col1=RS_1019._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
<-Map 41 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1012]
+ SHUFFLE [RS_1019]
PartitionCols:_col0, _col1
- Select Operator [SEL_1011] (rows=28798881 width=120)
+ Select Operator [SEL_1018] (rows=28798881 width=120)
Output:["_col0","_col1","_col2"]
TableScan [TS_14] (rows=28798881 width=337)
default@catalog_returns,catalog_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["cr_item_sk","cr_order_number","cr_refunded_cash","cr_reversed_charge","cr_store_credit"]
<-Map 38 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1010]
+ SHUFFLE [RS_1017]
PartitionCols:_col0, _col1
- Select Operator [SEL_1009] (rows=287989836 width=119)
+ Select Operator [SEL_1016] (rows=287989836 width=119)
Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_1008] (rows=287989836 width=119)
+ Filter Operator [FIL_1015] (rows=287989836 width=119)
predicate:(cs_item_sk BETWEEN DynamicValue(RS_49_item_i_item_sk_min) AND DynamicValue(RS_49_item_i_item_sk_max) and in_bloom_filter(cs_item_sk, DynamicValue(RS_49_item_i_item_sk_bloom_filter)))
TableScan [TS_12] (rows=287989836 width=119)
default@catalog_sales,catalog_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["cs_item_sk","cs_order_number","cs_ext_list_price"]
<-Reducer 20 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_986]
- Please refer to the previous Group By Operator [GBY_984]
+ BROADCAST [RS_1014]
+ Group By Operator [GBY_1013] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 19 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1007]
+ Group By Operator [GBY_1005] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_1002] (rows=4666 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_1000]
<-Reducer 35 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1059]
+ SHUFFLE [RS_1067]
PartitionCols:_col1, _col0, _col2
- Select Operator [SEL_1058] (rows=2364623 width=525)
+ Select Operator [SEL_1066] (rows=21299858 width=525)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
- Group By Operator [GBY_1057] (rows=2364623 width=1362)
+ Group By Operator [GBY_1065] (rows=21299858 width=1362)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count(VALUE._col0)","sum(VALUE._col1)","sum(VALUE._col2)","sum(VALUE._col3)"],keys:KEY._col0, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5, KEY._col6, KEY._col7, KEY._col8, KEY._col9, KEY._col10, KEY._col11, KEY._col12, KEY._col13
<-Reducer 34 [SIMPLE_EDGE]
SHUFFLE [RS_189]
PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13
- Group By Operator [GBY_188] (rows=2364623 width=1362)
+ Group By Operator [GBY_188] (rows=21299858 width=1362)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count()","sum(_col8)","sum(_col9)","sum(_col10)"],keys:_col24, _col11, _col25, _col12, _col29, _col31, _col37, _col38, _col39, _col40, _col42, _col43, _col44, _col45
- Merge Join Operator [MERGEJOIN_972] (rows=2364623 width=1155)
- Conds:RS_184._col17=RS_1040._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40","_col42","_col43","_col44","_col45"]
- <-Map 49 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1040]
+ Merge Join Operator [MERGEJOIN_972] (rows=21299858 width=1155)
+ Conds:RS_184._col17=RS_1047._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40","_col42","_col43","_col44","_col45"]
+ <-Map 51 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1047]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1036]
+ Please refer to the previous Select Operator [SEL_1043]
<-Reducer 33 [SIMPLE_EDGE]
SHUFFLE [RS_184]
PartitionCols:_col17
- Merge Join Operator [MERGEJOIN_971] (rows=2364623 width=798)
- Conds:RS_181._col5=RS_1039._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40"]
- <-Map 49 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1039]
+ Merge Join Operator [MERGEJOIN_971] (rows=21299858 width=798)
+ Conds:RS_181._col5=RS_1046._col0(Inner),Output:["_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col37","_col38","_col39","_col40"]
+ <-Map 51 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1046]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1036]
+ Please refer to the previous Select Operator [SEL_1043]
<-Reducer 32 [SIMPLE_EDGE]
SHUFFLE [RS_181]
PartitionCols:_col5
- Filter Operator [FIL_180] (rows=2364623 width=609)
+ Filter Operator [FIL_180] (rows=21299858 width=609)
predicate:(_col33 <> _col35)
- Merge Join Operator [MERGEJOIN_970] (rows=2364623 width=609)
- Conds:RS_177._col15=RS_1035._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col33","_col35"]
- <-Map 48 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1035]
+ Merge Join Operator [MERGEJOIN_970] (rows=21299858 width=609)
+ Conds:RS_177._col15=RS_1042._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col17","_col24","_col25","_col29","_col31","_col33","_col35"]
+ <-Map 50 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1042]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1031]
+ Please refer to the previous Select Operator [SEL_1038]
<-Reducer 31 [SIMPLE_EDGE]
SHUFFLE [RS_177]
PartitionCols:_col15
- Merge Join Operator [MERGEJOIN_969] (rows=2331651 width=523)
- Conds:RS_174._col3=RS_1034._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31","_col33"]
- <-Map 48 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1034]
+ Merge Join Operator [MERGEJOIN_969] (rows=21002853 width=525)
+ Conds:RS_174._col3=RS_1041._col0(Inner),Output:["_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31","_col33"]
+ <-Map 50 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1041]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1031]
+ Please refer to the previous Select Operator [SEL_1038]
<-Reducer 30 [SIMPLE_EDGE]
SHUFFLE [RS_174]
PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_968] (rows=2299139 width=436)
- Conds:RS_171._col18=RS_1000._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31"]
+ Merge Join Operator [MERGEJOIN_968] (rows=20709989 width=438)
+ Conds:RS_171._col18=RS_984._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col24","_col25","_col29","_col31"]
<-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1000]
+ PARTITION_ONLY_SHUFFLE [RS_984]
PartitionCols:_col0
- Select Operator [SEL_994] (rows=73049 width=8)
+ Select Operator [SEL_978] (rows=73049 width=8)
Output:["_col0","_col1"]
Please refer to the previous TableScan [TS_38]
<-Reducer 29 [SIMPLE_EDGE]
SHUFFLE [RS_171]
PartitionCols:_col18
- Merge Join Operator [MERGEJOIN_967] (rows=2299139 width=434)
- Conds:RS_168._col19=RS_999._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col24","_col25","_col29"]
+ Merge Join Operator [MERGEJOIN_967] (rows=20709989 width=438)
+ Conds:RS_168._col19=RS_983._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col24","_col25","_col29"]
<-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_999]
+ PARTITION_ONLY_SHUFFLE [RS_983]
PartitionCols:_col0
- Select Operator [SEL_993] (rows=73049 width=8)
+ Select Operator [SEL_977] (rows=73049 width=8)
Output:["_col0","_col1"]
Please refer to the previous TableScan [TS_38]
<-Reducer 28 [SIMPLE_EDGE]
SHUFFLE [RS_168]
PartitionCols:_col19
- Merge Join Operator [MERGEJOIN_966] (rows=2299139 width=432)
- Conds:RS_165._col16=RS_1030._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col19","_col24","_col25"]
+ Merge Join Operator [MERGEJOIN_966] (rows=20709989 width=437)
+ Conds:RS_165._col16=RS_1037._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col17","_col18","_col19","_col24","_col25"]
<-Map 46 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1030]
+ SHUFFLE [RS_1037]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1026]
+ Please refer to the previous Select Operator [SEL_1033]
<-Reducer 27 [SIMPLE_EDGE]
SHUFFLE [RS_165]
PartitionCols:_col16
- Merge Join Operator [MERGEJOIN_965] (rows=2299139 width=433)
- Conds:RS_162._col4=RS_1029._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
+ Merge Join Operator [MERGEJOIN_965] (rows=20709989 width=441)
+ Conds:RS_162._col4=RS_1036._col0(Inner),Output:["_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
<-Map 46 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1029]
+ SHUFFLE [RS_1036]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1026]
+ Please refer to the previous Select Operator [SEL_1033]
<-Reducer 26 [SIMPLE_EDGE]
SHUFFLE [RS_162]
PartitionCols:_col4
- Merge Join Operator [MERGEJOIN_964] (rows=2299139 width=435)
- Conds:RS_159._col6=RS_1024._col0(Inner),Output:["_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
+ Merge Join Operator [MERGEJOIN_964] (rows=20709989 width=443)
+ Conds:RS_159._col6=RS_1031._col0(Inner),Output:["_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19","_col24","_col25"]
<-Map 45 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1024]
+ SHUFFLE [RS_1031]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1022]
+ Please refer to the previous Select Operator [SEL_1029]
<-Reducer 25 [SIMPLE_EDGE]
SHUFFLE [RS_159]
PartitionCols:_col6
- Merge Join Operator [MERGEJOIN_963] (rows=2299139 width=259)
- Conds:RS_156._col1, _col7=RS_1020._col0, _col1(Inner),Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
+ Merge Join Operator [MERGEJOIN_963] (rows=20709989 width=267)
+ Conds:RS_156._col1, _col7=RS_1027._col0, _col1(Inner),Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
<-Map 44 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1020]
+ SHUFFLE [RS_1027]
PartitionCols:_col0, _col1
- Please refer to the previous Select Operator [SEL_1018]
+ Please refer to the previous Select Operator [SEL_1025]
<-Reducer 24 [SIMPLE_EDGE]
SHUFFLE [RS_156]
PartitionCols:_col1, _col7
- Merge Join Operator [MERGEJOIN_962] (rows=1394510 width=123)
- Conds:RS_153._col1=RS_1056._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
+ Merge Join Operator [MERGEJOIN_962] (rows=12561347 width=135)
+ Conds:RS_153._col1=RS_1064._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
<-Reducer 23 [SIMPLE_EDGE]
SHUFFLE [RS_153]
PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_960] (rows=1394510 width=123)
- Conds:RS_150._col2=RS_1007._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
+ Merge Join Operator [MERGEJOIN_960] (rows=12561347 width=135)
+ Conds:RS_150._col2=RS_1012._col0(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col15","_col16","_col17","_col18","_col19"]
<-Map 37 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1007]
+ SHUFFLE [RS_1012]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1005]
+ Please refer to the previous Select Operator [SEL_1010]
<-Reducer 22 [SIMPLE_EDGE]
SHUFFLE [RS_150]
PartitionCols:_col2
- Merge Join Operator [MERGEJOIN_959] (rows=1608052 width=119)
- Conds:RS_147._col0=RS_1003._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
+ Merge Join Operator [MERGEJOIN_959] (rows=14484878 width=119)
+ Conds:RS_147._col0=RS_988._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
<-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1003]
+ PARTITION_ONLY_SHUFFLE [RS_988]
PartitionCols:_col0
- Select Operator [SEL_1001] (rows=652 width=4)
+ Select Operator [SEL_985] (rows=652 width=4)
Output:["_col0"]
- Filter Operator [FIL_995] (rows=652 width=8)
+ Filter Operator [FIL_979] (rows=652 width=8)
predicate:(d_year = 2001)
Please refer to the previous TableScan [TS_38]
<-Reducer 21 [SIMPLE_EDGE]
SHUFFLE [RS_147]
PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_958] (rows=4503592 width=119)
- Conds:RS_1049._col1=RS_978._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
+ Merge Join Operator [MERGEJOIN_958] (rows=40567099 width=312)
+ Conds:RS_1055._col1=RS_1003._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"]
<-Map 19 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_978]
+ PARTITION_ONLY_SHUFFLE [RS_1003]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_975]
- <-Map 50 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1049]
+ Please refer to the previous Select Operator [SEL_1000]
+ <-Map 52 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1055]
PartitionCols:_col1
- Select Operator [SEL_1048] (rows=417313408 width=351)
+ Select Operator [SEL_1054] (rows=417313408 width=351)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10"]
- Filter Operator [FIL_1047] (rows=417313408 width=355)
- predicate:((ss_item_sk BETWEEN DynamicValue(RS_145_item_i_item_sk_min) AND DynamicValue(RS_145_item_i_item_sk_max) and in_bloom_filter(ss_item_sk, DynamicValue(RS_145_item_i_item_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null)
+ Filter Operator [FIL_1053] (rows=417313408 width=355)
+ predicate:((ss_sold_date_sk BETWEEN DynamicValue(RS_148_d1_d_date_sk_min) AND DynamicValue(RS_148_d1_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_148_d1_d_date_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null)
TableScan [TS_96] (rows=575995635 width=355)
default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_item_sk","ss_customer_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_promo_sk","ss_ticket_number","ss_wholesale_cost","ss_list_price","ss_coupon_amt"]
- <-Reducer 36 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_1045]
- Group By Operator [GBY_1044] (rows=1 width=12)
+ <-Reducer 49 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_1052]
+ Group By Operator [GBY_1051] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 19 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_983]
- Group By Operator [GBY_981] (rows=1 width=12)
+ <-Map 47 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_993]
+ Group By Operator [GBY_991] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_979] (rows=518 width=4)
+ Select Operator [SEL_989] (rows=652 width=4)
Output:["_col0"]
- Please refer to the previous Select Operator [SEL_975]
+ Please refer to the previous Select Operator [SEL_985]
<-Reducer 43 [ONE_TO_ONE_EDGE] vectorized
- FORWARD [RS_1056]
+ FORWARD [RS_1064]
PartitionCols:_col0
- Select Operator [SEL_1055] (rows=13257 width=4)
+ Select Operator [SEL_1063] (rows=13257 width=4)
Output:["_col0"]
- Filter Operator [FIL_1054] (rows=13257 width=228)
+ Filter Operator [FIL_1062] (rows=13257 width=228)
predicate:(_col1 > (2 * _col2))
- Group By Operator [GBY_1053] (rows=39773 width=228)
+ Group By Operator [GBY_1061] (rows=39773 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)"],keys:KEY._col0
<-Reducer 42 [SIMPLE_EDGE]
SHUFFLE [RS_117]
@@ -726,21 +736,29 @@ Stage-0
Group By Operator [GBY_116] (rows=6482999 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(_col2)","sum(_col5)"],keys:_col0
Merge Join Operator [MERGEJOIN_961] (rows=183085709 width=227)
- Conds:RS_1052._col0, _col1=RS_1013._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
+ Conds:RS_1060._col0, _col1=RS_1020._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
<-Map 41 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1013]
+ SHUFFLE [RS_1020]
PartitionCols:_col0, _col1
- Please refer to the previous Select Operator [SEL_1011]
- <-Map 51 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1052]
+ Please refer to the previous Select Operator [SEL_1018]
+ <-Map 53 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1060]
PartitionCols:_col0, _col1
- Select Operator [SEL_1051] (rows=287989836 width=119)
+ Select Operator [SEL_1059] (rows=287989836 width=119)
Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_1050] (rows=287989836 width=119)
+ Filter Operator [FIL_1058] (rows=287989836 width=119)
predicate:(cs_item_sk BETWEEN DynamicValue(RS_145_item_i_item_sk_min) AND DynamicValue(RS_145_item_i_item_sk_max) and in_bloom_filter(cs_item_sk, DynamicValue(RS_145_item_i_item_sk_bloom_filter)))
TableScan [TS_108] (rows=287989836 width=119)
default@catalog_sales,catalog_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["cs_item_sk","cs_order_number","cs_ext_list_price"]
<-Reducer 36 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_1046]
- Please refer to the previous Group By Operator [GBY_1044]
+ BROADCAST [RS_1057]
+ Group By Operator [GBY_1056] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 19 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1008]
+ Group By Operator [GBY_1006] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_1004] (rows=4666 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_1000]
diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query85.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query85.q.out
index 2aa27db..aeb3383 100644
--- a/ql/src/test/results/clientpositive/perf/tez/constraints/query85.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query85.q.out
@@ -183,136 +183,136 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 10 <- Reducer 12 (BROADCAST_EDGE)
-Reducer 12 <- Map 11 (CUSTOM_SIMPLE_EDGE)
-Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 10 (SIMPLE_EDGE)
-Reducer 3 <- Map 14 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
-Reducer 4 <- Map 11 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
-Reducer 5 <- Map 13 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
-Reducer 6 <- Map 14 (SIMPLE_EDGE), Reducer 5 (SIMPLE_EDGE)
-Reducer 7 <- Map 15 (SIMPLE_EDGE), Reducer 6 (SIMPLE_EDGE)
-Reducer 8 <- Reducer 7 (SIMPLE_EDGE)
-Reducer 9 <- Reducer 8 (SIMPLE_EDGE)
+Map 1 <- Reducer 8 (BROADCAST_EDGE)
+Reducer 10 <- Map 13 (SIMPLE_EDGE), Map 9 (SIMPLE_EDGE)
+Reducer 11 <- Map 14 (SIMPLE_EDGE), Reducer 10 (SIMPLE_EDGE)
+Reducer 12 <- Map 14 (SIMPLE_EDGE), Reducer 11 (SIMPLE_EDGE)
+Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 7 (SIMPLE_EDGE)
+Reducer 3 <- Reducer 12 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
+Reducer 4 <- Map 15 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
+Reducer 5 <- Reducer 4 (SIMPLE_EDGE)
+Reducer 6 <- Reducer 5 (SIMPLE_EDGE)
+Reducer 8 <- Map 7 (CUSTOM_SIMPLE_EDGE)
Stage-0
Fetch Operator
limit:-1
Stage-1
- Reducer 9 vectorized
- File Output Operator [FS_209]
- Limit [LIM_208] (rows=72 width=832)
+ Reducer 6 vectorized
+ File Output Operator [FS_207]
+ Limit [LIM_206] (rows=72 width=832)
Number of rows:100
- Select Operator [SEL_207] (rows=72 width=832)
+ Select Operator [SEL_205] (rows=72 width=832)
Output:["_col0","_col1","_col2","_col3"]
- <-Reducer 8 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_206]
- Select Operator [SEL_205] (rows=72 width=832)
+ <-Reducer 5 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_204]
+ Select Operator [SEL_203] (rows=72 width=832)
Output:["_col4","_col5","_col6","_col7"]
- Group By Operator [GBY_204] (rows=72 width=353)
+ Group By Operator [GBY_202] (rows=72 width=353)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(VALUE._col0)","count(VALUE._col1)","sum(VALUE._col2)","count(VALUE._col3)","sum(VALUE._col4)","count(VALUE._col5)"],keys:KEY._col0
- <-Reducer 7 [SIMPLE_EDGE]
- SHUFFLE [RS_42]
+ <-Reducer 4 [SIMPLE_EDGE]
+ SHUFFLE [RS_43]
PartitionCols:_col0
- Group By Operator [GBY_41] (rows=72 width=353)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(_col11)","count(_col11)","sum(_col7)","count(_col7)","sum(_col6)","count(_col6)"],keys:_col36
- Merge Join Operator [MERGEJOIN_179] (rows=16740 width=100)
- Conds:RS_37._col4=RS_203._col0(Inner),Output:["_col6","_col7","_col11","_col36"]
+ Group By Operator [GBY_42] (rows=288 width=353)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(_col3)","count(_col3)","sum(_col27)","count(_col27)","sum(_col26)","count(_col26)"],keys:_col36
+ Merge Join Operator [MERGEJOIN_177] (rows=2912400 width=313)
+ Conds:RS_38._col24=RS_201._col0(Inner),Output:["_col3","_col26","_col27","_col36"]
<-Map 15 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_203]
+ SHUFFLE [RS_201]
PartitionCols:_col0
- Select Operator [SEL_202] (rows=72 width=101)
+ Select Operator [SEL_200] (rows=72 width=101)
Output:["_col0","_col1"]
- TableScan [TS_18] (rows=72 width=101)
+ TableScan [TS_28] (rows=72 width=101)
default@reason,reason,Tbl:COMPLETE,Col:COMPLETE,Output:["r_reason_sk","r_reason_desc"]
- <-Reducer 6 [SIMPLE_EDGE]
- SHUFFLE [RS_37]
- PartitionCols:_col4
- Filter Operator [FIL_36] (rows=16740 width=39)
- predicate:((_col29 and _col30 and _col15) or (_col31 and _col32 and _col16) or (_col33 and _col34 and _col17))
- Merge Join Operator [MERGEJOIN_178] (rows=44640 width=39)
- Conds:RS_33._col20, _col1, _col19=RS_197._col2, _col0, _col1(Inner),Output:["_col4","_col6","_col7","_col11","_col15","_col16","_col17","_col29","_col30","_col31","_col32","_col33","_col34"]
- <-Map 14 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_197]
- PartitionCols:_col2, _col0, _col1
- Select Operator [SEL_195] (rows=265971 width=207)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"]
- Filter Operator [FIL_194] (rows=265971 width=183)
- predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U'))
- TableScan [TS_15] (rows=1861800 width=183)
- default@customer_demographics,cd1,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
- <-Reducer 5 [SIMPLE_EDGE]
- SHUFFLE [RS_33]
- PartitionCols:_col20, _col1, _col19
- Filter Operator [FIL_32] (rows=44640 width=218)
- predicate:((_col23 and _col12) or (_col24 and _col13) or (_col25 and _col14))
- Merge Join Operator [MERGEJOIN_177] (rows=59520 width=218)
- Conds:RS_29._col2=RS_201._col0(Inner),Output:["_col1","_col4","_col6","_col7","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col19","_col20","_col23","_col24","_col25"]
- <-Map 13 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_201]
- PartitionCols:_col0
- Select Operator [SEL_200] (rows=3529412 width=16)
- Output:["_col0","_col1","_col2","_col3"]
- Filter Operator [FIL_199] (rows=3529412 width=187)
- predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV'))
- TableScan [TS_12] (rows=40000000 width=187)
- default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
- <-Reducer 4 [SIMPLE_EDGE]
- SHUFFLE [RS_29]
- PartitionCols:_col2
- Merge Join Operator [MERGEJOIN_176] (rows=674551 width=254)
- Conds:RS_26._col8=RS_185._col0(Inner),Output:["_col1","_col2","_col4","_col6","_col7","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col19","_col20"]
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_185]
+ <-Reducer 3 [SIMPLE_EDGE]
+ SHUFFLE [RS_38]
+ PartitionCols:_col24
+ Filter Operator [FIL_36] (rows=2912400 width=280)
+ predicate:(((_col14 and _col15 and _col7) or (_col16 and _col17 and _col8) or (_col18 and _col19 and _col9)) and ((_col29 and _col4) or (_col30 and _col5) or (_col31 and _col6)))
+ Merge Join Operator [MERGEJOIN_176] (rows=10355208 width=280)
+ Conds:RS_33._col1, _col2=RS_34._col9, _col14(Inner),Output:["_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col14","_col15","_col16","_col17","_col18","_col19","_col24","_col26","_col27","_col29","_col30","_col31"]
+ <-Reducer 12 [SIMPLE_EDGE]
+ SHUFFLE [RS_34]
+ PartitionCols:_col9, _col14
+ Select Operator [SEL_27] (rows=1056644 width=155)
+ Output:["_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col13","_col14","_col15","_col16","_col18","_col19","_col20"]
+ Merge Join Operator [MERGEJOIN_175] (rows=1056644 width=155)
+ Conds:RS_24._col1, _col13, _col14=RS_198._col0, _col1, _col2(Inner),Output:["_col0","_col4","_col5","_col6","_col7","_col9","_col10","_col11","_col18","_col19","_col20","_col21","_col22","_col23"]
+ <-Map 14 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_198]
+ PartitionCols:_col0, _col1, _col2
+ Select Operator [SEL_196] (rows=265971 width=207)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"]
+ Filter Operator [FIL_195] (rows=265971 width=183)
+ predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U'))
+ TableScan [TS_15] (rows=1861800 width=183)
+ default@customer_demographics,cd1,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
+ <-Reducer 11 [SIMPLE_EDGE]
+ SHUFFLE [RS_24]
+ PartitionCols:_col1, _col13, _col14
+ Merge Join Operator [MERGEJOIN_174] (rows=1056644 width=312)
+ Conds:RS_21._col3=RS_199._col0(Inner),Output:["_col0","_col1","_col4","_col5","_col6","_col7","_col9","_col10","_col11","_col13","_col14"]
+ <-Map 14 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_199]
PartitionCols:_col0
- Select Operator [SEL_184] (rows=652 width=4)
- Output:["_col0"]
- Filter Operator [FIL_183] (rows=652 width=8)
- predicate:(d_year = 1998)
- TableScan [TS_9] (rows=73049 width=8)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
- <-Reducer 3 [SIMPLE_EDGE]
- SHUFFLE [RS_26]
- PartitionCols:_col8
- Merge Join Operator [MERGEJOIN_175] (rows=1889180 width=379)
- Conds:RS_23._col3=RS_198._col0(Inner),Output:["_col1","_col2","_col4","_col6","_col7","_col8","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col19","_col20"]
- <-Map 14 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_198]
+ Select Operator [SEL_197] (rows=265971 width=183)
+ Output:["_col0","_col1","_col2"]
+ Please refer to the previous Filter Operator [FIL_195]
+ <-Reducer 10 [SIMPLE_EDGE]
+ SHUFFLE [RS_21]
+ PartitionCols:_col3
+ Merge Join Operator [MERGEJOIN_173] (rows=1056644 width=135)
+ Conds:RS_191._col2=RS_194._col0(Inner),Output:["_col0","_col1","_col3","_col4","_col5","_col6","_col7","_col9","_col10","_col11"]
+ <-Map 13 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_194]
PartitionCols:_col0
- Select Operator [SEL_196] (rows=265971 width=183)
- Output:["_col0","_col1","_col2"]
- Please refer to the previous Filter Operator [FIL_194]
- <-Reducer 2 [SIMPLE_EDGE]
- SHUFFLE [RS_23]
- PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_174] (rows=13039884 width=262)
- Conds:RS_182._col0, _col5=RS_193._col1, _col2(Inner),Output:["_col1","_col2","_col3","_col4","_col6","_col7","_col8","_col11","_col12","_col13","_col14","_col15","_col16","_col17"]
- <-Map 1 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_182]
- PartitionCols:_col0, _col5
- Select Operator [SEL_181] (rows=11975292 width=237)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"]
- Filter Operator [FIL_180] (rows=11975292 width=237)
- predicate:(wr_reason_sk is not null and wr_refunded_addr_sk is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null)
- TableScan [TS_0] (rows=14398467 width=237)
- default@web_returns,web_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["wr_item_sk","wr_refunded_cdemo_sk","wr_refunded_addr_sk","wr_returning_cdemo_sk","wr_reason_sk","wr_order_number","wr_fee","wr_refunded_cash"]
- <-Map 10 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_193]
- PartitionCols:_col1, _col2
- Select Operator [SEL_192] (rows=15992347 width=39)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9"]
- Filter Operator [FIL_191] (rows=15992347 width=243)
- predicate:((ws_net_profit BETWEEN 100 AND 200 or ws_net_profit BETWEEN 150 AND 300 or ws_net_profit BETWEEN 50 AND 250) and (ws_sales_price BETWEEN 100 AND 150 or ws_sales_price BETWEEN 50 AND 100 or ws_sales_price BETWEEN 150 AND 200) and (ws_sold_date_sk BETWEEN DynamicValue(RS_27_date_dim_d_date_sk_min) AND DynamicValue(RS_27_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_27_date_dim_d_date_sk_bloom_fi [...]
