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Posted to commits@spark.apache.org by ma...@apache.org on 2015/05/06 19:43:16 UTC
[01/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Repository: spark
Updated Branches:
refs/heads/master c3eb441f5 -> f2c47082c
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e
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diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e
new file mode 100644
index 0000000..84b934f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 027056d..5ead5f3 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -300,7 +300,7 @@ abstract class HiveComparisonTest
val hiveQueries = queryList.map(new TestHive.HiveQLQueryExecution(_))
// Make sure we can at least parse everything before attempting hive execution.
- hiveQueries.foreach(_.analyzed)
+ hiveQueries.foreach(_.logical)
val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map {
case ((queryString, i), hiveQuery, cachedAnswerFile)=>
try {
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
new file mode 100644
index 0000000..934452f
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveWindowFunctionQuerySuite.scala
@@ -0,0 +1,845 @@
+/*
+ * 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.hive.execution
+
+import java.io.File
+import java.util.{Locale, TimeZone}
+
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.util.Utils
+
+/**
+ * The test suite for window functions. To actually compare results with Hive,
+ * every test should be created by `createQueryTest`. Because we are reusing tables
+ * for different tests and there are a few properties needed to let Hive generate golden
+ * files, every `createQueryTest` calls should explicitly set `reset` to `false`.
+ */
+abstract class HiveWindowFunctionQueryBaseSuite extends HiveComparisonTest with BeforeAndAfter {
+ private val originalTimeZone = TimeZone.getDefault
+ private val originalLocale = Locale.getDefault
+ private val testTempDir = Utils.createTempDir()
+
+ override def beforeAll() {
+ TestHive.cacheTables = true
+ // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
+ TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+ // Add Locale setting
+ Locale.setDefault(Locale.US)
+
+ // Create the table used in windowing.q
+ sql("DROP TABLE IF EXISTS part")
+ sql(
+ """
+ |CREATE TABLE part(
+ | p_partkey INT,
+ | p_name STRING,
+ | p_mfgr STRING,
+ | p_brand STRING,
+ | p_type STRING,
+ | p_size INT,
+ | p_container STRING,
+ | p_retailprice DOUBLE,
+ | p_comment STRING)
+ """.stripMargin)
+ val testData1 = TestHive.getHiveFile("data/files/part_tiny.txt").getCanonicalPath
+ sql(
+ s"""
+ |LOAD DATA LOCAL INPATH '$testData1' overwrite into table part
+ """.stripMargin)
+
+ sql("DROP TABLE IF EXISTS over1k")
+ sql(
+ """
+ |create table over1k(
+ | t tinyint,
+ | si smallint,
+ | i int,
+ | b bigint,
+ | f float,
+ | d double,
+ | bo boolean,
+ | s string,
+ | ts timestamp,
+ | dec decimal(4,2),
+ | bin binary)
+ |row format delimited
+ |fields terminated by '|'
+ """.stripMargin)
+ val testData2 = TestHive.getHiveFile("data/files/over1k").getCanonicalPath
+ sql(
+ s"""
+ |LOAD DATA LOCAL INPATH '$testData2' overwrite into table over1k
+ """.stripMargin)
+
+ // The following settings are used for generating golden files with Hive.
+ // We have to use kryo to correctly let Hive serialize plans with window functions.
+ // This is used to generate golden files.
+ sql("set hive.plan.serialization.format=kryo")
+ // Explicitly set fs to local fs.
+ sql(s"set fs.default.name=file://$testTempDir/")
+ // Ask Hive to run jobs in-process as a single map and reduce task.
+ sql("set mapred.job.tracker=local")
+ }
+
+ override def afterAll() {
+ TestHive.cacheTables = false
+ TimeZone.setDefault(originalTimeZone)
+ Locale.setDefault(originalLocale)
+ TestHive.reset()
+ }
+
+ /////////////////////////////////////////////////////////////////////////////
+ // Tests based on windowing_multipartitioning.q
+ // Results of the original query file are not deterministic.
+ /////////////////////////////////////////////////////////////////////////////
+ createQueryTest("windowing_multipartitioning.q (deterministic) 1",
+ s"""
+ |select s,
+ |rank() over (partition by s order by si) r,
+ |sum(b) over (partition by s order by si) sum
+ |from over1k
+ |order by s, r, sum;
+ """.stripMargin, reset = false)
+
+ /* timestamp comparison issue with Hive?
+ createQueryTest("windowing_multipartitioning.q (deterministic) 2",
+ s"""
+ |select s,
+ |rank() over (partition by s order by dec desc) r,
+ |sum(b) over (partition by s order by ts desc) as sum
+ |from over1k
+ |where s = 'tom allen' or s = 'bob steinbeck'
+ |order by s, r, sum;
+ """.stripMargin, reset = false)
+ */
+
+ createQueryTest("windowing_multipartitioning.q (deterministic) 3",
+ s"""
+ |select s, sum(i) over (partition by s), sum(f) over (partition by si)
+ |from over1k where s = 'tom allen' or s = 'bob steinbeck';
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing_multipartitioning.q (deterministic) 4",
+ s"""
+ |select s, rank() over (partition by s order by bo),
+ |rank() over (partition by si order by bin desc) from over1k
+ |where s = 'tom allen' or s = 'bob steinbeck';
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing_multipartitioning.q (deterministic) 5",
+ s"""
+ |select s, sum(f) over (partition by i), row_number() over (order by f)
+ |from over1k where s = 'tom allen' or s = 'bob steinbeck';
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing_multipartitioning.q (deterministic) 6",
+ s"""
+ |select s, rank() over w1,
+ |rank() over w2
+ |from over1k
+ |where s = 'tom allen' or s = 'bob steinbeck'
+ |window
+ |w1 as (partition by s order by dec),
+ |w2 as (partition by si order by f) ;
+ """.stripMargin, reset = false)
+
+ /////////////////////////////////////////////////////////////////////////////
+ // Tests based on windowing_navfn.q
+ // Results of the original query file are not deterministic.
+ // Also, the original query of
+ // select i, lead(s) over (partition by bin order by d,i desc) from over1k ;
+ /////////////////////////////////////////////////////////////////////////////
+ createQueryTest("windowing_navfn.q (deterministic)",
+ s"""
+ |select s, row_number() over (partition by d order by dec) rn from over1k
+ |order by s, rn desc;
+ |select i, lead(s) over (partition by cast(bin as string) order by d,i desc) as l
+ |from over1k
+ |order by i desc, l;
+ |select i, lag(dec) over (partition by i order by s,i,dec) l from over1k
+ |order by i, l;
+ |select s, last_value(t) over (partition by d order by f) l from over1k
+ |order by s, l;
+ |select s, first_value(s) over (partition by bo order by s) f from over1k
+ |order by s, f;
+ |select t, s, i, last_value(i) over (partition by t order by s)
+ |from over1k where (s = 'oscar allen' or s = 'oscar carson') and t = 10;
+ """.stripMargin, reset = false)
+
+ /////////////////////////////////////////////////////////////////////////////
+ // Tests based on windowing_ntile.q
+ // Results of the original query file are not deterministic.
+ /////////////////////////////////////////////////////////////////////////////
+ createQueryTest("windowing_ntile.q (deterministic)",
+ s"""
+ |select i, ntile(10) over (partition by s order by i) n from over1k
+ |order by i, n;
+ |select s, ntile(100) over (partition by i order by s) n from over1k
+ |order by s, n;
+ |select f, ntile(4) over (partition by d order by f) n from over1k
+ |order by f, n;
+ |select d, ntile(1000) over (partition by dec order by d) n from over1k
+ |order by d, n;
+ """.stripMargin, reset = false)
+
+ /////////////////////////////////////////////////////////////////////////////
+ // Tests based on windowing_udaf.q
+ // Results of the original query file are not deterministic.
+ /////////////////////////////////////////////////////////////////////////////
+ createQueryTest("windowing_udaf.q (deterministic)",
+ s"""
+ |select s, min(i) over (partition by s) m from over1k
+ |order by s, m;
+ |select s, avg(f) over (partition by si order by s) a from over1k
+ |order by s, a;
+ |select s, avg(i) over (partition by t, b order by s) a from over1k
+ |order by s, a;
+ |select max(i) over w m from over1k
+ |order by m window w as (partition by f) ;
+ |select s, avg(d) over (partition by t order by f) a from over1k
+ |order by s, a;
+ """.stripMargin, reset = false)
+
+ /////////////////////////////////////////////////////////////////////////////
+ // Tests based on windowing_windowspec.q
+ // Results of the original query file are not deterministic.
+ /////////////////////////////////////////////////////////////////////////////
+ createQueryTest("windowing_windowspec.q (deterministic)",
+ s"""
+ |select s, sum(b) over (partition by i order by s,b rows unbounded preceding) as sum
+ |from over1k order by s, sum;
+ |select s, sum(f) over (partition by d order by s,f rows unbounded preceding) as sum
+ |from over1k order by s, sum;
+ |select s, sum(f) over
+ |(partition by ts order by f range between current row and unbounded following) as sum
+ |from over1k order by s, sum;
+ |select s, avg(f)
+ |over (partition by ts order by s,f rows between current row and 5 following) avg
+ |from over1k order by s, avg;
+ |select s, avg(d) over
+ |(partition by t order by s,d desc rows between 5 preceding and 5 following) avg
+ |from over1k order by s, avg;
+ |select s, sum(i) over(partition by ts order by s) sum from over1k
+ |order by s, sum;
+ |select f, sum(f) over
+ |(partition by ts order by f range between unbounded preceding and current row) sum
+ |from over1k order by f, sum;
+ |select s, i, round(avg(d) over (partition by s order by i) / 10.0 , 2) avg
+ |from over1k order by s, i, avg;
+ |select s, i, round((avg(d) over w1 + 10.0) - (avg(d) over w1 - 10.0),2) avg
+ |from over1k
+ |order by s, i, avg window w1 as (partition by s order by i);
+ """.stripMargin, reset = false)
+
+ /////////////////////////////////////////////////////////////////////////////
+ // Tests based on windowing_rank.q
+ // Results of the original query file are not deterministic.
+ /////////////////////////////////////////////////////////////////////////////
+ createQueryTest("windowing_rank.q (deterministic) 1",
+ s"""
+ |select s, rank() over (partition by f order by t) r from over1k order by s, r;
+ |select s, dense_rank() over (partition by ts order by i,s desc) as r from over1k
+ |order by s desc, r desc;
+ |select s, cume_dist() over (partition by bo order by b,s) cd from over1k
+ |order by s, cd;
+ |select s, percent_rank() over (partition by dec order by f) r from over1k
+ |order by s desc, r desc;
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing_rank.q (deterministic) 2",
+ s"""
+ |select ts, dec, rnk
+ |from
+ | (select ts, dec,
+ | rank() over (partition by ts order by dec) as rnk
+ | from
+ | (select other.ts, other.dec
+ | from over1k other
+ | join over1k on (other.b = over1k.b)
+ | ) joined
+ | ) ranked
+ |where rnk = 1
+ |order by ts, dec, rnk;
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing_rank.q (deterministic) 3",
+ s"""
+ |select ts, dec, rnk
+ |from
+ | (select ts, dec,
+ | rank() over (partition by ts order by dec) as rnk
+ | from
+ | (select other.ts, other.dec
+ | from over1k other
+ | join over1k on (other.b = over1k.b)
+ | ) joined
+ | ) ranked
+ |where dec = 89.5
+ |order by ts, dec, rnk;
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing_rank.q (deterministic) 4",
+ s"""
+ |select ts, dec, rnk
+ |from
+ | (select ts, dec,
+ | rank() over (partition by ts order by dec) as rnk
+ | from
+ | (select other.ts, other.dec
+ | from over1k other
+ | join over1k on (other.b = over1k.b)
+ | where other.t < 10
+ | ) joined
+ | ) ranked
+ |where rnk = 1
+ |order by ts, dec, rnk;
+ """.stripMargin, reset = false)
+
+ /////////////////////////////////////////////////////////////////////////////
+ // Tests from windowing.q
+ // We port tests in windowing.q to here because this query file contains too
+ // many tests and the syntax of test "-- 7. testJoinWithWindowingAndPTF"
+ // is not supported right now.
+ /////////////////////////////////////////////////////////////////////////////
+ createQueryTest("windowing.q -- 1. testWindowing",
+ s"""
+ |select p_mfgr, p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |sum(p_retailprice) over
+ |(distribute by p_mfgr sort by p_name rows between unbounded preceding and current row) as s1
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 2. testGroupByWithPartitioning",
+ s"""
+ |select p_mfgr, p_name, p_size,
+ |min(p_retailprice),
+ |rank() over(distribute by p_mfgr sort by p_name)as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+ |from part
+ |group by p_mfgr, p_name, p_size
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 3. testGroupByHavingWithSWQ",
+ s"""
+ |select p_mfgr, p_name, p_size, min(p_retailprice),
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+ |from part
+ |group by p_mfgr, p_name, p_size
+ |having p_size > 0
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 4. testCount",
+ s"""
+ |select p_mfgr, p_name,
+ |count(p_size) over(distribute by p_mfgr sort by p_name) as cd
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 5. testCountWithWindowingUDAF",
+ s"""
+ |select p_mfgr, p_name,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |count(p_size) over(distribute by p_mfgr sort by p_name) as cd,
+ |p_retailprice, sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+ | rows between unbounded preceding and current row) as s1,
+ |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 6. testCountInSubQ",
+ s"""
+ |select sub1.r, sub1.dr, sub1.cd, sub1.s1, sub1.deltaSz
+ |from (select p_mfgr, p_name,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |count(p_size) over(distribute by p_mfgr sort by p_name) as cd,
+ |p_retailprice, sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+ | rows between unbounded preceding and current row) as s1,
+ |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+ |from part
+ |) sub1
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 8. testMixedCaseAlias",
+ s"""
+ |select p_mfgr, p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name, p_size desc) as R
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 9. testHavingWithWindowingNoGBY",
+ s"""
+ |select p_mfgr, p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+ | rows between unbounded preceding and current row) as s1
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 10. testHavingWithWindowingCondRankNoGBY",
+ s"""
+ |select p_mfgr, p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+ | rows between unbounded preceding and current row) as s1
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 11. testFirstLast",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |sum(p_size) over (distribute by p_mfgr sort by p_name
+ |rows between current row and current row) as s2,
+ |first_value(p_size) over w1 as f,
+ |last_value(p_size, false) over w1 as l
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 12. testFirstLastWithWhere",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |sum(p_size) over (distribute by p_mfgr sort by p_name
+ |rows between current row and current row) as s2,
+ |first_value(p_size) over w1 as f,
+ |last_value(p_size, false) over w1 as l
+ |from part
+ |where p_mfgr = 'Manufacturer#3'
+ |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 13. testSumWindow",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |sum(p_size) over w1 as s1,
+ |sum(p_size) over (distribute by p_mfgr sort by p_name
+ |rows between current row and current row) as s2
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 14. testNoSortClause",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 15. testExpressions",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |cume_dist() over(distribute by p_mfgr sort by p_name) as cud,
+ |percent_rank() over(distribute by p_mfgr sort by p_name) as pr,
+ |ntile(3) over(distribute by p_mfgr sort by p_name) as nt,
+ |count(p_size) over(distribute by p_mfgr sort by p_name) as ca,
+ |avg(p_size) over(distribute by p_mfgr sort by p_name) as avg,
+ |stddev(p_size) over(distribute by p_mfgr sort by p_name) as st,
+ |first_value(p_size % 5) over(distribute by p_mfgr sort by p_name) as fv,
+ |last_value(p_size) over(distribute by p_mfgr sort by p_name) as lv,
+ |first_value(p_size) over w1 as fvW1
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+ | rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 16. testMultipleWindows",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |cume_dist() over(distribute by p_mfgr sort by p_name) as cud,
+ |sum(p_size) over (distribute by p_mfgr sort by p_name
+ |range between unbounded preceding and current row) as s1,
+ |sum(p_size) over (distribute by p_mfgr sort by p_size
+ |range between 5 preceding and current row) as s2,
+ |first_value(p_size) over w1 as fv1
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+ | rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+
+ createQueryTest("windowing.q -- 17. testCountStar",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |count(*) over(distribute by p_mfgr sort by p_name ) as c,
+ |count(p_size) over(distribute by p_mfgr sort by p_name) as ca,
+ |first_value(p_size) over w1 as fvW1
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+ | rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 18. testUDAFs",
+ s"""
+ |select p_mfgr,p_name, p_size,
+ |sum(p_retailprice) over w1 as s,
+ |min(p_retailprice) over w1 as mi,
+ |max(p_retailprice) over w1 as ma,
+ |avg(p_retailprice) over w1 as ag
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+ | rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 19. testUDAFsWithGBY",
+ """
+ |select p_mfgr,p_name, p_size, p_retailprice,
+ |sum(p_retailprice) over w1 as s,
+ |min(p_retailprice) as mi ,
+ |max(p_retailprice) as ma ,
+ |avg(p_retailprice) over w1 as ag
+ |from part
+ |group by p_mfgr,p_name, p_size, p_retailprice
+ |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+ | rows between 2 preceding and 2 following);
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 20. testSTATs",
+ """
+ |select p_mfgr,p_name, p_size,
+ |stddev(p_retailprice) over w1 as sdev,
+ |stddev_pop(p_retailprice) over w1 as sdev_pop,
+ |collect_set(p_size) over w1 as uniq_size,
+ |variance(p_retailprice) over w1 as var,
+ |corr(p_size, p_retailprice) over w1 as cor,
+ |covar_pop(p_size, p_retailprice) over w1 as covarp
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+ | rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 21. testDISTs",
+ """
+ |select p_mfgr,p_name, p_size,
+ |histogram_numeric(p_retailprice, 5) over w1 as hist,
+ |percentile(p_partkey, 0.5) over w1 as per,
+ |row_number() over(distribute by p_mfgr sort by p_name) as rn
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_mfgr, p_name
+ | rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 24. testLateralViews",
+ """
+ |select p_mfgr, p_name,
+ |lv_col, p_size, sum(p_size) over w1 as s
+ |from (select p_mfgr, p_name, p_size, array(1,2,3) arr from part) p
+ |lateral view explode(arr) part_lv as lv_col
+ |window w1 as (distribute by p_mfgr sort by p_size, lv_col
+ | rows between 2 preceding and current row)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 26. testGroupByHavingWithSWQAndAlias",
+ """
+ |select p_mfgr, p_name, p_size, min(p_retailprice) as mi,
+ |rank() over(distribute by p_mfgr sort by p_name) as r,
+ |dense_rank() over(distribute by p_mfgr sort by p_name) as dr,
+ |p_size, p_size - lag(p_size,1,p_size) over(distribute by p_mfgr sort by p_name) as deltaSz
+ |from part
+ |group by p_mfgr, p_name, p_size
+ |having p_size > 0
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 27. testMultipleRangeWindows",
+ """
+ |select p_mfgr,p_name, p_size,
+ |sum(p_size) over (distribute by p_mfgr sort by p_size
+ |range between 10 preceding and current row) as s2,
+ |sum(p_size) over (distribute by p_mfgr sort by p_size
+ |range between current row and 10 following ) as s1
+ |from part
+ |window w1 as (rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 28. testPartOrderInUDAFInvoke",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_size) over (partition by p_mfgr order by p_name
+ |rows between 2 preceding and 2 following) as s
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 29. testPartOrderInWdwDef",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_size) over w1 as s
+ |from part
+ |window w1 as (partition by p_mfgr order by p_name
+ | rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 30. testDefaultPartitioningSpecRules",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_size) over w1 as s,
+ |sum(p_size) over w2 as s2
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following),
+ | w2 as (partition by p_mfgr order by p_name)
+ """.stripMargin, reset = false)
+
+ /* p_name is not a numeric column. What is Hive's semantic?
