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Posted to reviews@spark.apache.org by kiszk <gi...@git.apache.org> on 2017/05/23 05:18:49 UTC
[GitHub] spark pull request #18066: [SPARK-20822][SQL] Generate code to build table c...
GitHub user kiszk opened a pull request:
https://github.com/apache/spark/pull/18066
[SPARK-20822][SQL] Generate code to build table cache using ColumnarBatch and to get value from ColumnVector
## What changes were proposed in this pull request?
This PR generates the following Java code
1. Build each in-memory table cache using `ColumnarBatch` with `ColumnVector` instead of using CachedBatch with `Array[Byte]`.
2. Get a value for a column in `ColumnVector without using an iterator
As the first step, for ease of review, I supported only integer and double data types with whole-stage codegen. Another PR will address an execution path without whole-stage codegen
This PR implements the follings:
1. Keep a in-memory table cache using `ColumnarBatch` with `ColumnVector`. For supporting the new and coventional cache data structure, this PR declares `CachedBatch` as trait, and declares `CachedColumnarBatch` and `CachedBatchBytes` as actual implementations.
2. Generate Java code to build a in-memory table cache.
3. Generate Java code to directly get value from `ColumnVector`.
This PR improves runtime performance by
1. build in-memory table cache by eliminating lots of virtual calls and complicated data path.
2. eliminating data copy from column-oriented storage to `InternalRow` in a `SpecificColumnarIterator` iterator.
**Options**
A ColumnVector for all primitive data types in ColumnarBatch can be compressed. Currently, there are two ways to enable compression:
1. Set true into a property `spark.sql.inMemoryColumnarStorage.compressed (default is true)`, or
2. Call `DataFrame.persist(st)`, where st is `MEMORY_ONLY_SER`, `MEMORY_ONLY_SER_2`, `MEMORY_AND_DISK_SER`, or `MEMORY_AND_DISK_SER_2`.
**an example program**
```java
val df = sparkContext.parallelize((1 to 10), 1).map(i => (i, i.toDouble)).toDF("i", "d").cache
df.filter("i < 8 and 4.0 < d").show
```
**Generated code for building a in-memory table cache**
```
/* 001 */ import scala.collection.Iterator;
/* 002 */ import org.apache.spark.sql.types.DataType;
/* 003 */ import org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder;
/* 004 */ import org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter;
/* 005 */ import org.apache.spark.sql.execution.columnar.MutableUnsafeRow;
/* 006 */ import org.apache.spark.sql.execution.vectorized.ColumnVector;
/* 007 */
/* 008 */ public SpecificColumnarIterator generate(Object[] references) {
/* 009 */ return new SpecificColumnarIterator(references);
/* 010 */ }
/* 011 */
/* 012 */ class SpecificColumnarIterator extends org.apache.spark.sql.execution.columnar.ColumnarIterator {
/* 013 */ private ColumnVector[] colInstances;
/* 014 */ private UnsafeRow unsafeRow = new UnsafeRow(0);
/* 015 */ private BufferHolder bufferHolder = new BufferHolder(unsafeRow);
/* 016 */ private UnsafeRowWriter rowWriter = new UnsafeRowWriter(bufferHolder, 0);
/* 017 */ private MutableUnsafeRow mutableRow = null;
/* 018 */
/* 019 */ private int rowIdx = 0;
/* 020 */ private int numRowsInBatch = 0;
/* 021 */
/* 022 */ private scala.collection.Iterator input = null;
/* 023 */ private DataType[] columnTypes = null;
/* 024 */ private int[] columnIndexes = null;
/* 025 */
/* 026 */
/* 027 */
/* 028 */ public SpecificColumnarIterator(Object[] references) {
/* 029 */
/* 030 */ this.mutableRow = new MutableUnsafeRow(rowWriter);
/* 031 */ }
/* 032 */
/* 033 */ public void initialize(Iterator input, DataType[] columnTypes, int[] columnIndexes) {