- TableScan [TS_3] (rows=144002668 width=243)
- default@web_sales,web_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ws_sold_date_sk","ws_item_sk","ws_web_page_sk","ws_order_number","ws_quantity","ws_sales_price","ws_net_profit"]
- <-Reducer 12 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_190]
- Group By Operator [GBY_189] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 11 [CUSTOM_SIMPLE_EDGE] vectorized
- SHUFFLE [RS_188]
- Group By Operator [GBY_187] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_186] (rows=652 width=4)
- Output:["_col0"]
- Please refer to the previous Select Operator [SEL_184]
+ Select Operator [SEL_193] (rows=3529412 width=16)
+ Output:["_col0","_col1","_col2","_col3"]
+ Filter Operator [FIL_192] (rows=3529412 width=187)
+ predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV'))
+ TableScan [TS_9] (rows=40000000 width=187)
+ default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
+ <-Map 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_191]
+ PartitionCols:_col2
+ Select Operator [SEL_190] (rows=11975292 width=237)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"]
+ Filter Operator [FIL_189] (rows=11975292 width=237)
+ predicate:(wr_reason_sk is not null and wr_refunded_addr_sk is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null)
+ TableScan [TS_6] (rows=14398467 width=237)
+ default@web_returns,web_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["wr_item_sk","wr_refunded_cdemo_sk","wr_refunded_addr_sk","wr_returning_cdemo_sk","wr_reason_sk","wr_order_number","wr_fee","wr_refunded_cash"]
+ <-Reducer 2 [SIMPLE_EDGE]
+ SHUFFLE [RS_33]
+ PartitionCols:_col1, _col2
+ Merge Join Operator [MERGEJOIN_172] (rows=51392014 width=35)
+ Conds:RS_188._col0=RS_180._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9"]
+ <-Map 7 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_180]
+ PartitionCols:_col0
+ Select Operator [SEL_179] (rows=652 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_178] (rows=652 width=8)
+ predicate:(d_year = 1998)
+ TableScan [TS_3] (rows=73049 width=8)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
+ <-Map 1 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_188]
+ PartitionCols:_col0
+ Select Operator [SEL_187] (rows=143931136 width=39)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9"]
+ Filter Operator [FIL_186] (rows=143931136 width=243)
+ predicate:(((ws_net_profit >= 100) or (ws_net_profit <= 200) or ws_net_profit is not null or (ws_net_profit <= 300) or (ws_net_profit <= 250)) and ((ws_sales_price >= 100) or (ws_sales_price <= 150) or ws_sales_price is not null or (ws_sales_price <= 200)) and (ws_sold_date_sk BETWEEN DynamicValue(RS_31_date_dim_d_date_sk_min) AND DynamicValue(RS_31_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_31_date_dim_d_date_sk_b [...]
+ TableScan [TS_0] (rows=144002668 width=243)
+ default@web_sales,web_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ws_sold_date_sk","ws_item_sk","ws_web_page_sk","ws_order_number","ws_quantity","ws_sales_price","ws_net_profit"]
+ <-Reducer 8 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_185]
+ Group By Operator [GBY_184] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 7 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_183]
+ Group By Operator [GBY_182] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_181] (rows=652 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_179]
diff --git a/ql/src/test/results/clientpositive/perf/tez/query13.q.out b/ql/src/test/results/clientpositive/perf/tez/query13.q.out
index 2cf833b..60a73d7 100644
--- a/ql/src/test/results/clientpositive/perf/tez/query13.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/query13.q.out
@@ -115,14 +115,14 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 9 <- Reducer 8 (BROADCAST_EDGE)
-Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 9 (SIMPLE_EDGE)
+Map 1 <- Reducer 9 (BROADCAST_EDGE)
+Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 8 (SIMPLE_EDGE)
Reducer 3 <- Map 10 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
Reducer 4 <- Map 11 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
Reducer 5 <- Map 12 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
Reducer 6 <- Map 13 (SIMPLE_EDGE), Reducer 5 (SIMPLE_EDGE)
Reducer 7 <- Reducer 6 (CUSTOM_SIMPLE_EDGE)
-Reducer 8 <- Map 1 (CUSTOM_SIMPLE_EDGE)
+Reducer 9 <- Map 8 (CUSTOM_SIMPLE_EDGE)
Stage-0
Fetch Operator
@@ -137,98 +137,96 @@ Stage-0
<-Reducer 6 [CUSTOM_SIMPLE_EDGE]
PARTITION_ONLY_SHUFFLE [RS_37]
Group By Operator [GBY_36] (rows=1 width=256)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5"],aggregations:["sum(_col6)","count(_col6)","sum(_col7)","count(_col7)","sum(_col8)","count(_col8)"]
- Select Operator [SEL_35] (rows=40950 width=44)
- Output:["_col6","_col7","_col8"]
- Filter Operator [FIL_34] (rows=40950 width=44)
- predicate:((_col24 and _col25 and _col12 and _col17) or (_col26 and _col27 and _col13 and _col18) or (_col28 and _col29 and _col14 and _col18))
- Merge Join Operator [MERGEJOIN_121] (rows=218403 width=44)
- Conds:RS_31._col2=RS_144._col0(Inner),Output:["_col6","_col7","_col8","_col12","_col13","_col14","_col17","_col18","_col24","_col25","_col26","_col27","_col28","_col29"]
- <-Map 13 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_144]
- PartitionCols:_col0
- Select Operator [SEL_143] (rows=265971 width=28)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
- Filter Operator [FIL_142] (rows=265971 width=183)
- predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null)
- TableScan [TS_15] (rows=1861800 width=183)
- default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
- <-Reducer 5 [SIMPLE_EDGE]
- SHUFFLE [RS_31]
- PartitionCols:_col2
- Filter Operator [FIL_30] (rows=218403 width=44)
- predicate:((_col20 and _col9) or (_col21 and _col10) or (_col22 and _col11))
- Merge Join Operator [MERGEJOIN_120] (rows=291204 width=44)
- Conds:RS_27._col4=RS_141._col0(Inner),Output:["_col2","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col17","_col18","_col20","_col21","_col22"]
- <-Map 12 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_141]
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5"],aggregations:["sum(_col5)","count(_col5)","sum(_col6)","count(_col6)","sum(_col7)","count(_col7)"]
+ Merge Join Operator [MERGEJOIN_121] (rows=368553 width=0)
+ Conds:RS_32._col4=RS_144._col0(Inner),Output:["_col5","_col6","_col7"]
+ <-Map 13 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_144]
+ PartitionCols:_col0
+ Select Operator [SEL_143] (rows=1704 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_142] (rows=1704 width=4)
+ predicate:s_store_sk is not null
+ TableScan [TS_15] (rows=1704 width=4)
+ default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk"]
+ <-Reducer 5 [SIMPLE_EDGE]
+ SHUFFLE [RS_32]
+ PartitionCols:_col4
+ Filter Operator [FIL_31] (rows=368553 width=44)
+ predicate:((_col20 and _col21 and _col11 and _col27) or (_col22 and _col23 and _col12 and _col28) or (_col24 and _col25 and _col13 and _col28))
+ Merge Join Operator [MERGEJOIN_120] (rows=1965626 width=44)
+ Conds:RS_28._col2=RS_141._col0(Inner),Output:["_col4","_col5","_col6","_col7","_col11","_col12","_col13","_col20","_col21","_col22","_col23","_col24","_col25","_col27","_col28"]
+ <-Map 12 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_141]
+ PartitionCols:_col0
+ Select Operator [SEL_140] (rows=1309 width=12)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_139] (rows=1309 width=8)
+ predicate:((hd_dep_count) IN (3, 1) and hd_demo_sk is not null)
+ TableScan [TS_12] (rows=7200 width=8)
+ default@household_demographics,household_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_dep_count"]
+ <-Reducer 4 [SIMPLE_EDGE]
+ SHUFFLE [RS_28]
+ PartitionCols:_col2
+ Merge Join Operator [MERGEJOIN_119] (rows=10811694 width=36)
+ Conds:RS_25._col1=RS_138._col0(Inner),Output:["_col2","_col4","_col5","_col6","_col7","_col11","_col12","_col13","_col20","_col21","_col22","_col23","_col24","_col25"]
+ <-Map 11 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_138]
PartitionCols:_col0
- Select Operator [SEL_140] (rows=3529412 width=16)
- Output:["_col0","_col1","_col2","_col3"]
- Filter Operator [FIL_139] (rows=3529412 width=187)
- predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV') and ca_address_sk is not null)
- TableScan [TS_12] (rows=40000000 width=187)
- default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
- <-Reducer 4 [SIMPLE_EDGE]
- SHUFFLE [RS_27]
- PartitionCols:_col4
- Merge Join Operator [MERGEJOIN_119] (rows=3300311 width=104)
- Conds:RS_24._col3=RS_138._col0(Inner),Output:["_col2","_col4","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col17","_col18"]
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_138]
- PartitionCols:_col0
- Select Operator [SEL_137] (rows=1309 width=12)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_136] (rows=1309 width=8)
- predicate:((hd_dep_count) IN (3, 1) and hd_demo_sk is not null)
- TableScan [TS_9] (rows=7200 width=8)
- default@household_demographics,household_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_dep_count"]
- <-Reducer 3 [SIMPLE_EDGE]
- SHUFFLE [RS_24]
- PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_118] (rows=18152968 width=233)
- Conds:RS_21._col5=RS_135._col0(Inner),Output:["_col2","_col3","_col4","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14"]
- <-Map 10 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_135]
- PartitionCols:_col0
- Select Operator [SEL_134] (rows=1704 width=4)
- Output:["_col0"]
- Filter Operator [FIL_133] (rows=1704 width=4)
- predicate:s_store_sk is not null
- TableScan [TS_6] (rows=1704 width=4)
- default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk"]
- <-Reducer 2 [SIMPLE_EDGE]
- SHUFFLE [RS_21]
- PartitionCols:_col5
- Merge Join Operator [MERGEJOIN_117] (rows=18152968 width=237)
- Conds:RS_124._col0=RS_132._col0(Inner),Output:["_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14"]
- <-Map 1 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_124]
- PartitionCols:_col0
- Select Operator [SEL_123] (rows=652 width=4)
- Output:["_col0"]
- Filter Operator [FIL_122] (rows=652 width=8)
- predicate:((d_year = 2001) and d_date_sk is not null)
- TableScan [TS_0] (rows=73049 width=8)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
- <-Map 9 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_132]
- PartitionCols:_col0
- Select Operator [SEL_131] (rows=50840141 width=260)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- Filter Operator [FIL_130] (rows=50840141 width=450)
- predicate:((ss_net_profit BETWEEN 100 AND 200 or ss_net_profit BETWEEN 150 AND 300 or ss_net_profit BETWEEN 50 AND 250) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_sold_date_sk BETWEEN DynamicValue(RS_18_date_dim_d_date_sk_min) AND DynamicValue(RS_18_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_18_date_dim_d_date_sk_bloom_filter))) [...]
- TableScan [TS_3] (rows=575995635 width=450)
- default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_quantity","ss_sales_price","ss_ext_sales_price","ss_ext_wholesale_cost","ss_net_profit"]
- <-Reducer 8 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_129]
- Group By Operator [GBY_128] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 1 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_127]
- Group By Operator [GBY_126] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_125] (rows=652 width=4)
- Output:["_col0"]
- Please refer to the previous Select Operator [SEL_123]
+ Select Operator [SEL_137] (rows=265971 width=28)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
+ Filter Operator [FIL_136] (rows=265971 width=183)
+ predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null)
+ TableScan [TS_9] (rows=1861800 width=183)
+ default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
+ <-Reducer 3 [SIMPLE_EDGE]
+ SHUFFLE [RS_25]
+ PartitionCols:_col1
+ Filter Operator [FIL_24] (rows=10811694 width=36)
+ predicate:((_col16 and _col8) or (_col17 and _col9) or (_col18 and _col10))
+ Merge Join Operator [MERGEJOIN_118] (rows=14415593 width=36)
+ Conds:RS_21._col3=RS_135._col0(Inner),Output:["_col1","_col2","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col16","_col17","_col18"]
+ <-Map 10 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_135]
+ PartitionCols:_col0
+ Select Operator [SEL_134] (rows=3529412 width=16)
+ Output:["_col0","_col1","_col2","_col3"]
+ Filter Operator [FIL_133] (rows=3529412 width=187)
+ predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV') and ca_address_sk is not null)
+ TableScan [TS_6] (rows=40000000 width=187)
+ default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
+ <-Reducer 2 [SIMPLE_EDGE]
+ SHUFFLE [RS_21]
+ PartitionCols:_col3
+ Merge Join Operator [MERGEJOIN_117] (rows=163376714 width=237)
+ Conds:RS_132._col0=RS_124._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Map 8 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_124]
+ PartitionCols:_col0
+ Select Operator [SEL_123] (rows=652 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_122] (rows=652 width=8)
+ predicate:((d_year = 2001) and d_date_sk is not null)
+ TableScan [TS_3] (rows=73049 width=8)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
+ <-Map 1 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_132]
+ PartitionCols:_col0
+ Select Operator [SEL_131] (rows=457561292 width=260)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ Filter Operator [FIL_130] (rows=457561292 width=450)
+ predicate:(((ss_net_profit >= 100) or (ss_net_profit <= 200) or ss_net_profit is not null or (ss_net_profit <= 300) or (ss_net_profit <= 250)) and ((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and (ss_sold_date_sk BETWEEN DynamicValue(RS_19_date_dim_d_date_sk_min) AND DynamicValue(RS_19_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_19_date_dim_d_date_ [...]
+ TableScan [TS_0] (rows=575995635 width=450)
+ default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_quantity","ss_sales_price","ss_ext_sales_price","ss_ext_wholesale_cost","ss_net_profit"]
+ <-Reducer 9 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_129]
+ Group By Operator [GBY_128] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 8 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_127]
+ Group By Operator [GBY_126] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_125] (rows=652 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_123]
diff --git a/ql/src/test/results/clientpositive/perf/tez/query21.q.out b/ql/src/test/results/clientpositive/perf/tez/query21.q.out
index 382775a..0d10843 100644
--- a/ql/src/test/results/clientpositive/perf/tez/query21.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/query21.q.out
@@ -83,12 +83,12 @@ Stage-0
File Output Operator [FS_95]
Limit [LIM_94] (rows=100 width=216)
Number of rows:100
- Select Operator [SEL_93] (rows=231983 width=216)
+ Select Operator [SEL_93] (rows=115991 width=216)
Output:["_col0","_col1","_col2","_col3"]
<-Reducer 5 [SIMPLE_EDGE] vectorized
SHUFFLE [RS_92]
- Filter Operator [FIL_91] (rows=231983 width=216)
- predicate:CASE WHEN ((_col2 > 0L)) THEN ((UDFToDouble(_col3) / UDFToDouble(_col2)) BETWEEN 0.666667D AND 1.5D) ELSE (null) END
+ Filter Operator [FIL_91] (rows=115991 width=216)
+ predicate:(CASE WHEN ((_col2 > 0L)) THEN (((UDFToDouble(_col3) / UDFToDouble(_col2)) <= 1.5D)) ELSE (null) END and CASE WHEN ((_col2 > 0L)) THEN ((0.666667D <= (UDFToDouble(_col3) / UDFToDouble(_col2)))) ELSE (null) END)
Group By Operator [GBY_90] (rows=463966 width=216)
Output:["_col0","_col1","_col2","_col3"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)"],keys:KEY._col0, KEY._col1
<-Reducer 4 [SIMPLE_EDGE]
diff --git a/ql/src/test/results/clientpositive/perf/tez/query34.q.out b/ql/src/test/results/clientpositive/perf/tez/query34.q.out
index 4640899..d401e4c 100644
--- a/ql/src/test/results/clientpositive/perf/tez/query34.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/query34.q.out
@@ -155,7 +155,7 @@ Stage-0
Select Operator [SEL_105] (rows=595 width=4)
Output:["_col0"]
Filter Operator [FIL_104] (rows=595 width=12)
- predicate:((d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and (d_year) IN (2000, 2001, 2002) and d_date_sk is not null)
+ predicate:(((d_dom >= 1) or (d_dom <= 3) or (d_dom >= 25) or (d_dom <= 28)) and (d_dom BETWEEN 1 AND 3 or d_dom BETWEEN 25 AND 28) and (d_year) IN (2000, 2001, 2002) and d_date_sk is not null)
TableScan [TS_6] (rows=73049 width=12)
default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_dom"]
<-Map 4 [SIMPLE_EDGE] vectorized
diff --git a/ql/src/test/results/clientpositive/perf/tez/query48.q.out b/ql/src/test/results/clientpositive/perf/tez/query48.q.out
index 885be13..d64eec1 100644
--- a/ql/src/test/results/clientpositive/perf/tez/query48.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/query48.q.out
@@ -143,13 +143,13 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 7 <- Reducer 9 (BROADCAST_EDGE)
+Map 1 <- Reducer 8 (BROADCAST_EDGE)
Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 7 (SIMPLE_EDGE)
-Reducer 3 <- Map 8 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
+Reducer 3 <- Map 9 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
Reducer 4 <- Map 10 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
Reducer 5 <- Map 11 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
Reducer 6 <- Reducer 5 (CUSTOM_SIMPLE_EDGE)
-Reducer 9 <- Map 8 (CUSTOM_SIMPLE_EDGE)
+Reducer 8 <- Map 7 (CUSTOM_SIMPLE_EDGE)
Stage-0
Fetch Operator
@@ -162,82 +162,80 @@ Stage-0
<-Reducer 5 [CUSTOM_SIMPLE_EDGE]
PARTITION_ONLY_SHUFFLE [RS_30]
Group By Operator [GBY_29] (rows=1 width=8)
- Output:["_col0"],aggregations:["sum(_col5)"]
- Select Operator [SEL_28] (rows=20247 width=24)
- Output:["_col5"]
- Filter Operator [FIL_27] (rows=20247 width=24)
- predicate:((_col12 and _col6) or (_col13 and _col7) or (_col14 and _col8))
- Merge Join Operator [MERGEJOIN_96] (rows=26999 width=24)
- Conds:RS_24._col3=RS_116._col0(Inner),Output:["_col5","_col6","_col7","_col8","_col12","_col13","_col14"]
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_116]
- PartitionCols:_col0
- Select Operator [SEL_115] (rows=3529412 width=16)
- Output:["_col0","_col1","_col2","_col3"]
- Filter Operator [FIL_114] (rows=3529412 width=187)
- predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV') and ca_address_sk is not null)
- TableScan [TS_12] (rows=40000000 width=187)
- default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
- <-Reducer 4 [SIMPLE_EDGE]
- SHUFFLE [RS_24]
- PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_95] (rows=305980 width=12)
- Conds:RS_21._col4=RS_113._col0(Inner),Output:["_col3","_col5","_col6","_col7","_col8"]
- <-Map 10 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_113]
- PartitionCols:_col0
- Select Operator [SEL_112] (rows=1704 width=4)
- Output:["_col0"]
- Filter Operator [FIL_111] (rows=1704 width=4)
- predicate:s_store_sk is not null
- TableScan [TS_9] (rows=1704 width=4)
- default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk"]
- <-Reducer 3 [SIMPLE_EDGE]
- SHUFFLE [RS_21]
- PartitionCols:_col4
- Merge Join Operator [MERGEJOIN_94] (rows=305980 width=12)
- Conds:RS_18._col1=RS_102._col0(Inner),Output:["_col3","_col4","_col5","_col6","_col7","_col8"]
- <-Map 8 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_102]
- PartitionCols:_col0
- Select Operator [SEL_101] (rows=652 width=4)
- Output:["_col0"]
- Filter Operator [FIL_100] (rows=652 width=8)
- predicate:((d_year = 1998) and d_date_sk is not null)
- TableScan [TS_6] (rows=73049 width=8)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
- <-Reducer 2 [SIMPLE_EDGE]
- SHUFFLE [RS_18]
- PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_93] (rows=856943 width=12)
- Conds:RS_99._col0=RS_110._col1(Inner),Output:["_col1","_col3","_col4","_col5","_col6","_col7","_col8"]
- <-Map 1 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_99]
- PartitionCols:_col0
- Select Operator [SEL_98] (rows=29552 width=4)
- Output:["_col0"]
- Filter Operator [FIL_97] (rows=29552 width=183)
- predicate:((cd_education_status = '4 yr Degree') and (cd_marital_status = 'M') and cd_demo_sk is not null)
- TableScan [TS_0] (rows=1861800 width=183)
- default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
- <-Map 7 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_110]
- PartitionCols:_col1
- Select Operator [SEL_109] (rows=53235296 width=31)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"]
- Filter Operator [FIL_108] (rows=53235296 width=233)
- predicate:((ss_net_profit BETWEEN 0 AND 2000 or ss_net_profit BETWEEN 150 AND 3000 or ss_net_profit BETWEEN 50 AND 25000) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_sold_date_sk BETWEEN DynamicValue(RS_19_date_dim_d_date_sk_min) AND DynamicValue(RS_19_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_19_date_dim_d_date_sk_bloom_filter))) and ss [...]