+ createQueryTest("windowing.q -- 31. testWindowCrossReference",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_size) over w1 as s1,
+ |sum(p_size) over w2 as s2
+ |from part
+ |window w1 as (partition by p_mfgr order by p_name
+ | range between 2 preceding and 2 following),
+ | w2 as w1
+ """.stripMargin, reset = false)
+ */
+ /*
+ createQueryTest("windowing.q -- 32. testWindowInheritance",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_size) over w1 as s1,
+ |sum(p_size) over w2 as s2
+ |from part
+ |window w1 as (partition by p_mfgr order by p_name
+ | range between 2 preceding and 2 following),
+ | w2 as (w1 rows between unbounded preceding and current row)
+ """.stripMargin, reset = false)
+ */
+
+ /* p_name is not a numeric column. What is Hive's semantic?
+ createQueryTest("windowing.q -- 33. testWindowForwardReference",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_size) over w1 as s1,
+ |sum(p_size) over w2 as s2,
+ |sum(p_size) over w3 as s3
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_name
+ | range between 2 preceding and 2 following),
+ | w2 as w3,
+ | w3 as (distribute by p_mfgr sort by p_name
+ | range between unbounded preceding and current row)
+ """.stripMargin, reset = false)
+ */
+ /*
+ createQueryTest("windowing.q -- 34. testWindowDefinitionPropagation",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_size) over w1 as s1,
+ |sum(p_size) over w2 as s2,
+ |sum(p_size) over (w3 rows between 2 preceding and 2 following) as s3
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_name
+ | range between 2 preceding and 2 following),
+ | w2 as w3,
+ | w3 as (distribute by p_mfgr sort by p_name
+ | range between unbounded preceding and current row)
+ """.stripMargin, reset = false)
+ */
+
+ /* Seems Hive evaluate SELECT DISTINCT before window functions?
+ createQueryTest("windowing.q -- 35. testDistinctWithWindowing",
+ """
+ |select DISTINCT p_mfgr, p_name, p_size,
+ |sum(p_size) over w1 as s
+ |from part
+ |window w1 as (distribute by p_mfgr sort by p_name rows between 2 preceding and 2 following)
+ """.stripMargin, reset = false)
+ */
+
+ createQueryTest("windowing.q -- 36. testRankWithPartitioning",
+ """
+ |select p_mfgr, p_name, p_size,
+ |rank() over (partition by p_mfgr order by p_name ) as r
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 37. testPartitioningVariousForms",
+ """
+ |select p_mfgr,
+ |round(sum(p_retailprice) over (partition by p_mfgr order by p_mfgr),2) as s1,
+ |min(p_retailprice) over (partition by p_mfgr) as s2,
+ |max(p_retailprice) over (distribute by p_mfgr sort by p_mfgr) as s3,
+ |round(avg(p_retailprice) over (distribute by p_mfgr),2) as s4,
+ |count(p_retailprice) over (cluster by p_mfgr ) as s5
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 38. testPartitioningVariousForms2",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_retailprice) over (partition by p_mfgr, p_name order by p_mfgr, p_name
+ |rows between unbounded preceding and current row) as s1,
+ |min(p_retailprice) over (distribute by p_mfgr, p_name sort by p_mfgr, p_name
+ |rows between unbounded preceding and current row) as s2,
+ |max(p_retailprice) over (partition by p_mfgr, p_name order by p_name) as s3
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 39. testUDFOnOrderCols",
+ """
+ |select p_mfgr, p_type, substr(p_type, 2) as short_ptype,
+ |rank() over (partition by p_mfgr order by substr(p_type, 2)) as r
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 40. testNoBetweenForRows",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_retailprice) over (distribute by p_mfgr sort by p_name rows unbounded preceding) as s1
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 41. testNoBetweenForRange",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_retailprice) over (distribute by p_mfgr sort by p_size range unbounded preceding) as s1
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 42. testUnboundedFollowingForRows",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_retailprice) over (distribute by p_mfgr sort by p_name
+ |rows between current row and unbounded following) as s1
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 43. testUnboundedFollowingForRange",
+ """
+ |select p_mfgr, p_name, p_size,
+ |sum(p_retailprice) over (distribute by p_mfgr sort by p_size
+ |range between current row and unbounded following) as s1
+ |from part
+ """.stripMargin, reset = false)
+
+ createQueryTest("windowing.q -- 44. testOverNoPartitionSingleAggregate",
+ """
+ |select p_name, p_retailprice,
+ |round(avg(p_retailprice) over(),2)
+ |from part
+ |order by p_name
+ """.stripMargin, reset = false)
+}
+
+class HiveWindowFunctionQueryWithoutCodeGenSuite extends HiveWindowFunctionQueryBaseSuite {
+ var originalCodegenEnabled: Boolean = _
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ originalCodegenEnabled = conf.codegenEnabled
+ sql("set spark.sql.codegen=false")
+ }
+
+ override def afterAll(): Unit = {
+ sql(s"set spark.sql.codegen=$originalCodegenEnabled")
+ super.afterAll()
+ }
+}
+
+abstract class HiveWindowFunctionQueryFileBaseSuite
+ extends HiveCompatibilitySuite with BeforeAndAfter {
+ private val originalTimeZone = TimeZone.getDefault
+ private val originalLocale = Locale.getDefault
+ private val testTempDir = Utils.createTempDir()
+
+ override def beforeAll() {
+ TestHive.cacheTables = true
+ // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
+ TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+ // Add Locale setting
+ Locale.setDefault(Locale.US)
+
+ // The following settings are used for generating golden files with Hive.
+ // We have to use kryo to correctly let Hive serialize plans with window functions.
+ // This is used to generate golden files.
+ sql("set hive.plan.serialization.format=kryo")
+ // Explicitly set fs to local fs.
+ sql(s"set fs.default.name=file://$testTempDir/")
+ // Ask Hive to run jobs in-process as a single map and reduce task.
+ sql("set mapred.job.tracker=local")
+ }
+
+ override def afterAll() {
+ TestHive.cacheTables = false
+ TimeZone.setDefault(originalTimeZone)
+ Locale.setDefault(originalLocale)
+ TestHive.reset()
+ }
+
+ override def blackList: Seq[String] = Seq(
+ // Partitioned table functions are not supported.
+ "ptf*",
+ // tests of windowing.q are in HiveWindowFunctionQueryBaseSuite
+ "windowing.q",
+
+ // This one failed on the expression of
+ // sum(lag(p_retailprice,1,0.0)) over w1
+ // lag(p_retailprice,1,0.0) is a GenericUDF and the argument inspector of
+ // p_retailprice created by HiveInspectors is
+ // PrimitiveObjectInspectorFactory.javaDoubleObjectInspector.
+ // However, seems Hive assumes it is
+ // PrimitiveObjectInspectorFactory.writableDoubleObjectInspector, which introduces an error.
+ "windowing_expressions",
+
+ // Hive's results are not deterministic
+ "windowing_multipartitioning",
+ "windowing_navfn",
+ "windowing_ntile",
+ "windowing_udaf",
+ "windowing_windowspec",
+ "windowing_rank"
+ )
+
+ override def whiteList: Seq[String] = Seq(
+ "windowing_udaf2",
+ "windowing_columnPruning",
+ "windowing_adjust_rowcontainer_sz"
+ )
+
+ override def testCases: Seq[(String, File)] = super.testCases.filter {
+ case (name, _) => realWhiteList.contains(name)
+ }
+}
+
+class HiveWindowFunctionQueryFileWithoutCodeGenSuite extends HiveWindowFunctionQueryFileBaseSuite {
+ var originalCodegenEnabled: Boolean = _
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ originalCodegenEnabled = conf.codegenEnabled
+ sql("set spark.sql.codegen=false")
+ }
+
+ override def afterAll(): Unit = {
+ sql(s"set spark.sql.codegen=$originalCodegenEnabled")
+ super.afterAll()
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 630dec8..616352d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -48,6 +48,10 @@ case class Order(
state: String,
month: Int)
+case class WindowData(
+ month: Int,
+ area: String,
+ product: Int)
/** A SQL Dialect for testing purpose, and it can not be nested type */
class MyDialect extends DefaultDialect
@@ -604,4 +608,147 @@ class SQLQuerySuite extends QueryTest {
sql("SELECT TRANSFORM (d1, d2, d3) USING 'cat' AS (a,b,c) FROM script_trans")
.queryExecution.toRdd.count())
}
+
+ test("window function: udaf with aggregate expressin") {
+ val data = Seq(
+ WindowData(1, "a", 5),
+ WindowData(2, "a", 6),
+ WindowData(3, "b", 7),
+ WindowData(4, "b", 8),
+ WindowData(5, "c", 9),
+ WindowData(6, "c", 10)
+ )
+ sparkContext.parallelize(data).toDF().registerTempTable("windowData")
+
+ checkAnswer(
+ sql(
+ """
+ |select area, sum(product), sum(sum(product)) over (partition by area)
+ |from windowData group by month, area
+ """.stripMargin),
+ Seq(
+ ("a", 5, 11),
+ ("a", 6, 11),
+ ("b", 7, 15),
+ ("b", 8, 15),
+ ("c", 9, 19),
+ ("c", 10, 19)
+ ).map(i => Row(i._1, i._2, i._3)))
+
+ checkAnswer(
+ sql(
+ """
+ |select area, sum(product) - 1, sum(sum(product)) over (partition by area)
+ |from windowData group by month, area
+ """.stripMargin),
+ Seq(
+ ("a", 4, 11),
+ ("a", 5, 11),
+ ("b", 6, 15),
+ ("b", 7, 15),
+ ("c", 8, 19),
+ ("c", 9, 19)
+ ).map(i => Row(i._1, i._2, i._3)))
+
+ checkAnswer(
+ sql(
+ """
+ |select area, sum(product), sum(product) / sum(sum(product)) over (partition by area)
+ |from windowData group by month, area
+ """.stripMargin),
+ Seq(
+ ("a", 5, 5d/11),
+ ("a", 6, 6d/11),
+ ("b", 7, 7d/15),
+ ("b", 8, 8d/15),
+ ("c", 10, 10d/19),
+ ("c", 9, 9d/19)
+ ).map(i => Row(i._1, i._2, i._3)))
+
+ checkAnswer(
+ sql(
+ """
+ |select area, sum(product), sum(product) / sum(sum(product) - 1) over (partition by area)
+ |from windowData group by month, area
+ """.stripMargin),
+ Seq(
+ ("a", 5, 5d/9),
+ ("a", 6, 6d/9),
+ ("b", 7, 7d/13),
+ ("b", 8, 8d/13),
+ ("c", 10, 10d/17),
+ ("c", 9, 9d/17)
+ ).map(i => Row(i._1, i._2, i._3)))
+ }
+
+ test("window function: partition and order expressions") {
+ val data = Seq(
+ WindowData(1, "a", 5),
+ WindowData(2, "a", 6),
+ WindowData(3, "b", 7),
+ WindowData(4, "b", 8),
+ WindowData(5, "c", 9),
+ WindowData(6, "c", 10)
+ )
+ sparkContext.parallelize(data).toDF().registerTempTable("windowData")
+
+ checkAnswer(
+ sql(
+ """
+ |select month, area, product, sum(product + 1) over (partition by 1 order by 2)
+ |from windowData
+ """.stripMargin),
+ Seq(
+ (1, "a", 5, 51),
+ (2, "a", 6, 51),
+ (3, "b", 7, 51),
+ (4, "b", 8, 51),
+ (5, "c", 9, 51),
+ (6, "c", 10, 51)
+ ).map(i => Row(i._1, i._2, i._3, i._4)))
+
+ checkAnswer(
+ sql(
+ """
+ |select month, area, product, sum(product)
+ |over (partition by month % 2 order by 10 - product)
+ |from windowData
+ """.stripMargin),
+ Seq(
+ (1, "a", 5, 21),
+ (2, "a", 6, 24),
+ (3, "b", 7, 16),
+ (4, "b", 8, 18),
+ (5, "c", 9, 9),
+ (6, "c", 10, 10)
+ ).map(i => Row(i._1, i._2, i._3, i._4)))
+ }
+
+ test("window function: expressions in arguments of a window functions") {
+ val data = Seq(
+ WindowData(1, "a", 5),
+ WindowData(2, "a", 6),
+ WindowData(3, "b", 7),
+ WindowData(4, "b", 8),
+ WindowData(5, "c", 9),
+ WindowData(6, "c", 10)
+ )
+ sparkContext.parallelize(data).toDF().registerTempTable("windowData")
+
+ checkAnswer(
+ sql(
+ """
+ |select month, area, month % 2,
+ |lag(product, 1 + 1, product) over (partition by month % 2 order by area)
+ |from windowData
+ """.stripMargin),
+ Seq(
+ (1, "a", 1, 5),
+ (2, "a", 0, 6),
+ (3, "b", 1, 7),
+ (4, "b", 0, 8),
+ (5, "c", 1, 5),
+ (6, "c", 0, 6)
+ ).map(i => Row(i._1, i._2, i._3, i._4)))
+ }
}
---------------------------------------------------------------------
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[05/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1 b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1
new file mode 100644
index 0000000..9cc7e7e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-4-f2e4d659b65a833e9281b6786d3d55c1
@@ -0,0 +1,1049 @@
+ 24.37875
+ 27.900000000000002
+ 43.64
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---------------------------------------------------------------------
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[07/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5
new file mode 100644
index 0000000..9091a91
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 2-0-81bb7f49a55385878637c8aac4d08e5
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diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 4-0-12cc78f3953c3e6b5411ddc729541bf0 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 4-0-12cc78f3953c3e6b5411ddc729541bf0
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----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-0-6642a21d87e0401ba1a668ea8b244f0c b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-0-6642a21d87e0401ba1a668ea8b244f0c
new file mode 100644
index 0000000..119dd71
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-0-6642a21d87e0401ba1a668ea8b244f0c
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---------------------------------------------------------------------
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[11/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39 b/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39
new file mode 100644
index 0000000..49d003b
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 43. testUnboundedFollowingForRange-0-3cd04e5f2398853c4850f4f86142bb39
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 8749.730000000001
+Manufacturer#1 almond antique burnished rose metallic 2 8749.730000000001
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 6403.43
+Manufacturer#1 almond aquamarine burnished black steel 28 4800.84
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3386.42
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66
+Manufacturer#2 almond aquamarine midnight light salmon 2 8923.62
+Manufacturer#2 almond antique violet chocolate turquoise 14 6891.639999999999
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5200.96
+Manufacturer#2 almond aquamarine rose maroon antique 25 3499.36
+Manufacturer#2 almond antique violet turquoise frosted 40 1800.7
+Manufacturer#3 almond antique misty red olive 1 7532.610000000001
+Manufacturer#3 almond antique forest lavender goldenrod 14 5609.63
+Manufacturer#3 almond antique chartreuse khaki white 17 4419.360000000001
+Manufacturer#3 almond antique metallic orange dim 19 2747.6800000000003
+Manufacturer#3 almond antique olive coral navajo 45 1337.29
+Manufacturer#4 almond aquamarine yellow dodger mint 7 7337.620000000001
+Manufacturer#4 almond antique gainsboro frosted violet 10 5492.7
+Manufacturer#4 almond azure aquamarine papaya violet 12 3872.0299999999997
+Manufacturer#4 almond aquamarine floral ivory bisque 27 2581.6800000000003
+Manufacturer#4 almond antique violet mint lemon 39 1375.42
+Manufacturer#5 almond antique sky peru orange 2 7672.660000000002
+Manufacturer#5 almond antique medium spring khaki 6 5883.93
+Manufacturer#5 almond azure blanched chiffon midnight 23 4272.27
+Manufacturer#5 almond antique blue firebrick mint 31 2807.79
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae b/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae
new file mode 100644
index 0000000..5982c9e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 44. testOverNoPartitionSingleAggregate-0-cb3d2f8c1296044dc2658876bb6103ae
@@ -0,0 +1,26 @@
+almond antique blue firebrick mint 1789.69 1546.78
+almond antique burnished rose metallic 1173.15 1546.78
+almond antique burnished rose metallic 1173.15 1546.78
+almond antique chartreuse khaki white 1671.68 1546.78
+almond antique chartreuse lavender yellow 1753.76 1546.78
+almond antique forest lavender goldenrod 1190.27 1546.78