/* 034 */ this.input = input;
/* 035 */ this.columnTypes = columnTypes;
/* 036 */ this.columnIndexes = columnIndexes;
/* 037 */ }
/* 038 */
/* 039 */
/* 040 */
/* 041 */ public boolean hasNext() {
/* 042 */ if (rowIdx < numRowsInBatch) {
/* 043 */ return true;
/* 044 */ }
/* 045 */ if (!input.hasNext()) {
/* 046 */ return false;
/* 047 */ }
/* 048 */
/* 049 */ org.apache.spark.sql.execution.columnar.CachedColumnarBatch cachedBatch =
/* 050 */ (org.apache.spark.sql.execution.columnar.CachedColumnarBatch) input.next();
/* 051 */ org.apache.spark.sql.execution.vectorized.ColumnarBatch batch = cachedBatch.columnarBatch();
/* 052 */ rowIdx = 0;
/* 053 */ numRowsInBatch = cachedBatch.getNumRows();
/* 054 */ colInstances = new ColumnVector[columnIndexes.length];
/* 055 */ for (int i = 0; i < columnIndexes.length; i ++) {
/* 056 */ colInstances[i] = batch.column(columnIndexes[i]);
/* 057 */ }
/* 058 */
/* 059 */ return hasNext();
/* 060 */ }
/* 061 */
/* 062 */ public InternalRow next() {
/* 063 */ bufferHolder.reset();
/* 064 */ rowWriter.zeroOutNullBytes();
/* 065 */
/* 066 */ unsafeRow.setTotalSize(bufferHolder.totalSize());
/* 067 */ rowIdx += 1;
/* 068 */ return unsafeRow;
/* 069 */ }
/* 070 */ }
```
**Generated code by whole-stage codegen (lines 75-78 are major changes)**
```
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private boolean agg_initAgg;
/* 009 */ private boolean agg_bufIsNull;
/* 010 */ private long agg_bufValue;
/* 011 */ private scala.collection.Iterator inmemorytablescan_input;
/* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric inmemorytablescan_numOutputRows;
/* 013 */ private org.apache.spark.sql.execution.metric.SQLMetric inmemorytablescan_scanTime;
/* 014 */ private long inmemorytablescan_scanTime1;
/* 015 */ private org.apache.spark.sql.execution.vectorized.ColumnarBatch inmemorytablescan_batch;
/* 016 */ private int inmemorytablescan_batchIdx;
/* 017 */ private org.apache.spark.sql.execution.vectorized.ColumnVector inmemorytablescan_colInstance0;
/* 018 */ private org.apache.spark.sql.execution.vectorized.ColumnVector inmemorytablescan_colInstance1;
/* 019 */ private UnsafeRow inmemorytablescan_result;
/* 020 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder inmemorytablescan_holder;
/* 021 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter inmemorytablescan_rowWriter;
/* 022 */ private org.apache.spark.sql.execution.metric.SQLMetric filter_numOutputRows;
/* 023 */ private UnsafeRow filter_result;
/* 024 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder filter_holder;
/* 025 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter filter_rowWriter;
/* 026 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_numOutputRows;
/* 027 */ private org.apache.spark.sql.execution.metric.SQLMetric agg_aggTime;
/* 028 */ private UnsafeRow agg_result;
/* 029 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder agg_holder;
/* 030 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter agg_rowWriter;
/* 031 */
/* 032 */ public GeneratedIterator(Object[] references) {
/* 033 */ this.references = references;
/* 034 */ }
/* 035 */
/* 036 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 037 */ partitionIndex = index;
/* 038 */ this.inputs = inputs;
/* 039 */ wholestagecodegen_init_0();
/* 040 */ wholestagecodegen_init_1();
/* 041 */
/* 042 */ }
/* 043 */
/* 044 */ private void wholestagecodegen_init_0() {
/* 045 */ agg_initAgg = false;
/* 046 */
/* 047 */ inmemorytablescan_input = inputs[0];
/* 048 */ this.inmemorytablescan_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0];
/* 049 */ this.inmemorytablescan_scanTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[1];