- TableScan [TS_3] (rows=575995635 width=233)
- default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_cdemo_sk","ss_addr_sk","ss_store_sk","ss_quantity","ss_sales_price","ss_net_profit"]
- <-Reducer 9 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_107]
- Group By Operator [GBY_106] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 8 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_105]
- Group By Operator [GBY_104] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_103] (rows=652 width=4)
- Output:["_col0"]
- Please refer to the previous Select Operator [SEL_101]
+ Output:["_col0"],aggregations:["sum(_col4)"]
+ Merge Join Operator [MERGEJOIN_96] (rows=170127 width=0)
+ Conds:RS_25._col3=RS_116._col0(Inner),Output:["_col4"]
+ <-Map 11 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_116]
+ PartitionCols:_col0
+ Select Operator [SEL_115] (rows=1704 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_114] (rows=1704 width=4)
+ predicate:s_store_sk is not null
+ TableScan [TS_12] (rows=1704 width=4)
+ default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk"]
+ <-Reducer 4 [SIMPLE_EDGE]
+ SHUFFLE [RS_25]
+ PartitionCols:_col3
+ Filter Operator [FIL_24] (rows=170127 width=24)
+ predicate:((_col11 and _col5) or (_col12 and _col6) or (_col13 and _col7))
+ Merge Join Operator [MERGEJOIN_95] (rows=226838 width=24)
+ Conds:RS_21._col2=RS_113._col0(Inner),Output:["_col3","_col4","_col5","_col6","_col7","_col11","_col12","_col13"]
+ <-Map 10 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_113]
+ PartitionCols:_col0
+ Select Operator [SEL_112] (rows=3529412 width=16)
+ Output:["_col0","_col1","_col2","_col3"]
+ Filter Operator [FIL_111] (rows=3529412 width=187)
+ predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV') and ca_address_sk is not null)
+ TableScan [TS_9] (rows=40000000 width=187)
+ default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
+ <-Reducer 3 [SIMPLE_EDGE]
+ SHUFFLE [RS_21]
+ PartitionCols:_col2
+ Merge Join Operator [MERGEJOIN_94] (rows=2570826 width=12)
+ Conds:RS_18._col1=RS_110._col0(Inner),Output:["_col2","_col3","_col4","_col5","_col6","_col7"]
+ <-Map 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_110]
+ PartitionCols:_col0
+ Select Operator [SEL_109] (rows=29552 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_108] (rows=29552 width=183)
+ predicate:((cd_education_status = '4 yr Degree') and (cd_marital_status = 'M') and cd_demo_sk is not null)
+ TableScan [TS_6] (rows=1861800 width=183)
+ default@customer_demographics,customer_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
+ <-Reducer 2 [SIMPLE_EDGE]
+ SHUFFLE [RS_18]
+ PartitionCols:_col1
+ Merge Join Operator [MERGEJOIN_93] (rows=57024544 width=25)
+ Conds:RS_107._col0=RS_99._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7"]
+ <-Map 7 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_99]
+ PartitionCols:_col0
+ Select Operator [SEL_98] (rows=652 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_97] (rows=652 width=8)
+ predicate:((d_year = 1998) and d_date_sk is not null)
+ TableScan [TS_3] (rows=73049 width=8)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
+ <-Map 1 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_107]
+ PartitionCols:_col0
+ Select Operator [SEL_106] (rows=159705893 width=31)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"]
+ Filter Operator [FIL_105] (rows=159705893 width=233)
+ predicate:(((ss_net_profit >= 0) or (ss_net_profit <= 2000) or ss_net_profit is not null or (ss_net_profit <= 3000) or (ss_net_profit <= 25000)) and ((ss_sales_price >= 100) or (ss_sales_price <= 150) or ss_sales_price is not null or (ss_sales_price <= 200)) and (ss_sales_price BETWEEN 100 AND 150 or ss_sales_price BETWEEN 50 AND 100 or ss_sales_price BETWEEN 150 AND 200) and (ss_sold_date_sk BETWEEN DynamicValue(RS_16_date_dim_d_date_sk_min) AND Dynam [...]
+ TableScan [TS_0] (rows=575995635 width=233)
+ default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_cdemo_sk","ss_addr_sk","ss_store_sk","ss_quantity","ss_sales_price","ss_net_profit"]
+ <-Reducer 8 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_104]
+ Group By Operator [GBY_103] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 7 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_102]
+ Group By Operator [GBY_101] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_100] (rows=652 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_98]
diff --git a/ql/src/test/results/clientpositive/perf/tez/query54.q.out b/ql/src/test/results/clientpositive/perf/tez/query54.q.out
index a029634..a7d5101 100644
--- a/ql/src/test/results/clientpositive/perf/tez/query54.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/query54.q.out
@@ -1,7 +1,7 @@
-Warning: Shuffle Join MERGEJOIN[271][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3]] in Stage 'Reducer 4' is a cross product
-Warning: Shuffle Join MERGEJOIN[272][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4]] in Stage 'Reducer 5' is a cross product
-Warning: Shuffle Join MERGEJOIN[270][tables = [$hdt$_1, $hdt$_2]] in Stage 'Reducer 30' is a cross product
-Warning: Shuffle Join MERGEJOIN[273][tables = [$hdt$_1, $hdt$_2, $hdt$_0]] in Stage 'Reducer 6' is a cross product
+Warning: Shuffle Join MERGEJOIN[269][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3]] in Stage 'Reducer 4' is a cross product
+Warning: Shuffle Join MERGEJOIN[270][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4]] in Stage 'Reducer 5' is a cross product
+Warning: Shuffle Join MERGEJOIN[271][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4, $hdt$_5]] in Stage 'Reducer 6' is a cross product
+Warning: Shuffle Join MERGEJOIN[272][tables = [$hdt$_0, $hdt$_1, $hdt$_2, $hdt$_3, $hdt$_4, $hdt$_5, $hdt$_6]] in Stage 'Reducer 7' is a cross product
PREHOOK: query: explain
with my_customers as (
select distinct c_customer_sk
@@ -133,27 +133,28 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 15 <- Reducer 23 (BROADCAST_EDGE), Union 16 (CONTAINS)
-Map 21 <- Reducer 23 (BROADCAST_EDGE), Union 16 (CONTAINS)
-Reducer 12 <- Map 11 (SIMPLE_EDGE), Map 14 (SIMPLE_EDGE)
-Reducer 13 <- Reducer 12 (SIMPLE_EDGE), Reducer 20 (SIMPLE_EDGE)
-Reducer 17 <- Map 22 (SIMPLE_EDGE), Union 16 (SIMPLE_EDGE)
-Reducer 18 <- Map 24 (SIMPLE_EDGE), Reducer 17 (SIMPLE_EDGE)
+Map 16 <- Reducer 24 (BROADCAST_EDGE), Union 17 (CONTAINS)
+Map 22 <- Reducer 24 (BROADCAST_EDGE), Union 17 (CONTAINS)
+Reducer 10 <- Reducer 9 (SIMPLE_EDGE)
+Reducer 13 <- Map 12 (SIMPLE_EDGE), Map 15 (SIMPLE_EDGE)
+Reducer 14 <- Reducer 13 (SIMPLE_EDGE), Reducer 21 (SIMPLE_EDGE)
+Reducer 18 <- Map 23 (SIMPLE_EDGE), Union 17 (SIMPLE_EDGE)
Reducer 19 <- Map 25 (SIMPLE_EDGE), Reducer 18 (SIMPLE_EDGE)
-Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 10 (SIMPLE_EDGE)
-Reducer 20 <- Reducer 19 (SIMPLE_EDGE)
-Reducer 23 <- Map 22 (CUSTOM_SIMPLE_EDGE)
-Reducer 27 <- Map 26 (SIMPLE_EDGE)
-Reducer 28 <- Reducer 27 (CUSTOM_SIMPLE_EDGE)
-Reducer 29 <- Map 26 (SIMPLE_EDGE)
-Reducer 3 <- Reducer 13 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
-Reducer 30 <- Reducer 29 (CUSTOM_SIMPLE_EDGE), Reducer 32 (CUSTOM_SIMPLE_EDGE)
-Reducer 31 <- Map 26 (SIMPLE_EDGE)
-Reducer 32 <- Reducer 31 (CUSTOM_SIMPLE_EDGE)
-Reducer 4 <- Reducer 28 (CUSTOM_SIMPLE_EDGE), Reducer 3 (CUSTOM_SIMPLE_EDGE)
-Reducer 5 <- Reducer 27 (CUSTOM_SIMPLE_EDGE), Reducer 4 (CUSTOM_SIMPLE_EDGE)
-Reducer 6 <- Reducer 30 (CUSTOM_SIMPLE_EDGE), Reducer 5 (CUSTOM_SIMPLE_EDGE)
-Reducer 7 <- Reducer 6 (SIMPLE_EDGE)
+Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 11 (SIMPLE_EDGE)
+Reducer 20 <- Map 26 (SIMPLE_EDGE), Reducer 19 (SIMPLE_EDGE)
+Reducer 21 <- Reducer 20 (SIMPLE_EDGE)
+Reducer 24 <- Map 23 (CUSTOM_SIMPLE_EDGE)
+Reducer 28 <- Map 27 (SIMPLE_EDGE)
+Reducer 29 <- Reducer 28 (CUSTOM_SIMPLE_EDGE)
+Reducer 3 <- Reducer 14 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
+Reducer 30 <- Map 27 (SIMPLE_EDGE)
+Reducer 31 <- Reducer 30 (CUSTOM_SIMPLE_EDGE)
+Reducer 32 <- Map 27 (SIMPLE_EDGE)
+Reducer 33 <- Map 27 (SIMPLE_EDGE)
+Reducer 4 <- Reducer 29 (CUSTOM_SIMPLE_EDGE), Reducer 3 (CUSTOM_SIMPLE_EDGE)
+Reducer 5 <- Reducer 31 (CUSTOM_SIMPLE_EDGE), Reducer 4 (CUSTOM_SIMPLE_EDGE)
+Reducer 6 <- Reducer 32 (CUSTOM_SIMPLE_EDGE), Reducer 5 (CUSTOM_SIMPLE_EDGE)
+Reducer 7 <- Reducer 33 (CUSTOM_SIMPLE_EDGE), Reducer 6 (CUSTOM_SIMPLE_EDGE)
Reducer 8 <- Reducer 7 (SIMPLE_EDGE)
Reducer 9 <- Reducer 8 (SIMPLE_EDGE)
@@ -161,253 +162,260 @@ Stage-0
Fetch Operator
limit:100
Stage-1
- Reducer 9 vectorized
- File Output Operator [FS_349]
- Limit [LIM_348] (rows=1 width=16)
+ Reducer 10 vectorized
+ File Output Operator [FS_352]
+ Limit [LIM_351] (rows=1 width=16)
Number of rows:100
- Select Operator [SEL_347] (rows=1 width=16)
+ Select Operator [SEL_350] (rows=1 width=16)
Output:["_col0","_col1","_col2"]
- <-Reducer 8 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_346]
- Select Operator [SEL_345] (rows=1 width=16)
+ <-Reducer 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_349]
+ Select Operator [SEL_348] (rows=1 width=16)
Output:["_col0","_col1","_col2"]
- Group By Operator [GBY_344] (rows=1 width=12)
+ Group By Operator [GBY_347] (rows=1 width=12)
Output:["_col0","_col1"],aggregations:["count(VALUE._col0)"],keys:KEY._col0
- <-Reducer 7 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_343]
+ <-Reducer 8 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_346]
PartitionCols:_col0
- Group By Operator [GBY_342] (rows=1 width=12)
+ Group By Operator [GBY_345] (rows=1 width=12)
Output:["_col0","_col1"],aggregations:["count()"],keys:_col0
- Select Operator [SEL_341] (rows=1 width=116)
+ Select Operator [SEL_344] (rows=1 width=116)
Output:["_col0"]
- Group By Operator [GBY_340] (rows=1 width=116)
+ Group By Operator [GBY_343] (rows=1 width=116)
Output:["_col0","_col1"],aggregations:["sum(VALUE._col0)"],keys:KEY._col0
- <-Reducer 6 [SIMPLE_EDGE]
- SHUFFLE [RS_119]
+ <-Reducer 7 [SIMPLE_EDGE]
+ SHUFFLE [RS_118]
PartitionCols:_col0
- Group By Operator [GBY_118] (rows=312 width=116)
- Output:["_col0","_col1"],aggregations:["sum(_col1)"],keys:_col0
- Select Operator [SEL_117] (rows=624257222 width=127)
- Output:["_col0","_col1"]
- Filter Operator [FIL_116] (rows=624257222 width=127)
- predicate:_col2 BETWEEN _col3 AND _col4
- Select Operator [SEL_115] (rows=5618315000 width=127)
- Output:["_col0","_col1","_col2","_col3","_col4"]
- Merge Join Operator [MERGEJOIN_273] (rows=5618315000 width=127)
- Conds:(Inner),Output:["_col0","_col2","_col6","_col13","_col15"]
- <-Reducer 30 [CUSTOM_SIMPLE_EDGE]
- PARTITION_ONLY_SHUFFLE [RS_112]
- Merge Join Operator [MERGEJOIN_270] (rows=25 width=4)
- Conds:(Right Outer),Output:["_col0"]
- <-Reducer 29 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_331]
- Group By Operator [GBY_330] (rows=25 width=4)
+ Group By Operator [GBY_117] (rows=302 width=116)
+ Output:["_col0","_col1"],aggregations:["sum(_col2)"],keys:_col10
+ Select Operator [SEL_116] (rows=624257216 width=123)
+ Output:["_col2","_col10"]
+ Filter Operator [FIL_115] (rows=624257216 width=123)
+ predicate:(_col4 <= _col15)
+ Merge Join Operator [MERGEJOIN_272] (rows=1872771650 width=123)
+ Conds:(Inner),Output:["_col2","_col4","_col10","_col15"]
+ <-Reducer 33 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_342]
+ Group By Operator [GBY_341] (rows=25 width=4)
+ Output:["_col0"],keys:KEY._col0
+ <-Map 27 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_322]
+ PartitionCols:_col0
+ Group By Operator [GBY_318] (rows=25 width=4)
+ Output:["_col0"],keys:_col0
+ Select Operator [SEL_314] (rows=50 width=12)
+ Output:["_col0"]
+ Filter Operator [FIL_310] (rows=50 width=12)
+ predicate:((d_moy = 3) and (d_year = 1999))
+ TableScan [TS_50] (rows=73049 width=12)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_month_seq","d_year","d_moy"]
+ <-Reducer 6 [CUSTOM_SIMPLE_EDGE]
+ PARTITION_ONLY_SHUFFLE [RS_112]
+ Filter Operator [FIL_111] (rows=74910866 width=119)
+ predicate:(_col14 <= _col4)
+ Merge Join Operator [MERGEJOIN_271] (rows=224732600 width=119)
+ Conds:(Inner),Output:["_col2","_col4","_col10","_col14"]
+ <-Reducer 32 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_340]
+ Group By Operator [GBY_339] (rows=25 width=4)
Output:["_col0"],keys:KEY._col0
- <-Map 26 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_319]
+ <-Map 27 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_321]
PartitionCols:_col0
- Group By Operator [GBY_316] (rows=25 width=4)
+ Group By Operator [GBY_317] (rows=25 width=4)
Output:["_col0"],keys:_col0
Select Operator [SEL_313] (rows=50 width=12)
Output:["_col0"]
- Filter Operator [FIL_311] (rows=50 width=12)
- predicate:((d_moy = 3) and (d_year = 1999))
- TableScan [TS_73] (rows=73049 width=12)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_month_seq","d_year","d_moy"]
- <-Reducer 32 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_339]
- Select Operator [SEL_338] (rows=1 width=8)
- Filter Operator [FIL_337] (rows=1 width=8)
- predicate:(sq_count_check(_col0) <= 1)
- Group By Operator [GBY_336] (rows=1 width=8)
- Output:["_col0"],aggregations:["count(VALUE._col0)"]
- <-Reducer 31 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_335]
- Group By Operator [GBY_334] (rows=1 width=8)
- Output:["_col0"],aggregations:["count()"]
- Select Operator [SEL_333] (rows=25 width=4)
- Group By Operator [GBY_332] (rows=25 width=4)
- Output:["_col0"],keys:KEY._col0
- <-Map 26 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_320]
+ Please refer to the previous Filter Operator [FIL_310]
+ <-Reducer 5 [CUSTOM_SIMPLE_EDGE]
+ PARTITION_ONLY_SHUFFLE [RS_108]
+ Merge Join Operator [MERGEJOIN_270] (rows=8989304 width=8)
+ Conds:(Inner),Output:["_col2","_col4","_col10"]
+ <-Reducer 31 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_338]
+ Select Operator [SEL_337] (rows=1 width=8)
+ Filter Operator [FIL_336] (rows=1 width=8)
+ predicate:(sq_count_check(_col0) <= 1)
+ Group By Operator [GBY_335] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count(VALUE._col0)"]
+ <-Reducer 30 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_334]
+ Group By Operator [GBY_333] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count()"]
+ Select Operator [SEL_332] (rows=25 width=4)
+ Group By Operator [GBY_331] (rows=25 width=4)
+ Output:["_col0"],keys:KEY._col0
+ <-Map 27 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_320]
+ PartitionCols:_col0
+ Group By Operator [GBY_316] (rows=25 width=4)
+ Output:["_col0"],keys:_col0
+ Select Operator [SEL_312] (rows=50 width=12)
+ Output:["_col0"]
+ Please refer to the previous Filter Operator [FIL_310]
+ <-Reducer 4 [CUSTOM_SIMPLE_EDGE]
+ PARTITION_ONLY_SHUFFLE [RS_105]
+ Merge Join Operator [MERGEJOIN_269] (rows=8989304 width=8)
+ Conds:(Inner),Output:["_col2","_col4","_col10"]
+ <-Reducer 29 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_330]
+ Select Operator [SEL_329] (rows=1 width=8)
+ Filter Operator [FIL_328] (rows=1 width=8)
+ predicate:(sq_count_check(_col0) <= 1)
+ Group By Operator [GBY_327] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count(VALUE._col0)"]
+ <-Reducer 28 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_326]
+ Group By Operator [GBY_325] (rows=1 width=8)
+ Output:["_col0"],aggregations:["count()"]
+ Select Operator [SEL_324] (rows=25 width=4)
+ Group By Operator [GBY_323] (rows=25 width=4)
+ Output:["_col0"],keys:KEY._col0
+ <-Map 27 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_319]
+ PartitionCols:_col0
+ Group By Operator [GBY_315] (rows=25 width=4)
+ Output:["_col0"],keys:_col0
+ Select Operator [SEL_311] (rows=50 width=12)
+ Output:["_col0"]
+ Please refer to the previous Filter Operator [FIL_310]
+ <-Reducer 3 [CUSTOM_SIMPLE_EDGE]
+ PARTITION_ONLY_SHUFFLE [RS_102]
+ Merge Join Operator [MERGEJOIN_268] (rows=8989304 width=8)
+ Conds:RS_99._col1=RS_100._col5(Inner),Output:["_col2","_col4","_col10"]
+ <-Reducer 14 [SIMPLE_EDGE]
+ SHUFFLE [RS_100]
+ PartitionCols:_col5
+ Merge Join Operator [MERGEJOIN_267] (rows=55046 width=4)
+ Conds:RS_46._col0=RS_309._col1(Inner),Output:["_col5"]
+ <-Reducer 13 [SIMPLE_EDGE]
+ SHUFFLE [RS_46]
PartitionCols:_col0
- Group By Operator [GBY_317] (rows=25 width=4)
- Output:["_col0"],keys:_col0
- Select Operator [SEL_314] (rows=50 width=12)
- Output:["_col0"]
- Please refer to the previous Filter Operator [FIL_311]
- <-Reducer 5 [CUSTOM_SIMPLE_EDGE]
- PARTITION_ONLY_SHUFFLE [RS_113]
- Select Operator [SEL_108] (rows=224732600 width=119)
- Output:["_col0","_col4","_col11","_col13"]
- Merge Join Operator [MERGEJOIN_272] (rows=224732600 width=119)
- Conds:(Left Outer),Output:["_col2","_col4","_col10","_col13"]
- <-Reducer 27 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_323]
- Group By Operator [GBY_321] (rows=25 width=4)
- Output:["_col0"],keys:KEY._col0
- <-Map 26 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_318]
- PartitionCols:_col0
- Group By Operator [GBY_315] (rows=25 width=4)
- Output:["_col0"],keys:_col0
- Select Operator [SEL_312] (rows=50 width=12)
- Output:["_col0"]
- Please refer to the previous Filter Operator [FIL_311]
- <-Reducer 4 [CUSTOM_SIMPLE_EDGE]
- PARTITION_ONLY_SHUFFLE [RS_105]
- Merge Join Operator [MERGEJOIN_271] (rows=8989304 width=8)
- Conds:(Inner),Output:["_col2","_col4","_col10"]
- <-Reducer 28 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_329]
- Select Operator [SEL_328] (rows=1 width=8)
- Filter Operator [FIL_327] (rows=1 width=8)
- predicate:(sq_count_check(_col0) <= 1)
- Group By Operator [GBY_326] (rows=1 width=8)
- Output:["_col0"],aggregations:["count(VALUE._col0)"]
- <-Reducer 27 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_325]
- Group By Operator [GBY_324] (rows=1 width=8)
- Output:["_col0"],aggregations:["count()"]
- Select Operator [SEL_322] (rows=25 width=4)
- Please refer to the previous Group By Operator [GBY_321]
- <-Reducer 3 [CUSTOM_SIMPLE_EDGE]
- PARTITION_ONLY_SHUFFLE [RS_102]
- Merge Join Operator [MERGEJOIN_269] (rows=8989304 width=8)
- Conds:RS_99._col1=RS_100._col5(Inner),Output:["_col2","_col4","_col10"]
- <-Reducer 13 [SIMPLE_EDGE]
- SHUFFLE [RS_100]
- PartitionCols:_col5
- Merge Join Operator [MERGEJOIN_268] (rows=55046 width=4)
- Conds:RS_69._col0=RS_310._col1(Inner),Output:["_col5"]
- <-Reducer 12 [SIMPLE_EDGE]
- SHUFFLE [RS_69]
- PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_264] (rows=39720279 width=4)
- Conds:RS_292._col1, _col2=RS_295._col0, _col1(Inner),Output:["_col0"]
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_292]
- PartitionCols:_col1, _col2
- Select Operator [SEL_291] (rows=40000000 width=188)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_290] (rows=40000000 width=188)
- predicate:(ca_address_sk is not null and ca_county is not null and ca_state is not null)
- TableScan [TS_29] (rows=40000000 width=188)
- default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_county","ca_state"]
- <-Map 14 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_295]
- PartitionCols:_col0, _col1
- Select Operator [SEL_294] (rows=1704 width=184)
- Output:["_col0","_col1"]
- Filter Operator [FIL_293] (rows=1704 width=184)
- predicate:(s_county is not null and s_state is not null)
- TableScan [TS_32] (rows=1704 width=184)
- default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_county","s_state"]
- <-Reducer 20 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_310]
- PartitionCols:_col1
- Select Operator [SEL_309] (rows=55046 width=8)
- Output:["_col0","_col1"]
- Group By Operator [GBY_308] (rows=55046 width=8)
- Output:["_col0","_col1"],keys:KEY._col0, KEY._col1