+almond antique gainsboro frosted violet 1620.67 1546.78
+almond antique medium spring khaki 1611.66 1546.78
+almond antique metallic orange dim 1410.39 1546.78
+almond antique misty red olive 1922.98 1546.78
+almond antique olive coral navajo 1337.29 1546.78
+almond antique salmon chartreuse burlywood 1602.59 1546.78
+almond antique sky peru orange 1788.73 1546.78
+almond antique violet chocolate turquoise 1690.68 1546.78
+almond antique violet mint lemon 1375.42 1546.78
+almond antique violet turquoise frosted 1800.7 1546.78
+almond aquamarine burnished black steel 1414.42 1546.78
+almond aquamarine dodger light gainsboro 1018.1 1546.78
+almond aquamarine floral ivory bisque 1206.26 1546.78
+almond aquamarine midnight light salmon 2031.98 1546.78
+almond aquamarine pink moccasin thistle 1632.66 1546.78
+almond aquamarine rose maroon antique 1698.66 1546.78
+almond aquamarine sandy cyan gainsboro 1701.6 1546.78
+almond aquamarine yellow dodger mint 1844.92 1546.78
+almond azure aquamarine papaya violet 1290.35 1546.78
+almond azure blanched chiffon midnight 1464.48 1546.78
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f b/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f
new file mode 100644
index 0000000..00d41fc
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 5. testCountWithWindowingUDAF-0-3bde93728761b780a745c2ce0398aa0f
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 1 1 2 1173.15 1173.15 2 0
+Manufacturer#1 almond antique burnished rose metallic 1 1 2 1173.15 2346.3 2 0
+Manufacturer#1 almond antique chartreuse lavender yellow 3 2 3 1753.76 4100.06 34 32
+Manufacturer#1 almond antique salmon chartreuse burlywood 4 3 4 1602.59 5702.650000000001 6 -28
+Manufacturer#1 almond aquamarine burnished black steel 5 4 5 1414.42 7117.070000000001 28 22
+Manufacturer#1 almond aquamarine pink moccasin thistle 6 5 6 1632.66 8749.730000000001 42 14
+Manufacturer#2 almond antique violet chocolate turquoise 1 1 1 1690.68 1690.68 14 0
+Manufacturer#2 almond antique violet turquoise frosted 2 2 2 1800.7 3491.38 40 26
+Manufacturer#2 almond aquamarine midnight light salmon 3 3 3 2031.98 5523.360000000001 2 -38
+Manufacturer#2 almond aquamarine rose maroon antique 4 4 4 1698.66 7222.02 25 23
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 5 5 5 1701.6 8923.62 18 -7
+Manufacturer#3 almond antique chartreuse khaki white 1 1 1 1671.68 1671.68 17 0
+Manufacturer#3 almond antique forest lavender goldenrod 2 2 2 1190.27 2861.95 14 -3
+Manufacturer#3 almond antique metallic orange dim 3 3 3 1410.39 4272.34 19 5
+Manufacturer#3 almond antique misty red olive 4 4 4 1922.98 6195.32 1 -18
+Manufacturer#3 almond antique olive coral navajo 5 5 5 1337.29 7532.61 45 44
+Manufacturer#4 almond antique gainsboro frosted violet 1 1 1 1620.67 1620.67 10 0
+Manufacturer#4 almond antique violet mint lemon 2 2 2 1375.42 2996.09 39 29
+Manufacturer#4 almond aquamarine floral ivory bisque 3 3 3 1206.26 4202.35 27 -12
+Manufacturer#4 almond aquamarine yellow dodger mint 4 4 4 1844.92 6047.27 7 -20
+Manufacturer#4 almond azure aquamarine papaya violet 5 5 5 1290.35 7337.620000000001 12 5
+Manufacturer#5 almond antique blue firebrick mint 1 1 1 1789.69 1789.69 31 0
+Manufacturer#5 almond antique medium spring khaki 2 2 2 1611.66 3401.3500000000004 6 -25
+Manufacturer#5 almond antique sky peru orange 3 3 3 1788.73 5190.08 2 -4
+Manufacturer#5 almond aquamarine dodger light gainsboro 4 4 4 1018.1 6208.18 46 44
+Manufacturer#5 almond azure blanched chiffon midnight 5 5 5 1464.48 7672.66 23 -23
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce b/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce
new file mode 100644
index 0000000..98c09e4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 6. testCountInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce
@@ -0,0 +1,26 @@
+1 1 2 1173.15 0
+1 1 2 2346.3 0
+3 2 3 4100.06 32
+4 3 4 5702.650000000001 -28
+5 4 5 7117.070000000001 22
+6 5 6 8749.730000000001 14
+1 1 1 1690.68 0
+2 2 2 3491.38 26
+3 3 3 5523.360000000001 -38
+4 4 4 7222.02 23
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+1 1 1 1671.68 0
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+1 1 1 1620.67 0
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+5 5 5 7337.620000000001 5
+1 1 1 1789.69 0
+2 2 2 3401.3500000000004 -25
+3 3 3 5190.08 -4
+4 4 4 6208.18 44
+5 5 5 7672.66 -23
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722 b/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722
new file mode 100644
index 0000000..e35257d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 8. testMixedCaseAlias-0-4b1ad2515fb079012467e987f484a722
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1
+Manufacturer#1 almond antique burnished rose metallic 2 1
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4
+Manufacturer#1 almond aquamarine burnished black steel 28 5
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6
+Manufacturer#2 almond antique violet chocolate turquoise 14 1
+Manufacturer#2 almond antique violet turquoise frosted 40 2
+Manufacturer#2 almond aquamarine midnight light salmon 2 3
+Manufacturer#2 almond aquamarine rose maroon antique 25 4
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5
+Manufacturer#3 almond antique chartreuse khaki white 17 1
+Manufacturer#3 almond antique forest lavender goldenrod 14 2
+Manufacturer#3 almond antique metallic orange dim 19 3
+Manufacturer#3 almond antique misty red olive 1 4
+Manufacturer#3 almond antique olive coral navajo 45 5
+Manufacturer#4 almond antique gainsboro frosted violet 10 1
+Manufacturer#4 almond antique violet mint lemon 39 2
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3
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+Manufacturer#4 almond azure aquamarine papaya violet 12 5
+Manufacturer#5 almond antique blue firebrick mint 31 1
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---------------------------------------------------------------------
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[03/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194
new file mode 100644
index 0000000..ae2a1e9
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-3-6f104992e0050576085064815de43194
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e
new file mode 100644
index 0000000..ee1c26e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e
@@ -0,0 +1,1049 @@
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---------------------------------------------------------------------
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[08/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65
new file mode 100644
index 0000000..c38e7bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-2-ac487cc1b94130bf9ce00e07c7075f65
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----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-3-b82dfa24123047be4b4e3c27c3997d34 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-3-b82dfa24123047be4b4e3c27c3997d34
new file mode 100644
index 0000000..1e0cf03
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-3-b82dfa24123047be4b4e3c27c3997d34
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---------------------------------------------------------------------
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[06/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e
new file mode 100644
index 0000000..657e81a
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-1-2bf20f39e6ffef258858f7943a974e7e
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f
new file mode 100644
index 0000000..6cfa5ad
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-2-16239d2b069789ba99fbac50c4f0724f
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7 b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7
new file mode 100644
index 0000000..072a8a8
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_udaf.q (deterministic)-3-d90b27fca067b0b3c48d873b3ef32af7
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---------------------------------------------------------------------
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[12/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
----------------------------------------------------------------------
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
index 4b6f0ad..fd0b6f0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala
@@ -17,27 +17,27 @@
package org.apache.spark.sql.hive
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator.AggregationBuffer
import org.apache.hadoop.hive.ql.udf.generic.GenericUDFUtils.ConversionHelper
+import org.apache.spark.sql.AnalysisException
import scala.collection.mutable.ArrayBuffer
import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ConstantObjectInspector}
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory.ObjectInspectorOptions
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory
-import org.apache.hadoop.hive.ql.exec.{UDF, UDAF}
-import org.apache.hadoop.hive.ql.exec.{FunctionInfo, FunctionRegistry}
+import org.apache.hadoop.hive.ql.exec._
import org.apache.hadoop.hive.ql.udf.{UDFType => HiveUDFType}
import org.apache.hadoop.hive.ql.udf.generic._
import org.apache.hadoop.hive.ql.udf.generic.GenericUDF._
import org.apache.spark.Logging
import org.apache.spark.sql.catalyst.analysis
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical.{Generate, Project, LogicalPlan}
+import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.types._
-import org.apache.spark.sql.catalyst.analysis.MultiAlias
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
/* Implicit conversions */
import scala.collection.JavaConversions._
@@ -189,6 +189,219 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr
}
}
+/**
+ * Resolves [[UnresolvedWindowFunction]] to [[HiveWindowFunction]].
+ */
+private[spark] object ResolveHiveWindowFunction extends Rule[LogicalPlan] {
+ def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+ case p: LogicalPlan if !p.childrenResolved => p
+
+ // We are resolving WindowExpressions at here. When we get here, we have already
+ // replaced those WindowSpecReferences.
+ case p: LogicalPlan =>
+ p transformExpressions {
+ case WindowExpression(
+ UnresolvedWindowFunction(name, children),
+ windowSpec: WindowSpecDefinition) =>
+ // First, let's find the window function info.
+ val windowFunctionInfo: WindowFunctionInfo =
+ Option(FunctionRegistry.getWindowFunctionInfo(name.toLowerCase)).getOrElse(
+ throw new AnalysisException(s"Couldn't find window function $name"))
+
+ // Get the class of this function.
+ // In Hive 0.12, there is no windowFunctionInfo.getFunctionClass. So, we use
+ // windowFunctionInfo.getfInfo().getFunctionClass for both Hive 0.13 and Hive 0.13.1.
+ val functionClass = windowFunctionInfo.getfInfo().getFunctionClass
+ val newChildren =
+ // Rank(), DENSE_RANK(), CUME_DIST(), and PERCENT_RANK() do not take explicit
+ // input parameters and requires implicit parameters, which
+ // are expressions in Order By clause.
+ if (classOf[GenericUDAFRank].isAssignableFrom(functionClass)) {
+ if (children.nonEmpty) {
+ throw new AnalysisException(s"$name does not take input parameters.")
+ }
+ windowSpec.orderSpec.map(_.child)
+ } else {
+ children
+ }
+
+ // If the class is UDAF, we need to use UDAFBridge.
+ val isUDAFBridgeRequired =
+ if (classOf[UDAF].isAssignableFrom(functionClass)) {
+ true
+ } else {
+ false
+ }
+
+ // Create the HiveWindowFunction. For the meaning of isPivotResult, see the doc of
+ // HiveWindowFunction.
+ val windowFunction =
+ HiveWindowFunction(
+ new HiveFunctionWrapper(functionClass.getName),
+ windowFunctionInfo.isPivotResult,
+ isUDAFBridgeRequired,
+ newChildren)
+
+ // Second, check if the specified window function can accept window definition.
+ windowSpec.frameSpecification match {
+ case frame: SpecifiedWindowFrame if !windowFunctionInfo.isSupportsWindow =>
+ // This Hive window function does not support user-speficied window frame.
+ throw new AnalysisException(
+ s"Window function $name does not take a frame specification.")
+ case frame: SpecifiedWindowFrame if windowFunctionInfo.isSupportsWindow &&
+ windowFunctionInfo.isPivotResult =>
+ // These two should not be true at the same time when a window frame is defined.
+ // If so, throw an exception.
+ throw new AnalysisException(s"Could not handle Hive window function $name because " +
+ s"it supports both a user specified window frame and pivot result.")
+ case _ => // OK
+ }
+ // Resolve those UnspecifiedWindowFrame because the physical Window operator still needs
+ // a window frame specification to work.
+ val newWindowSpec = windowSpec.frameSpecification match {
+ case UnspecifiedFrame =>
+ val newWindowFrame =
+ SpecifiedWindowFrame.defaultWindowFrame(
+ windowSpec.orderSpec.nonEmpty,
+ windowFunctionInfo.isSupportsWindow)
+ WindowSpecDefinition(windowSpec.partitionSpec, windowSpec.orderSpec, newWindowFrame)
+ case _ => windowSpec
+ }
+
+ // Finally, we create a WindowExpression with the resolved window function and
+ // specified window spec.
+ WindowExpression(windowFunction, newWindowSpec)
+ }
+ }
+}
+
+/**
+ * A [[WindowFunction]] implementation wrapping Hive's window function.
+ * @param funcWrapper The wrapper for the Hive Window Function.
+ * @param pivotResult If it is true, the Hive function will return a list of values representing
+ * the values of the added columns. Otherwise, a single value is returned for
+ * current row.
+ * @param isUDAFBridgeRequired If it is true, the function returned by functionWrapper's
+ * createFunction is UDAF, we need to use GenericUDAFBridge to wrap
+ * it as a GenericUDAFResolver2.
+ * @param children Input parameters.
+ */
+private[hive] case class HiveWindowFunction(
+ funcWrapper: HiveFunctionWrapper,
+ pivotResult: Boolean,
+ isUDAFBridgeRequired: Boolean,
+ children: Seq[Expression]) extends WindowFunction
+ with HiveInspectors {
+
+ // Hive window functions are based on GenericUDAFResolver2.
+ type UDFType = GenericUDAFResolver2
+
+ @transient
+ protected lazy val resolver: GenericUDAFResolver2 =
+ if (isUDAFBridgeRequired) {
+ new GenericUDAFBridge(funcWrapper.createFunction[UDAF]())
+ } else {
+ funcWrapper.createFunction[GenericUDAFResolver2]()
+ }
+
+ @transient
+ protected lazy val inputInspectors = children.map(toInspector).toArray
+
+ // The GenericUDAFEvaluator used to evaluate the window function.
+ @transient
+ protected lazy val evaluator: GenericUDAFEvaluator = {
+ val parameterInfo = new SimpleGenericUDAFParameterInfo(inputInspectors, false, false)
+ resolver.getEvaluator(parameterInfo)
+ }
+
+ // The object inspector of values returned from the Hive window function.
+ @transient
+ protected lazy val returnInspector = {
+ evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors)
+ }
+
+ def dataType: DataType =
+ if (!pivotResult) {
+ inspectorToDataType(returnInspector)
+ } else {
+ // If pivotResult is true, we should take the element type out as the data type of this
+ // function.
+ inspectorToDataType(returnInspector) match {
+ case ArrayType(dt, _) => dt
+ case _ =>
+ sys.error(
+ s"error resolve the data type of window function ${funcWrapper.functionClassName}")
+ }
+ }
+
+ def nullable: Boolean = true
+
+ override type EvaluatedType = Any
+
+ override def eval(input: Row): Any =
+ throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+
+ @transient
+ lazy val inputProjection = new InterpretedProjection(children)
+
+ @transient
+ private var hiveEvaluatorBuffer: AggregationBuffer = _
+ // Output buffer.