/* 050 */ inmemorytablescan_scanTime1 = 0;
/* 051 */ inmemorytablescan_batch = null;
/* 052 */ inmemorytablescan_batchIdx = 0;
/* 053 */ inmemorytablescan_colInstance0 = null;
/* 054 */ inmemorytablescan_colInstance1 = null;
/* 055 */ inmemorytablescan_result = new UnsafeRow(2);
/* 056 */ this.inmemorytablescan_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(inmemorytablescan_result, 0);
/* 057 */ this.inmemorytablescan_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(inmemorytablescan_holder, 2);
/* 058 */ this.filter_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[2];
/* 059 */ filter_result = new UnsafeRow(2);
/* 060 */ this.filter_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(filter_result, 0);
/* 061 */ this.filter_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(filter_holder, 2);
/* 062 */
/* 063 */ }
/* 064 */
/* 065 */ private void agg_doAggregateWithoutKey() throws java.io.IOException {
/* 066 */ // initialize aggregation buffer
/* 067 */ agg_bufIsNull = false;
/* 068 */ agg_bufValue = 0L;
/* 069 */
/* 070 */ if (inmemorytablescan_batch == null) {
/* 071 */ inmemorytablescan_nextBatch();
/* 072 */ }
/* 073 */ while (inmemorytablescan_batch != null) {
/* 074 */ int inmemorytablescan_numRows = inmemorytablescan_batch.numRows();
/* 075 */ int inmemorytablescan_localEnd = inmemorytablescan_numRows - inmemorytablescan_batchIdx;
/* 076 */ for (int inmemorytablescan_localIdx = 0; inmemorytablescan_localIdx < inmemorytablescan_localEnd; inmemorytablescan_localIdx++) {
/* 077 */ int inmemorytablescan_rowIdx = inmemorytablescan_batchIdx + inmemorytablescan_localIdx;
/* 078 */ int inmemorytablescan_value = inmemorytablescan_colInstance0.getInt(inmemorytablescan_rowIdx);
/* 079 */
/* 080 */ boolean filter_isNull = false;
/* 081 */
/* 082 */ boolean filter_value = false;
/* 083 */ filter_value = inmemorytablescan_value > 4;
/* 084 */ if (!filter_value) continue;
/* 085 */ double inmemorytablescan_value1 = inmemorytablescan_colInstance1.getDouble(inmemorytablescan_rowIdx);
/* 086 */
/* 087 */ boolean filter_isNull3 = false;
/* 088 */
/* 089 */ boolean filter_value3 = false;
/* 090 */ filter_value3 = org.apache.spark.util.Utils.nanSafeCompareDoubles(inmemorytablescan_value1, 10.0D) > 0;
/* 091 */ if (!filter_value3) continue;
/* 092 */
/* 093 */ filter_numOutputRows.add(1);
/* 094 */
/* 095 */ // do aggregate
/* 096 */ // common sub-expressions
/* 097 */
/* 098 */ // evaluate aggregate function
/* 099 */ boolean agg_isNull1 = false;
/* 100 */
/* 101 */ long agg_value1 = -1L;
/* 102 */ agg_value1 = agg_bufValue + 1L;
/* 103 */ // update aggregation buffer
/* 104 */ agg_bufIsNull = false;
/* 105 */ agg_bufValue = agg_value1;
/* 106 */ // shouldStop check is eliminated
/* 107 */ }
/* 108 */ inmemorytablescan_batchIdx = inmemorytablescan_numRows;
/* 109 */ inmemorytablescan_batch = null;
/* 110 */ inmemorytablescan_nextBatch();
/* 111 */ }
/* 112 */ inmemorytablescan_scanTime.add(inmemorytablescan_scanTime1 / (1000 * 1000));
/* 113 */ inmemorytablescan_scanTime1 = 0;
/* 114 */
/* 115 */ }
/* 116 */
/* 117 */ private void inmemorytablescan_nextBatch() throws java.io.IOException {
/* 118 */ long getBatchStart = System.nanoTime();
/* 119 */ if (inmemorytablescan_input.hasNext()) {
/* 120 */ inmemorytablescan_batch = (org.apache.spark.sql.execution.vectorized.ColumnarBatch)inmemorytablescan_input.next();
/* 121 */ inmemorytablescan_numOutputRows.add(inmemorytablescan_batch.numRows());
/* 122 */ inmemorytablescan_batchIdx = 0;
/* 123 */ inmemorytablescan_colInstance0 = inmemorytablescan_batch.column(0);
/* 124 */ inmemorytablescan_colInstance1 = inmemorytablescan_batch.column(1);
/* 125 */
/* 126 */ }