- <-Reducer 19 [SIMPLE_EDGE]
- SHUFFLE [RS_63]
+ Merge Join Operator [MERGEJOIN_263] (rows=39720279 width=4)
+ Conds:RS_291._col1, _col2=RS_294._col0, _col1(Inner),Output:["_col0"]
+ <-Map 12 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_291]
+ PartitionCols:_col1, _col2
+ Select Operator [SEL_290] (rows=40000000 width=188)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_289] (rows=40000000 width=188)
+ predicate:(ca_address_sk is not null and ca_county is not null and ca_state is not null)
+ TableScan [TS_6] (rows=40000000 width=188)
+ default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_county","ca_state"]
+ <-Map 15 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_294]
PartitionCols:_col0, _col1
- Group By Operator [GBY_62] (rows=55046 width=8)
- Output:["_col0","_col1"],keys:_col6, _col5
- Merge Join Operator [MERGEJOIN_267] (rows=110092 width=8)
- Conds:RS_58._col1=RS_307._col0(Inner),Output:["_col5","_col6"]
- <-Map 25 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_307]
- PartitionCols:_col0
- Select Operator [SEL_306] (rows=80000000 width=8)
- Output:["_col0","_col1"]
- Filter Operator [FIL_305] (rows=80000000 width=8)
- predicate:(c_current_addr_sk is not null and c_customer_sk is not null)
- TableScan [TS_49] (rows=80000000 width=8)
- default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_addr_sk"]
- <-Reducer 18 [SIMPLE_EDGE]
- SHUFFLE [RS_58]
- PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_266] (rows=110092 width=0)
- Conds:RS_55._col2=RS_304._col0(Inner),Output:["_col1"]
- <-Map 24 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_304]
- PartitionCols:_col0
- Select Operator [SEL_303] (rows=453 width=4)
- Output:["_col0"]
- Filter Operator [FIL_302] (rows=453 width=186)
- predicate:((i_category = 'Jewelry') and (i_class = 'consignment') and i_item_sk is not null)
- TableScan [TS_46] (rows=462000 width=186)
- default@item,item,Tbl:COMPLETE,Col:COMPLETE,Output:["i_item_sk","i_class","i_category"]
- <-Reducer 17 [SIMPLE_EDGE]
- SHUFFLE [RS_55]
- PartitionCols:_col2
- Merge Join Operator [MERGEJOIN_265] (rows=11665117 width=7)
- Conds:Union 16._col0=RS_298._col0(Inner),Output:["_col1","_col2"]
- <-Map 22 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_298]
- PartitionCols:_col0
- Select Operator [SEL_297] (rows=50 width=4)
- Output:["_col0"]
- Filter Operator [FIL_296] (rows=50 width=12)
- predicate:((d_moy = 3) and (d_year = 1999) and d_date_sk is not null)
- TableScan [TS_43] (rows=73049 width=12)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_moy"]
- <-Union 16 [SIMPLE_EDGE]
- <-Map 15 [CONTAINS] vectorized
- Reduce Output Operator [RS_355]
- PartitionCols:_col0
- Select Operator [SEL_354] (rows=285117831 width=11)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_353] (rows=285117831 width=11)
- predicate:((cs_sold_date_sk BETWEEN DynamicValue(RS_53_date_dim_d_date_sk_min) AND DynamicValue(RS_53_date_dim_d_date_sk_max) and in_bloom_filter(cs_sold_date_sk, DynamicValue(RS_53_date_dim_d_date_sk_bloom_filter))) and cs_bill_customer_sk is not null and cs_item_sk is not null and cs_sold_date_sk is not null)
- TableScan [TS_274] (rows=287989836 width=11)
- Output:["cs_sold_date_sk","cs_bill_customer_sk","cs_item_sk"]
- <-Reducer 23 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_351]
- Group By Operator [GBY_350] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 22 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_301]
- Group By Operator [GBY_300] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_299] (rows=50 width=4)
- Output:["_col0"]
- Please refer to the previous Select Operator [SEL_297]
- <-Map 21 [CONTAINS] vectorized
- Reduce Output Operator [RS_358]
+ Select Operator [SEL_293] (rows=1704 width=184)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_292] (rows=1704 width=184)
+ predicate:(s_county is not null and s_state is not null)
+ TableScan [TS_9] (rows=1704 width=184)
+ default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_county","s_state"]
+ <-Reducer 21 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_309]
+ PartitionCols:_col1
+ Select Operator [SEL_308] (rows=55046 width=8)
+ Output:["_col0","_col1"]
+ Group By Operator [GBY_307] (rows=55046 width=8)
+ Output:["_col0","_col1"],keys:KEY._col0, KEY._col1
+ <-Reducer 20 [SIMPLE_EDGE]
+ SHUFFLE [RS_40]
+ PartitionCols:_col0, _col1
+ Group By Operator [GBY_39] (rows=55046 width=8)
+ Output:["_col0","_col1"],keys:_col6, _col5
+ Merge Join Operator [MERGEJOIN_266] (rows=110092 width=8)
+ Conds:RS_35._col1=RS_306._col0(Inner),Output:["_col5","_col6"]
+ <-Map 26 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_306]
+ PartitionCols:_col0
+ Select Operator [SEL_305] (rows=80000000 width=8)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_304] (rows=80000000 width=8)
+ predicate:(c_current_addr_sk is not null and c_customer_sk is not null)
+ TableScan [TS_26] (rows=80000000 width=8)
+ default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_addr_sk"]
+ <-Reducer 19 [SIMPLE_EDGE]
+ SHUFFLE [RS_35]
+ PartitionCols:_col1
+ Merge Join Operator [MERGEJOIN_265] (rows=110092 width=0)
+ Conds:RS_32._col2=RS_303._col0(Inner),Output:["_col1"]
+ <-Map 25 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_303]
+ PartitionCols:_col0
+ Select Operator [SEL_302] (rows=453 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_301] (rows=453 width=186)
+ predicate:((i_category = 'Jewelry') and (i_class = 'consignment') and i_item_sk is not null)
+ TableScan [TS_23] (rows=462000 width=186)
+ default@item,item,Tbl:COMPLETE,Col:COMPLETE,Output:["i_item_sk","i_class","i_category"]
+ <-Reducer 18 [SIMPLE_EDGE]
+ SHUFFLE [RS_32]
+ PartitionCols:_col2
+ Merge Join Operator [MERGEJOIN_264] (rows=11665117 width=7)
+ Conds:Union 17._col0=RS_297._col0(Inner),Output:["_col1","_col2"]
+ <-Map 23 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_297]
PartitionCols:_col0
- Select Operator [SEL_357] (rows=143930993 width=11)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_356] (rows=143930993 width=11)
- predicate:((ws_sold_date_sk BETWEEN DynamicValue(RS_53_date_dim_d_date_sk_min) AND DynamicValue(RS_53_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_53_date_dim_d_date_sk_bloom_filter))) and ws_bill_customer_sk is not null and ws_item_sk is not null and ws_sold_date_sk is not null)
- TableScan [TS_279] (rows=144002668 width=11)
- Output:["ws_sold_date_sk","ws_item_sk","ws_bill_customer_sk"]
- <-Reducer 23 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_352]
- Please refer to the previous Group By Operator [GBY_350]
- <-Reducer 2 [SIMPLE_EDGE]
- SHUFFLE [RS_99]
- PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_263] (rows=525327388 width=114)
- Conds:RS_286._col0=RS_289._col0(Inner),Output:["_col1","_col2","_col4"]
- <-Map 1 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_286]
- PartitionCols:_col0
- Select Operator [SEL_285] (rows=525327388 width=114)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_284] (rows=525327388 width=114)
- predicate:(ss_customer_sk is not null and ss_sold_date_sk is not null)
- TableScan [TS_23] (rows=575995635 width=114)
- default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_customer_sk","ss_ext_sales_price"]
- <-Map 10 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_289]
- PartitionCols:_col0
- Select Operator [SEL_288] (rows=73049 width=8)
- Output:["_col0","_col1"]
- Filter Operator [FIL_287] (rows=73049 width=8)
- predicate:d_date_sk is not null
- TableScan [TS_26] (rows=73049 width=8)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_month_seq"]
+ Select Operator [SEL_296] (rows=50 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_295] (rows=50 width=12)
+ predicate:((d_moy = 3) and (d_year = 1999) and d_date_sk is not null)
+ TableScan [TS_20] (rows=73049 width=12)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_moy"]
+ <-Union 17 [SIMPLE_EDGE]
+ <-Map 16 [CONTAINS] vectorized
+ Reduce Output Operator [RS_358]
+ PartitionCols:_col0
+ Select Operator [SEL_357] (rows=285117831 width=11)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_356] (rows=285117831 width=11)
+ predicate:((cs_sold_date_sk BETWEEN DynamicValue(RS_30_date_dim_d_date_sk_min) AND DynamicValue(RS_30_date_dim_d_date_sk_max) and in_bloom_filter(cs_sold_date_sk, DynamicValue(RS_30_date_dim_d_date_sk_bloom_filter))) and cs_bill_customer_sk is not null and cs_item_sk is not null and cs_sold_date_sk is not null)
+ TableScan [TS_273] (rows=287989836 width=11)
+ Output:["cs_sold_date_sk","cs_bill_customer_sk","cs_item_sk"]
+ <-Reducer 24 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_354]
+ Group By Operator [GBY_353] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 23 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_300]
+ Group By Operator [GBY_299] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_298] (rows=50 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_296]
+ <-Map 22 [CONTAINS] vectorized
+ Reduce Output Operator [RS_361]
+ PartitionCols:_col0
+ Select Operator [SEL_360] (rows=143930993 width=11)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_359] (rows=143930993 width=11)
+ predicate:((ws_sold_date_sk BETWEEN DynamicValue(RS_30_date_dim_d_date_sk_min) AND DynamicValue(RS_30_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_30_date_dim_d_date_sk_bloom_filter))) and ws_bill_customer_sk is not null and ws_item_sk is not null and ws_sold_date_sk is not null)
+ TableScan [TS_278] (rows=144002668 width=11)
+ Output:["ws_sold_date_sk","ws_item_sk","ws_bill_customer_sk"]
+ <-Reducer 24 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_355]
+ Please refer to the previous Group By Operator [GBY_353]
+ <-Reducer 2 [SIMPLE_EDGE]
+ SHUFFLE [RS_99]
+ PartitionCols:_col1
+ Merge Join Operator [MERGEJOIN_262] (rows=525327388 width=114)
+ Conds:RS_285._col0=RS_288._col0(Inner),Output:["_col1","_col2","_col4"]
+ <-Map 1 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_285]
+ PartitionCols:_col0
+ Select Operator [SEL_284] (rows=525327388 width=114)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_283] (rows=525327388 width=114)
+ predicate:(ss_customer_sk is not null and ss_sold_date_sk is not null)
+ TableScan [TS_0] (rows=575995635 width=114)
+ default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_customer_sk","ss_ext_sales_price"]
+ <-Map 11 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_288]
+ PartitionCols:_col0
+ Select Operator [SEL_287] (rows=73049 width=8)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_286] (rows=73049 width=8)
+ predicate:d_date_sk is not null
+ TableScan [TS_3] (rows=73049 width=8)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_month_seq"]
diff --git a/ql/src/test/results/clientpositive/perf/tez/query64.q.out b/ql/src/test/results/clientpositive/perf/tez/query64.q.out
index 0010c469..14bc6f0 100644
--- a/ql/src/test/results/clientpositive/perf/tez/query64.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/query64.q.out
@@ -265,488 +265,518 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 34 <- Reducer 37 (BROADCAST_EDGE)
-Map 40 <- Reducer 37 (BROADCAST_EDGE)
-Map 50 <- Reducer 39 (BROADCAST_EDGE)
-Map 51 <- Reducer 39 (BROADCAST_EDGE)
-Reducer 10 <- Reducer 14 (SIMPLE_EDGE), Reducer 9 (SIMPLE_EDGE)
-Reducer 11 <- Reducer 10 (SIMPLE_EDGE)
-Reducer 12 <- Reducer 29 (SIMPLE_EDGE), Reducer 6 (SIMPLE_EDGE)
-Reducer 13 <- Map 49 (SIMPLE_EDGE), Reducer 12 (SIMPLE_EDGE)
-Reducer 14 <- Reducer 13 (SIMPLE_EDGE)
-Reducer 16 <- Map 15 (SIMPLE_EDGE), Reducer 35 (SIMPLE_EDGE)
-Reducer 17 <- Reducer 16 (SIMPLE_EDGE), Reducer 42 (ONE_TO_ONE_EDGE)
-Reducer 18 <- Map 46 (SIMPLE_EDGE), Reducer 17 (SIMPLE_EDGE)
-Reducer 19 <- Reducer 18 (SIMPLE_EDGE), Reducer 31 (SIMPLE_EDGE)
-Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 15 (SIMPLE_EDGE)
-Reducer 20 <- Map 47 (SIMPLE_EDGE), Reducer 19 (SIMPLE_EDGE)
-Reducer 21 <- Map 33 (SIMPLE_EDGE), Reducer 20 (SIMPLE_EDGE)
-Reducer 22 <- Map 48 (SIMPLE_EDGE), Reducer 21 (SIMPLE_EDGE)
-Reducer 23 <- Map 15 (SIMPLE_EDGE), Reducer 38 (SIMPLE_EDGE)
-Reducer 24 <- Reducer 23 (SIMPLE_EDGE), Reducer 45 (ONE_TO_ONE_EDGE)
-Reducer 25 <- Map 46 (SIMPLE_EDGE), Reducer 24 (SIMPLE_EDGE)
-Reducer 26 <- Reducer 25 (SIMPLE_EDGE), Reducer 31 (SIMPLE_EDGE)
-Reducer 27 <- Map 47 (SIMPLE_EDGE), Reducer 26 (SIMPLE_EDGE)
-Reducer 28 <- Map 33 (SIMPLE_EDGE), Reducer 27 (SIMPLE_EDGE)
-Reducer 29 <- Map 48 (SIMPLE_EDGE), Reducer 28 (SIMPLE_EDGE)
-Reducer 3 <- Map 15 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
-Reducer 31 <- Map 30 (SIMPLE_EDGE), Map 32 (SIMPLE_EDGE)
+Map 38 <- Reducer 25 (BROADCAST_EDGE)
+Map 44 <- Reducer 41 (BROADCAST_EDGE)
+Map 54 <- Reducer 33 (BROADCAST_EDGE)
+Map 55 <- Reducer 43 (BROADCAST_EDGE)
+Reducer 10 <- Reducer 9 (SIMPLE_EDGE)
+Reducer 11 <- Reducer 10 (SIMPLE_EDGE), Reducer 16 (SIMPLE_EDGE)
+Reducer 12 <- Reducer 11 (SIMPLE_EDGE)
+Reducer 13 <- Reducer 32 (SIMPLE_EDGE), Reducer 6 (SIMPLE_EDGE)
+Reducer 14 <- Map 53 (SIMPLE_EDGE), Reducer 13 (SIMPLE_EDGE)
+Reducer 15 <- Map 36 (SIMPLE_EDGE), Reducer 14 (SIMPLE_EDGE)
+Reducer 16 <- Reducer 15 (SIMPLE_EDGE)
+Reducer 18 <- Map 17 (SIMPLE_EDGE), Reducer 39 (SIMPLE_EDGE)
+Reducer 19 <- Reducer 18 (SIMPLE_EDGE), Reducer 46 (ONE_TO_ONE_EDGE)
+Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 17 (SIMPLE_EDGE)
+Reducer 20 <- Map 50 (SIMPLE_EDGE), Reducer 19 (SIMPLE_EDGE)
+Reducer 21 <- Map 34 (SIMPLE_EDGE), Reducer 20 (SIMPLE_EDGE)
+Reducer 22 <- Map 51 (SIMPLE_EDGE), Reducer 21 (SIMPLE_EDGE)
+Reducer 23 <- Map 37 (SIMPLE_EDGE), Reducer 22 (SIMPLE_EDGE)
+Reducer 24 <- Map 52 (SIMPLE_EDGE), Reducer 23 (SIMPLE_EDGE)
+Reducer 25 <- Map 17 (CUSTOM_SIMPLE_EDGE)
+Reducer 26 <- Map 17 (SIMPLE_EDGE), Reducer 42 (SIMPLE_EDGE)
+Reducer 27 <- Reducer 26 (SIMPLE_EDGE), Reducer 49 (ONE_TO_ONE_EDGE)
+Reducer 28 <- Map 50 (SIMPLE_EDGE), Reducer 27 (SIMPLE_EDGE)
+Reducer 29 <- Map 34 (SIMPLE_EDGE), Reducer 28 (SIMPLE_EDGE)
+Reducer 3 <- Map 17 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
+Reducer 30 <- Map 51 (SIMPLE_EDGE), Reducer 29 (SIMPLE_EDGE)
+Reducer 31 <- Map 37 (SIMPLE_EDGE), Reducer 30 (SIMPLE_EDGE)
+Reducer 32 <- Map 52 (SIMPLE_EDGE), Reducer 31 (SIMPLE_EDGE)
+Reducer 33 <- Map 17 (CUSTOM_SIMPLE_EDGE)
Reducer 35 <- Map 34 (SIMPLE_EDGE), Map 36 (SIMPLE_EDGE)
-Reducer 37 <- Map 36 (CUSTOM_SIMPLE_EDGE)
-Reducer 38 <- Map 36 (SIMPLE_EDGE), Map 50 (SIMPLE_EDGE)
-Reducer 39 <- Map 36 (CUSTOM_SIMPLE_EDGE)
-Reducer 4 <- Reducer 3 (SIMPLE_EDGE), Reducer 31 (SIMPLE_EDGE)
-Reducer 41 <- Map 40 (SIMPLE_EDGE), Map 43 (SIMPLE_EDGE)
-Reducer 42 <- Reducer 41 (SIMPLE_EDGE)
-Reducer 44 <- Map 43 (SIMPLE_EDGE), Map 51 (SIMPLE_EDGE)
-Reducer 45 <- Reducer 44 (SIMPLE_EDGE)
-Reducer 5 <- Map 33 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
-Reducer 6 <- Map 49 (SIMPLE_EDGE), Reducer 5 (SIMPLE_EDGE)
-Reducer 7 <- Reducer 22 (SIMPLE_EDGE), Reducer 6 (SIMPLE_EDGE)
-Reducer 8 <- Map 49 (SIMPLE_EDGE), Reducer 7 (SIMPLE_EDGE)
-Reducer 9 <- Reducer 8 (SIMPLE_EDGE)
+Reducer 39 <- Map 38 (SIMPLE_EDGE), Map 40 (SIMPLE_EDGE)
+Reducer 4 <- Reducer 3 (SIMPLE_EDGE), Reducer 35 (SIMPLE_EDGE)
+Reducer 41 <- Map 40 (CUSTOM_SIMPLE_EDGE)
+Reducer 42 <- Map 40 (SIMPLE_EDGE), Map 54 (SIMPLE_EDGE)
+Reducer 43 <- Map 40 (CUSTOM_SIMPLE_EDGE)
+Reducer 45 <- Map 44 (SIMPLE_EDGE), Map 47 (SIMPLE_EDGE)
+Reducer 46 <- Reducer 45 (SIMPLE_EDGE)
+Reducer 48 <- Map 47 (SIMPLE_EDGE), Map 55 (SIMPLE_EDGE)
+Reducer 49 <- Reducer 48 (SIMPLE_EDGE)
+Reducer 5 <- Map 37 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
+Reducer 6 <- Map 53 (SIMPLE_EDGE), Reducer 5 (SIMPLE_EDGE)
+Reducer 7 <- Reducer 24 (SIMPLE_EDGE), Reducer 6 (SIMPLE_EDGE)
+Reducer 8 <- Map 53 (SIMPLE_EDGE), Reducer 7 (SIMPLE_EDGE)
+Reducer 9 <- Map 36 (SIMPLE_EDGE), Reducer 8 (SIMPLE_EDGE)
Stage-0
Fetch Operator
limit:-1
Stage-1
- Reducer 11 vectorized
- File Output Operator [FS_1176]
- Select Operator [SEL_1175] (rows=2169965329 width=1702)
+ Reducer 12 vectorized
+ File Output Operator [FS_1208]
+ Select Operator [SEL_1207] (rows=98871277768 width=1702)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19","_col20"]
- <-Reducer 10 [SIMPLE_EDGE]
- SHUFFLE [RS_257]
- Select Operator [SEL_256] (rows=2169965329 width=1694)
+ <-Reducer 11 [SIMPLE_EDGE]
+ SHUFFLE [RS_255]
+ Select Operator [SEL_254] (rows=98871277768 width=1694)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18"]
- Filter Operator [FIL_255] (rows=2169965329 width=1694)
+ Filter Operator [FIL_253] (rows=98871277768 width=1694)
predicate:(_col19 <= _col12)
- Merge Join Operator [MERGEJOIN_1087] (rows=6509895988 width=1694)
- Conds:RS_1158._col2, _col1, _col3=RS_1174._col1, _col0, _col2(Inner),Output:["_col0","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col19","_col20","_col21","_col22"]
- <-Reducer 14 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1174]
- PartitionCols:_col1, _col0, _col2
- Select Operator [SEL_1173] (rows=2299138 width=525)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
- Group By Operator [GBY_1172] (rows=2299138 width=1362)
+ Merge Join Operator [MERGEJOIN_1107] (rows=296613833305 width=1694)
+ Conds:RS_1189._col2, _col1, _col3=RS_1206._col1, _col0, _col2(Inner),Output:["_col0","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col19","_col20","_col21","_col22"]
+ <-Reducer 10 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1189]
+ PartitionCols:_col2, _col1, _col3
+ Select Operator [SEL_1188] (rows=20709988 width=1354)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15"]
+ Group By Operator [GBY_1187] (rows=20709988 width=1362)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count(VALUE._col0)","sum(VALUE._col1)","sum(VALUE._col2)","sum(VALUE._col3)"],keys:KEY._col0, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5, KEY._col6, KEY._col7, KEY._col8, KEY._col9, KEY._col10, KEY._col11, KEY._col12, KEY._col13
- <-Reducer 13 [SIMPLE_EDGE]
- SHUFFLE [RS_249]
+ <-Reducer 9 [SIMPLE_EDGE]
+ SHUFFLE [RS_122]
PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13
- Group By Operator [GBY_248] (rows=2299138 width=1362)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count()","sum(_col43)","sum(_col44)","sum(_col45)"],keys:_col28, _col46, _col29, _col7, _col9, _col14, _col15, _col16, _col17, _col23, _col24, _col25, _col26, _col47
- Select Operator [SEL_247] (rows=2331650 width=1292)
- Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col23","_col24","_col25","_col26","_col28","_col29","_col43","_col44","_col45","_col46","_col47"]
- Filter Operator [FIL_246] (rows=2331650 width=1292)
- predicate:(_col51 <> _col19)
- Merge Join Operator [MERGEJOIN_1086] (rows=2331650 width=1292)
- Conds:RS_243._col37=RS_1116._col0(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col43","_col44","_col45","_col46","_col47","_col51"]
- <-Map 49 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1116]
- PartitionCols:_col0
- Select Operator [SEL_1113] (rows=1861800 width=89)
- Output:["_col0","_col1"]
- Filter Operator [FIL_1112] (rows=1861800 width=89)
- predicate:cd_demo_sk is not null
- TableScan [TS_96] (rows=1861800 width=89)
- default@customer_demographics,cd1,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status"]
- <-Reducer 12 [SIMPLE_EDGE]
- SHUFFLE [RS_243]
- PartitionCols:_col37
- Merge Join Operator [MERGEJOIN_1085] (rows=2299138 width=1205)
- Conds:RS_240._col0=RS_241._col16(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col37","_col43","_col44","_col45","_col46","_col47"]
- <-Reducer 6 [SIMPLE_EDGE]
- SHUFFLE [RS_240]
- PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_1056] (rows=70357394 width=458)
- Conds:RS_111._col1=RS_1115._col0(Inner),Output:["_col0","_col7","_col9","_col14","_col15","_col16","_col17","_col19"]
- <-Map 49 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1115]
- PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1113]
- <-Reducer 5 [SIMPLE_EDGE]
- SHUFFLE [RS_111]
- PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_1055] (rows=69376329 width=376)
- Conds:RS_108._col3=RS_1109._col0(Inner),Output:["_col0","_col1","_col7","_col9","_col14","_col15","_col16","_col17"]
- <-Map 33 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1109]
- PartitionCols:_col0
- Select Operator [SEL_1108] (rows=40000000 width=365)
- Output:["_col0","_col1","_col2","_col3","_col4"]
- Filter Operator [FIL_1107] (rows=40000000 width=365)
- predicate:ca_address_sk is not null
- TableScan [TS_19] (rows=40000000 width=365)
- default@customer_address,ad2,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_street_number","ca_street_name","ca_city","ca_zip"]
- <-Reducer 4 [SIMPLE_EDGE]
- SHUFFLE [RS_108]
- PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_1054] (rows=69376329 width=19)
- Conds:RS_105._col2=RS_106._col0(Inner),Output:["_col0","_col1","_col3","_col7","_col9"]
- <-Reducer 31 [SIMPLE_EDGE]
- SHUFFLE [RS_106]
- PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_1053] (rows=7200 width=4)
- Conds:RS_1103._col1=RS_1106._col0(Inner),Output:["_col0"]
- <-Map 30 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1103]
- PartitionCols:_col1
- Select Operator [SEL_1102] (rows=7200 width=8)
- Output:["_col0","_col1"]
- Filter Operator [FIL_1101] (rows=7200 width=8)
- predicate:(hd_demo_sk is not null and hd_income_band_sk is not null)
- TableScan [TS_9] (rows=7200 width=8)
- default@household_demographics,hd2,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_income_band_sk"]
- <-Map 32 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1106]
- PartitionCols:_col0
- Select Operator [SEL_1105] (rows=20 width=4)
- Output:["_col0"]
- Filter Operator [FIL_1104] (rows=20 width=4)
- predicate:ib_income_band_sk is not null
- TableScan [TS_12] (rows=20 width=4)
- default@income_band,ib2,Tbl:COMPLETE,Col:COMPLETE,Output:["ib_income_band_sk"]
- <-Reducer 3 [SIMPLE_EDGE]
- SHUFFLE [RS_105]
- PartitionCols:_col2
- Merge Join Operator [MERGEJOIN_1052] (rows=69376329 width=23)
- Conds:RS_102._col4=RS_1098._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col7","_col9"]
- <-Map 15 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1098]
- PartitionCols:_col0
- Select Operator [SEL_1094] (rows=73049 width=8)
- Output:["_col0","_col1"]
- Filter Operator [FIL_1091] (rows=73049 width=8)
- predicate:d_date_sk is not null
- TableScan [TS_3] (rows=73049 width=8)
- default@date_dim,d2,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
- <-Reducer 2 [SIMPLE_EDGE]
- SHUFFLE [RS_102]
- PartitionCols:_col4
- Merge Join Operator [MERGEJOIN_1051] (rows=69376329 width=23)
- Conds:RS_1090._col5=RS_1097._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col7"]
- <-Map 15 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1097]
- PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1094]
- <-Map 1 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1090]
- PartitionCols:_col5
- Select Operator [SEL_1089] (rows=69376329 width=23)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5"]
- Filter Operator [FIL_1088] (rows=69376329 width=23)
- predicate:(c_current_addr_sk is not null and c_current_cdemo_sk is not null and c_current_hdemo_sk is not null and c_customer_sk is not null and c_first_sales_date_sk is not null and c_first_shipto_date_sk is not null)
- TableScan [TS_0] (rows=80000000 width=23)
- default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_cdemo_sk","c_current_hdemo_sk","c_current_addr_sk","c_first_shipto_date_sk","c_first_sales_date_sk"]
- <-Reducer 29 [SIMPLE_EDGE]
- SHUFFLE [RS_241]
- PartitionCols:_col16
- Select Operator [SEL_221] (rows=2651207 width=784)
- Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col16","_col17","_col23","_col24","_col25","_col26","_col27"]
- Merge Join Operator [MERGEJOIN_1084] (rows=2651207 width=784)
- Conds:RS_218._col5, _col12=RS_1155._col0, _col1(Inner),Output:["_col6","_col7","_col13","_col14","_col15","_col16","_col17","_col21","_col22","_col24","_col25","_col26","_col27"]
- <-Map 48 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1155]
- PartitionCols:_col0, _col1
- Select Operator [SEL_1153] (rows=57591150 width=8)
- Output:["_col0","_col1"]
- Filter Operator [FIL_1152] (rows=57591150 width=8)
- predicate:(sr_item_sk is not null and sr_ticket_number is not null)
- TableScan [TS_77] (rows=57591150 width=8)
- default@store_returns,store_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["sr_item_sk","sr_ticket_number"]
- <-Reducer 28 [SIMPLE_EDGE]
- SHUFFLE [RS_218]
- PartitionCols:_col5, _col12
- Merge Join Operator [MERGEJOIN_1083] (rows=1608052 width=657)
- Conds:RS_215._col9=RS_1111._col0(Inner),Output:["_col5","_col6","_col7","_col12","_col13","_col14","_col15","_col16","_col17","_col21","_col22","_col24","_col25","_col26","_col27"]
- <-Map 33 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1111]
+ Group By Operator [GBY_121] (rows=20709988 width=1362)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count()","sum(_col42)","sum(_col43)","sum(_col44)"],keys:_col28, _col45, _col29, _col7, _col9, _col14, _col15, _col16, _col17, _col23, _col24, _col25, _col26, _col46
+ Merge Join Operator [MERGEJOIN_1088] (rows=21002852 width=1122)
+ Conds:RS_117._col31=RS_1135._col0(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col23","_col24","_col25","_col26","_col28","_col29","_col42","_col43","_col44","_col45","_col46"]
+ <-Map 36 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1135]
+ PartitionCols:_col0
+ Select Operator [SEL_1133] (rows=20 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_1132] (rows=20 width=4)
+ predicate:ib_income_band_sk is not null
+ TableScan [TS_12] (rows=20 width=4)
+ default@income_band,ib2,Tbl:COMPLETE,Col:COMPLETE,Output:["ib_income_band_sk"]
+ <-Reducer 8 [SIMPLE_EDGE]
+ SHUFFLE [RS_117]
+ PartitionCols:_col31
+ Filter Operator [FIL_116] (rows=21002852 width=1296)
+ predicate:(_col50 <> _col19)
+ Merge Join Operator [MERGEJOIN_1087] (rows=21002852 width=1296)
+ Conds:RS_113._col36=RS_1144._col0(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col31","_col42","_col43","_col44","_col45","_col46","_col50"]
+ <-Map 53 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1144]
+ PartitionCols:_col0
+ Select Operator [SEL_1143] (rows=1861800 width=89)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_1142] (rows=1861800 width=89)
+ predicate:cd_demo_sk is not null
+ TableScan [TS_89] (rows=1861800 width=89)
+ default@customer_demographics,cd1,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status"]
+ <-Reducer 7 [SIMPLE_EDGE]
+ SHUFFLE [RS_113]
+ PartitionCols:_col36
+ Merge Join Operator [MERGEJOIN_1086] (rows=20709988 width=1209)
+ Conds:RS_110._col0=RS_111._col15(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col31","_col36","_col42","_col43","_col44","_col45","_col46"]
+ <-Reducer 6 [SIMPLE_EDGE]
+ SHUFFLE [RS_110]
+ PartitionCols:_col0
+ Merge Join Operator [MERGEJOIN_1076] (rows=70357394 width=458)
+ Conds:RS_107._col1=RS_1145._col0(Inner),Output:["_col0","_col7","_col9","_col14","_col15","_col16","_col17","_col19"]
+ <-Map 53 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1145]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1143]
+ <-Reducer 5 [SIMPLE_EDGE]
+ SHUFFLE [RS_107]
+ PartitionCols:_col1
+ Merge Join Operator [MERGEJOIN_1075] (rows=69376329 width=376)
+ Conds:RS_104._col3=RS_1139._col0(Inner),Output:["_col0","_col1","_col7","_col9","_col14","_col15","_col16","_col17"]
+ <-Map 37 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1139]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1108]
- <-Reducer 27 [SIMPLE_EDGE]
- SHUFFLE [RS_215]
- PartitionCols:_col9
- Merge Join Operator [MERGEJOIN_1082] (rows=1608052 width=296)
- Conds:RS_212._col10=RS_1151._col0(Inner),Output:["_col5","_col6","_col7","_col9","_col12","_col13","_col14","_col15","_col16","_col17","_col21","_col22"]
- <-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1151]
+ Select Operator [SEL_1138] (rows=40000000 width=365)
+ Output:["_col0","_col1","_col2","_col3","_col4"]
+ Filter Operator [FIL_1137] (rows=40000000 width=365)
+ predicate:ca_address_sk is not null
+ TableScan [TS_19] (rows=40000000 width=365)
+ default@customer_address,ad2,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_street_number","ca_street_name","ca_city","ca_zip"]
+ <-Reducer 4 [SIMPLE_EDGE]
+ SHUFFLE [RS_104]
+ PartitionCols:_col3
+ Merge Join Operator [MERGEJOIN_1074] (rows=69376329 width=19)
+ Conds:RS_101._col2=RS_102._col0(Inner),Output:["_col0","_col1","_col3","_col7","_col9"]
+ <-Reducer 3 [SIMPLE_EDGE]
+ SHUFFLE [RS_101]
+ PartitionCols:_col2
+ Merge Join Operator [MERGEJOIN_1072] (rows=69376329 width=23)
+ Conds:RS_98._col4=RS_1118._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col7","_col9"]
+ <-Map 17 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1118]
+ PartitionCols:_col0
+ Select Operator [SEL_1114] (rows=73049 width=8)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_1111] (rows=73049 width=8)
+ predicate:d_date_sk is not null
+ TableScan [TS_3] (rows=73049 width=8)
+ default@date_dim,d2,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
+ <-Reducer 2 [SIMPLE_EDGE]
+ SHUFFLE [RS_98]
+ PartitionCols:_col4
+ Merge Join Operator [MERGEJOIN_1071] (rows=69376329 width=23)
+ Conds:RS_1110._col5=RS_1117._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col7"]
+ <-Map 17 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1117]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1114]
+ <-Map 1 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1110]
+ PartitionCols:_col5
+ Select Operator [SEL_1109] (rows=69376329 width=23)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5"]
+ Filter Operator [FIL_1108] (rows=69376329 width=23)
+ predicate:(c_current_addr_sk is not null and c_current_cdemo_sk is not null and c_current_hdemo_sk is not null and c_customer_sk is not null and c_first_sales_date_sk is not null and c_first_shipto_date_sk is not null)
+ TableScan [TS_0] (rows=80000000 width=23)
+ default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_cdemo_sk","c_current_hdemo_sk","c_current_addr_sk","c_first_shipto_date_sk","c_first_sales_date_sk"]
+ <-Reducer 35 [SIMPLE_EDGE]
+ SHUFFLE [RS_102]
PartitionCols:_col0
- Select Operator [SEL_1149] (rows=1704 width=181)
- Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_1148] (rows=1704 width=181)
- predicate:(s_store_name is not null and s_store_sk is not null and s_zip is not null)
- TableScan [TS_71] (rows=1704 width=181)
- default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk","s_store_name","s_zip"]
- <-Reducer 26 [SIMPLE_EDGE]
- SHUFFLE [RS_212]
- PartitionCols:_col10
- Merge Join Operator [MERGEJOIN_1081] (rows=1608052 width=119)
- Conds:RS_209._col0=RS_210._col5(Inner),Output:["_col5","_col6","_col7","_col9","_col10","_col12","_col13","_col14","_col15","_col16","_col17"]
- <-Reducer 31 [SIMPLE_EDGE]
- SHUFFLE [RS_209]
+ Merge Join Operator [MERGEJOIN_1073] (rows=7200 width=4)
+ Conds:RS_1129._col1=RS_1134._col0(Inner),Output:["_col0"]
+ <-Map 34 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1129]
+ PartitionCols:_col1
+ Select Operator [SEL_1128] (rows=7200 width=8)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_1127] (rows=7200 width=8)
+ predicate:(hd_demo_sk is not null and hd_income_band_sk is not null)
+ TableScan [TS_9] (rows=7200 width=8)
+ default@household_demographics,hd2,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_income_band_sk"]
+ <-Map 36 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1134]
PartitionCols:_col0
- Please refer to the previous Merge Join Operator [MERGEJOIN_1053]
- <-Reducer 25 [SIMPLE_EDGE]
- SHUFFLE [RS_210]
- PartitionCols:_col5
- Select Operator [SEL_196] (rows=1608052 width=119)
- Output:["_col2","_col3","_col4","_col5","_col6","_col7","_col9","_col10","_col11","_col12","_col13","_col14"]
- Merge Join Operator [MERGEJOIN_1080] (rows=1608052 width=119)
- Conds:RS_193._col7=RS_1147._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Map 46 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1147]
+ Please refer to the previous Select Operator [SEL_1133]
+ <-Reducer 24 [SIMPLE_EDGE]
+ SHUFFLE [RS_111]
+ PartitionCols:_col15
+ Select Operator [SEL_88] (rows=23881330 width=788)
+ Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col11","_col15","_col16","_col22","_col23","_col24","_col25","_col26"]
+ Merge Join Operator [MERGEJOIN_1085] (rows=23881330 width=788)
+ Conds:RS_85._col1, _col8=RS_1185._col0, _col1(Inner),Output:["_col2","_col3","_col9","_col10","_col11","_col12","_col13","_col18","_col20","_col21","_col23","_col24","_col25","_col26"]
+ <-Map 52 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1185]
+ PartitionCols:_col0, _col1
+ Select Operator [SEL_1184] (rows=57591150 width=8)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_1183] (rows=57591150 width=8)
+ predicate:(sr_item_sk is not null and sr_ticket_number is not null)
+ TableScan [TS_61] (rows=57591150 width=8)
+ default@store_returns,store_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["sr_item_sk","sr_ticket_number"]
+ <-Reducer 23 [SIMPLE_EDGE]
+ SHUFFLE [RS_85]
+ PartitionCols:_col1, _col8
+ Merge Join Operator [MERGEJOIN_1084] (rows=14484878 width=661)
+ Conds:RS_82._col5=RS_1140._col0(Inner),Output:["_col1","_col2","_col3","_col8","_col9","_col10","_col11","_col12","_col13","_col18","_col20","_col21","_col23","_col24","_col25","_col26"]
+ <-Map 37 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1140]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1138]
+ <-Reducer 22 [SIMPLE_EDGE]
+ SHUFFLE [RS_82]
+ PartitionCols:_col5
+ Merge Join Operator [MERGEJOIN_1083] (rows=14484878 width=300)
+ Conds:RS_79._col6=RS_1181._col0(Inner),Output:["_col1","_col2","_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col18","_col20","_col21"]
+ <-Map 51 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1181]
+ PartitionCols:_col0
+ Select Operator [SEL_1180] (rows=1704 width=181)
+ Output:["_col0","_col1","_col2"]
+ Filter Operator [FIL_1179] (rows=1704 width=181)
+ predicate:(s_store_name is not null and s_store_sk is not null and s_zip is not null)
+ TableScan [TS_55] (rows=1704 width=181)
+ default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk","s_store_name","s_zip"]
+ <-Reducer 21 [SIMPLE_EDGE]
+ SHUFFLE [RS_79]
+ PartitionCols:_col6
+ Merge Join Operator [MERGEJOIN_1082] (rows=14484878 width=123)
+ Conds:RS_76._col4=RS_1130._col0(Inner),Output:["_col1","_col2","_col3","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col13","_col18"]
+ <-Map 34 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1130]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1128]
+ <-Reducer 20 [SIMPLE_EDGE]
+ SHUFFLE [RS_76]
+ PartitionCols:_col4
+ Merge Join Operator [MERGEJOIN_1081] (rows=14484878 width=119)
+ Conds:RS_73._col7=RS_1177._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Map 50 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1177]
PartitionCols:_col0
- Select Operator [SEL_1145] (rows=2300 width=4)
+ Select Operator [SEL_1176] (rows=2300 width=4)
Output:["_col0"]
- Filter Operator [FIL_1144] (rows=2300 width=4)
+ Filter Operator [FIL_1175] (rows=2300 width=4)
predicate:p_promo_sk is not null
- TableScan [TS_55] (rows=2300 width=4)
+ TableScan [TS_49] (rows=2300 width=4)
default@promotion,promotion,Tbl:COMPLETE,Col:COMPLETE,Output:["p_promo_sk"]
- <-Reducer 24 [SIMPLE_EDGE]
- SHUFFLE [RS_193]
+ <-Reducer 19 [SIMPLE_EDGE]
+ SHUFFLE [RS_73]
PartitionCols:_col7
- Merge Join Operator [MERGEJOIN_1079] (rows=1608052 width=119)
- Conds:RS_190._col1=RS_1171._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Reducer 23 [SIMPLE_EDGE]
- SHUFFLE [RS_190]
+ Merge Join Operator [MERGEJOIN_1080] (rows=14484878 width=119)
+ Conds:RS_70._col1=RS_1174._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Reducer 18 [SIMPLE_EDGE]
+ SHUFFLE [RS_70]
PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_1077] (rows=1608052 width=119)
- Conds:RS_187._col0=RS_1100._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Map 15 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1100]
+ Merge Join Operator [MERGEJOIN_1078] (rows=14484878 width=119)
+ Conds:RS_67._col0=RS_1119._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Map 17 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1119]
PartitionCols:_col0
- Select Operator [SEL_1096] (rows=652 width=4)
+ Select Operator [SEL_1115] (rows=652 width=4)
Output:["_col0"]
- Filter Operator [FIL_1093] (rows=652 width=8)
- predicate:((d_year = 2001) and d_date_sk is not null)
+ Filter Operator [FIL_1112] (rows=652 width=8)
+ predicate:((d_year = 2000) and d_date_sk is not null)
Please refer to the previous TableScan [TS_3]
- <-Reducer 38 [SIMPLE_EDGE]
- SHUFFLE [RS_187]
+ <-Reducer 39 [SIMPLE_EDGE]
+ SHUFFLE [RS_67]
PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_1076] (rows=4503592 width=119)
- Conds:RS_1164._col1=RS_1121._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Map 36 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_1121]
+ Merge Join Operator [MERGEJOIN_1077] (rows=40567099 width=314)
+ Conds:RS_1151._col1=RS_1154._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Map 40 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1154]
PartitionCols:_col0
- Select Operator [SEL_1118] (rows=518 width=111)
+ Select Operator [SEL_1153] (rows=4666 width=111)
Output:["_col0","_col1"]
- Filter Operator [FIL_1117] (rows=518 width=312)
- predicate:((i_color) IN ('maroon', 'burnished', 'dim', 'steel', 'navajo', 'chocolate') and i_current_price BETWEEN 35 AND 45 and i_current_price BETWEEN 36 AND 50 and i_item_sk is not null)
- TableScan [TS_34] (rows=462000 width=311)
+ Filter Operator [FIL_1152] (rows=4666 width=311)
+ predicate:((i_color) IN ('maroon', 'burnished', 'dim', 'steel', 'navajo', 'chocolate') and i_current_price BETWEEN 36 AND 45 and i_item_sk is not null)
+ TableScan [TS_28] (rows=462000 width=311)
default@item,item,Tbl:COMPLETE,Col:COMPLETE,Output:["i_item_sk","i_current_price","i_color","i_product_name"]
- <-Map 50 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1164]
+ <-Map 38 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1151]
PartitionCols:_col1
- Select Operator [SEL_1163] (rows=417313408 width=355)
+ Select Operator [SEL_1150] (rows=417313408 width=355)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11"]
- Filter Operator [FIL_1162] (rows=417313408 width=355)
- predicate:((ss_item_sk BETWEEN DynamicValue(RS_185_item_i_item_sk_min) AND DynamicValue(RS_185_item_i_item_sk_max) and in_bloom_filter(ss_item_sk, DynamicValue(RS_185_item_i_item_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_item_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_store_ [...]
- TableScan [TS_157] (rows=575995635 width=355)
+ Filter Operator [FIL_1149] (rows=417313408 width=355)
+ predicate:((ss_sold_date_sk BETWEEN DynamicValue(RS_68_d1_d_date_sk_min) AND DynamicValue(RS_68_d1_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_68_d1_d_date_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_item_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_store [...]