+ private var outputBuffer: Any = _
+
+ override def init(): Unit = {
+ evaluator.init(GenericUDAFEvaluator.Mode.COMPLETE, inputInspectors)
+ }
+
+ // Reset the hiveEvaluatorBuffer and outputPosition
+ override def reset(): Unit = {
+ // We create a new aggregation buffer to workaround the bug in GenericUDAFRowNumber.
+ // Basically, GenericUDAFRowNumberEvaluator.reset calls RowNumberBuffer.init.
+ // However, RowNumberBuffer.init does not really reset this buffer.
+ hiveEvaluatorBuffer = evaluator.getNewAggregationBuffer
+ evaluator.reset(hiveEvaluatorBuffer)
+ }
+
+ override def prepareInputParameters(input: Row): AnyRef = {
+ wrap(inputProjection(input), inputInspectors, new Array[AnyRef](children.length))
+ }
+ // Add input parameters for a single row.
+ override def update(input: AnyRef): Unit = {
+ evaluator.iterate(hiveEvaluatorBuffer, input.asInstanceOf[Array[AnyRef]])
+ }
+
+ override def batchUpdate(inputs: Array[AnyRef]): Unit = {
+ var i = 0
+ while (i < inputs.length) {
+ evaluator.iterate(hiveEvaluatorBuffer, inputs(i).asInstanceOf[Array[AnyRef]])
+ i += 1
+ }
+ }
+
+ override def evaluate(): Unit = {
+ outputBuffer = unwrap(evaluator.evaluate(hiveEvaluatorBuffer), returnInspector)
+ }
+
+ override def get(index: Int): Any = {
+ if (!pivotResult) {
+ // if pivotResult is false, we will get a single value for all rows in the frame.
+ outputBuffer
+ } else {
+ // if pivotResult is true, we will get a Seq having the same size with the size
+ // of the window frame. At here, we will return the result at the position of
+ // index in the output buffer.
+ outputBuffer.asInstanceOf[Seq[Any]].get(index)
+ }
+ }
+
+ override def toString: String = {
+ s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})"
+ }
+
+ override def newInstance: WindowFunction =
+ new HiveWindowFunction(funcWrapper, pivotResult, isUDAFBridgeRequired, children)
+}
+
private[hive] case class HiveGenericUdaf(
funcWrapper: HiveFunctionWrapper,
children: Seq[Expression]) extends AggregateExpression
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522 b/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522
new file mode 100644
index 0000000..850c41c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 1. testWindowing-0-327a8cd39fe30255ff492ee86f660522
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 1173.15
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 2346.3
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 4100.06
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 5702.650000000001
+Manufacturer#1 almond aquamarine burnished black steel 28 5 4 7117.070000000001
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 8749.730000000001
+Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 1690.68
+Manufacturer#2 almond antique violet turquoise frosted 40 2 2 3491.38
+Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 5523.360000000001
+Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 7222.02
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 8923.62
+Manufacturer#3 almond antique chartreuse khaki white 17 1 1 1671.68
+Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 2861.95
+Manufacturer#3 almond antique metallic orange dim 19 3 3 4272.34
+Manufacturer#3 almond antique misty red olive 1 4 4 6195.32
+Manufacturer#3 almond antique olive coral navajo 45 5 5 7532.61
+Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 1620.67
+Manufacturer#4 almond antique violet mint lemon 39 2 2 2996.09
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 4202.35
+Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 6047.27
+Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 7337.620000000001
+Manufacturer#5 almond antique blue firebrick mint 31 1 1 1789.69
+Manufacturer#5 almond antique medium spring khaki 6 2 2 3401.3500000000004
+Manufacturer#5 almond antique sky peru orange 2 3 3 5190.08
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 6208.18
+Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 7672.66
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd b/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd
new file mode 100644
index 0000000..850c41c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 10. testHavingWithWindowingCondRankNoGBY-0-fef4bf638d52a9a601845347010602fd
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 1173.15
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 2346.3
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 4100.06
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 5702.650000000001
+Manufacturer#1 almond aquamarine burnished black steel 28 5 4 7117.070000000001
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 8749.730000000001
+Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 1690.68
+Manufacturer#2 almond antique violet turquoise frosted 40 2 2 3491.38
+Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 5523.360000000001
+Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 7222.02
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 8923.62
+Manufacturer#3 almond antique chartreuse khaki white 17 1 1 1671.68
+Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 2861.95
+Manufacturer#3 almond antique metallic orange dim 19 3 3 4272.34
+Manufacturer#3 almond antique misty red olive 1 4 4 6195.32
+Manufacturer#3 almond antique olive coral navajo 45 5 5 7532.61
+Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 1620.67
+Manufacturer#4 almond antique violet mint lemon 39 2 2 2996.09
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 4202.35
+Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 6047.27
+Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 7337.620000000001
+Manufacturer#5 almond antique blue firebrick mint 31 1 1 1789.69
+Manufacturer#5 almond antique medium spring khaki 6 2 2 3401.3500000000004
+Manufacturer#5 almond antique sky peru orange 2 3 3 5190.08
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 6208.18
+Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 7672.66
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6 b/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6
new file mode 100644
index 0000000..921679c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 11. testFirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 2 2 34
+Manufacturer#1 almond antique burnished rose metallic 2 2 2 6
+Manufacturer#1 almond antique chartreuse lavender yellow 34 34 2 28
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 6 2 42
+Manufacturer#1 almond aquamarine burnished black steel 28 28 34 42
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 42 6 42
+Manufacturer#2 almond antique violet chocolate turquoise 14 14 14 2
+Manufacturer#2 almond antique violet turquoise frosted 40 40 14 25
+Manufacturer#2 almond aquamarine midnight light salmon 2 2 14 18
+Manufacturer#2 almond aquamarine rose maroon antique 25 25 40 18
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 18 2 18
+Manufacturer#3 almond antique chartreuse khaki white 17 17 17 19
+Manufacturer#3 almond antique forest lavender goldenrod 14 14 17 1
+Manufacturer#3 almond antique metallic orange dim 19 19 17 45
+Manufacturer#3 almond antique misty red olive 1 1 14 45
+Manufacturer#3 almond antique olive coral navajo 45 45 19 45
+Manufacturer#4 almond antique gainsboro frosted violet 10 10 10 27
+Manufacturer#4 almond antique violet mint lemon 39 39 10 7
+Manufacturer#4 almond aquamarine floral ivory bisque 27 27 10 12
+Manufacturer#4 almond aquamarine yellow dodger mint 7 7 39 12
+Manufacturer#4 almond azure aquamarine papaya violet 12 12 27 12
+Manufacturer#5 almond antique blue firebrick mint 31 31 31 2
+Manufacturer#5 almond antique medium spring khaki 6 6 31 46
+Manufacturer#5 almond antique sky peru orange 2 2 31 23
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 46 6 23
+Manufacturer#5 almond azure blanched chiffon midnight 23 23 2 23
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5 b/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5
new file mode 100644
index 0000000..09e30c7
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 12. testFirstLastWithWhere-0-84345a9f685ba63b87caa4bb16b122b5
@@ -0,0 +1,5 @@
+Manufacturer#3 almond antique chartreuse khaki white 17 1 17 17 19
+Manufacturer#3 almond antique forest lavender goldenrod 14 2 14 17 1
+Manufacturer#3 almond antique metallic orange dim 19 3 19 17 45
+Manufacturer#3 almond antique misty red olive 1 4 1 14 45
+Manufacturer#3 almond antique olive coral navajo 45 5 45 19 45
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f b/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f
new file mode 100644
index 0000000..01ee88f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 13. testSumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 38 2
+Manufacturer#1 almond antique burnished rose metallic 2 44 2
+Manufacturer#1 almond antique chartreuse lavender yellow 34 72 34
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 112 6
+Manufacturer#1 almond aquamarine burnished black steel 28 110 28
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 42
+Manufacturer#2 almond antique violet chocolate turquoise 14 56 14
+Manufacturer#2 almond antique violet turquoise frosted 40 81 40
+Manufacturer#2 almond aquamarine midnight light salmon 2 99 2
+Manufacturer#2 almond aquamarine rose maroon antique 25 85 25
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45 18
+Manufacturer#3 almond antique chartreuse khaki white 17 50 17
+Manufacturer#3 almond antique forest lavender goldenrod 14 51 14
+Manufacturer#3 almond antique metallic orange dim 19 96 19
+Manufacturer#3 almond antique misty red olive 1 79 1
+Manufacturer#3 almond antique olive coral navajo 45 65 45
+Manufacturer#4 almond antique gainsboro frosted violet 10 76 10
+Manufacturer#4 almond antique violet mint lemon 39 83 39
+Manufacturer#4 almond aquamarine floral ivory bisque 27 95 27
+Manufacturer#4 almond aquamarine yellow dodger mint 7 85 7
+Manufacturer#4 almond azure aquamarine papaya violet 12 46 12
+Manufacturer#5 almond antique blue firebrick mint 31 39 31
+Manufacturer#5 almond antique medium spring khaki 6 85 6
+Manufacturer#5 almond antique sky peru orange 2 108 2
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 77 46
+Manufacturer#5 almond azure blanched chiffon midnight 23 71 23
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1 b/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1
new file mode 100644
index 0000000..c78eb64
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 14. testNoSortClause-0-88d96a526d3cae6ed8168c5b228974d1
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1 1
+Manufacturer#1 almond antique burnished rose metallic 2 1 1
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3
+Manufacturer#1 almond aquamarine burnished black steel 28 5 4
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5
+Manufacturer#2 almond antique violet chocolate turquoise 14 1 1
+Manufacturer#2 almond antique violet turquoise frosted 40 2 2
+Manufacturer#2 almond aquamarine midnight light salmon 2 3 3
+Manufacturer#2 almond aquamarine rose maroon antique 25 4 4
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5
+Manufacturer#3 almond antique chartreuse khaki white 17 1 1
+Manufacturer#3 almond antique forest lavender goldenrod 14 2 2
+Manufacturer#3 almond antique metallic orange dim 19 3 3
+Manufacturer#3 almond antique misty red olive 1 4 4
+Manufacturer#3 almond antique olive coral navajo 45 5 5
+Manufacturer#4 almond antique gainsboro frosted violet 10 1 1
+Manufacturer#4 almond antique violet mint lemon 39 2 2
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3
+Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4
+Manufacturer#4 almond azure aquamarine papaya violet 12 5 5
+Manufacturer#5 almond antique blue firebrick mint 31 1 1
+Manufacturer#5 almond antique medium spring khaki 6 2 2
+Manufacturer#5 almond antique sky peru orange 2 3 3
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4
+Manufacturer#5 almond azure blanched chiffon midnight 23 5 5
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e b/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e
new file mode 100644
index 0000000..050138c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 15. testExpressions-0-11f6c13cf2710ce7054654cca136e73e
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 0.0 1 2 2.0 0.0 2 2 2
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 0.0 1 2 2.0 0.0 2 2 2
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 0.5 0.4 2 3 12.666666666666666 15.084944665313014 2 34 2
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 0.6666666666666666 0.6 2 4 11.0 13.379088160259652 2 6 2
+Manufacturer#1 almond aquamarine burnished black steel 28 5 4 0.8333333333333334 0.8 3 5 14.4 13.763720427268202 2 28 34
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 1.0 1.0 3 6 19.0 16.237815945091466 2 42 6
+Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 0.2 0.0 1 1 14.0 0.0 4 14 14
+Manufacturer#2 almond antique violet turquoise frosted 40 2 2 0.4 0.25 1 2 27.0 13.0 4 40 14
+Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 0.6 0.5 2 3 18.666666666666668 15.86050300449376 4 2 14
+Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 0.8 0.75 2 4 20.25 14.00669482783144 4 25 40
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 1.0 1.0 3 5 19.8 12.560254774486067 4 18 2
+Manufacturer#3 almond antique chartreuse khaki white 17 1 1 0.2 0.0 1 1 17.0 0.0 2 17 17
+Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 0.4 0.25 1 2 15.5 1.5 2 14 17
+Manufacturer#3 almond antique metallic orange dim 19 3 3 0.6 0.5 2 3 16.666666666666668 2.0548046676563256 2 19 17
+Manufacturer#3 almond antique misty red olive 1 4 4 0.8 0.75 2 4 12.75 7.013380069552769 2 1 14
+Manufacturer#3 almond antique olive coral navajo 45 5 5 1.0 1.0 3 5 19.2 14.344336861632886 2 45 19
+Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 0.2 0.0 1 1 10.0 0.0 0 10 10
+Manufacturer#4 almond antique violet mint lemon 39 2 2 0.4 0.25 1 2 24.5 14.5 0 39 10
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 0.6 0.5 2 3 25.333333333333332 11.897712198383164 0 27 10
+Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 0.8 0.75 2 4 20.75 13.007209539328564 0 7 39
+Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 1.0 1.0 3 5 19.0 12.149074038789951 0 12 27
+Manufacturer#5 almond antique blue firebrick mint 31 1 1 0.2 0.0 1 1 31.0 0.0 1 31 31
+Manufacturer#5 almond antique medium spring khaki 6 2 2 0.4 0.25 1 2 18.5 12.5 1 6 31
+Manufacturer#5 almond antique sky peru orange 2 3 3 0.6 0.5 2 3 13.0 12.832251036613439 1 2 31
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 0.8 0.75 2 4 21.25 18.102140757380052 1 46 6
+Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 1.0 1.0 3 5 21.6 16.206171663906314 1 23 2
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a b/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a
new file mode 100644
index 0000000..c108888
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 16. testMultipleWindows-0-efd1476255eeb1b1961149144f574b7a
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 4 4 2
+Manufacturer#1 almond antique burnished rose metallic 2 1 1 0.3333333333333333 4 4 2
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3 2 0.5 38 34 2
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 3 0.6666666666666666 44 10 2
+Manufacturer#1 almond aquamarine burnished black steel 28 5 4 0.8333333333333334 72 28 34
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 5 1.0 114 42 6
+Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 0.2 14 14 14
+Manufacturer#2 almond antique violet turquoise frosted 40 2 2 0.4 54 40 14
+Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 0.6 56 2 14
+Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 0.8 81 25 40
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 1.0 99 32 2
+Manufacturer#3 almond antique chartreuse khaki white 17 1 1 0.2 17 31 17
+Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 0.4 31 14 17
+Manufacturer#3 almond antique metallic orange dim 19 3 3 0.6 50 50 17
+Manufacturer#3 almond antique misty red olive 1 4 4 0.8 51 1 14
+Manufacturer#3 almond antique olive coral navajo 45 5 5 1.0 96 45 19
+Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 0.2 10 17 10
+Manufacturer#4 almond antique violet mint lemon 39 2 2 0.4 49 39 10
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 0.6 76 27 10
+Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 0.8 83 7 39
+Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 1.0 95 29 27
+Manufacturer#5 almond antique blue firebrick mint 31 1 1 0.2 31 31 31
+Manufacturer#5 almond antique medium spring khaki 6 2 2 0.4 37 8 31
+Manufacturer#5 almond antique sky peru orange 2 3 3 0.6 39 2 31
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 0.8 85 46 6
+Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 1.0 108 23 2
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d b/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d
new file mode 100644
index 0000000..b1309a4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 17. testCountStar-0-1b1fc185c8fddf68e58e92f29052ab2d
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 2 2 2
+Manufacturer#1 almond antique burnished rose metallic 2 2 2 2
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3 3 2
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4 4 2
+Manufacturer#1 almond aquamarine burnished black steel 28 5 5 34
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6 6 6
+Manufacturer#2 almond antique violet chocolate turquoise 14 1 1 14
+Manufacturer#2 almond antique violet turquoise frosted 40 2 2 14
+Manufacturer#2 almond aquamarine midnight light salmon 2 3 3 14
+Manufacturer#2 almond aquamarine rose maroon antique 25 4 4 40
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5 5 2
+Manufacturer#3 almond antique chartreuse khaki white 17 1 1 17
+Manufacturer#3 almond antique forest lavender goldenrod 14 2 2 17
+Manufacturer#3 almond antique metallic orange dim 19 3 3 17
+Manufacturer#3 almond antique misty red olive 1 4 4 14
+Manufacturer#3 almond antique olive coral navajo 45 5 5 19
+Manufacturer#4 almond antique gainsboro frosted violet 10 1 1 10
+Manufacturer#4 almond antique violet mint lemon 39 2 2 10
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3 3 10
+Manufacturer#4 almond aquamarine yellow dodger mint 7 4 4 39
+Manufacturer#4 almond azure aquamarine papaya violet 12 5 5 27
+Manufacturer#5 almond antique blue firebrick mint 31 1 1 31
+Manufacturer#5 almond antique medium spring khaki 6 2 2 31
+Manufacturer#5 almond antique sky peru orange 2 3 3 31
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 4 4 6
+Manufacturer#5 almond azure blanched chiffon midnight 23 5 5 2