/* 127 */ inmemorytablescan_scanTime1 += System.nanoTime() - getBatchStart;
/* 128 */ }
/* 129 */
/* 130 */ private void wholestagecodegen_init_1() {
/* 131 */ this.agg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[3];
/* 132 */ this.agg_aggTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[4];
/* 133 */ agg_result = new UnsafeRow(1);
/* 134 */ this.agg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(agg_result, 0);
/* 135 */ this.agg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(agg_holder, 1);
/* 136 */
/* 137 */ }
/* 138 */
/* 139 */ protected void processNext() throws java.io.IOException {
/* 140 */ while (!agg_initAgg) {
/* 141 */ agg_initAgg = true;
/* 142 */ long agg_beforeAgg = System.nanoTime();
/* 143 */ agg_doAggregateWithoutKey();
/* 144 */ agg_aggTime.add((System.nanoTime() - agg_beforeAgg) / 1000000);
/* 145 */
/* 146 */ // output the result
/* 147 */
/* 148 */ agg_numOutputRows.add(1);
/* 149 */ agg_rowWriter.zeroOutNullBytes();
/* 150 */
/* 151 */ if (agg_bufIsNull) {
/* 152 */ agg_rowWriter.setNullAt(0);
/* 153 */ } else {
/* 154 */ agg_rowWriter.write(0, agg_bufValue);
/* 155 */ }
/* 156 */ append(agg_result);
/* 157 */ }
/* 158 */ }
/* 159 */ }
```
## How was this patch tested?
Add test suites for wider columns
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/kiszk/spark SPARK-20822
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/18066.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #18066
----
commit 6ed3d3fa51cd9b09e2f137bda87dcb16e5a9fb1a
Author: Kazuaki Ishizaki <is...@jp.ibm.com>
Date: 2017-05-23T04:54:25Z
initial commit
----
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by kiszk <gi...@git.apache.org>.
Github user kiszk commented on the issue:
https://github.com/apache/spark/pull/18066
#18747 is another PR for this JIRA entry
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
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Merged build finished. Test FAILed.
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18066
**[Test build #77222 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/77222/testReport)** for PR 18066 at commit [`6ed3d3f`](https://github.com/apache/spark/commit/6ed3d3fa51cd9b09e2f137bda87dcb16e5a9fb1a).
* This patch **fails Scala style tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `class GenerateColumnAccessor(useColumnarBatch: Boolean)`
* `class GenerateColumnarBatch(
`
* ` class GeneratedColumnarBatchIterator extends $`
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by kiszk <gi...@git.apache.org>.
Github user kiszk commented on the issue:
https://github.com/apache/spark/pull/18066
@hvanhovell @sameeragarwal Would you please review this?
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by kiszk <gi...@git.apache.org>.
Github user kiszk commented on the issue:
https://github.com/apache/spark/pull/18066
ping @hvanhovell @sameeragarwal
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[GitHub] spark pull request #18066: [SPARK-20822][SQL] Generate code to build table c...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/18066#discussion_r120038491
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/GeneratedColumnarBatch.scala ---
@@ -0,0 +1,220 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.columnar
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.execution.vectorized.ColumnarBatch
+import org.apache.spark.sql.types._
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.storage.StorageLevel._
+
+
+/**
+ * A helper class to expose the scala iterator to Java.
+ */
+abstract class ColumnarBatchIterator extends Iterator[ColumnarBatch]
+
+
+/**
+ * Generate code to batch [[InternalRow]]s into [[ColumnarBatch]]es.