+ TableScan [TS_25] (rows=575995635 width=355)
default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_item_sk","ss_customer_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_promo_sk","ss_ticket_number","ss_wholesale_cost","ss_list_price","ss_coupon_amt"]
- <-Reducer 39 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_1160]
- Group By Operator [GBY_1159] (rows=1 width=12)
+ <-Reducer 25 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_1148]
+ Group By Operator [GBY_1147] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 36 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_1126]
- Group By Operator [GBY_1124] (rows=1 width=12)
+ <-Map 17 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1125]
+ Group By Operator [GBY_1123] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_1122] (rows=518 width=4)
+ Select Operator [SEL_1120] (rows=652 width=4)
Output:["_col0"]
- Please refer to the previous Select Operator [SEL_1118]
- <-Reducer 45 [ONE_TO_ONE_EDGE] vectorized
- FORWARD [RS_1171]
+ Please refer to the previous Select Operator [SEL_1115]
+ <-Reducer 46 [ONE_TO_ONE_EDGE] vectorized
+ FORWARD [RS_1174]
PartitionCols:_col0
- Select Operator [SEL_1170] (rows=13257 width=4)
+ Select Operator [SEL_1173] (rows=13257 width=4)
Output:["_col0"]
- Filter Operator [FIL_1169] (rows=13257 width=228)
+ Filter Operator [FIL_1172] (rows=13257 width=228)
predicate:(_col1 > (2 * _col2))
- Group By Operator [GBY_1168] (rows=39773 width=228)
+ Group By Operator [GBY_1171] (rows=39773 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)"],keys:KEY._col0
- <-Reducer 44 [SIMPLE_EDGE]
- SHUFFLE [RS_177]
+ <-Reducer 45 [SIMPLE_EDGE]
+ SHUFFLE [RS_45]
PartitionCols:_col0
- Group By Operator [GBY_176] (rows=6482999 width=228)
+ Group By Operator [GBY_44] (rows=6482999 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(_col2)","sum(_col5)"],keys:_col0
- Merge Join Operator [MERGEJOIN_1078] (rows=183085709 width=227)
- Conds:RS_1167._col0, _col1=RS_1139._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
- <-Map 43 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1139]
+ Merge Join Operator [MERGEJOIN_1079] (rows=183085709 width=227)
+ Conds:RS_1166._col0, _col1=RS_1169._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
+ <-Map 47 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1169]
PartitionCols:_col0, _col1
- Select Operator [SEL_1137] (rows=28798881 width=120)
+ Select Operator [SEL_1168] (rows=28798881 width=120)
Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_1136] (rows=28798881 width=337)
+ Filter Operator [FIL_1167] (rows=28798881 width=337)
predicate:(cr_item_sk is not null and cr_order_number is not null)
- TableScan [TS_43] (rows=28798881 width=337)
+ TableScan [TS_37] (rows=28798881 width=337)
default@catalog_returns,catalog_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["cr_item_sk","cr_order_number","cr_refunded_cash","cr_reversed_charge","cr_store_credit"]
- <-Map 51 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1167]
+ <-Map 44 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1166]
PartitionCols:_col0, _col1
- Select Operator [SEL_1166] (rows=287989836 width=119)
+ Select Operator [SEL_1165] (rows=287989836 width=119)
Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_1165] (rows=287989836 width=119)
- predicate:((cs_item_sk BETWEEN DynamicValue(RS_185_item_i_item_sk_min) AND DynamicValue(RS_185_item_i_item_sk_max) and in_bloom_filter(cs_item_sk, DynamicValue(RS_185_item_i_item_sk_bloom_filter))) and cs_item_sk is not null and cs_order_number is not null)
- TableScan [TS_166] (rows=287989836 width=119)
+ Filter Operator [FIL_1164] (rows=287989836 width=119)
+ predicate:((cs_item_sk BETWEEN DynamicValue(RS_65_item_i_item_sk_min) AND DynamicValue(RS_65_item_i_item_sk_max) and in_bloom_filter(cs_item_sk, DynamicValue(RS_65_item_i_item_sk_bloom_filter))) and cs_item_sk is not null and cs_order_number is not null)
+ TableScan [TS_34] (rows=287989836 width=119)
default@catalog_sales,catalog_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["cs_item_sk","cs_order_number","cs_ext_list_price"]
- <-Reducer 39 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_1161]
- Please refer to the previous Group By Operator [GBY_1159]
- <-Reducer 9 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1158]
- PartitionCols:_col2, _col1, _col3
- Select Operator [SEL_1157] (rows=2299138 width=1354)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15"]
- Group By Operator [GBY_1156] (rows=2299138 width=1362)
+ <-Reducer 41 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_1163]
+ Group By Operator [GBY_1162] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 40 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1160]
+ Group By Operator [GBY_1158] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_1155] (rows=4666 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_1153]
+ <-Reducer 16 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1206]
+ PartitionCols:_col1, _col0, _col2
+ Select Operator [SEL_1205] (rows=20709988 width=525)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"]
+ Group By Operator [GBY_1204] (rows=20709988 width=1362)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count(VALUE._col0)","sum(VALUE._col1)","sum(VALUE._col2)","sum(VALUE._col3)"],keys:KEY._col0, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5, KEY._col6, KEY._col7, KEY._col8, KEY._col9, KEY._col10, KEY._col11, KEY._col12, KEY._col13
- <-Reducer 8 [SIMPLE_EDGE]
- SHUFFLE [RS_123]
+ <-Reducer 15 [SIMPLE_EDGE]
+ SHUFFLE [RS_247]
PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13
- Group By Operator [GBY_122] (rows=2299138 width=1362)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count()","sum(_col43)","sum(_col44)","sum(_col45)"],keys:_col28, _col46, _col29, _col7, _col9, _col14, _col15, _col16, _col17, _col23, _col24, _col25, _col26, _col47
- Select Operator [SEL_121] (rows=2331650 width=1292)
- Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col23","_col24","_col25","_col26","_col28","_col29","_col43","_col44","_col45","_col46","_col47"]
- Filter Operator [FIL_120] (rows=2331650 width=1292)
- predicate:(_col51 <> _col19)
- Merge Join Operator [MERGEJOIN_1068] (rows=2331650 width=1292)
- Conds:RS_117._col37=RS_1114._col0(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col43","_col44","_col45","_col46","_col47","_col51"]
- <-Map 49 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1114]
- PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1113]
- <-Reducer 7 [SIMPLE_EDGE]
- SHUFFLE [RS_117]
- PartitionCols:_col37
- Merge Join Operator [MERGEJOIN_1067] (rows=2299138 width=1205)
- Conds:RS_114._col0=RS_115._col16(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col37","_col43","_col44","_col45","_col46","_col47"]
- <-Reducer 6 [SIMPLE_EDGE]
- SHUFFLE [RS_114]
- PartitionCols:_col0
- Please refer to the previous Merge Join Operator [MERGEJOIN_1056]
- <-Reducer 22 [SIMPLE_EDGE]
- SHUFFLE [RS_115]
- PartitionCols:_col16
- Select Operator [SEL_95] (rows=2651207 width=784)
- Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col16","_col17","_col23","_col24","_col25","_col26","_col27"]
- Merge Join Operator [MERGEJOIN_1066] (rows=2651207 width=784)
- Conds:RS_92._col5, _col12=RS_1154._col0, _col1(Inner),Output:["_col6","_col7","_col13","_col14","_col15","_col16","_col17","_col21","_col22","_col24","_col25","_col26","_col27"]
- <-Map 48 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1154]
- PartitionCols:_col0, _col1
- Please refer to the previous Select Operator [SEL_1153]
- <-Reducer 21 [SIMPLE_EDGE]
- SHUFFLE [RS_92]
- PartitionCols:_col5, _col12
- Merge Join Operator [MERGEJOIN_1065] (rows=1608052 width=657)
- Conds:RS_89._col9=RS_1110._col0(Inner),Output:["_col5","_col6","_col7","_col12","_col13","_col14","_col15","_col16","_col17","_col21","_col22","_col24","_col25","_col26","_col27"]
- <-Map 33 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1110]
- PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1108]
- <-Reducer 20 [SIMPLE_EDGE]
- SHUFFLE [RS_89]
- PartitionCols:_col9
- Merge Join Operator [MERGEJOIN_1064] (rows=1608052 width=296)
- Conds:RS_86._col10=RS_1150._col0(Inner),Output:["_col5","_col6","_col7","_col9","_col12","_col13","_col14","_col15","_col16","_col17","_col21","_col22"]
- <-Map 47 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1150]
- PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1149]
- <-Reducer 19 [SIMPLE_EDGE]
- SHUFFLE [RS_86]
- PartitionCols:_col10
- Merge Join Operator [MERGEJOIN_1063] (rows=1608052 width=119)
- Conds:RS_83._col0=RS_84._col5(Inner),Output:["_col5","_col6","_col7","_col9","_col10","_col12","_col13","_col14","_col15","_col16","_col17"]
- <-Reducer 31 [SIMPLE_EDGE]
- SHUFFLE [RS_83]
- PartitionCols:_col0
- Please refer to the previous Merge Join Operator [MERGEJOIN_1053]
- <-Reducer 18 [SIMPLE_EDGE]
- SHUFFLE [RS_84]
- PartitionCols:_col5
- Select Operator [SEL_70] (rows=1608052 width=119)
- Output:["_col2","_col3","_col4","_col5","_col6","_col7","_col9","_col10","_col11","_col12","_col13","_col14"]
- Merge Join Operator [MERGEJOIN_1062] (rows=1608052 width=119)
- Conds:RS_67._col7=RS_1146._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Map 46 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1146]
+ Group By Operator [GBY_246] (rows=20709988 width=1362)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17"],aggregations:["count()","sum(_col42)","sum(_col43)","sum(_col44)"],keys:_col28, _col45, _col29, _col7, _col9, _col14, _col15, _col16, _col17, _col23, _col24, _col25, _col26, _col46
+ Merge Join Operator [MERGEJOIN_1106] (rows=21002852 width=1122)
+ Conds:RS_242._col31=RS_1136._col0(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col23","_col24","_col25","_col26","_col28","_col29","_col42","_col43","_col44","_col45","_col46"]
+ <-Map 36 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1136]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1133]
+ <-Reducer 14 [SIMPLE_EDGE]
+ SHUFFLE [RS_242]
+ PartitionCols:_col31
+ Filter Operator [FIL_241] (rows=21002852 width=1296)
+ predicate:(_col50 <> _col19)
+ Merge Join Operator [MERGEJOIN_1105] (rows=21002852 width=1296)
+ Conds:RS_238._col36=RS_1146._col0(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col31","_col42","_col43","_col44","_col45","_col46","_col50"]
+ <-Map 53 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1146]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1143]
+ <-Reducer 13 [SIMPLE_EDGE]
+ SHUFFLE [RS_238]
+ PartitionCols:_col36
+ Merge Join Operator [MERGEJOIN_1104] (rows=20709988 width=1209)
+ Conds:RS_235._col0=RS_236._col15(Inner),Output:["_col7","_col9","_col14","_col15","_col16","_col17","_col19","_col23","_col24","_col25","_col26","_col28","_col29","_col31","_col36","_col42","_col43","_col44","_col45","_col46"]
+ <-Reducer 6 [SIMPLE_EDGE]
+ SHUFFLE [RS_235]
+ PartitionCols:_col0
+ Please refer to the previous Merge Join Operator [MERGEJOIN_1076]
+ <-Reducer 32 [SIMPLE_EDGE]
+ SHUFFLE [RS_236]
+ PartitionCols:_col15
+ Select Operator [SEL_213] (rows=23881330 width=788)
+ Output:["_col3","_col4","_col5","_col6","_col8","_col9","_col11","_col15","_col16","_col22","_col23","_col24","_col25","_col26"]
+ Merge Join Operator [MERGEJOIN_1103] (rows=23881330 width=788)
+ Conds:RS_210._col1, _col8=RS_1186._col0, _col1(Inner),Output:["_col2","_col3","_col9","_col10","_col11","_col12","_col13","_col18","_col20","_col21","_col23","_col24","_col25","_col26"]
+ <-Map 52 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1186]
+ PartitionCols:_col0, _col1
+ Please refer to the previous Select Operator [SEL_1184]
+ <-Reducer 31 [SIMPLE_EDGE]
+ SHUFFLE [RS_210]
+ PartitionCols:_col1, _col8
+ Merge Join Operator [MERGEJOIN_1102] (rows=14484878 width=661)
+ Conds:RS_207._col5=RS_1141._col0(Inner),Output:["_col1","_col2","_col3","_col8","_col9","_col10","_col11","_col12","_col13","_col18","_col20","_col21","_col23","_col24","_col25","_col26"]
+ <-Map 37 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1141]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1138]
+ <-Reducer 30 [SIMPLE_EDGE]
+ SHUFFLE [RS_207]
+ PartitionCols:_col5
+ Merge Join Operator [MERGEJOIN_1101] (rows=14484878 width=300)
+ Conds:RS_204._col6=RS_1182._col0(Inner),Output:["_col1","_col2","_col3","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col18","_col20","_col21"]
+ <-Map 51 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1182]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1180]
+ <-Reducer 29 [SIMPLE_EDGE]
+ SHUFFLE [RS_204]
+ PartitionCols:_col6
+ Merge Join Operator [MERGEJOIN_1100] (rows=14484878 width=123)
+ Conds:RS_201._col4=RS_1131._col0(Inner),Output:["_col1","_col2","_col3","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col13","_col18"]
+ <-Map 34 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1131]
+ PartitionCols:_col0
+ Please refer to the previous Select Operator [SEL_1128]
+ <-Reducer 28 [SIMPLE_EDGE]
+ SHUFFLE [RS_201]
+ PartitionCols:_col4
+ Merge Join Operator [MERGEJOIN_1099] (rows=14484878 width=119)
+ Conds:RS_198._col7=RS_1178._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Map 50 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1178]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1145]
- <-Reducer 17 [SIMPLE_EDGE]
- SHUFFLE [RS_67]
+ Please refer to the previous Select Operator [SEL_1176]
+ <-Reducer 27 [SIMPLE_EDGE]
+ SHUFFLE [RS_198]
PartitionCols:_col7
- Merge Join Operator [MERGEJOIN_1061] (rows=1608052 width=119)
- Conds:RS_64._col1=RS_1143._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Reducer 16 [SIMPLE_EDGE]
- SHUFFLE [RS_64]
+ Merge Join Operator [MERGEJOIN_1098] (rows=14484878 width=119)
+ Conds:RS_195._col1=RS_1203._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Reducer 26 [SIMPLE_EDGE]
+ SHUFFLE [RS_195]
PartitionCols:_col1
- Merge Join Operator [MERGEJOIN_1059] (rows=1608052 width=119)
- Conds:RS_61._col0=RS_1099._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Map 15 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1099]
+ Merge Join Operator [MERGEJOIN_1096] (rows=14484878 width=119)
+ Conds:RS_192._col0=RS_1121._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Map 17 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1121]
PartitionCols:_col0
- Select Operator [SEL_1095] (rows=652 width=4)
+ Select Operator [SEL_1116] (rows=652 width=4)
Output:["_col0"]
- Filter Operator [FIL_1092] (rows=652 width=8)
- predicate:((d_year = 2000) and d_date_sk is not null)
+ Filter Operator [FIL_1113] (rows=652 width=8)
+ predicate:((d_year = 2001) and d_date_sk is not null)
Please refer to the previous TableScan [TS_3]
- <-Reducer 35 [SIMPLE_EDGE]
- SHUFFLE [RS_61]
+ <-Reducer 42 [SIMPLE_EDGE]
+ SHUFFLE [RS_192]
PartitionCols:_col0
- Merge Join Operator [MERGEJOIN_1058] (rows=4503592 width=119)
- Conds:RS_1132._col1=RS_1119._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
- <-Map 36 [SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_1119]
+ Merge Join Operator [MERGEJOIN_1095] (rows=40567099 width=314)
+ Conds:RS_1194._col1=RS_1156._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12","_col13"]
+ <-Map 40 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1156]
PartitionCols:_col0
- Please refer to the previous Select Operator [SEL_1118]
- <-Map 34 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1132]
+ Please refer to the previous Select Operator [SEL_1153]
+ <-Map 54 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1194]
PartitionCols:_col1
- Select Operator [SEL_1131] (rows=417313408 width=355)
+ Select Operator [SEL_1193] (rows=417313408 width=355)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11"]
- Filter Operator [FIL_1130] (rows=417313408 width=355)
- predicate:((ss_item_sk BETWEEN DynamicValue(RS_59_item_i_item_sk_min) AND DynamicValue(RS_59_item_i_item_sk_max) and in_bloom_filter(ss_item_sk, DynamicValue(RS_59_item_i_item_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_item_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_store_sk [...]
- TableScan [TS_31] (rows=575995635 width=355)
+ Filter Operator [FIL_1192] (rows=417313408 width=355)
+ predicate:((ss_sold_date_sk BETWEEN DynamicValue(RS_193_d1_d_date_sk_min) AND DynamicValue(RS_193_d1_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_193_d1_d_date_sk_bloom_filter))) and ss_addr_sk is not null and ss_cdemo_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_item_sk is not null and ss_promo_sk is not null and ss_sold_date_sk is not null and ss_st [...]
+ TableScan [TS_150] (rows=575995635 width=355)
default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_item_sk","ss_customer_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_promo_sk","ss_ticket_number","ss_wholesale_cost","ss_list_price","ss_coupon_amt"]
- <-Reducer 37 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_1128]
- Group By Operator [GBY_1127] (rows=1 width=12)
+ <-Reducer 33 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_1191]
+ Group By Operator [GBY_1190] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 36 [CUSTOM_SIMPLE_EDGE] vectorized
- PARTITION_ONLY_SHUFFLE [RS_1125]
- Group By Operator [GBY_1123] (rows=1 width=12)
+ <-Map 17 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1126]
+ Group By Operator [GBY_1124] (rows=1 width=12)
Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_1120] (rows=518 width=4)
+ Select Operator [SEL_1122] (rows=652 width=4)
Output:["_col0"]
- Please refer to the previous Select Operator [SEL_1118]
- <-Reducer 42 [ONE_TO_ONE_EDGE] vectorized
- FORWARD [RS_1143]
+ Please refer to the previous Select Operator [SEL_1116]
+ <-Reducer 49 [ONE_TO_ONE_EDGE] vectorized
+ FORWARD [RS_1203]
PartitionCols:_col0
- Select Operator [SEL_1142] (rows=13257 width=4)
+ Select Operator [SEL_1202] (rows=13257 width=4)
Output:["_col0"]
- Filter Operator [FIL_1141] (rows=13257 width=228)
+ Filter Operator [FIL_1201] (rows=13257 width=228)
predicate:(_col1 > (2 * _col2))
- Group By Operator [GBY_1140] (rows=39773 width=228)
+ Group By Operator [GBY_1200] (rows=39773 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)"],keys:KEY._col0
- <-Reducer 41 [SIMPLE_EDGE]
- SHUFFLE [RS_51]
+ <-Reducer 48 [SIMPLE_EDGE]
+ SHUFFLE [RS_170]
PartitionCols:_col0
- Group By Operator [GBY_50] (rows=6482999 width=228)
+ Group By Operator [GBY_169] (rows=6482999 width=228)
Output:["_col0","_col1","_col2"],aggregations:["sum(_col2)","sum(_col5)"],keys:_col0
- Merge Join Operator [MERGEJOIN_1060] (rows=183085709 width=227)
- Conds:RS_1135._col0, _col1=RS_1138._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
- <-Map 43 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1138]
+ Merge Join Operator [MERGEJOIN_1097] (rows=183085709 width=227)
+ Conds:RS_1199._col0, _col1=RS_1170._col0, _col1(Inner),Output:["_col0","_col2","_col5"]
+ <-Map 47 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1170]
PartitionCols:_col0, _col1
- Please refer to the previous Select Operator [SEL_1137]
- <-Map 40 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_1135]
+ Please refer to the previous Select Operator [SEL_1168]
+ <-Map 55 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_1199]
PartitionCols:_col0, _col1
- Select Operator [SEL_1134] (rows=287989836 width=119)
+ Select Operator [SEL_1198] (rows=287989836 width=119)
Output:["_col0","_col1","_col2"]
- Filter Operator [FIL_1133] (rows=287989836 width=119)
- predicate:((cs_item_sk BETWEEN DynamicValue(RS_59_item_i_item_sk_min) AND DynamicValue(RS_59_item_i_item_sk_max) and in_bloom_filter(cs_item_sk, DynamicValue(RS_59_item_i_item_sk_bloom_filter))) and cs_item_sk is not null and cs_order_number is not null)
- TableScan [TS_40] (rows=287989836 width=119)
+ Filter Operator [FIL_1197] (rows=287989836 width=119)
+ predicate:((cs_item_sk BETWEEN DynamicValue(RS_190_item_i_item_sk_min) AND DynamicValue(RS_190_item_i_item_sk_max) and in_bloom_filter(cs_item_sk, DynamicValue(RS_190_item_i_item_sk_bloom_filter))) and cs_item_sk is not null and cs_order_number is not null)
+ TableScan [TS_159] (rows=287989836 width=119)
default@catalog_sales,catalog_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["cs_item_sk","cs_order_number","cs_ext_list_price"]
- <-Reducer 37 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_1129]
- Please refer to the previous Group By Operator [GBY_1127]
+ <-Reducer 43 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_1196]
+ Group By Operator [GBY_1195] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 40 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_1161]
+ Group By Operator [GBY_1159] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_1157] (rows=4666 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_1153]
diff --git a/ql/src/test/results/clientpositive/perf/tez/query85.q.out b/ql/src/test/results/clientpositive/perf/tez/query85.q.out
index 1ada394..5b8ad0b 100644
--- a/ql/src/test/results/clientpositive/perf/tez/query85.q.out
+++ b/ql/src/test/results/clientpositive/perf/tez/query85.q.out
@@ -183,155 +183,153 @@ POSTHOOK: Output: hdfs://### HDFS PATH ###
Plan optimized by CBO.