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a b/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a
new file mode 100644
index 0000000..52d2ee8
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 18. testUDAFs-0-6974e5959e41a661e09db18547fef58a
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 4100.06 1173.15 1753.76 1366.6866666666667
+Manufacturer#1 almond antique burnished rose metallic 2 5702.650000000001 1173.15 1753.76 1425.6625000000001
+Manufacturer#1 almond antique chartreuse lavender yellow 34 7117.070000000001 1173.15 1753.76 1423.4140000000002
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 7576.58 1173.15 1753.76 1515.316
+Manufacturer#1 almond aquamarine burnished black steel 28 6403.43 1414.42 1753.76 1600.8575
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 4649.67 1414.42 1632.66 1549.89
+Manufacturer#2 almond antique violet chocolate turquoise 14 5523.360000000001 1690.68 2031.98 1841.1200000000001
+Manufacturer#2 almond antique violet turquoise frosted 40 7222.02 1690.68 2031.98 1805.505
+Manufacturer#2 almond aquamarine midnight light salmon 2 8923.62 1690.68 2031.98 1784.7240000000002
+Manufacturer#2 almond aquamarine rose maroon antique 25 7232.9400000000005 1698.66 2031.98 1808.2350000000001
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5432.24 1698.66 2031.98 1810.7466666666667
+Manufacturer#3 almond antique chartreuse khaki white 17 4272.34 1190.27 1671.68 1424.1133333333335
+Manufacturer#3 almond antique forest lavender goldenrod 14 6195.32 1190.27 1922.98 1548.83
+Manufacturer#3 almond antique metallic orange dim 19 7532.61 1190.27 1922.98 1506.522
+Manufacturer#3 almond antique misty red olive 1 5860.929999999999 1190.27 1922.98 1465.2324999999998
+Manufacturer#3 almond antique olive coral navajo 45 4670.66 1337.29 1922.98 1556.8866666666665
+Manufacturer#4 almond antique gainsboro frosted violet 10 4202.35 1206.26 1620.67 1400.7833333333335
+Manufacturer#4 almond antique violet mint lemon 39 6047.27 1206.26 1844.92 1511.8175
+Manufacturer#4 almond aquamarine floral ivory bisque 27 7337.620000000001 1206.26 1844.92 1467.5240000000001
+Manufacturer#4 almond aquamarine yellow dodger mint 7 5716.950000000001 1206.26 1844.92 1429.2375000000002
+Manufacturer#4 almond azure aquamarine papaya violet 12 4341.530000000001 1206.26 1844.92 1447.176666666667
+Manufacturer#5 almond antique blue firebrick mint 31 5190.08 1611.66 1789.69 1730.0266666666666
+Manufacturer#5 almond antique medium spring khaki 6 6208.18 1018.1 1789.69 1552.045
+Manufacturer#5 almond antique sky peru orange 2 7672.66 1018.1 1789.69 1534.532
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 5882.970000000001 1018.1 1788.73 1470.7425000000003
+Manufacturer#5 almond azure blanched chiffon midnight 23 4271.3099999999995 1018.1 1788.73 1423.7699999999998
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35 b/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35
new file mode 100644
index 0000000..6461642
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 19. testUDAFsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35
@@ -0,0 +1,25 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1173.15 4529.5 1173.15 1173.15 1509.8333333333333
+Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 5943.92 1753.76 1753.76 1485.98
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 7576.58 1602.59 1602.59 1515.316
+Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 6403.43 1414.42 1414.42 1600.8575
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 4649.67 1632.66 1632.66 1549.89
+Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 5523.360000000001 1690.68 1690.68 1841.1200000000001
+Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 7222.02 1800.7 1800.7 1805.505
+Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 8923.62 2031.98 2031.98 1784.7240000000002
+Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 7232.9400000000005 1698.66 1698.66 1808.2350000000001
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5432.24 1701.6 1701.6 1810.7466666666667
+Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 4272.34 1671.68 1671.68 1424.1133333333335
+Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 6195.32 1190.27 1190.27 1548.83
+Manufacturer#3 almond antique metallic orange dim 19 1410.39 7532.61 1410.39 1410.39 1506.522
+Manufacturer#3 almond antique misty red olive 1 1922.98 5860.929999999999 1922.98 1922.98 1465.2324999999998
+Manufacturer#3 almond antique olive coral navajo 45 1337.29 4670.66 1337.29 1337.29 1556.8866666666665
+Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 4202.35 1620.67 1620.67 1400.7833333333335
+Manufacturer#4 almond antique violet mint lemon 39 1375.42 6047.27 1375.42 1375.42 1511.8175
+Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 7337.620000000001 1206.26 1206.26 1467.5240000000001
+Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 5716.950000000001 1844.92 1844.92 1429.2375000000002
+Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 4341.530000000001 1290.35 1290.35 1447.176666666667
+Manufacturer#5 almond antique blue firebrick mint 31 1789.69 5190.08 1789.69 1789.69 1730.0266666666666
+Manufacturer#5 almond antique medium spring khaki 6 1611.66 6208.18 1611.66 1611.66 1552.045
+Manufacturer#5 almond antique sky peru orange 2 1788.73 7672.66 1788.73 1788.73 1534.532
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 5882.970000000001 1018.1 1018.1 1470.7425000000003
+Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 4271.3099999999995 1464.48 1464.48 1423.7699999999998
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251 b/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251
new file mode 100644
index 0000000..2c30e65
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 2. testGroupByWithPartitioning-0-cb5618b1e626f3a9d4a030b508b5d251
@@ -0,0 +1,25 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1 1 2 0
+Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 2 2 34 32
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 3 3 6 -28
+Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 4 4 28 22
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 5 5 42 14
+Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1 1 14 0
+Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 2 2 40 26
+Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 3 3 2 -38
+Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 4 4 25 23
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5 5 18 -7
+Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1 1 17 0
+Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 2 2 14 -3
+Manufacturer#3 almond antique metallic orange dim 19 1410.39 3 3 19 5
+Manufacturer#3 almond antique misty red olive 1 1922.98 4 4 1 -18
+Manufacturer#3 almond antique olive coral navajo 45 1337.29 5 5 45 44
+Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1 1 10 0
+Manufacturer#4 almond antique violet mint lemon 39 1375.42 2 2 39 29
+Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 3 3 27 -12
+Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 4 4 7 -20
+Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 5 5 12 5
+Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1 1 31 0
+Manufacturer#5 almond antique medium spring khaki 6 1611.66 2 2 6 -25
+Manufacturer#5 almond antique sky peru orange 2 1788.73 3 3 2 -4
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 4 4 46 44
+Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 5 5 23 -23
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838 b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838
new file mode 100644
index 0000000..1f7e8a5
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 20. testSTATs-0-da0e0cca69e42118a96b8609b8fa5838
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 273.70217881648074 273.70217881648074 [34,2] 74912.8826888888 1.0 4128.782222222221
+Manufacturer#1 almond antique burnished rose metallic 2 258.10677784349235 258.10677784349235 [34,2,6] 66619.10876874991 0.811328754177887 2801.7074999999995
+Manufacturer#1 almond antique chartreuse lavender yellow 34 230.90151585470358 230.90151585470358 [34,2,6,28] 53315.51002399992 0.695639377397664 2210.7864
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 202.73109328368946 202.73109328368946 [34,2,6,42,28] 41099.896184 0.630785977101214 2009.9536000000007
+Manufacturer#1 almond aquamarine burnished black steel 28 121.6064517973862 121.6064517973862 [34,6,42,28] 14788.129118750014 0.2036684720435979 331.1337500000004
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 96.5751586416853 96.5751586416853 [6,42,28] 9326.761266666683 -1.4442181184933883E-4 -0.20666666666708502
+Manufacturer#2 almond antique violet chocolate turquoise 14 142.2363169751898 142.2363169751898 [2,40,14] 20231.169866666663 -0.49369526554523185 -1113.7466666666658
+Manufacturer#2 almond antique violet turquoise frosted 40 137.76306498840682 137.76306498840682 [2,25,40,14] 18978.662075 -0.5205630897335946 -1004.4812499999995
+Manufacturer#2 almond aquamarine midnight light salmon 2 130.03972279269132 130.03972279269132 [2,18,25,40,14] 16910.329504000005 -0.46908967495720255 -766.1791999999995
+Manufacturer#2 almond aquamarine rose maroon antique 25 135.55100986344584 135.55100986344584 [2,18,25,40] 18374.07627499999 -0.6091405874714462 -1128.1787499999987
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 156.44019460768044 156.44019460768044 [2,18,25] 24473.534488888927 -0.9571686373491608 -1441.4466666666676
+Manufacturer#3 almond antique chartreuse khaki white 17 196.7742266885805 196.7742266885805 [17,19,14] 38720.09628888887 0.5557168646224995 224.6944444444446
+Manufacturer#3 almond antique forest lavender goldenrod 14 275.14144189852607 275.14144189852607 [17,1,19,14] 75702.81305 -0.6720833036576083 -1296.9000000000003
+Manufacturer#3 almond antique metallic orange dim 19 260.23473614412046 260.23473614412046 [17,1,19,14,45] 67722.117896 -0.5703526513979519 -2129.0664
+Manufacturer#3 almond antique misty red olive 1 275.9139962356932 275.9139962356932 [1,19,14,45] 76128.53331875012 -0.577476899644802 -2547.7868749999993
+Manufacturer#3 almond antique olive coral navajo 45 260.5815918713796 260.5815918713796 [1,19,45] 67902.76602222225 -0.8710736366736884 -4099.731111111111
+Manufacturer#4 almond antique gainsboro frosted violet 10 170.13011889596618 170.13011889596618 [39,27,10] 28944.25735555559 -0.6656975320098423 -1347.4777777777779
+Manufacturer#4 almond antique violet mint lemon 39 242.26834609323197 242.26834609323197 [39,7,27,10] 58693.95151875002 -0.8051852719193339 -2537.328125
+Manufacturer#4 almond aquamarine floral ivory bisque 27 234.10001662537326 234.10001662537326 [39,7,27,10,12] 54802.817784000035 -0.6046935574240581 -1719.8079999999995
+Manufacturer#4 almond aquamarine yellow dodger mint 7 247.3342714197732 247.3342714197732 [39,7,27,12] 61174.24181875003 -0.5508665654707869 -1719.0368749999975
+Manufacturer#4 almond azure aquamarine papaya violet 12 283.3344330566893 283.3344330566893 [7,27,12] 80278.40095555557 -0.7755740084632333 -1867.4888888888881
+Manufacturer#5 almond antique blue firebrick mint 31 83.69879024746363 83.69879024746363 [2,6,31] 7005.487488888913 0.39004303087285047 418.9233333333353
+Manufacturer#5 almond antique medium spring khaki 6 316.68049612345885 316.68049612345885 [2,6,46,31] 100286.53662500004 -0.713612911776183 -4090.853749999999
+Manufacturer#5 almond antique sky peru orange 2 285.40506298242155 285.40506298242155 [2,23,6,46,31] 81456.04997600002 -0.712858514567818 -3297.2011999999986
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 285.43749038756283 285.43749038756283 [2,23,6,46] 81474.56091875004 -0.984128787153391 -4871.028125000002
+Manufacturer#5 almond azure blanched chiffon midnight 23 315.9225931564038 315.9225931564038 [2,23,46] 99807.08486666664 -0.9978877469246936 -5664.856666666666
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad b/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad
new file mode 100644
index 0000000..e7c39f4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 21. testDISTs-0-672d4cb385b7ced2e446f132474293ad
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 [{"x":1173.15,"y":2.0},{"x":1753.76,"y":1.0}] 121152.0 1
+Manufacturer#1 almond antique burnished rose metallic 2 [{"x":1173.15,"y":2.0},{"x":1602.59,"y":1.0},{"x":1753.76,"y":1.0}] 115872.0 2
+Manufacturer#1 almond antique chartreuse lavender yellow 34 [{"x":1173.15,"y":2.0},{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1753.76,"y":1.0}] 110592.0 3
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 [{"x":1173.15,"y":1.0},{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0},{"x":1753.76,"y":1.0}] 86428.0 4
+Manufacturer#1 almond aquamarine burnished black steel 28 [{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0},{"x":1753.76,"y":1.0}] 86098.0 5
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 [{"x":1414.42,"y":1.0},{"x":1602.59,"y":1.0},{"x":1632.66,"y":1.0}] 86428.0 6
+Manufacturer#2 almond antique violet chocolate turquoise 14 [{"x":1690.68,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 146985.0 1
+Manufacturer#2 almond antique violet turquoise frosted 40 [{"x":1690.68,"y":1.0},{"x":1698.66,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 139825.5 2
+Manufacturer#2 almond aquamarine midnight light salmon 2 [{"x":1690.68,"y":1.0},{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 146985.0 3
+Manufacturer#2 almond aquamarine rose maroon antique 25 [{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":1800.7,"y":1.0},{"x":2031.98,"y":1.0}] 169347.0 4
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 [{"x":1698.66,"y":1.0},{"x":1701.6,"y":1.0},{"x":2031.98,"y":1.0}] 146985.0 5
+Manufacturer#3 almond antique chartreuse khaki white 17 [{"x":1190.27,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0}] 90681.0 1
+Manufacturer#3 almond antique forest lavender goldenrod 14 [{"x":1190.27,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0},{"x":1922.98,"y":1.0}] 65831.5 2
+Manufacturer#3 almond antique metallic orange dim 19 [{"x":1190.27,"y":1.0},{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1671.68,"y":1.0},{"x":1922.98,"y":1.0}] 90681.0 3
+Manufacturer#3 almond antique misty red olive 1 [{"x":1190.27,"y":1.0},{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1922.98,"y":1.0}] 76690.0 4
+Manufacturer#3 almond antique olive coral navajo 45 [{"x":1337.29,"y":1.0},{"x":1410.39,"y":1.0},{"x":1922.98,"y":1.0}] 112398.0 5
+Manufacturer#4 almond antique gainsboro frosted violet 10 [{"x":1206.26,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0}] 48427.0 1
+Manufacturer#4 almond antique violet mint lemon 39 [{"x":1206.26,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0},{"x":1844.92,"y":1.0}] 46844.0 2
+Manufacturer#4 almond aquamarine floral ivory bisque 27 [{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1375.42,"y":1.0},{"x":1620.67,"y":1.0},{"x":1844.92,"y":1.0}] 45261.0 3
+Manufacturer#4 almond aquamarine yellow dodger mint 7 [{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1375.42,"y":1.0},{"x":1844.92,"y":1.0}] 39309.0 4
+Manufacturer#4 almond azure aquamarine papaya violet 12 [{"x":1206.26,"y":1.0},{"x":1290.35,"y":1.0},{"x":1844.92,"y":1.0}] 33357.0 5
+Manufacturer#5 almond antique blue firebrick mint 31 [{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}] 155733.0 1
+Manufacturer#5 almond antique medium spring khaki 6 [{"x":1018.1,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}] 99201.0 2
+Manufacturer#5 almond antique sky peru orange 2 [{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0},{"x":1789.69,"y":1.0}] 78486.0 3
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 [{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1611.66,"y":1.0},{"x":1788.73,"y":1.0}] 60577.5 4
+Manufacturer#5 almond azure blanched chiffon midnight 23 [{"x":1018.1,"y":1.0},{"x":1464.48,"y":1.0},{"x":1788.73,"y":1.0}] 78486.0 5
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a b/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a
new file mode 100644
index 0000000..dc83c9f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 24. testLateralViews-0-dea06072f0a64fe4537fae854944ed5a
@@ -0,0 +1,78 @@
+Manufacturer#1 almond antique burnished rose metallic 1 2 2
+Manufacturer#1 almond antique burnished rose metallic 1 2 4
+Manufacturer#1 almond antique burnished rose metallic 2 2 6
+Manufacturer#1 almond antique burnished rose metallic 2 2 6
+Manufacturer#1 almond antique burnished rose metallic 3 2 6
+Manufacturer#1 almond antique burnished rose metallic 3 2 6
+Manufacturer#1 almond antique salmon chartreuse burlywood 1 6 10
+Manufacturer#1 almond antique salmon chartreuse burlywood 2 6 14
+Manufacturer#1 almond antique salmon chartreuse burlywood 3 6 18
+Manufacturer#1 almond aquamarine burnished black steel 1 28 40
+Manufacturer#1 almond aquamarine burnished black steel 2 28 62
+Manufacturer#1 almond aquamarine burnished black steel 3 28 84
+Manufacturer#1 almond antique chartreuse lavender yellow 1 34 90
+Manufacturer#1 almond antique chartreuse lavender yellow 2 34 96
+Manufacturer#1 almond antique chartreuse lavender yellow 3 34 102
+Manufacturer#1 almond aquamarine pink moccasin thistle 1 42 110
+Manufacturer#1 almond aquamarine pink moccasin thistle 2 42 118
+Manufacturer#1 almond aquamarine pink moccasin thistle 3 42 126
+Manufacturer#2 almond aquamarine midnight light salmon 1 2 2
+Manufacturer#2 almond aquamarine midnight light salmon 2 2 4
+Manufacturer#2 almond aquamarine midnight light salmon 3 2 6
+Manufacturer#2 almond antique violet chocolate turquoise 1 14 18
+Manufacturer#2 almond antique violet chocolate turquoise 2 14 30
+Manufacturer#2 almond antique violet chocolate turquoise 3 14 42
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 1 18 46
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 2 18 50
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 3 18 54
+Manufacturer#2 almond aquamarine rose maroon antique 1 25 61
+Manufacturer#2 almond aquamarine rose maroon antique 2 25 68
+Manufacturer#2 almond aquamarine rose maroon antique 3 25 75
+Manufacturer#2 almond antique violet turquoise frosted 1 40 90
+Manufacturer#2 almond antique violet turquoise frosted 2 40 105
+Manufacturer#2 almond antique violet turquoise frosted 3 40 120
+Manufacturer#3 almond antique misty red olive 1 1 1
+Manufacturer#3 almond antique misty red olive 2 1 2
+Manufacturer#3 almond antique misty red olive 3 1 3
+Manufacturer#3 almond antique forest lavender goldenrod 1 14 16
+Manufacturer#3 almond antique forest lavender goldenrod 2 14 29
+Manufacturer#3 almond antique forest lavender goldenrod 3 14 42
+Manufacturer#3 almond antique chartreuse khaki white 1 17 45