+ */
+class GenerateColumnarBatch(
+ schema: StructType,
+ batchSize: Int,
+ storageLevel: StorageLevel)
+ extends CodeGenerator[Iterator[InternalRow], Iterator[CachedColumnarBatch]] {
+
+ protected def canonicalize(in: Iterator[InternalRow]): Iterator[InternalRow] = in
+
+ protected def bind(
+ in: Iterator[InternalRow], inputSchema: Seq[Attribute]): Iterator[InternalRow] = {
+ in
+ }
+
+ protected def create(rowIterator: Iterator[InternalRow]): Iterator[CachedColumnarBatch] = {
+ import scala.collection.JavaConverters._
+ val ctx = newCodeGenContext()
+ val columnStatsCls = classOf[ColumnStats].getName
+ val rowVar = ctx.freshName("row")
+ val batchVar = ctx.freshName("columnarBatch")
+ val rowNumVar = ctx.freshName("rowNum")
+ val numBytesVar = ctx.freshName("bytesInBatch")
+ ctx.addMutableState("long", numBytesVar, s"$numBytesVar = 0;")
+ val rowIterVar = ctx.addReferenceObj(
+ "rowIterator", rowIterator.asJava, classOf[java.util.Iterator[_]].getName)
+ val schemas = StructType(
+ schema.fields.map(s => StructField(s.name,
+ s.dataType match {
+ case udt: UserDefinedType[_] => udt.sqlType
+ case other => other
+ }, s.nullable))
+ )
+ val schemaVar = ctx.addReferenceObj("schema", schemas, classOf[StructType].getName)
+ val maxNumBytes = ColumnBuilder.MAX_BATCH_SIZE_IN_BYTE
+ val numColumns = schema.fields.length
+
+ val colStatVars = (0 to numColumns - 1).map(i => ctx.freshName("colStat" + i))
+ val colStatCode = ctx.splitExpressions(
+ (schemas.fields zip colStatVars).zipWithIndex.map {
+ case ((field, varName), i) =>
+ val columnStatsCls = field.dataType match {
+ case IntegerType => classOf[IntColumnStats].getName
+ case DoubleType => classOf[DoubleColumnStats].getName
+ case others => throw new UnsupportedOperationException(s"$others is not supported yet")
+ }
+ ctx.addMutableState(columnStatsCls, varName, "")
+ s"$varName = new $columnStatsCls(); statsArray[$i] = $varName;\n"
+ },
+ "apply",
+ Seq.empty
+ )
+
+ val populateColumnVectorsCode = ctx.splitExpressions(
+ (schemas.fields zip colStatVars).zipWithIndex.map {
+ case ((field, colStatVar), i) =>
+ GenerateColumnarBatch.putColumnCode(ctx, field.dataType, field.nullable,
+ batchVar, rowVar, rowNumVar, colStatVar, i, numBytesVar).trim + "\n"
+ },
+ "apply",
+ Seq(("InternalRow", rowVar), ("ColumnarBatch", batchVar), ("int", rowNumVar))
+ )
+
+ val code = s"""
+ import org.apache.spark.memory.MemoryMode;
+ import org.apache.spark.sql.catalyst.InternalRow;
+ import org.apache.spark.sql.execution.columnar.CachedColumnarBatch;
+ import org.apache.spark.sql.execution.columnar.GenerateColumnarBatch;
+ import org.apache.spark.sql.execution.vectorized.ColumnarBatch;
+ import org.apache.spark.sql.execution.vectorized.ColumnVector;
+
+ public GeneratedColumnarBatchIterator generate(Object[] references) {
+ return new GeneratedColumnarBatchIterator(references);
+ }
+
+ class GeneratedColumnarBatchIterator extends ${classOf[ColumnarBatchIterator].getName} {
+ private Object[] references;
+ ${ctx.declareMutableStates()}
+
+ public GeneratedColumnarBatchIterator(Object[] references) {
+ this.references = references;
+ ${ctx.initMutableStates()}
+ }
+
+ ${ctx.declareAddedFunctions()}
+
+ $columnStatsCls[] statsArray = new $columnStatsCls[$numColumns];
+ private void allocateColumnStats() {
+ ${colStatCode.trim}
+ }
+
+ @Override
+ public boolean hasNext() {
+ return $rowIterVar.hasNext();
+ }
+
+ @Override
+ public CachedColumnarBatch next() {
+ ColumnarBatch $batchVar =
+ ColumnarBatch.allocate($schemaVar, MemoryMode.ON_HEAP, $batchSize);
+ allocateColumnStats();
+ int $rowNumVar = 0;
+ $numBytesVar = 0;
+ while ($rowIterVar.hasNext() && $rowNumVar < $batchSize && $numBytesVar < $maxNumBytes) {
+ InternalRow $rowVar = (InternalRow) $rowIterVar.next();
+ $populateColumnVectorsCode
+ $rowNumVar += 1;
+ }
+ $batchVar.setNumRows($rowNumVar);
+ return CachedColumnarBatch.apply(
+ $batchVar, GenerateColumnarBatch.generateStats(statsArray));
+ }
+ }
+ """
+ val formattedCode = CodeFormatter.stripOverlappingComments(