Vertex dependency in root stage
-Map 11 <- Reducer 13 (BROADCAST_EDGE)
-Reducer 10 <- Reducer 9 (SIMPLE_EDGE)
-Reducer 13 <- Map 12 (CUSTOM_SIMPLE_EDGE)
-Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 11 (SIMPLE_EDGE)
-Reducer 3 <- Map 17 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
-Reducer 4 <- Map 12 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
-Reducer 5 <- Map 14 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE)
-Reducer 6 <- Map 15 (SIMPLE_EDGE), Reducer 5 (SIMPLE_EDGE)
-Reducer 7 <- Map 16 (SIMPLE_EDGE), Reducer 6 (SIMPLE_EDGE)
-Reducer 8 <- Map 17 (SIMPLE_EDGE), Reducer 7 (SIMPLE_EDGE)
-Reducer 9 <- Reducer 8 (SIMPLE_EDGE)
+Map 1 <- Reducer 8 (BROADCAST_EDGE)
+Reducer 10 <- Map 14 (SIMPLE_EDGE), Map 9 (SIMPLE_EDGE)
+Reducer 11 <- Map 15 (SIMPLE_EDGE), Reducer 10 (SIMPLE_EDGE)
+Reducer 12 <- Map 15 (SIMPLE_EDGE), Reducer 11 (SIMPLE_EDGE)
+Reducer 13 <- Map 16 (SIMPLE_EDGE), Reducer 12 (SIMPLE_EDGE)
+Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 7 (SIMPLE_EDGE)
+Reducer 3 <- Reducer 13 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE)
+Reducer 4 <- Map 17 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE)
+Reducer 5 <- Reducer 4 (SIMPLE_EDGE)
+Reducer 6 <- Reducer 5 (SIMPLE_EDGE)
+Reducer 8 <- Map 7 (CUSTOM_SIMPLE_EDGE)
Stage-0
Fetch Operator
limit:-1
Stage-1
- Reducer 10 vectorized
- File Output Operator [FS_240]
- Limit [LIM_239] (rows=7 width=832)
+ Reducer 6 vectorized
+ File Output Operator [FS_237]
+ Limit [LIM_236] (rows=72 width=832)
Number of rows:100
- Select Operator [SEL_238] (rows=7 width=832)
+ Select Operator [SEL_235] (rows=72 width=832)
Output:["_col0","_col1","_col2","_col3"]
- <-Reducer 9 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_237]
- Select Operator [SEL_236] (rows=7 width=832)
+ <-Reducer 5 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_234]
+ Select Operator [SEL_233] (rows=72 width=832)
Output:["_col4","_col5","_col6","_col7"]
- Group By Operator [GBY_235] (rows=7 width=353)
+ Group By Operator [GBY_232] (rows=72 width=353)
Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(VALUE._col0)","count(VALUE._col1)","sum(VALUE._col2)","count(VALUE._col3)","sum(VALUE._col4)","count(VALUE._col5)"],keys:KEY._col0
- <-Reducer 8 [SIMPLE_EDGE]
- SHUFFLE [RS_49]
+ <-Reducer 4 [SIMPLE_EDGE]
+ SHUFFLE [RS_50]
PartitionCols:_col0
- Group By Operator [GBY_48] (rows=7 width=353)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(_col12)","count(_col12)","sum(_col7)","count(_col7)","sum(_col6)","count(_col6)"],keys:_col25
- Select Operator [SEL_47] (rows=16740 width=136)
- Output:["_col6","_col7","_col12","_col25"]
- Filter Operator [FIL_46] (rows=16740 width=136)
- predicate:((_col33 and _col34 and _col16) or (_col35 and _col36 and _col17) or (_col37 and _col38 and _col18))
- Merge Join Operator [MERGEJOIN_206] (rows=44640 width=136)
- Conds:RS_43._col1, _col20, _col21=RS_224._col0, _col1, _col2(Inner),Output:["_col6","_col7","_col12","_col16","_col17","_col18","_col25","_col33","_col34","_col35","_col36","_col37","_col38"]
- <-Map 17 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_224]
- PartitionCols:_col0, _col1, _col2
- Select Operator [SEL_222] (rows=265971 width=207)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"]
- Filter Operator [FIL_221] (rows=265971 width=183)
- predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null)
- TableScan [TS_21] (rows=1861800 width=183)
- default@customer_demographics,cd1,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
- <-Reducer 7 [SIMPLE_EDGE]
- SHUFFLE [RS_43]
- PartitionCols:_col1, _col20, _col21
- Filter Operator [FIL_42] (rows=44640 width=315)
- predicate:((_col27 and _col13) or (_col28 and _col14) or (_col29 and _col15))
- Merge Join Operator [MERGEJOIN_205] (rows=59520 width=315)
- Conds:RS_39._col2=RS_234._col0(Inner),Output:["_col1","_col6","_col7","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col20","_col21","_col25","_col27","_col28","_col29"]
- <-Map 16 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_234]
+ Group By Operator [GBY_49] (rows=288 width=353)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(_col4)","count(_col4)","sum(_col28)","count(_col28)","sum(_col27)","count(_col27)"],keys:_col37
+ Merge Join Operator [MERGEJOIN_203] (rows=2912400 width=313)
+ Conds:RS_45._col2=RS_231._col0(Inner),Output:["_col4","_col27","_col28","_col37"]
+ <-Map 17 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_231]
+ PartitionCols:_col0
+ Select Operator [SEL_230] (rows=4602 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_229] (rows=4602 width=4)
+ predicate:wp_web_page_sk is not null
+ TableScan [TS_34] (rows=4602 width=4)
+ default@web_page,web_page,Tbl:COMPLETE,Col:COMPLETE,Output:["wp_web_page_sk"]
+ <-Reducer 3 [SIMPLE_EDGE]
+ SHUFFLE [RS_45]
+ PartitionCols:_col2
+ Filter Operator [FIL_43] (rows=2912400 width=377)
+ predicate:(((_col15 and _col16 and _col8) or (_col17 and _col18 and _col9) or (_col19 and _col20 and _col10)) and ((_col30 and _col5) or (_col31 and _col6) or (_col32 and _col7)))
+ Merge Join Operator [MERGEJOIN_202] (rows=10355208 width=377)
+ Conds:RS_40._col1, _col3=RS_41._col9, _col14(Inner),Output:["_col2","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col15","_col16","_col17","_col18","_col19","_col20","_col27","_col28","_col30","_col31","_col32","_col37"]
+ <-Reducer 13 [SIMPLE_EDGE]
+ SHUFFLE [RS_41]
+ PartitionCols:_col9, _col14
+ Select Operator [SEL_33] (rows=1056644 width=250)
+ Output:["_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col14","_col15","_col16","_col18","_col19","_col20","_col25"]
+ Merge Join Operator [MERGEJOIN_201] (rows=1056644 width=250)
+ Conds:RS_30._col4=RS_228._col0(Inner),Output:["_col0","_col5","_col6","_col7","_col9","_col10","_col11","_col18","_col19","_col20","_col21","_col22","_col23","_col25"]
+ <-Map 16 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_228]
+ PartitionCols:_col0
+ Select Operator [SEL_227] (rows=72 width=101)
+ Output:["_col0","_col1"]
+ Filter Operator [FIL_226] (rows=72 width=101)
+ predicate:r_reason_sk is not null
+ TableScan [TS_18] (rows=72 width=101)
+ default@reason,reason,Tbl:COMPLETE,Col:COMPLETE,Output:["r_reason_sk","r_reason_desc"]
+ <-Reducer 12 [SIMPLE_EDGE]
+ SHUFFLE [RS_30]
+ PartitionCols:_col4
+ Merge Join Operator [MERGEJOIN_200] (rows=1056644 width=155)
+ Conds:RS_27._col1, _col13, _col14=RS_224._col0, _col1, _col2(Inner),Output:["_col0","_col4","_col5","_col6","_col7","_col9","_col10","_col11","_col18","_col19","_col20","_col21","_col22","_col23"]
+ <-Map 15 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_224]
+ PartitionCols:_col0, _col1, _col2
+ Select Operator [SEL_222] (rows=265971 width=207)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"]
+ Filter Operator [FIL_221] (rows=265971 width=183)
+ predicate:((cd_education_status) IN ('4 yr Degree', 'Primary', 'Advanced Degree') and (cd_marital_status) IN ('M', 'D', 'U') and cd_demo_sk is not null)
+ TableScan [TS_15] (rows=1861800 width=183)
+ default@customer_demographics,cd1,Tbl:COMPLETE,Col:COMPLETE,Output:["cd_demo_sk","cd_marital_status","cd_education_status"]
+ <-Reducer 11 [SIMPLE_EDGE]
+ SHUFFLE [RS_27]
+ PartitionCols:_col1, _col13, _col14
+ Merge Join Operator [MERGEJOIN_199] (rows=1056644 width=312)
+ Conds:RS_24._col3=RS_225._col0(Inner),Output:["_col0","_col1","_col4","_col5","_col6","_col7","_col9","_col10","_col11","_col13","_col14"]
+ <-Map 15 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_225]
+ PartitionCols:_col0
+ Select Operator [SEL_223] (rows=265971 width=183)
+ Output:["_col0","_col1","_col2"]
+ Please refer to the previous Filter Operator [FIL_221]
+ <-Reducer 10 [SIMPLE_EDGE]
+ SHUFFLE [RS_24]
+ PartitionCols:_col3
+ Merge Join Operator [MERGEJOIN_198] (rows=1056644 width=135)
+ Conds:RS_217._col2=RS_220._col0(Inner),Output:["_col0","_col1","_col3","_col4","_col5","_col6","_col7","_col9","_col10","_col11"]
+ <-Map 14 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_220]
+ PartitionCols:_col0
+ Select Operator [SEL_219] (rows=3529412 width=16)
+ Output:["_col0","_col1","_col2","_col3"]
+ Filter Operator [FIL_218] (rows=3529412 width=187)
+ predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV') and ca_address_sk is not null)
+ TableScan [TS_9] (rows=40000000 width=187)
+ default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
+ <-Map 9 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_217]
+ PartitionCols:_col2
+ Select Operator [SEL_216] (rows=11975292 width=237)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"]
+ Filter Operator [FIL_215] (rows=11975292 width=237)
+ predicate:(wr_item_sk is not null and wr_order_number is not null and wr_reason_sk is not null and wr_refunded_addr_sk is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null)
+ TableScan [TS_6] (rows=14398467 width=237)
+ default@web_returns,web_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["wr_item_sk","wr_refunded_cdemo_sk","wr_refunded_addr_sk","wr_returning_cdemo_sk","wr_reason_sk","wr_order_number","wr_fee","wr_refunded_cash"]
+ <-Reducer 2 [SIMPLE_EDGE]
+ SHUFFLE [RS_40]
+ PartitionCols:_col1, _col3
+ Merge Join Operator [MERGEJOIN_197] (rows=51392014 width=39)
+ Conds:RS_214._col0=RS_206._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10"]
+ <-Map 7 [SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_206]
PartitionCols:_col0
- Select Operator [SEL_233] (rows=3529412 width=16)
- Output:["_col0","_col1","_col2","_col3"]
- Filter Operator [FIL_232] (rows=3529412 width=187)
- predicate:((ca_country = 'United States') and (ca_state) IN ('KY', 'GA', 'NM', 'MT', 'OR', 'IN', 'WI', 'MO', 'WV') and ca_address_sk is not null)
- TableScan [TS_18] (rows=40000000 width=187)
- default@customer_address,customer_address,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_state","ca_country"]
- <-Reducer 6 [SIMPLE_EDGE]
- SHUFFLE [RS_39]
- PartitionCols:_col2
- Merge Join Operator [MERGEJOIN_204] (rows=674551 width=350)
- Conds:RS_36._col4=RS_231._col0(Inner),Output:["_col1","_col2","_col6","_col7","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col20","_col21","_col25"]
- <-Map 15 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_231]
- PartitionCols:_col0
- Select Operator [SEL_230] (rows=72 width=101)
- Output:["_col0","_col1"]
- Filter Operator [FIL_229] (rows=72 width=101)
- predicate:r_reason_sk is not null
- TableScan [TS_15] (rows=72 width=101)
- default@reason,reason,Tbl:COMPLETE,Col:COMPLETE,Output:["r_reason_sk","r_reason_desc"]
- <-Reducer 5 [SIMPLE_EDGE]
- SHUFFLE [RS_36]
- PartitionCols:_col4
- Merge Join Operator [MERGEJOIN_203] (rows=674551 width=254)
- Conds:RS_33._col10=RS_228._col0(Inner),Output:["_col1","_col2","_col4","_col6","_col7","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col20","_col21"]
- <-Map 14 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_228]
- PartitionCols:_col0
- Select Operator [SEL_227] (rows=4602 width=4)
- Output:["_col0"]
- Filter Operator [FIL_226] (rows=4602 width=4)
- predicate:wp_web_page_sk is not null
- TableScan [TS_12] (rows=4602 width=4)
- default@web_page,web_page,Tbl:COMPLETE,Col:COMPLETE,Output:["wp_web_page_sk"]
- <-Reducer 4 [SIMPLE_EDGE]
- SHUFFLE [RS_33]
- PartitionCols:_col10
- Merge Join Operator [MERGEJOIN_202] (rows=674551 width=258)
- Conds:RS_30._col8=RS_212._col0(Inner),Output:["_col1","_col2","_col4","_col6","_col7","_col10","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col20","_col21"]
- <-Map 12 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_212]
- PartitionCols:_col0
- Select Operator [SEL_211] (rows=652 width=4)
- Output:["_col0"]
- Filter Operator [FIL_210] (rows=652 width=8)
- predicate:((d_year = 1998) and d_date_sk is not null)
- TableScan [TS_9] (rows=73049 width=8)
- default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
- <-Reducer 3 [SIMPLE_EDGE]
- SHUFFLE [RS_30]
- PartitionCols:_col8
- Merge Join Operator [MERGEJOIN_201] (rows=1889180 width=383)
- Conds:RS_27._col3=RS_225._col0(Inner),Output:["_col1","_col2","_col4","_col6","_col7","_col8","_col10","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col20","_col21"]
- <-Map 17 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_225]
- PartitionCols:_col0
- Select Operator [SEL_223] (rows=265971 width=183)
- Output:["_col0","_col1","_col2"]
- Please refer to the previous Filter Operator [FIL_221]
- <-Reducer 2 [SIMPLE_EDGE]
- SHUFFLE [RS_27]
- PartitionCols:_col3
- Merge Join Operator [MERGEJOIN_200] (rows=13039884 width=266)
- Conds:RS_209._col0, _col5=RS_220._col1, _col3(Inner),Output:["_col1","_col2","_col3","_col4","_col6","_col7","_col8","_col10","_col12","_col13","_col14","_col15","_col16","_col17","_col18"]
- <-Map 1 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_209]
- PartitionCols:_col0, _col5
- Select Operator [SEL_208] (rows=11975292 width=237)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"]
- Filter Operator [FIL_207] (rows=11975292 width=237)
- predicate:(wr_item_sk is not null and wr_order_number is not null and wr_reason_sk is not null and wr_refunded_addr_sk is not null and wr_refunded_cdemo_sk is not null and wr_returning_cdemo_sk is not null)
- TableScan [TS_0] (rows=14398467 width=237)
- default@web_returns,web_returns,Tbl:COMPLETE,Col:COMPLETE,Output:["wr_item_sk","wr_refunded_cdemo_sk","wr_refunded_addr_sk","wr_returning_cdemo_sk","wr_reason_sk","wr_order_number","wr_fee","wr_refunded_cash"]
- <-Map 11 [SIMPLE_EDGE] vectorized
- SHUFFLE [RS_220]
- PartitionCols:_col1, _col3
- Select Operator [SEL_219] (rows=15992347 width=43)
- Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10"]
- Filter Operator [FIL_218] (rows=15992347 width=243)
- predicate:((ws_net_profit BETWEEN 100 AND 200 or ws_net_profit BETWEEN 150 AND 300 or ws_net_profit BETWEEN 50 AND 250) and (ws_sales_price BETWEEN 100 AND 150 or ws_sales_price BETWEEN 50 AND 100 or ws_sales_price BETWEEN 150 AND 200) and (ws_sold_date_sk BETWEEN DynamicValue(RS_31_date_dim_d_date_sk_min) AND DynamicValue(RS_31_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_31_date_dim_d_date_sk_bl [...]
- TableScan [TS_3] (rows=144002668 width=243)
- default@web_sales,web_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ws_sold_date_sk","ws_item_sk","ws_web_page_sk","ws_order_number","ws_quantity","ws_sales_price","ws_net_profit"]
- <-Reducer 13 [BROADCAST_EDGE] vectorized
- BROADCAST [RS_217]
- Group By Operator [GBY_216] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
- <-Map 12 [CUSTOM_SIMPLE_EDGE] vectorized
- SHUFFLE [RS_215]
- Group By Operator [GBY_214] (rows=1 width=12)
- Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
- Select Operator [SEL_213] (rows=652 width=4)
- Output:["_col0"]
- Please refer to the previous Select Operator [SEL_211]
+ Select Operator [SEL_205] (rows=652 width=4)
+ Output:["_col0"]
+ Filter Operator [FIL_204] (rows=652 width=8)
+ predicate:((d_year = 1998) and d_date_sk is not null)
+ TableScan [TS_3] (rows=73049 width=8)
+ default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year"]
+ <-Map 1 [SIMPLE_EDGE] vectorized
+ SHUFFLE [RS_214]
+ PartitionCols:_col0
+ Select Operator [SEL_213] (rows=143931136 width=43)
+ Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10"]
+ Filter Operator [FIL_212] (rows=143931136 width=243)
+ predicate:(((ws_net_profit >= 100) or (ws_net_profit <= 200) or ws_net_profit is not null or (ws_net_profit <= 300) or (ws_net_profit <= 250)) and ((ws_sales_price >= 100) or (ws_sales_price <= 150) or ws_sales_price is not null or (ws_sales_price <= 200)) and (ws_sold_date_sk BETWEEN DynamicValue(RS_38_date_dim_d_date_sk_min) AND DynamicValue(RS_38_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_38_date_dim_d_date_sk_b [...]
+ TableScan [TS_0] (rows=144002668 width=243)
+ default@web_sales,web_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ws_sold_date_sk","ws_item_sk","ws_web_page_sk","ws_order_number","ws_quantity","ws_sales_price","ws_net_profit"]
+ <-Reducer 8 [BROADCAST_EDGE] vectorized
+ BROADCAST [RS_211]
+ Group By Operator [GBY_210] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"]
+ <-Map 7 [CUSTOM_SIMPLE_EDGE] vectorized
+ PARTITION_ONLY_SHUFFLE [RS_209]
+ Group By Operator [GBY_208] (rows=1 width=12)
+ Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"]
+ Select Operator [SEL_207] (rows=652 width=4)
+ Output:["_col0"]
+ Please refer to the previous Select Operator [SEL_205]
diff --git a/ql/src/test/results/clientpositive/rand_partitionpruner3.q.out b/ql/src/test/results/clientpositive/rand_partitionpruner3.q.out
index 008b761..5e19d87 100644
--- a/ql/src/test/results/clientpositive/rand_partitionpruner3.q.out
+++ b/ql/src/test/results/clientpositive/rand_partitionpruner3.q.out
@@ -8,9 +8,6 @@ POSTHOOK: type: QUERY
POSTHOOK: Input: default@srcpart
POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12
#### A masked pattern was here ####
-OPTIMIZED SQL: SELECT `key`, `value`, CAST('2008-04-08' AS STRING) AS `ds`, `hr`
-FROM `default`.`srcpart`
-WHERE RAND(1) < 0.1 AND `ds` = '2008-04-08' AND `key` <= 50 AND `key` >= 10 AND `hr` LIKE '%2'
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1
@@ -25,7 +22,7 @@ STAGE PLANS:
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: ((UDFToDouble(key) <= 50.0D) and (UDFToDouble(key) >= 10.0D) and (rand(1) < 0.1D)) (type: boolean)
+ predicate: ((rand(1) < 0.1D) and UDFToDouble(key) BETWEEN 10.0D AND 50.0D) (type: boolean)
Statistics: Num rows: 18 Data size: 6516 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string), '2008-04-08' (type: string), hr (type: string)
@@ -140,9 +137,6 @@ POSTHOOK: type: QUERY
POSTHOOK: Input: default@srcpart
POSTHOOK: Input: default@srcpart@ds=2008-04-08/hr=12
#### A masked pattern was here ####
-OPTIMIZED SQL: SELECT `key`, `value`, CAST('2008-04-08' AS STRING) AS `ds`, `hr`
-FROM `default`.`srcpart`
-WHERE `ds` = '2008-04-08' AND `key` <= 50 AND `key` >= 10 AND `hr` LIKE '%2'
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1
@@ -153,12 +147,12 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: a
- filterExpr: ((UDFToDouble(key) <= 50.0D) and (UDFToDouble(key) >= 10.0D)) (type: boolean)
+ filterExpr: ((ds = '2008-04-08') and UDFToDouble(key) BETWEEN 10.0D AND 50.0D and (hr like '%2')) (type: boolean)
Statistics: Num rows: 500 Data size: 181000 Basic stats: COMPLETE Column stats: COMPLETE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: ((UDFToDouble(key) <= 50.0D) and (UDFToDouble(key) >= 10.0D)) (type: boolean)
+ predicate: UDFToDouble(key) BETWEEN 10.0D AND 50.0D (type: boolean)
Statistics: Num rows: 55 Data size: 19910 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string), '2008-04-08' (type: string), hr (type: string)
diff --git a/ql/src/test/results/clientpositive/select_unquote_or.q.out b/ql/src/test/results/clientpositive/select_unquote_or.q.out
index 4b9ee0c..bb4df39 100644
--- a/ql/src/test/results/clientpositive/select_unquote_or.q.out
+++ b/ql/src/test/results/clientpositive/select_unquote_or.q.out
@@ -61,18 +61,18 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: npe_test
- filterExpr: ((UDFToDouble(ds) > 1970.0D) or (UDFToDouble(ds) < 1985.0D)) (type: boolean)
+ filterExpr: UDFToDouble(ds) NOT BETWEEN 1985.0D AND 1970.0D (type: boolean)
Statistics: Num rows: 498 Data size: 180276 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: ((UDFToDouble(ds) < 1985.0D) or (UDFToDouble(ds) > 1970.0D)) (type: boolean)
- Statistics: Num rows: 332 Data size: 120184 Basic stats: COMPLETE Column stats: COMPLETE
+ predicate: UDFToDouble(ds) NOT BETWEEN 1985.0D AND 1970.0D (type: boolean)
+ Statistics: Num rows: 443 Data size: 160366 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string), ds (type: string)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 332 Data size: 120184 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 443 Data size: 160366 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
- Statistics: Num rows: 332 Data size: 120184 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 443 Data size: 160366 Basic stats: COMPLETE Column stats: COMPLETE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
diff --git a/ql/src/test/results/clientpositive/smb_mapjoin_47.q.out b/ql/src/test/results/clientpositive/smb_mapjoin_47.q.out
index 57a543c..20a7348 100644
--- a/ql/src/test/results/clientpositive/smb_mapjoin_47.q.out
+++ b/ql/src/test/results/clientpositive/smb_mapjoin_47.q.out
@@ -549,14 +549,14 @@ STAGE PLANS:
0
1
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5
- residual filter predicates: {((_col0 + _col3) >= 100)} {((_col0 + _col3) <= 102)}
- Statistics: Num rows: 2 Data size: 382 Basic stats: COMPLETE Column stats: COMPLETE
+ residual filter predicates: {(_col0 + _col3) BETWEEN 100 AND 102}
+ Statistics: Num rows: 12 Data size: 2292 Basic stats: COMPLETE Column stats: COMPLETE
Limit
Number of rows: 10
- Statistics: Num rows: 2 Data size: 382 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 10 Data size: 1910 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
- Statistics: Num rows: 2 Data size: 382 Basic stats: COMPLETE Column stats: COMPLETE
+ Statistics: Num rows: 10 Data size: 1910 Basic stats: COMPLETE Column stats: COMPLETE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
diff --git a/ql/src/test/results/clientpositive/spark/join34.q.out b/ql/src/test/results/clientpositive/spark/join34.q.out
index 7ec6094..652f1af 100644
--- a/ql/src/test/results/clientpositive/spark/join34.q.out
+++ b/ql/src/test/results/clientpositive/spark/join34.q.out
@@ -32,17 +32,6 @@ POSTHOOK: type: QUERY
POSTHOOK: Input: default@src
POSTHOOK: Input: default@src1
POSTHOOK: Output: default@dest_j1_n1
-OPTIMIZED SQL: SELECT `t5`.`key`, `t5`.`value`, `t3`.`value` AS `value1`
-FROM (SELECT `key`, `value`
-FROM `default`.`src`
-WHERE `key` < 20
-UNION ALL
-SELECT `key`, `value`
-FROM `default`.`src`
-WHERE `key` > 100) AS `t3`
-INNER JOIN (SELECT `key`, `value`
-FROM `default`.`src1`
-WHERE (`key` < 20 OR `key` > 100) AND `key` IS NOT NULL) AS `t5` ON `t3`.`key` = `t5`.`key`
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1
@@ -59,23 +48,23 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (UDFToDouble(key) < 20.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) < 20.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 332 Data size: 3526 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 296 Data size: 3144 Basic stats: COMPLETE Column stats: NONE
tag: 0
value expressions: _col1 (type: string)
auto parallelism: false
@@ -137,23 +126,23 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x1
- filterExpr: (UDFToDouble(key) > 100.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) > 100.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 332 Data size: 3526 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 296 Data size: 3144 Basic stats: COMPLETE Column stats: NONE
tag: 0
value expressions: _col1 (type: string)
auto parallelism: false
@@ -215,23 +204,23 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
+ filterExpr: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
Statistics: Num rows: 25 Data size: 191 Basic stats: COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
- Statistics: Num rows: 16 Data size: 122 Basic stats: COMPLETE Column stats: NONE
+ predicate: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
+ Statistics: Num rows: 23 Data size: 175 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 16 Data size: 122 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 23 Data size: 175 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 16 Data size: 122 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 23 Data size: 175 Basic stats: COMPLETE Column stats: NONE
tag: 1
value expressions: _col1 (type: string)
auto parallelism: false
@@ -299,17 +288,17 @@ STAGE PLANS:
0 _col0 (type: string)
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
- Statistics: Num rows: 365 Data size: 3878 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 325 Data size: 3458 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), _col1 (type: string)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 365 Data size: 3878 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 325 Data size: 3458 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
GlobalTableId: 1
#### A masked pattern was here ####
NumFilesPerFileSink: 1
- Statistics: Num rows: 365 Data size: 3878 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 325 Data size: 3458 Basic stats: COMPLETE Column stats: NONE
#### A masked pattern was here ####
table:
input format: org.apache.hadoop.mapred.TextInputFormat
diff --git a/ql/src/test/results/clientpositive/spark/join35.q.out b/ql/src/test/results/clientpositive/spark/join35.q.out
index 8794053..cce7a77 100644
--- a/ql/src/test/results/clientpositive/spark/join35.q.out
+++ b/ql/src/test/results/clientpositive/spark/join35.q.out
@@ -32,19 +32,6 @@ POSTHOOK: type: QUERY
POSTHOOK: Input: default@src
POSTHOOK: Input: default@src1