+Manufacturer#3 almond antique chartreuse khaki white 2 17 48
+Manufacturer#3 almond antique chartreuse khaki white 3 17 51
+Manufacturer#3 almond antique metallic orange dim 1 19 53
+Manufacturer#3 almond antique metallic orange dim 2 19 55
+Manufacturer#3 almond antique metallic orange dim 3 19 57
+Manufacturer#3 almond antique olive coral navajo 1 45 83
+Manufacturer#3 almond antique olive coral navajo 2 45 109
+Manufacturer#3 almond antique olive coral navajo 3 45 135
+Manufacturer#4 almond aquamarine yellow dodger mint 1 7 7
+Manufacturer#4 almond aquamarine yellow dodger mint 2 7 14
+Manufacturer#4 almond aquamarine yellow dodger mint 3 7 21
+Manufacturer#4 almond antique gainsboro frosted violet 1 10 24
+Manufacturer#4 almond antique gainsboro frosted violet 2 10 27
+Manufacturer#4 almond antique gainsboro frosted violet 3 10 30
+Manufacturer#4 almond azure aquamarine papaya violet 1 12 32
+Manufacturer#4 almond azure aquamarine papaya violet 2 12 34
+Manufacturer#4 almond azure aquamarine papaya violet 3 12 36
+Manufacturer#4 almond aquamarine floral ivory bisque 1 27 51
+Manufacturer#4 almond aquamarine floral ivory bisque 2 27 66
+Manufacturer#4 almond aquamarine floral ivory bisque 3 27 81
+Manufacturer#4 almond antique violet mint lemon 1 39 93
+Manufacturer#4 almond antique violet mint lemon 2 39 105
+Manufacturer#4 almond antique violet mint lemon 3 39 117
+Manufacturer#5 almond antique sky peru orange 1 2 2
+Manufacturer#5 almond antique sky peru orange 2 2 4
+Manufacturer#5 almond antique sky peru orange 3 2 6
+Manufacturer#5 almond antique medium spring khaki 1 6 10
+Manufacturer#5 almond antique medium spring khaki 2 6 14
+Manufacturer#5 almond antique medium spring khaki 3 6 18
+Manufacturer#5 almond azure blanched chiffon midnight 1 23 35
+Manufacturer#5 almond azure blanched chiffon midnight 2 23 52
+Manufacturer#5 almond azure blanched chiffon midnight 3 23 69
+Manufacturer#5 almond antique blue firebrick mint 1 31 77
+Manufacturer#5 almond antique blue firebrick mint 2 31 85
+Manufacturer#5 almond antique blue firebrick mint 3 31 93
+Manufacturer#5 almond aquamarine dodger light gainsboro 1 46 108
+Manufacturer#5 almond aquamarine dodger light gainsboro 2 46 123
+Manufacturer#5 almond aquamarine dodger light gainsboro 3 46 138
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc b/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc
new file mode 100644
index 0000000..2c30e65
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 26. testGroupByHavingWithSWQAndAlias-0-b996a664b06e5741c08079d5c38241bc
@@ -0,0 +1,25 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1 1 2 0
+Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 2 2 34 32
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 3 3 6 -28
+Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 4 4 28 22
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 5 5 42 14
+Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1 1 14 0
+Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 2 2 40 26
+Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 3 3 2 -38
+Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 4 4 25 23
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5 5 18 -7
+Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1 1 17 0
+Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 2 2 14 -3
+Manufacturer#3 almond antique metallic orange dim 19 1410.39 3 3 19 5
+Manufacturer#3 almond antique misty red olive 1 1922.98 4 4 1 -18
+Manufacturer#3 almond antique olive coral navajo 45 1337.29 5 5 45 44
+Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1 1 10 0
+Manufacturer#4 almond antique violet mint lemon 39 1375.42 2 2 39 29
+Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 3 3 27 -12
+Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 4 4 7 -20
+Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 5 5 12 5
+Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1 1 31 0
+Manufacturer#5 almond antique medium spring khaki 6 1611.66 2 2 6 -25
+Manufacturer#5 almond antique sky peru orange 2 1788.73 3 3 2 -4
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 4 4 46 44
+Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 5 5 23 -23
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4 b/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4
new file mode 100644
index 0000000..b2a91ba
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 27. testMultipleRangeWindows-0-227e080e337d734dd88ff814b3b412e4
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 4 10
+Manufacturer#1 almond antique burnished rose metallic 2 4 10
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 10 6
+Manufacturer#1 almond aquamarine burnished black steel 28 28 62
+Manufacturer#1 almond antique chartreuse lavender yellow 34 62 76
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 42
+Manufacturer#2 almond aquamarine midnight light salmon 2 2 2
+Manufacturer#2 almond antique violet chocolate turquoise 14 14 32
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 32 43
+Manufacturer#2 almond aquamarine rose maroon antique 25 43 25
+Manufacturer#2 almond antique violet turquoise frosted 40 40 40
+Manufacturer#3 almond antique misty red olive 1 1 1
+Manufacturer#3 almond antique forest lavender goldenrod 14 14 50
+Manufacturer#3 almond antique chartreuse khaki white 17 31 36
+Manufacturer#3 almond antique metallic orange dim 19 50 19
+Manufacturer#3 almond antique olive coral navajo 45 45 45
+Manufacturer#4 almond aquamarine yellow dodger mint 7 7 29
+Manufacturer#4 almond antique gainsboro frosted violet 10 17 22
+Manufacturer#4 almond azure aquamarine papaya violet 12 29 12
+Manufacturer#4 almond aquamarine floral ivory bisque 27 27 27
+Manufacturer#4 almond antique violet mint lemon 39 39 39
+Manufacturer#5 almond antique sky peru orange 2 2 8
+Manufacturer#5 almond antique medium spring khaki 6 8 6
+Manufacturer#5 almond azure blanched chiffon midnight 23 23 54
+Manufacturer#5 almond antique blue firebrick mint 31 54 31
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 46 46
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db b/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db
new file mode 100644
index 0000000..5bcb0fa
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 28. testPartOrderInUDAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 38
+Manufacturer#1 almond antique burnished rose metallic 2 44
+Manufacturer#1 almond antique chartreuse lavender yellow 34 72
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 112
+Manufacturer#1 almond aquamarine burnished black steel 28 110
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 76
+Manufacturer#2 almond antique violet chocolate turquoise 14 56
+Manufacturer#2 almond antique violet turquoise frosted 40 81
+Manufacturer#2 almond aquamarine midnight light salmon 2 99
+Manufacturer#2 almond aquamarine rose maroon antique 25 85
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45
+Manufacturer#3 almond antique chartreuse khaki white 17 50
+Manufacturer#3 almond antique forest lavender goldenrod 14 51
+Manufacturer#3 almond antique metallic orange dim 19 96
+Manufacturer#3 almond antique misty red olive 1 79
+Manufacturer#3 almond antique olive coral navajo 45 65
+Manufacturer#4 almond antique gainsboro frosted violet 10 76
+Manufacturer#4 almond antique violet mint lemon 39 83
+Manufacturer#4 almond aquamarine floral ivory bisque 27 95
+Manufacturer#4 almond aquamarine yellow dodger mint 7 85
+Manufacturer#4 almond azure aquamarine papaya violet 12 46
+Manufacturer#5 almond antique blue firebrick mint 31 39
+Manufacturer#5 almond antique medium spring khaki 6 85
+Manufacturer#5 almond antique sky peru orange 2 108
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 77
+Manufacturer#5 almond azure blanched chiffon midnight 23 71
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a b/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a
new file mode 100644
index 0000000..5bcb0fa
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 29. testPartOrderInWdwDef-0-88945892370ccbc1125a927a3d55342a
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 38
+Manufacturer#1 almond antique burnished rose metallic 2 44
+Manufacturer#1 almond antique chartreuse lavender yellow 34 72
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 112
+Manufacturer#1 almond aquamarine burnished black steel 28 110
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 76
+Manufacturer#2 almond antique violet chocolate turquoise 14 56
+Manufacturer#2 almond antique violet turquoise frosted 40 81
+Manufacturer#2 almond aquamarine midnight light salmon 2 99
+Manufacturer#2 almond aquamarine rose maroon antique 25 85
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45
+Manufacturer#3 almond antique chartreuse khaki white 17 50
+Manufacturer#3 almond antique forest lavender goldenrod 14 51
+Manufacturer#3 almond antique metallic orange dim 19 96
+Manufacturer#3 almond antique misty red olive 1 79
+Manufacturer#3 almond antique olive coral navajo 45 65
+Manufacturer#4 almond antique gainsboro frosted violet 10 76
+Manufacturer#4 almond antique violet mint lemon 39 83
+Manufacturer#4 almond aquamarine floral ivory bisque 27 95
+Manufacturer#4 almond aquamarine yellow dodger mint 7 85
+Manufacturer#4 almond azure aquamarine papaya violet 12 46
+Manufacturer#5 almond antique blue firebrick mint 31 39
+Manufacturer#5 almond antique medium spring khaki 6 85
+Manufacturer#5 almond antique sky peru orange 2 108
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 77
+Manufacturer#5 almond azure blanched chiffon midnight 23 71
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100 b/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100
new file mode 100644
index 0000000..2c30e65
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 3. testGroupByHavingWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100
@@ -0,0 +1,25 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1 1 2 0
+Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 2 2 34 32
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 3 3 6 -28
+Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 4 4 28 22
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 5 5 42 14
+Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1 1 14 0
+Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 2 2 40 26
+Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 3 3 2 -38
+Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 4 4 25 23
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 5 5 18 -7
+Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1 1 17 0
+Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 2 2 14 -3
+Manufacturer#3 almond antique metallic orange dim 19 1410.39 3 3 19 5
+Manufacturer#3 almond antique misty red olive 1 1922.98 4 4 1 -18
+Manufacturer#3 almond antique olive coral navajo 45 1337.29 5 5 45 44
+Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1 1 10 0
+Manufacturer#4 almond antique violet mint lemon 39 1375.42 2 2 39 29
+Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 3 3 27 -12
+Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 4 4 7 -20
+Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 5 5 12 5
+Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1 1 31 0
+Manufacturer#5 almond antique medium spring khaki 6 1611.66 2 2 6 -25
+Manufacturer#5 almond antique sky peru orange 2 1788.73 3 3 2 -4
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 4 4 46 44
+Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 5 5 23 -23
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada b/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada
new file mode 100644
index 0000000..698a443
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 30. testDefaultPartitioningSpecRules-0-fa80b09c99e3c1487de48ea71a88dada
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 38 4
+Manufacturer#1 almond antique burnished rose metallic 2 44 4
+Manufacturer#1 almond antique chartreuse lavender yellow 34 72 38
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 112 44
+Manufacturer#1 almond aquamarine burnished black steel 28 110 72
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 76 114
+Manufacturer#2 almond antique violet chocolate turquoise 14 56 14
+Manufacturer#2 almond antique violet turquoise frosted 40 81 54
+Manufacturer#2 almond aquamarine midnight light salmon 2 99 56
+Manufacturer#2 almond aquamarine rose maroon antique 25 85 81
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 45 99
+Manufacturer#3 almond antique chartreuse khaki white 17 50 17
+Manufacturer#3 almond antique forest lavender goldenrod 14 51 31
+Manufacturer#3 almond antique metallic orange dim 19 96 50
+Manufacturer#3 almond antique misty red olive 1 79 51
+Manufacturer#3 almond antique olive coral navajo 45 65 96
+Manufacturer#4 almond antique gainsboro frosted violet 10 76 10
+Manufacturer#4 almond antique violet mint lemon 39 83 49
+Manufacturer#4 almond aquamarine floral ivory bisque 27 95 76
+Manufacturer#4 almond aquamarine yellow dodger mint 7 85 83
+Manufacturer#4 almond azure aquamarine papaya violet 12 46 95
+Manufacturer#5 almond antique blue firebrick mint 31 39 31
+Manufacturer#5 almond antique medium spring khaki 6 85 37
+Manufacturer#5 almond antique sky peru orange 2 108 39
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 77 85
+Manufacturer#5 almond azure blanched chiffon midnight 23 71 108
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768 b/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768
new file mode 100644
index 0000000..e35257d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 36. testRankWithPartitioning-0-45ccbaf0ee083858f7661c66b11d4768
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1
+Manufacturer#1 almond antique burnished rose metallic 2 1
+Manufacturer#1 almond antique chartreuse lavender yellow 34 3
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4
+Manufacturer#1 almond aquamarine burnished black steel 28 5
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 6
+Manufacturer#2 almond antique violet chocolate turquoise 14 1
+Manufacturer#2 almond antique violet turquoise frosted 40 2
+Manufacturer#2 almond aquamarine midnight light salmon 2 3
+Manufacturer#2 almond aquamarine rose maroon antique 25 4
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5
+Manufacturer#3 almond antique chartreuse khaki white 17 1
+Manufacturer#3 almond antique forest lavender goldenrod 14 2
+Manufacturer#3 almond antique metallic orange dim 19 3
+Manufacturer#3 almond antique misty red olive 1 4
+Manufacturer#3 almond antique olive coral navajo 45 5
+Manufacturer#4 almond antique gainsboro frosted violet 10 1
+Manufacturer#4 almond antique violet mint lemon 39 2
+Manufacturer#4 almond aquamarine floral ivory bisque 27 3
+Manufacturer#4 almond aquamarine yellow dodger mint 7 4
+Manufacturer#4 almond azure aquamarine papaya violet 12 5
+Manufacturer#5 almond antique blue firebrick mint 31 1
+Manufacturer#5 almond antique medium spring khaki 6 2
+Manufacturer#5 almond antique sky peru orange 2 3
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 4
+Manufacturer#5 almond azure blanched chiffon midnight 23 5
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a b/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a
new file mode 100644
index 0000000..9c0ca6c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 37. testPartitioningVariousForms-0-3436e50214f9afdec84334e10faa931a
@@ -0,0 +1,26 @@
+Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6
+Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6
+Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6
+Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6
+Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6
+Manufacturer#1 8749.73 1173.15 1753.76 1458.29 6
+Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5
+Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5
+Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5
+Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5
+Manufacturer#2 8923.62 1690.68 2031.98 1784.72 5
+Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5
+Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5
+Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5
+Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5
+Manufacturer#3 7532.61 1190.27 1922.98 1506.52 5
+Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5
+Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5
+Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5
+Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5
+Manufacturer#4 7337.62 1206.26 1844.92 1467.52 5
+Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5
+Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5
+Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5
+Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5
+Manufacturer#5 7672.66 1018.1 1789.69 1534.53 5
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e b/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e
new file mode 100644
index 0000000..fc27df2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 38. testPartitioningVariousForms2-0-cba9d84a6b1bb5e36595338d4602377e
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1173.15 1173.15 1173.15
+Manufacturer#1 almond antique burnished rose metallic 2 2346.3 1173.15 1173.15
+Manufacturer#1 almond antique chartreuse lavender yellow 34 1753.76 1753.76 1753.76
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 1602.59 1602.59 1602.59
+Manufacturer#1 almond aquamarine burnished black steel 28 1414.42 1414.42 1414.42
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66 1632.66 1632.66
+Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68 1690.68 1690.68
+Manufacturer#2 almond antique violet turquoise frosted 40 1800.7 1800.7 1800.7
+Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98 2031.98 2031.98
+Manufacturer#2 almond aquamarine rose maroon antique 25 1698.66 1698.66 1698.66
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6 1701.6 1701.6
+Manufacturer#3 almond antique chartreuse khaki white 17 1671.68 1671.68 1671.68
+Manufacturer#3 almond antique forest lavender goldenrod 14 1190.27 1190.27 1190.27
+Manufacturer#3 almond antique metallic orange dim 19 1410.39 1410.39 1410.39
+Manufacturer#3 almond antique misty red olive 1 1922.98 1922.98 1922.98
+Manufacturer#3 almond antique olive coral navajo 45 1337.29 1337.29 1337.29
+Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67 1620.67 1620.67
+Manufacturer#4 almond antique violet mint lemon 39 1375.42 1375.42 1375.42
+Manufacturer#4 almond aquamarine floral ivory bisque 27 1206.26 1206.26 1206.26
+Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92 1844.92 1844.92
+Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35 1290.35 1290.35
+Manufacturer#5 almond antique blue firebrick mint 31 1789.69 1789.69 1789.69
+Manufacturer#5 almond antique medium spring khaki 6 1611.66 1611.66 1611.66
+Manufacturer#5 almond antique sky peru orange 2 1788.73 1788.73 1788.73
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 1018.1 1018.1 1018.1
+Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48 1464.48 1464.48
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423 b/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423
new file mode 100644
index 0000000..e5a541f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 39. testUDFOnOrderCols-0-7647562850dd367ef1e6c63117805423
@@ -0,0 +1,26 @@
+Manufacturer#1 LARGE BRUSHED STEEL ARGE BRUSHED STEEL 1
+Manufacturer#1 LARGE BURNISHED STEEL ARGE BURNISHED STEEL 2
+Manufacturer#1 PROMO BURNISHED NICKEL ROMO BURNISHED NICKEL 3
+Manufacturer#1 PROMO PLATED TIN ROMO PLATED TIN 4
+Manufacturer#1 PROMO PLATED TIN ROMO PLATED TIN 4
+Manufacturer#1 STANDARD ANODIZED STEEL TANDARD ANODIZED STEEL 6
+Manufacturer#2 ECONOMY POLISHED STEEL CONOMY POLISHED STEEL 1
+Manufacturer#2 MEDIUM ANODIZED COPPER EDIUM ANODIZED COPPER 2
+Manufacturer#2 MEDIUM BURNISHED COPPER EDIUM BURNISHED COPPER 3
+Manufacturer#2 SMALL POLISHED NICKEL MALL POLISHED NICKEL 4
+Manufacturer#2 STANDARD PLATED TIN TANDARD PLATED TIN 5
+Manufacturer#3 ECONOMY PLATED COPPER CONOMY PLATED COPPER 1
+Manufacturer#3 MEDIUM BURNISHED BRASS EDIUM BURNISHED BRASS 2
+Manufacturer#3 MEDIUM BURNISHED TIN EDIUM BURNISHED TIN 3
+Manufacturer#3 PROMO ANODIZED TIN ROMO ANODIZED TIN 4
+Manufacturer#3 STANDARD POLISHED STEEL TANDARD POLISHED STEEL 5
+Manufacturer#4 ECONOMY BRUSHED COPPER CONOMY BRUSHED COPPER 1
+Manufacturer#4 SMALL BRUSHED BRASS MALL BRUSHED BRASS 2
+Manufacturer#4 SMALL PLATED STEEL MALL PLATED STEEL 3
+Manufacturer#4 PROMO POLISHED STEEL ROMO POLISHED STEEL 4
+Manufacturer#4 STANDARD ANODIZED TIN TANDARD ANODIZED TIN 5
+Manufacturer#5 LARGE BRUSHED BRASS ARGE BRUSHED BRASS 1
+Manufacturer#5 ECONOMY BURNISHED STEEL CONOMY BURNISHED STEEL 2
+Manufacturer#5 MEDIUM BURNISHED TIN EDIUM BURNISHED TIN 3
+Manufacturer#5 SMALL PLATED BRASS MALL PLATED BRASS 4
+Manufacturer#5 STANDARD BURNISHED TIN TANDARD BURNISHED TIN 5
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c b/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c
new file mode 100644
index 0000000..bf8e620
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 4. testCount-0-e6e97e884327df86f16b870527ec026c
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2
+Manufacturer#1 almond antique burnished rose metallic 2
+Manufacturer#1 almond antique chartreuse lavender yellow 3
+Manufacturer#1 almond antique salmon chartreuse burlywood 4
+Manufacturer#1 almond aquamarine burnished black steel 5
+Manufacturer#1 almond aquamarine pink moccasin thistle 6
+Manufacturer#2 almond antique violet chocolate turquoise 1
+Manufacturer#2 almond antique violet turquoise frosted 2
+Manufacturer#2 almond aquamarine midnight light salmon 3
+Manufacturer#2 almond aquamarine rose maroon antique 4
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 5
+Manufacturer#3 almond antique chartreuse khaki white 1
+Manufacturer#3 almond antique forest lavender goldenrod 2
+Manufacturer#3 almond antique metallic orange dim 3
+Manufacturer#3 almond antique misty red olive 4
+Manufacturer#3 almond antique olive coral navajo 5
+Manufacturer#4 almond antique gainsboro frosted violet 1
+Manufacturer#4 almond antique violet mint lemon 2
+Manufacturer#4 almond aquamarine floral ivory bisque 3
+Manufacturer#4 almond aquamarine yellow dodger mint 4
+Manufacturer#4 almond azure aquamarine papaya violet 5
+Manufacturer#5 almond antique blue firebrick mint 1
+Manufacturer#5 almond antique medium spring khaki 2
+Manufacturer#5 almond antique sky peru orange 3
+Manufacturer#5 almond aquamarine dodger light gainsboro 4
+Manufacturer#5 almond azure blanched chiffon midnight 5
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1 b/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1
new file mode 100644
index 0000000..1e29df6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 40. testNoBetweenForRows-0-99007f45b6406869e048b0e4eb9213f1
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 1173.15
+Manufacturer#1 almond antique burnished rose metallic 2 2346.3
+Manufacturer#1 almond antique chartreuse lavender yellow 34 4100.06
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 5702.650000000001
+Manufacturer#1 almond aquamarine burnished black steel 28 7117.070000000001
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 8749.730000000001
+Manufacturer#2 almond antique violet chocolate turquoise 14 1690.68
+Manufacturer#2 almond antique violet turquoise frosted 40 3491.38
+Manufacturer#2 almond aquamarine midnight light salmon 2 5523.360000000001
+Manufacturer#2 almond aquamarine rose maroon antique 25 7222.02
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 8923.62
+Manufacturer#3 almond antique chartreuse khaki white 17 1671.68
+Manufacturer#3 almond antique forest lavender goldenrod 14 2861.95
+Manufacturer#3 almond antique metallic orange dim 19 4272.34
+Manufacturer#3 almond antique misty red olive 1 6195.32
+Manufacturer#3 almond antique olive coral navajo 45 7532.61
+Manufacturer#4 almond antique gainsboro frosted violet 10 1620.67
+Manufacturer#4 almond antique violet mint lemon 39 2996.09
+Manufacturer#4 almond aquamarine floral ivory bisque 27 4202.35
+Manufacturer#4 almond aquamarine yellow dodger mint 7 6047.27
+Manufacturer#4 almond azure aquamarine papaya violet 12 7337.620000000001
+Manufacturer#5 almond antique blue firebrick mint 31 1789.69
+Manufacturer#5 almond antique medium spring khaki 6 3401.3500000000004
+Manufacturer#5 almond antique sky peru orange 2 5190.08
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 6208.18
+Manufacturer#5 almond azure blanched chiffon midnight 23 7672.66
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283 b/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283
new file mode 100644
index 0000000..a620479
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 41. testNoBetweenForRange-0-d81a591e90950de291d2f133793e9283
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 2346.3
+Manufacturer#1 almond antique burnished rose metallic 2 2346.3
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 3948.8900000000003
+Manufacturer#1 almond aquamarine burnished black steel 28 5363.31
+Manufacturer#1 almond antique chartreuse lavender yellow 34 7117.070000000001
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 8749.730000000001
+Manufacturer#2 almond aquamarine midnight light salmon 2 2031.98
+Manufacturer#2 almond antique violet chocolate turquoise 14 3722.66
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 5424.26
+Manufacturer#2 almond aquamarine rose maroon antique 25 7122.92
+Manufacturer#2 almond antique violet turquoise frosted 40 8923.62
+Manufacturer#3 almond antique misty red olive 1 1922.98
+Manufacturer#3 almond antique forest lavender goldenrod 14 3113.25
+Manufacturer#3 almond antique chartreuse khaki white 17 4784.93
+Manufacturer#3 almond antique metallic orange dim 19 6195.320000000001
+Manufacturer#3 almond antique olive coral navajo 45 7532.610000000001
+Manufacturer#4 almond aquamarine yellow dodger mint 7 1844.92
+Manufacturer#4 almond antique gainsboro frosted violet 10 3465.59
+Manufacturer#4 almond azure aquamarine papaya violet 12 4755.9400000000005
+Manufacturer#4 almond aquamarine floral ivory bisque 27 5962.200000000001
+Manufacturer#4 almond antique violet mint lemon 39 7337.620000000001
+Manufacturer#5 almond antique sky peru orange 2 1788.73
+Manufacturer#5 almond antique medium spring khaki 6 3400.3900000000003
+Manufacturer#5 almond azure blanched chiffon midnight 23 4864.870000000001
+Manufacturer#5 almond antique blue firebrick mint 31 6654.560000000001
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 7672.660000000002
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0
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diff --git a/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0 b/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0
new file mode 100644
index 0000000..74147d2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing.q -- 42. testUnboundedFollowingForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0
@@ -0,0 +1,26 @@
+Manufacturer#1 almond antique burnished rose metallic 2 8749.730000000001
+Manufacturer#1 almond antique burnished rose metallic 2 7576.58
+Manufacturer#1 almond antique chartreuse lavender yellow 34 6403.43
+Manufacturer#1 almond antique salmon chartreuse burlywood 6 4649.67
+Manufacturer#1 almond aquamarine burnished black steel 28 3047.08
+Manufacturer#1 almond aquamarine pink moccasin thistle 42 1632.66
+Manufacturer#2 almond antique violet chocolate turquoise 14 8923.62
+Manufacturer#2 almond antique violet turquoise frosted 40 7232.9400000000005
+Manufacturer#2 almond aquamarine midnight light salmon 2 5432.24
+Manufacturer#2 almond aquamarine rose maroon antique 25 3400.26
+Manufacturer#2 almond aquamarine sandy cyan gainsboro 18 1701.6
+Manufacturer#3 almond antique chartreuse khaki white 17 7532.61
+Manufacturer#3 almond antique forest lavender goldenrod 14 5860.929999999999
+Manufacturer#3 almond antique metallic orange dim 19 4670.66
+Manufacturer#3 almond antique misty red olive 1 3260.27
+Manufacturer#3 almond antique olive coral navajo 45 1337.29
+Manufacturer#4 almond antique gainsboro frosted violet 10 7337.620000000001
+Manufacturer#4 almond antique violet mint lemon 39 5716.950000000001
+Manufacturer#4 almond aquamarine floral ivory bisque 27 4341.530000000001
+Manufacturer#4 almond aquamarine yellow dodger mint 7 3135.27
+Manufacturer#4 almond azure aquamarine papaya violet 12 1290.35
+Manufacturer#5 almond antique blue firebrick mint 31 7672.66
+Manufacturer#5 almond antique medium spring khaki 6 5882.970000000001
+Manufacturer#5 almond antique sky peru orange 2 4271.3099999999995
+Manufacturer#5 almond aquamarine dodger light gainsboro 46 2482.58
+Manufacturer#5 almond azure blanched chiffon midnight 23 1464.48
---------------------------------------------------------------------
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[09/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965
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diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965 b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965
new file mode 100644
index 0000000..dc72606
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-1-a3d352560ac835993001665db6954965
@@ -0,0 +1,1049 @@
+ 1
+ 1
+ 1
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562 b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562
new file mode 100644
index 0000000..76cbeb2
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-2-fafa16c0f7697ca28aeb6f2698799562
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-3-bda0e7c77d6f4712a03389cb5032bc6d
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diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-3-bda0e7c77d6f4712a03389cb5032bc6d b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-3-bda0e7c77d6f4712a03389cb5032bc6d
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index 0000000..a9ec53c
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7
new file mode 100644
index 0000000..612bdf4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7
@@ -0,0 +1,1049 @@
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1 b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1
new file mode 100644
index 0000000..22a6f27
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_rank.q (deterministic) 1-1-5c5f373e325115d710a7a23fcb1626f1
@@ -0,0 +1,1049 @@
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---------------------------------------------------------------------
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[02/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145
new file mode 100644
index 0000000..a9ae190
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-5-ee44c5cdc80e1c832b702f9fb76d8145
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0
new file mode 100644
index 0000000..b3f0881
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-6-4d78f7b1d172d20c91f5867bc13a42a0
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c
new file mode 100644
index 0000000..1a45289
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-7-20fdc99aa046b2c41d9b85ab338c749c
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---------------------------------------------------------------------
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[04/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6
new file mode 100644
index 0000000..1436509
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-1-6378faf36ffd3f61e61cee6c0cb70e6
@@ -0,0 +1,1049 @@
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56 b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56
new file mode 100644
index 0000000..e55bede
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_windowspec.q (deterministic)-2-5f0eab306ea3c22b11ace9b542a7ee56
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---------------------------------------------------------------------
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[10/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4
new file mode 100644
index 0000000..275772e
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-1-9ee79e711248dd6e0a6ce27e439e55f4
@@ -0,0 +1,1049 @@
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04
new file mode 100644
index 0000000..62d71ab
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-2-1e88e0ba414a00195f7ebf6b8600ac04
@@ -0,0 +1,1049 @@
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c
new file mode 100644
index 0000000..569c1d4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-3-34d9ee4120f21d0d0ae914fba0acc60c
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4
----------------------------------------------------------------------
diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4 b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4
new file mode 100644
index 0000000..86ca4e4
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-4-dfd39236756a3951bc1ec354799d69e4
@@ -0,0 +1,1049 @@
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diff --git a/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-5-8d0ee3e1605f38214bfad28a5ce897cc b/sql/hive/src/test/resources/golden/windowing_navfn.q (deterministic)-5-8d0ee3e1605f38214bfad28a5ce897cc
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http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-0-b7cb25303831392a51cd996e758ac79a
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diff --git a/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-0-b7cb25303831392a51cd996e758ac79a b/sql/hive/src/test/resources/golden/windowing_ntile.q (deterministic)-0-b7cb25303831392a51cd996e758ac79a
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[13/13] spark git commit: [SPARK-1442] [SQL] Window Function Support
for Spark SQL
Posted by ma...@apache.org.
[SPARK-1442] [SQL] Window Function Support for Spark SQL
Adding more information about the implementation...
This PR is adding the support of window functions to Spark SQL (specifically OVER and WINDOW clause). For every expression having a OVER clause, we use a WindowExpression as the container of a WindowFunction and the corresponding WindowSpecDefinition (the definition of a window frame, i.e. partition specification, order specification, and frame specification appearing in a OVER clause).
# Implementation #
The high level work flow of the implementation is described as follows.
* Query parsing: In the query parse process, all WindowExpressions are originally placed in the projectList of a Project operator or the aggregateExpressions of an Aggregate operator. It makes our changes to simple and keep all of parsing rules for window functions at a single place (nodesToWindowSpecification). For the WINDOWclause in a query, we use a WithWindowDefinition as the container as the mapping from the name of a window specification to a WindowSpecDefinition. This changes is similar with our common table expression support.
* Analysis: The query analysis process has three steps for window functions.
* Resolve all WindowSpecReferences by replacing them with WindowSpecReferences according to the mapping table stored in the node of WithWindowDefinition.
* Resolve WindowFunctions in the projectList of a Project operator or the aggregateExpressions of an Aggregate operator. For this PR, we use Hive's functions for window functions because we will have a major refactoring of our internal UDAFs and it is better to switch our UDAFs after that refactoring work.
* Once we have resolved all WindowFunctions, we will use ResolveWindowFunction to extract WindowExpressions from projectList and aggregateExpressions and then create a Window operator for every distinct WindowSpecDefinition. With this choice, at the execution time, we can rely on the Exchange operator to do all of work on reorganizing the table and we do not need to worry about it in the physical Window operator. An example analyzed plan is shown as follows
```
sql("""
SELECT
year, country, product, sales,
avg(sales) over(partition by product) avg_product,
sum(sales) over(partition by country) sum_country
FROM sales
ORDER BY year, country, product
""").explain(true)
== Analyzed Logical Plan ==
Sort [year#34 ASC,country#35 ASC,product#36 ASC], true
Project [year#34,country#35,product#36,sales#37,avg_product#27,sum_country#28]
Window [year#34,country#35,product#36,sales#37,avg_product#27], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum(sales#37) WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS sum_country#28], WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Window [year#34,country#35,product#36,sales#37], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage(sales#37) WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS avg_product#27], WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Project [year#34,country#35,product#36,sales#37]
MetastoreRelation default, sales, None
```
* Query planning: In the process of query planning, we simple generate the physical Window operator based on the logical Window operator. Then, to prepare the executedPlan, the EnsureRequirements rule will add Exchange and Sort operators if necessary. The EnsureRequirements rule will analyze the data properties and try to not add unnecessary shuffle and sort. The physical plan for the above example query is shown below.