+ new CodeAndComment(code, ctx.getPlaceHolderToComments()))
+ CodeGenerator.compile(formattedCode).generate(ctx.references.toArray)
+ .asInstanceOf[Iterator[CachedColumnarBatch]]
+ }
+}
+
+
+private[sql] object GenerateColumnarBatch {
+ def compressStorageLevel(storageLevel: StorageLevel, useCompression: Boolean): StorageLevel = {
+ if (!useCompression) return storageLevel
+ storageLevel match {
+ case MEMORY_ONLY => MEMORY_ONLY_SER
+ case MEMORY_ONLY_2 => MEMORY_ONLY_SER_2
+ case MEMORY_AND_DISK => MEMORY_AND_DISK_SER
+ case MEMORY_AND_DISK_2 => MEMORY_AND_DISK_SER_2
+ case sl => sl
+ }
+ }
+
+ def isCompress(storageLevel: StorageLevel) : Boolean = {
+ (storageLevel == MEMORY_ONLY_SER || storageLevel == MEMORY_ONLY_SER_2 ||
+ storageLevel == MEMORY_AND_DISK_SER || storageLevel == MEMORY_AND_DISK_SER_2)
+ }
+
+ private val typeToName = Map[AbstractDataType, String](
--- End diff --
Hi, @kiszk .
Is there any reason having only two types, `int` and `double`?
The PR looks more general to me.
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18066
**[Test build #77702 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/77702/testReport)** for PR 18066 at commit [`c183032`](https://github.com/apache/spark/commit/c183032a0edc3837bd0e697a15b298b47a2ab5ab).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request #18066: [SPARK-20822][SQL] Generate code to build table c...
Posted by kiszk <gi...@git.apache.org>.
Github user kiszk closed the pull request at:
https://github.com/apache/spark/pull/18066
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18066
**[Test build #77222 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/77222/testReport)** for PR 18066 at commit [`6ed3d3f`](https://github.com/apache/spark/commit/6ed3d3fa51cd9b09e2f137bda87dcb16e5a9fb1a).
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by kiszk <gi...@git.apache.org>.
Github user kiszk commented on the issue:
https://github.com/apache/spark/pull/18066
ping @hvanhovell
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/18066
Merged build finished. Test PASSed.
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by kiszk <gi...@git.apache.org>.
Github user kiszk commented on the issue:
https://github.com/apache/spark/pull/18066
ping @hvanhovell @sameeragarwal
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18066
**[Test build #77702 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/77702/testReport)** for PR 18066 at commit [`c183032`](https://github.com/apache/spark/commit/c183032a0edc3837bd0e697a15b298b47a2ab5ab).
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18066
**[Test build #77235 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/77235/testReport)** for PR 18066 at commit [`513acfb`](https://github.com/apache/spark/commit/513acfbf746e0299ceb1174e0ed76fe3dadad8a3).
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/18066
Merged build finished. Test PASSed.
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[GitHub] spark issue #18066: [SPARK-20822][SQL] Generate code to build table cache us...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/18066
**[Test build #77235 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/77235/testReport)** for PR 18066 at commit [`513acfb`](https://github.com/apache/spark/commit/513acfbf746e0299ceb1174e0ed76fe3dadad8a3).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request #18066: [SPARK-20822][SQL] Generate code to build table c...
Posted by kiszk <gi...@git.apache.org>.
Github user kiszk commented on a diff in the pull request:
https://github.com/apache/spark/pull/18066#discussion_r120042739
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/GeneratedColumnarBatch.scala ---
@@ -0,0 +1,220 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.columnar
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.execution.vectorized.ColumnarBatch
+import org.apache.spark.sql.types._
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.storage.StorageLevel._
+
+
+/**
+ * A helper class to expose the scala iterator to Java.