POSTHOOK: Output: default@dest_j1_n24
-OPTIMIZED SQL: SELECT `t5`.`key`, `t5`.`value`, `t3`.`$f1` AS `cnt`
-FROM (SELECT `key`, COUNT(*) AS `$f1`
-FROM `default`.`src`
-WHERE `key` < 20
-GROUP BY `key`
-UNION ALL
-SELECT `key`, COUNT(*) AS `$f1`
-FROM `default`.`src`
-WHERE `key` > 100
-GROUP BY `key`) AS `t3`
-INNER JOIN (SELECT `key`, `value`
-FROM `default`.`src1`
-WHERE (`key` < 20 OR `key` > 100) AND `key` IS NOT NULL) AS `t5` ON `t3`.`key` = `t5`.`key`
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1
@@ -63,25 +50,25 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (UDFToDouble(key) < 20.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) < 20.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((UDFToDouble(key) < 20.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
tag: -1
value expressions: _col1 (type: bigint)
auto parallelism: false
@@ -143,25 +130,25 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x1
- filterExpr: (UDFToDouble(key) > 100.0D) (type: boolean)
+ filterExpr: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
Statistics: Num rows: 500 Data size: 5312 Basic stats: COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (UDFToDouble(key) > 100.0D) (type: boolean)
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((UDFToDouble(key) > 100.0D) and UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D) (type: boolean)
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Group By Operator
aggregations: count()
keys: key (type: string)
mode: hash
outputColumnNames: _col0, _col1
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 1763 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
tag: -1
value expressions: _col1 (type: bigint)
auto parallelism: false
@@ -223,23 +210,23 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: x
- filterExpr: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
+ filterExpr: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
Statistics: Num rows: 25 Data size: 191 Basic stats: COMPLETE Column stats: NONE
GatherStats: false
Filter Operator
isSamplingPred: false
- predicate: (((UDFToDouble(key) < 20.0D) or (UDFToDouble(key) > 100.0D)) and key is not null) (type: boolean)
- Statistics: Num rows: 16 Data size: 122 Basic stats: COMPLETE Column stats: NONE
+ predicate: (UDFToDouble(key) NOT BETWEEN 20.0D AND 100.0D and key is not null) (type: boolean)
+ Statistics: Num rows: 23 Data size: 175 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: key (type: string), value (type: string)
outputColumnNames: _col0, _col1
- Statistics: Num rows: 16 Data size: 122 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 23 Data size: 175 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 16 Data size: 122 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 23 Data size: 175 Basic stats: COMPLETE Column stats: NONE
tag: 1
value expressions: _col1 (type: string)
auto parallelism: false
@@ -306,13 +293,13 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 881 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 74 Data size: 786 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 1762 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
tag: 0
value expressions: _col1 (type: bigint)
auto parallelism: false
@@ -326,17 +313,17 @@ STAGE PLANS:
0 _col0 (type: string)
1 _col0 (type: string)
outputColumnNames: _col1, _col2, _col3
- Statistics: Num rows: 182 Data size: 1938 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 162 Data size: 1729 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col2 (type: string), _col3 (type: string), UDFToInteger(_col1) (type: int)
outputColumnNames: _col0, _col1, _col2
- Statistics: Num rows: 182 Data size: 1938 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 162 Data size: 1729 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
GlobalTableId: 1
#### A masked pattern was here ####
NumFilesPerFileSink: 1
- Statistics: Num rows: 182 Data size: 1938 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 162 Data size: 1729 Basic stats: COMPLETE Column stats: NONE
#### A masked pattern was here ####
table:
input format: org.apache.hadoop.mapred.TextInputFormat
@@ -373,13 +360,13 @@ STAGE PLANS:
keys: KEY._col0 (type: string)
mode: mergepartial
outputColumnNames: _col0, _col1
- Statistics: Num rows: 83 Data size: 881 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 74 Data size: 786 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
key expressions: _col0 (type: string)
null sort order: a
sort order: +
Map-reduce partition columns: _col0 (type: string)
- Statistics: Num rows: 166 Data size: 1762 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 148 Data size: 1572 Basic stats: COMPLETE Column stats: NONE
tag: 0
value expressions: _col1 (type: bigint)
auto parallelism: false
diff --git a/ql/src/test/results/clientpositive/spark/pcr.q.out b/ql/src/test/results/clientpositive/spark/pcr.q.out
index 270f55a..2c5b15a 100644
--- a/ql/src/test/results/clientpositive/spark/pcr.q.out
+++ b/ql/src/test/results/clientpositive/spark/pcr.q.out
@@ -3102,10 +3102,6 @@ POSTHOOK: Input: default@pcr_t1@ds=2000-04-09
POSTHOOK: Input: default@pcr_t1@ds=2000-04-10
POSTHOOK: Input: default@pcr_t1@ds=2000-04-11
#### A masked pattern was here ####
-OPTIMIZED SQL: SELECT `key`, `value`, `ds`
-FROM `default`.`pcr_t1`
-WHERE `ds` > '2000-04-08' AND `ds` < '2000-04-11' OR `ds` >= '2000-04-08' AND `ds` <= '2000-04-11' AND `key` = 2
-ORDER BY `key`, `value`, `ds`
STAGE DEPENDENCIES:
Stage-1 is a root stage
Stage-0 depends on stages: Stage-1
diff --git a/ql/src/test/results/clientpositive/spark/subquery_scalar.q.out b/ql/src/test/results/clientpositive/spark/subquery_scalar.q.out
index d7d652f..82b080e 100644
--- a/ql/src/test/results/clientpositive/spark/subquery_scalar.q.out
+++ b/ql/src/test/results/clientpositive/spark/subquery_scalar.q.out
@@ -805,8 +805,7 @@ POSTHOOK: Input: default@part_null_n0
85768 almond antique chartreuse lavender yellow Manufacturer#1 Brand#12 LARGE BRUSHED STEEL 34 SM BAG 1753.76 refull
86428 almond aquamarine burnished black steel Manufacturer#1 Brand#12 STANDARD ANODIZED STEEL 28 WRAP BAG 1414.42 arefully
90681 almond antique chartreuse khaki white Manufacturer#3 Brand#31 MEDIUM BURNISHED TIN 17 SM CASE 1671.68 are slyly after the sl
-Warning: Shuffle Join JOIN[16][tables = [$hdt$_0, $hdt$_1]] in Work 'Reducer 2' is a cross product
-Warning: Shuffle Join JOIN[19][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 3' is a cross product
+Warning: Shuffle Join JOIN[17][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 2' is a cross product
PREHOOK: query: explain select * from part where p_size between (select min(p_size) from part) and (select avg(p_size) from part)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
@@ -823,10 +822,9 @@ STAGE PLANS:
Stage: Stage-1
Spark
Edges:
- Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 1), Reducer 5 (PARTITION-LEVEL SORT, 1)
- Reducer 3 <- Reducer 2 (PARTITION-LEVEL SORT, 1), Reducer 7 (PARTITION-LEVEL SORT, 1)
- Reducer 5 <- Map 4 (GROUP, 1)
- Reducer 7 <- Map 6 (GROUP, 1)
+ Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 1), Reducer 4 (PARTITION-LEVEL SORT, 1), Reducer 6 (PARTITION-LEVEL SORT, 1)
+ Reducer 4 <- Map 3 (GROUP, 1)
+ Reducer 6 <- Map 5 (GROUP, 1)
#### A masked pattern was here ####
Vertices:
Map 1
@@ -835,15 +833,15 @@ STAGE PLANS:
alias: part
Statistics: Num rows: 26 Data size: 3147 Basic stats: COMPLETE Column stats: NONE
Select Operator
- expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
+ expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string), UDFToDouble(p_size) (type: double)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
Statistics: Num rows: 26 Data size: 3147 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
sort order:
Statistics: Num rows: 26 Data size: 3147 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
+ value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string), _col9 (type: double)
Execution mode: vectorized
- Map 4
+ Map 3
Map Operator Tree:
TableScan
alias: part
@@ -862,7 +860,7 @@ STAGE PLANS:
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
value expressions: _col0 (type: int)
Execution mode: vectorized
- Map 6
+ Map 5
Map Operator Tree:
TableScan
alias: part
@@ -885,41 +883,29 @@ STAGE PLANS:
Reduce Operator Tree:
Join Operator
condition map:
- Left Outer Join 0 to 1
- keys:
- 0
- 1
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
- Statistics: Num rows: 26 Data size: 3277 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 26 Data size: 3277 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string), _col9 (type: int)
- Reducer 3
- Reduce Operator Tree:
- Join Operator
- condition map:
- Left Outer Join 0 to 1
+ Inner Join 0 to 1
+ Inner Join 0 to 2
keys:
0
1
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
- Statistics: Num rows: 26 Data size: 3719 Basic stats: COMPLETE Column stats: NONE
+ 2
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11
+ Statistics: Num rows: 26 Data size: 3693 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: UDFToDouble(_col5) BETWEEN UDFToDouble(_col9) AND _col10 (type: boolean)
- Statistics: Num rows: 2 Data size: 286 Basic stats: COMPLETE Column stats: NONE
+ predicate: ((_col10 <= _col9) and (_col9 <= _col11)) (type: boolean)
+ Statistics: Num rows: 2 Data size: 284 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
- Statistics: Num rows: 2 Data size: 286 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 2 Data size: 284 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
- Statistics: Num rows: 2 Data size: 286 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 2 Data size: 284 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
- Reducer 5
+ Reducer 4
Execution mode: vectorized
Reduce Operator Tree:
Group By Operator
@@ -927,11 +913,15 @@ STAGE PLANS:
mode: mergepartial
outputColumnNames: _col0
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
+ Select Operator
+ expressions: UDFToDouble(_col0) (type: double)
+ outputColumnNames: _col0
Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int)
- Reducer 7
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 1 Data size: 4 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: double)
+ Reducer 6
Execution mode: vectorized
Reduce Operator Tree:
Group By Operator
@@ -954,8 +944,7 @@ STAGE PLANS:
Processor Tree:
ListSink
-Warning: Shuffle Join JOIN[16][tables = [$hdt$_0, $hdt$_1]] in Work 'Reducer 2' is a cross product
-Warning: Shuffle Join JOIN[19][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 3' is a cross product
+Warning: Shuffle Join JOIN[17][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 2' is a cross product
PREHOOK: query: select * from part where p_size between (select min(p_size) from part) and (select avg(p_size) from part)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
@@ -1749,7 +1738,7 @@ POSTHOOK: Input: default@part
POSTHOOK: Input: default@part_null_n0
#### A masked pattern was here ####
192697 almond antique blue firebrick mint Manufacturer#5 Brand#52 MEDIUM BURNISHED TIN 31 LG DRUM 1789.69 ickly ir
-Warning: Shuffle Join JOIN[20][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 3' is a cross product
+Warning: Shuffle Join JOIN[22][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 3' is a cross product
PREHOOK: query: explain select * from part where p_size BETWEEN (select min(p_size) from part_null_n0 where part_null_n0.p_type = part.p_type) AND (select max(p_size) from part_null_n0)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
@@ -1778,17 +1767,21 @@ STAGE PLANS:
Map Operator Tree:
TableScan
alias: part
+ filterExpr: p_type is not null (type: boolean)
Statistics: Num rows: 26 Data size: 3147 Basic stats: COMPLETE Column stats: NONE
- Select Operator
- expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
+ Filter Operator
+ predicate: p_type is not null (type: boolean)
Statistics: Num rows: 26 Data size: 3147 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- key expressions: _col4 (type: string)
- sort order: +
- Map-reduce partition columns: _col4 (type: string)
+ Select Operator
+ expressions: p_partkey (type: int), p_name (type: string), p_mfgr (type: string), p_brand (type: string), p_type (type: string), p_size (type: int), p_container (type: string), p_retailprice (type: double), p_comment (type: string)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
Statistics: Num rows: 26 Data size: 3147 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
+ Reduce Output Operator
+ key expressions: _col4 (type: string)
+ sort order: +
+ Map-reduce partition columns: _col4 (type: string)
+ Statistics: Num rows: 26 Data size: 3147 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
Execution mode: vectorized
Map 4
Map Operator Tree:
@@ -1835,36 +1828,39 @@ STAGE PLANS:
Reduce Operator Tree:
Join Operator
condition map:
- Left Outer Join 0 to 1
+ Inner Join 0 to 1
keys:
0 _col4 (type: string)
- 1 _col2 (type: string)
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10
+ 1 _col1 (type: string)
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9
Statistics: Num rows: 28 Data size: 3461 Basic stats: COMPLETE Column stats: NONE
- Reduce Output Operator
- sort order:
- Statistics: Num rows: 28 Data size: 3461 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string), _col9 (type: int), _col10 (type: boolean)
+ Filter Operator
+ predicate: (_col9 <= _col5) (type: boolean)
+ Statistics: Num rows: 9 Data size: 1112 Basic stats: COMPLETE Column stats: NONE
+ Reduce Output Operator
+ sort order:
+ Statistics: Num rows: 9 Data size: 1112 Basic stats: COMPLETE Column stats: NONE
+ value expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
Reducer 3
Reduce Operator Tree:
Join Operator
condition map:
- Left Outer Join 0 to 1
+ Inner Join 0 to 1
keys:
0
1
- outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col12
- Statistics: Num rows: 28 Data size: 3601 Basic stats: COMPLETE Column stats: NONE
+ outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col11
+ Statistics: Num rows: 9 Data size: 1157 Basic stats: COMPLETE Column stats: NONE
Filter Operator
- predicate: CASE WHEN (_col10 is null) THEN (_col5 BETWEEN null AND _col12) ELSE (_col5 BETWEEN _col9 AND _col12) END (type: boolean)
- Statistics: Num rows: 14 Data size: 1800 Basic stats: COMPLETE Column stats: NONE
+ predicate: (_col5 <= _col11) (type: boolean)
+ Statistics: Num rows: 3 Data size: 385 Basic stats: COMPLETE Column stats: NONE
Select Operator
expressions: _col0 (type: int), _col1 (type: string), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: int), _col6 (type: string), _col7 (type: double), _col8 (type: string)
outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8
- Statistics: Num rows: 14 Data size: 1800 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 3 Data size: 385 Basic stats: COMPLETE Column stats: NONE
File Output Operator
compressed: false
- Statistics: Num rows: 14 Data size: 1800 Basic stats: COMPLETE Column stats: NONE
+ Statistics: Num rows: 3 Data size: 385 Basic stats: COMPLETE Column stats: NONE
table:
input format: org.apache.hadoop.mapred.SequenceFileInputFormat
output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
@@ -1879,15 +1875,15 @@ STAGE PLANS:
outputColumnNames: _col0, _col1
Statistics: Num rows: 1 Data size: 32560 Basic stats: COMPLETE Column stats: NONE
Select Operator
- expressions: _col1 (type: int), true (type: boolean), _col0 (type: string)
- outputColumnNames: _col0, _col1, _col2
+ expressions: _col1 (type: int), _col0 (type: string)
+ outputColumnNames: _col0, _col1
Statistics: Num rows: 1 Data size: 32560 Basic stats: COMPLETE Column stats: NONE
Reduce Output Operator
- key expressions: _col2 (type: string)
+ key expressions: _col1 (type: string)
sort order: +
- Map-reduce partition columns: _col2 (type: string)
+ Map-reduce partition columns: _col1 (type: string)
Statistics: Num rows: 1 Data size: 32560 Basic stats: COMPLETE Column stats: NONE
- value expressions: _col0 (type: int), _col1 (type: boolean)
+ value expressions: _col0 (type: int)
Reducer 7
Execution mode: vectorized
Reduce Operator Tree:
@@ -1907,7 +1903,7 @@ STAGE PLANS:
Processor Tree:
ListSink
-Warning: Shuffle Join JOIN[20][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 3' is a cross product
+Warning: Shuffle Join JOIN[22][tables = [$hdt$_0, $hdt$_1, $hdt$_2]] in Work 'Reducer 3' is a cross product
PREHOOK: query: select * from part where p_size BETWEEN (select min(p_size) from part_null_n0 where part_null_n0.p_type = part.p_type) AND (select max(p_size) from part_null_n0)
PREHOOK: type: QUERY
PREHOOK: Input: default@part
diff --git a/ql/src/test/results/clientpositive/udf_between.q.out b/ql/src/test/results/clientpositive/udf_between.q.out
index 9ba685a..1a4eb07 100644
--- a/ql/src/test/results/clientpositive/udf_between.q.out
+++ b/ql/src/test/results/clientpositive/udf_between.q.out
@@ -88,10 +88,10 @@ STAGE PLANS:
Processor Tree:
TableScan
alias: src
- filterExpr: (not (UDFToDouble(key) + 100.0D) BETWEEN 100.0D AND 200.0D) (type: boolean)
+ filterExpr: (UDFToDouble(key) + 100.0D) NOT BETWEEN 100.0D AND 200.0D (type: boolean)
Statistics: Num rows: 500 Data size: 89000 Basic stats: COMPLETE Column stats: COMPLETE
Filter Operator
- predicate: (not (UDFToDouble(key) + 100.0D) BETWEEN 100.0D AND 200.0D) (type: boolean)
+ predicate: (UDFToDouble(key) + 100.0D) NOT BETWEEN 100.0D AND 200.0D (type: boolean)
Statistics: Num rows: 445 Data size: 79210 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: key (type: string), value (type: string)
@@ -204,3 +204,177 @@ POSTHOOK: type: QUERY
POSTHOOK: Input: default@src
#### A masked pattern was here ####
238 val_238
+PREHOOK: query: create table t(i int)
+PREHOOK: type: CREATETABLE
+PREHOOK: Output: database:default
+PREHOOK: Output: default@t
+POSTHOOK: query: create table t(i int)
+POSTHOOK: type: CREATETABLE
+POSTHOOK: Output: database:default
+POSTHOOK: Output: default@t
+PREHOOK: query: insert into t values (0),(1),(2),(3),(4),(5),(6),(7),(8),(9),(10),(11)
+PREHOOK: type: QUERY
+PREHOOK: Input: _dummy_database@_dummy_table
+PREHOOK: Output: default@t
+POSTHOOK: query: insert into t values (0),(1),(2),(3),(4),(5),(6),(7),(8),(9),(10),(11)
+POSTHOOK: type: QUERY
+POSTHOOK: Input: _dummy_database@_dummy_table
+POSTHOOK: Output: default@t
+POSTHOOK: Lineage: t.i SCRIPT []
+PREHOOK: query: SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10
+PREHOOK: type: QUERY
+PREHOOK: Input: default@t
+#### A masked pattern was here ####
+POSTHOOK: query: SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10
+POSTHOOK: type: QUERY
+POSTHOOK: Input: default@t
+#### A masked pattern was here ####
+8
+9
+10
+PREHOOK: query: explain
+SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10
+PREHOOK: type: QUERY
+PREHOOK: Input: default@t
+#### A masked pattern was here ####
+POSTHOOK: query: explain
+SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10
+POSTHOOK: type: QUERY
+POSTHOOK: Input: default@t
+#### A masked pattern was here ####
+STAGE DEPENDENCIES:
+ Stage-0 is a root stage
+
+STAGE PLANS:
+ Stage: Stage-0
+ Fetch Operator
+ limit: -1
+ Processor Tree:
+ TableScan
+ alias: t
+ filterExpr: (i BETWEEN 8 AND 9 or i BETWEEN 9 AND 10) (type: boolean)
+ Statistics: Num rows: 12 Data size: 48 Basic stats: COMPLETE Column stats: COMPLETE
+ Filter Operator
+ predicate: (i BETWEEN 8 AND 9 or i BETWEEN 9 AND 10) (type: boolean)
+ Statistics: Num rows: 4 Data size: 16 Basic stats: COMPLETE Column stats: COMPLETE
+ Select Operator
+ expressions: i (type: int)
+ outputColumnNames: _col0
+ Statistics: Num rows: 4 Data size: 16 Basic stats: COMPLETE Column stats: COMPLETE
+ ListSink
+
+PREHOOK: query: SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10
+PREHOOK: type: QUERY
+PREHOOK: Input: default@t
+#### A masked pattern was here ####
+POSTHOOK: query: SELECT * FROM t where i between 8 and 9
+ or i between 9 and 10
+POSTHOOK: type: QUERY
+POSTHOOK: Input: default@t
+#### A masked pattern was here ####
+8
+9
+10
+PREHOOK: query: explain
+SELECT * FROM t where i between 6 and 7
+ or i between 9 and 10
+PREHOOK: type: QUERY
+PREHOOK: Input: default@t
+#### A masked pattern was here ####
+POSTHOOK: query: explain
+SELECT * FROM t where i between 6 and 7
+ or i between 9 and 10
+POSTHOOK: type: QUERY
+POSTHOOK: Input: default@t
+#### A masked pattern was here ####
+STAGE DEPENDENCIES:
+ Stage-0 is a root stage
+
+STAGE PLANS:
+ Stage: Stage-0
+ Fetch Operator
+ limit: -1
+ Processor Tree:
+ TableScan
+ alias: t
+ filterExpr: (i BETWEEN 6 AND 7 or i BETWEEN 9 AND 10) (type: boolean)
+ Statistics: Num rows: 12 Data size: 48 Basic stats: COMPLETE Column stats: COMPLETE
+ Filter Operator
+ predicate: (i BETWEEN 6 AND 7 or i BETWEEN 9 AND 10) (type: boolean)
+ Statistics: Num rows: 4 Data size: 16 Basic stats: COMPLETE Column stats: COMPLETE
+ Select Operator
+ expressions: i (type: int)
+ outputColumnNames: _col0
+ Statistics: Num rows: 4 Data size: 16 Basic stats: COMPLETE Column stats: COMPLETE
+ ListSink
+
+PREHOOK: query: SELECT * FROM t where i between 6 and 7
+ or i between 9 and 10
+PREHOOK: type: QUERY
+PREHOOK: Input: default@t
+#### A masked pattern was here ####
+POSTHOOK: query: SELECT * FROM t where i between 6 and 7
+ or i between 9 and 10
+POSTHOOK: type: QUERY
+POSTHOOK: Input: default@t
+#### A masked pattern was here ####
+6
+7
+9
+10
+PREHOOK: query: explain
+SELECT * FROM t where i not between 6 and 7
+ and i not between 9 and 10
+PREHOOK: type: QUERY
+PREHOOK: Input: default@t
+#### A masked pattern was here ####
+POSTHOOK: query: explain
+SELECT * FROM t where i not between 6 and 7
+ and i not between 9 and 10
+POSTHOOK: type: QUERY
+POSTHOOK: Input: default@t
+#### A masked pattern was here ####
+STAGE DEPENDENCIES:
+ Stage-0 is a root stage
+
+STAGE PLANS:
+ Stage: Stage-0
+ Fetch Operator
+ limit: -1
+ Processor Tree:
+ TableScan
+ alias: t
+ filterExpr: (i NOT BETWEEN 6 AND 7 and i NOT BETWEEN 9 AND 10) (type: boolean)
+ Statistics: Num rows: 12 Data size: 48 Basic stats: COMPLETE Column stats: COMPLETE
+ Filter Operator
+ predicate: (i NOT BETWEEN 6 AND 7 and i NOT BETWEEN 9 AND 10) (type: boolean)
+ Statistics: Num rows: 2 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE
+ Select Operator
+ expressions: i (type: int)
+ outputColumnNames: _col0
+ Statistics: Num rows: 2 Data size: 8 Basic stats: COMPLETE Column stats: COMPLETE
+ ListSink
+
+PREHOOK: query: SELECT * FROM t where i not between 6 and 7
+ and i not between 9 and 10
+PREHOOK: type: QUERY
+PREHOOK: Input: default@t
+#### A masked pattern was here ####
+POSTHOOK: query: SELECT * FROM t where i not between 6 and 7
+ and i not between 9 and 10
+POSTHOOK: type: QUERY
+POSTHOOK: Input: default@t
+#### A masked pattern was here ####
+0
+1
+2
+3
+4
+5
+8
+11
diff --git a/ql/src/test/results/clientpositive/vector_between_columns.q.out b/ql/src/test/results/clientpositive/vector_between_columns.q.out
index 4336505..28bc1cd 100644
--- a/ql/src/test/results/clientpositive/vector_between_columns.q.out
+++ b/ql/src/test/results/clientpositive/vector_between_columns.q.out
@@ -157,13 +157,13 @@ STAGE PLANS:
outputColumnNames: _col0, _col1, _col2, _col3, _col4
Statistics: Num rows: 36 Data size: 708 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
- expressions: _col0 (type: int), _col2 (type: int), _col1 (type: int), _col3 (type: smallint), CASE WHEN (_col1 BETWEEN _col4 AND _col4) THEN ('Ok') ELSE ('NoOk') END (type: string)
+ expressions: _col0 (type: int), _col2 (type: int), _col1 (type: int), _col3 (type: smallint), CASE WHEN (_col4 BETWEEN _col1 AND _col1) THEN ('Ok') ELSE ('NoOk') END (type: string)
outputColumnNames: _col0, _col1, _col2, _col3, _col4
Select Vectorization:
className: VectorSelectOperator
native: true
projectedOutputColumnNums: [0, 2, 1, 3, 6]
- selectExpressions: IfExprStringScalarStringScalar(col 5:boolean, val Ok, val NoOk)(children: VectorUDFAdaptor(_col1 BETWEEN _col4 AND _col4) -> 5:boolean) -> 6:string
+ selectExpressions: IfExprStringScalarStringScalar(col 5:boolean, val Ok, val NoOk)(children: VectorUDFAdaptor(_col4 BETWEEN _col1 AND _col1) -> 5:boolean) -> 6:string
Statistics: Num rows: 36 Data size: 7192 Basic stats: COMPLETE Column stats: COMPLETE
File Output Operator
compressed: false
@@ -320,8 +320,8 @@ STAGE PLANS:
Filter Vectorization:
className: VectorFilterOperator
native: true
- predicateExpression: SelectColumnIsTrue(col 5:boolean)(children: VectorUDFAdaptor(_col1 BETWEEN _col4 AND _col4) -> 5:boolean)
- predicate: _col1 BETWEEN _col4 AND _col4 (type: boolean)
+ predicateExpression: SelectColumnIsTrue(col 5:boolean)(children: VectorUDFAdaptor(_col4 BETWEEN _col1 AND _col1) -> 5:boolean)
+ predicate: _col4 BETWEEN _col1 AND _col1 (type: boolean)
Statistics: Num rows: 4 Data size: 80 Basic stats: COMPLETE Column stats: COMPLETE
Select Operator
expressions: _col0 (type: int), _col2 (type: int), _col1 (type: int), _col3 (type: smallint)