+ */
+abstract class ColumnarBatchIterator extends Iterator[ColumnarBatch]
+
+
+/**
+ * Generate code to batch [[InternalRow]]s into [[ColumnarBatch]]es.
+ */
+class GenerateColumnarBatch(
+ schema: StructType,
+ batchSize: Int,
+ storageLevel: StorageLevel)
+ extends CodeGenerator[Iterator[InternalRow], Iterator[CachedColumnarBatch]] {
+
+ protected def canonicalize(in: Iterator[InternalRow]): Iterator[InternalRow] = in
+
+ protected def bind(
+ in: Iterator[InternalRow], inputSchema: Seq[Attribute]): Iterator[InternalRow] = {
+ in
+ }
+
+ protected def create(rowIterator: Iterator[InternalRow]): Iterator[CachedColumnarBatch] = {
+ import scala.collection.JavaConverters._
+ val ctx = newCodeGenContext()
+ val columnStatsCls = classOf[ColumnStats].getName
+ val rowVar = ctx.freshName("row")
+ val batchVar = ctx.freshName("columnarBatch")
+ val rowNumVar = ctx.freshName("rowNum")
+ val numBytesVar = ctx.freshName("bytesInBatch")
+ ctx.addMutableState("long", numBytesVar, s"$numBytesVar = 0;")
+ val rowIterVar = ctx.addReferenceObj(
+ "rowIterator", rowIterator.asJava, classOf[java.util.Iterator[_]].getName)
+ val schemas = StructType(
+ schema.fields.map(s => StructField(s.name,
+ s.dataType match {
+ case udt: UserDefinedType[_] => udt.sqlType
+ case other => other
+ }, s.nullable))
+ )
+ val schemaVar = ctx.addReferenceObj("schema", schemas, classOf[StructType].getName)
+ val maxNumBytes = ColumnBuilder.MAX_BATCH_SIZE_IN_BYTE
+ val numColumns = schema.fields.length
+
+ val colStatVars = (0 to numColumns - 1).map(i => ctx.freshName("colStat" + i))
+ val colStatCode = ctx.splitExpressions(
+ (schemas.fields zip colStatVars).zipWithIndex.map {
+ case ((field, varName), i) =>
+ val columnStatsCls = field.dataType match {
+ case IntegerType => classOf[IntColumnStats].getName
+ case DoubleType => classOf[DoubleColumnStats].getName
+ case others => throw new UnsupportedOperationException(s"$others is not supported yet")
+ }
+ ctx.addMutableState(columnStatsCls, varName, "")
+ s"$varName = new $columnStatsCls(); statsArray[$i] = $varName;\n"
+ },
+ "apply",
+ Seq.empty
+ )
+
+ val populateColumnVectorsCode = ctx.splitExpressions(
+ (schemas.fields zip colStatVars).zipWithIndex.map {
+ case ((field, colStatVar), i) =>
+ GenerateColumnarBatch.putColumnCode(ctx, field.dataType, field.nullable,
+ batchVar, rowVar, rowNumVar, colStatVar, i, numBytesVar).trim + "\n"
+ },
+ "apply",
+ Seq(("InternalRow", rowVar), ("ColumnarBatch", batchVar), ("int", rowNumVar))
+ )
+
+ val code = s"""
+ import org.apache.spark.memory.MemoryMode;
+ import org.apache.spark.sql.catalyst.InternalRow;
+ import org.apache.spark.sql.execution.columnar.CachedColumnarBatch;
+ import org.apache.spark.sql.execution.columnar.GenerateColumnarBatch;
+ import org.apache.spark.sql.execution.vectorized.ColumnarBatch;
+ import org.apache.spark.sql.execution.vectorized.ColumnVector;
+
+ public GeneratedColumnarBatchIterator generate(Object[] references) {
+ return new GeneratedColumnarBatchIterator(references);
+ }
+
+ class GeneratedColumnarBatchIterator extends ${classOf[ColumnarBatchIterator].getName} {
+ private Object[] references;
+ ${ctx.declareMutableStates()}
+
+ public GeneratedColumnarBatchIterator(Object[] references) {
+ this.references = references;
+ ${ctx.initMutableStates()}
+ }
+
+ ${ctx.declareAddedFunctions()}
+
+ $columnStatsCls[] statsArray = new $columnStatsCls[$numColumns];
+ private void allocateColumnStats() {
+ ${colStatCode.trim}
+ }
+
+ @Override
+ public boolean hasNext() {
+ return $rowIterVar.hasNext();
+ }
+
+ @Override
+ public CachedColumnarBatch next() {
+ ColumnarBatch $batchVar =
+ ColumnarBatch.allocate($schemaVar, MemoryMode.ON_HEAP, $batchSize);
+ allocateColumnStats();
+ int $rowNumVar = 0;
+ $numBytesVar = 0;
+ while ($rowIterVar.hasNext() && $rowNumVar < $batchSize && $numBytesVar < $maxNumBytes) {
+ InternalRow $rowVar = (InternalRow) $rowIterVar.next();
+ $populateColumnVectorsCode
+ $rowNumVar += 1;
+ }
+ $batchVar.setNumRows($rowNumVar);
+ return CachedColumnarBatch.apply(
+ $batchVar, GenerateColumnarBatch.generateStats(statsArray));
+ }
+ }
+ """
+ val formattedCode = CodeFormatter.stripOverlappingComments(
+ new CodeAndComment(code, ctx.getPlaceHolderToComments()))
+ CodeGenerator.compile(formattedCode).generate(ctx.references.toArray)
+ .asInstanceOf[Iterator[CachedColumnarBatch]]
+ }
+}
+
+
+private[sql] object GenerateColumnarBatch {
+ def compressStorageLevel(storageLevel: StorageLevel, useCompression: Boolean): StorageLevel = {
+ if (!useCompression) return storageLevel
+ storageLevel match {
+ case MEMORY_ONLY => MEMORY_ONLY_SER
+ case MEMORY_ONLY_2 => MEMORY_ONLY_SER_2
+ case MEMORY_AND_DISK => MEMORY_AND_DISK_SER
+ case MEMORY_AND_DISK_2 => MEMORY_AND_DISK_SER_2
+ case sl => sl
+ }
+ }
+
+ def isCompress(storageLevel: StorageLevel) : Boolean = {
+ (storageLevel == MEMORY_ONLY_SER || storageLevel == MEMORY_ONLY_SER_2 ||
+ storageLevel == MEMORY_AND_DISK_SER || storageLevel == MEMORY_AND_DISK_SER_2)
+ }
+
+ private val typeToName = Map[AbstractDataType, String](
--- End diff --
As I described in the description, this is for ease of review.
>As the first step, for ease of review, I supported only integer and double data types with whole-stage codegen. Another PR will address an execution path without whole-stage codegen
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[GitHub] spark pull request #18066: [SPARK-20822][SQL] Generate code to build table c...
Posted by dongjoon-hyun <gi...@git.apache.org>.
Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/18066#discussion_r120038532
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala ---
@@ -64,6 +88,32 @@ case class InMemoryRelation(
val batchStats: LongAccumulator = child.sqlContext.sparkContext.longAccumulator)
extends logical.LeafNode with MultiInstanceRelation {
+ /**
+ * If true, store the input rows using [[CachedColumnarBatch]]es, which are generally faster.
+ * If false, store the input rows using [[CachedBatchBytes]].
+ */
+ private def numOfNestedFields(dataType: DataType): Int = dataType match {
+ case dt: StructType => dt.fields.map(f => numOfNestedFields(f.dataType)).sum
+ case m: MapType => numOfNestedFields(m.keyType) + numOfNestedFields(m.valueType)
+ case a: ArrayType => numOfNestedFields(a.elementType)
+ case u: UserDefinedType[_] => numOfNestedFields(u.sqlType)
+ case _ => 1
+ }
+
+ private[columnar] val useColumnarBatches: Boolean = {
+ // In the initial implementation, for ease of review
+ // support only integer and double and # of fields is less than wholeStageMaxNumFields
--- End diff --
Oh, I see. Here is the comment about the reason.
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