```
== Physical Plan ==
Sort [year#34 ASC,country#35 ASC,product#36 ASC], true
Exchange (RangePartitioning [year#34 ASC,country#35 ASC,product#36 ASC], 200), []
Window [year#34,country#35,product#36,sales#37,avg_product#27], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFSum(sales#37) WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS sum_country#28], WindowSpecDefinition [country#35], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Exchange (HashPartitioning [country#35], 200), [country#35 ASC]
Window [year#34,country#35,product#36,sales#37], [HiveWindowFunction#org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage(sales#37) WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING AS avg_product#27], WindowSpecDefinition [product#36], [], ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
Exchange (HashPartitioning [product#36], 200), [product#36 ASC]
HiveTableScan [year#34,country#35,product#36,sales#37], (MetastoreRelation default, sales, None), None
```
* Execution time: At execution time, a physical Window operator buffers all rows in a partition specified in the partition spec of a OVER clause. If necessary, it also maintains a sliding window frame. The current implementation tries to buffer the input parameters of a window function according to the window frame to avoid evaluating a row multiple times.
# Future work #
Here are three improvements that are not hard to add:
* Taking advantage of the window frame specification to reduce the number of rows buffered in the physical Window operator. For some cases, we only need to buffer the rows appearing in the sliding window. But for other cases, we will not be able to reduce the number of rows buffered (e.g. ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING).
* When aRAGEN frame is used, for <value> PRECEDING and <value> FOLLOWING, it will be great if the <value> part is an expression (we can start with Literal). So, when the data type of ORDER BY expression is a FractionalType, we can support FractionalType as the type <value> (<value> still needs to be evaluated as a positive value).
* When aRAGEN frame is used, we need to support DateType and TimestampType as the data type of the expression appearing in the order specification. Then, the <value> part of <value> PRECEDING and <value> FOLLOWING can support interval types (once we support them).
This is a joint work with guowei2 and yhuai
Thanks hbutani hvanhovell for his comments
Thanks scwf for his comments and unit tests
Author: Yin Huai <yh...@databricks.com>
Closes #5604 from guowei2/windowImplement and squashes the following commits:
76fe1c8 [Yin Huai] Implementation.
aa2b0ae [Yin Huai] Tests.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/f2c47082
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/f2c47082
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/f2c47082
Branch: refs/heads/master
Commit: f2c47082c3412a4cf8cbabe12585147c5ec3ea40
Parents: c3eb441
Author: Yin Huai <yh...@databricks.com>
Authored: Wed May 6 10:43:00 2015 -0700
Committer: Michael Armbrust <mi...@databricks.com>
Committed: Wed May 6 10:43:00 2015 -0700
----------------------------------------------------------------------
.../spark/sql/catalyst/analysis/Analyzer.scala | 200 +++
.../sql/catalyst/analysis/CheckAnalysis.scala | 5 +
.../sql/catalyst/expressions/Projection.scala | 94 ++
.../expressions/windowExpressions.scala | 340 +++++
.../catalyst/plans/logical/basicOperators.scala | 30 +-
.../spark/sql/catalyst/trees/TreeNode.scala | 23 +
.../sql/catalyst/trees/TreeNodeSuite.scala | 44 +-
.../spark/sql/execution/SparkStrategies.scala | 2 +
.../org/apache/spark/sql/execution/Window.scala | 480 +++++++
.../hive/execution/HiveCompatibilitySuite.scala | 2 +-
.../org/apache/spark/sql/hive/HiveContext.scala | 1 +
.../org/apache/spark/sql/hive/HiveQl.scala | 175 ++-
.../org/apache/spark/sql/hive/hiveUdfs.scala | 223 ++-
...Windowing-0-327a8cd39fe30255ff492ee86f660522 | 26 +
...RankNoGBY-0-fef4bf638d52a9a601845347010602fd | 26 +
...FirstLast-0-86bb9c97d92fdcd941bcb5143513e2e6 | 26 +
...WithWhere-0-84345a9f685ba63b87caa4bb16b122b5 | 5 +
...SumWindow-0-6cfc8840d3a4469b0fe11d63182cb59f | 26 +
...ortClause-0-88d96a526d3cae6ed8168c5b228974d1 | 26 +
...pressions-0-11f6c13cf2710ce7054654cca136e73e | 26 +
...leWindows-0-efd1476255eeb1b1961149144f574b7a | 26 +
...CountStar-0-1b1fc185c8fddf68e58e92f29052ab2d | 26 +
...testUDAFs-0-6974e5959e41a661e09db18547fef58a | 26 +
...FsWithGBY-0-67d15ee5915ac64a738fd4b60d75eb35 | 25 +
...titioning-0-cb5618b1e626f3a9d4a030b508b5d251 | 25 +
...testSTATs-0-da0e0cca69e42118a96b8609b8fa5838 | 26 +
...testDISTs-0-672d4cb385b7ced2e446f132474293ad | 26 +
...eralViews-0-dea06072f0a64fe4537fae854944ed5a | 78 ++
...QAndAlias-0-b996a664b06e5741c08079d5c38241bc | 25 +
...geWindows-0-227e080e337d734dd88ff814b3b412e4 | 26 +
...DAFInvoke-0-25912ae7d18c91cc09e17e57968fb5db | 26 +
...rInWdwDef-0-88945892370ccbc1125a927a3d55342a | 26 +
...ngWithSWQ-0-a5a5339330a6a6660d32ccb0cc5d7100 | 25 +
...SpecRules-0-fa80b09c99e3c1487de48ea71a88dada | 26 +
...titioning-0-45ccbaf0ee083858f7661c66b11d4768 | 26 +
...iousForms-0-3436e50214f9afdec84334e10faa931a | 26 +
...ousForms2-0-cba9d84a6b1bb5e36595338d4602377e | 26 +
...OrderCols-0-7647562850dd367ef1e6c63117805423 | 26 +
...testCount-0-e6e97e884327df86f16b870527ec026c | 26 +
...enForRows-0-99007f45b6406869e048b0e4eb9213f1 | 26 +
...nForRange-0-d81a591e90950de291d2f133793e9283 | 26 +
...ngForRows-0-fb8648e82e4dd56d6bdcfd739dd1edf0 | 26 +
...gForRange-0-3cd04e5f2398853c4850f4f86142bb39 | 26 +
...Aggregate-0-cb3d2f8c1296044dc2658876bb6103ae | 26 +
...owingUDAF-0-3bde93728761b780a745c2ce0398aa0f | 26 +
...untInSubQ-0-73d5274a21d4f4fd51d2a0f1d98516ce | 26 +
...CaseAlias-0-4b1ad2515fb079012467e987f484a722 | 26 +
...wingNoGBY-0-70cdc0555a61ef08534a9ebebb95ebbf | 26 +
...tainer_sz-0-d3f50875bd5dff172cf813fdb7d738eb | 0
...tainer_sz-1-dda16565b98926fc3587de937b9401c7 | 0
...ntainer_sz-2-374e39786feb745cd70f25be58bfa24 | 0
...tainer_sz-3-d2b5e23edec42a62e61750b110ecbaac | 1 +
...tainer_sz-4-50d0c630159068b5b8ccdeb76493f1f7 | 26 +
...tainer_sz-5-3f95cd6f4add7a2d0101fe3dd97e5082 | 1 +
...mnPruning-0-d3f50875bd5dff172cf813fdb7d738eb | 0
...mnPruning-1-dda16565b98926fc3587de937b9401c7 | 0
...umnPruning-2-374e39786feb745cd70f25be58bfa24 | 0
...mnPruning-3-9294b4a22bc396ff2accabd53c5da98b | 26 +
...umnPruning-4-445cab062581c449ceffcb368cdf133 | 26 +
...mnPruning-5-89110070c761eafb992eb9315128b53f | 26 +
...nistic) 1-0-12a92d8800e0da8b515ba3eaf6a7fd0f | 1049 ++++++++++++++
...nistic) 3-0-455e41d9949a2d22bab634fd8e42f2b1 | 1 +
...nistic) 4-0-cfad06ae8eba6b047d32a6a61dd59392 | 1 +
...nistic) 5-0-d7ca7a61377cef3a9f721a28afdae012 | 1 +
...nistic) 6-0-287bcc7679822bc7b684532b267bf11f | 1 +
...ministic)-0-36217f6074daaacddb9fcb50a3f4fb5b | 1049 ++++++++++++++
...ministic)-1-9ee79e711248dd6e0a6ce27e439e55f4 | 1049 ++++++++++++++
...ministic)-2-1e88e0ba414a00195f7ebf6b8600ac04 | 1049 ++++++++++++++
...ministic)-3-34d9ee4120f21d0d0ae914fba0acc60c | 1049 ++++++++++++++
...ministic)-4-dfd39236756a3951bc1ec354799d69e4 | 1049 ++++++++++++++
...ministic)-5-8d0ee3e1605f38214bfad28a5ce897cc | 1 +
...ministic)-0-b7cb25303831392a51cd996e758ac79a | 1049 ++++++++++++++
...ministic)-1-a3d352560ac835993001665db6954965 | 1049 ++++++++++++++
...ministic)-2-fafa16c0f7697ca28aeb6f2698799562 | 1049 ++++++++++++++
...ministic)-3-bda0e7c77d6f4712a03389cb5032bc6d | 1049 ++++++++++++++
...nistic) 1-0-2e0cbc2d7c5f16657edacd9e7209e6e7 | 1049 ++++++++++++++
...nistic) 1-1-5c5f373e325115d710a7a23fcb1626f1 | 1049 ++++++++++++++
...nistic) 1-2-ac487cc1b94130bf9ce00e07c7075f65 | 1049 ++++++++++++++
...nistic) 1-3-b82dfa24123047be4b4e3c27c3997d34 | 1049 ++++++++++++++
...inistic) 2-0-81bb7f49a55385878637c8aac4d08e5 | 1294 ++++++++++++++++++
...nistic) 3-0-58a982694ba2b1e34de82b1de54936a0 | 0
...nistic) 4-0-12cc78f3953c3e6b5411ddc729541bf0 | 474 +++++++
...ministic)-0-6642a21d87e0401ba1a668ea8b244f0c | 1049 ++++++++++++++
...ministic)-1-2bf20f39e6ffef258858f7943a974e7e | 1049 ++++++++++++++
...ministic)-2-16239d2b069789ba99fbac50c4f0724f | 1049 ++++++++++++++
...ministic)-3-d90b27fca067b0b3c48d873b3ef32af7 | 1049 ++++++++++++++
...ministic)-4-f2e4d659b65a833e9281b6786d3d55c1 | 1049 ++++++++++++++
...ing_udaf2-0-96659fde37d7a38ea15b367b47f59ce2 | 0
...ing_udaf2-1-b4bdee4908b1cb8e240c549ae5cfe4c0 | 1 +
...ministic)-0-f498cccf82480be03022d2a36f87651e | 1049 ++++++++++++++
...rministic)-1-6378faf36ffd3f61e61cee6c0cb70e6 | 1049 ++++++++++++++
...ministic)-2-5f0eab306ea3c22b11ace9b542a7ee56 | 1049 ++++++++++++++
...ministic)-3-6f104992e0050576085064815de43194 | 1049 ++++++++++++++
...ministic)-4-cd2e3d2344810cb3ba843d4c01c81d7e | 1049 ++++++++++++++
...ministic)-5-ee44c5cdc80e1c832b702f9fb76d8145 | 1049 ++++++++++++++
...ministic)-6-4d78f7b1d172d20c91f5867bc13a42a0 | 1049 ++++++++++++++
...ministic)-7-20fdc99aa046b2c41d9b85ab338c749c | 1049 ++++++++++++++
...ministic)-8-45a1d7c2aba45d761e19ff4dfdf5463e | 1049 ++++++++++++++
.../sql/hive/execution/HiveComparisonTest.scala | 2 +-
.../HiveWindowFunctionQuerySuite.scala | 845 ++++++++++++
.../sql/hive/execution/SQLQuerySuite.scala | 147 ++
101 files changed, 34768 insertions(+), 34 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 5e42b40..7b543b6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -17,6 +17,8 @@
package org.apache.spark.sql.catalyst.analysis
+import scala.collection.mutable.ArrayBuffer
+
import org.apache.spark.util.collection.OpenHashSet
import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.expressions._
@@ -61,6 +63,7 @@ class Analyzer(
ResolveGenerate ::
ImplicitGenerate ::
ResolveFunctions ::
+ ExtractWindowExpressions ::
GlobalAggregates ::
UnresolvedHavingClauseAttributes ::
TrimGroupingAliases ::
@@ -529,6 +532,203 @@ class Analyzer(
makeGeneratorOutput(p.generator, p.generatorOutput), p.child)
}
}
+
+ /**
+ * Extracts [[WindowExpression]]s from the projectList of a [[Project]] operator and
+ * aggregateExpressions of an [[Aggregate]] operator and creates individual [[Window]]
+ * operators for every distinct [[WindowSpecDefinition]].
+ *
+ * This rule handles three cases:
+ * - A [[Project]] having [[WindowExpression]]s in its projectList;
+ * - An [[Aggregate]] having [[WindowExpression]]s in its aggregateExpressions.
+ * - An [[Filter]]->[[Aggregate]] pattern representing GROUP BY with a HAVING
+ * clause and the [[Aggregate]] has [[WindowExpression]]s in its aggregateExpressions.
+ * Note: If there is a GROUP BY clause in the query, aggregations and corresponding
+ * filters (expressions in the HAVING clause) should be evaluated before any
+ * [[WindowExpression]]. If a query has SELECT DISTINCT, the DISTINCT part should be
+ * evaluated after all [[WindowExpression]]s.
+ *
+ * For every case, the transformation works as follows:
+ * 1. For a list of [[Expression]]s (a projectList or an aggregateExpressions), partitions
+ * it two lists of [[Expression]]s, one for all [[WindowExpression]]s and another for
+ * all regular expressions.
+ * 2. For all [[WindowExpression]]s, groups them based on their [[WindowSpecDefinition]]s.
+ * 3. For every distinct [[WindowSpecDefinition]], creates a [[Window]] operator and inserts
+ * it into the plan tree.
+ */
+ object ExtractWindowExpressions extends Rule[LogicalPlan] {
+ def hasWindowFunction(projectList: Seq[NamedExpression]): Boolean =
+ projectList.exists(hasWindowFunction)
+
+ def hasWindowFunction(expr: NamedExpression): Boolean = {
+ expr.find {
+ case window: WindowExpression => true
+ case _ => false
+ }.isDefined
+ }
+
+ /**
+ * From a Seq of [[NamedExpression]]s, extract window expressions and
+ * other regular expressions.
+ */
+ def extract(
+ expressions: Seq[NamedExpression]): (Seq[NamedExpression], Seq[NamedExpression]) = {
+ // First, we simple partition the input expressions to two part, one having
+ // WindowExpressions and another one without WindowExpressions.
+ val (windowExpressions, regularExpressions) = expressions.partition(hasWindowFunction)
+
+ // Then, we need to extract those regular expressions used in the WindowExpression.
+ // For example, when we have col1 - Sum(col2 + col3) OVER (PARTITION BY col4 ORDER BY col5),
+ // we need to make sure that col1 to col5 are all projected from the child of the Window
+ // operator.
+ val extractedExprBuffer = new ArrayBuffer[NamedExpression]()
+ def extractExpr(expr: Expression): Expression = expr match {
+ case ne: NamedExpression =>
+ // If a named expression is not in regularExpressions, add extract it and replace it
+ // with an AttributeReference.
+ val missingExpr =
+ AttributeSet(Seq(expr)) -- (regularExpressions ++ extractedExprBuffer)
+ if (missingExpr.nonEmpty) {
+ extractedExprBuffer += ne
+ }
+ ne.toAttribute
+ case e: Expression if e.foldable =>
+ e // No need to create an attribute reference if it will be evaluated as a Literal.
+ case e: Expression =>
+ // For other expressions, we extract it and replace it with an AttributeReference (with
+ // an interal column name, e.g. "_w0").
+ val withName = Alias(e, s"_w${extractedExprBuffer.length}")()
+ extractedExprBuffer += withName
+ withName.toAttribute
+ }
+
+ // Now, we extract expressions from windowExpressions by using extractExpr.
+ val newWindowExpressions = windowExpressions.map {
+ _.transform {
+ // Extracts children expressions of a WindowFunction (input parameters of
+ // a WindowFunction).
+ case wf : WindowFunction =>
+ val newChildren = wf.children.map(extractExpr(_))
+ wf.withNewChildren(newChildren)
+
+ // Extracts expressions from the partition spec and order spec.
+ case wsc @ WindowSpecDefinition(partitionSpec, orderSpec, _) =>
+ val newPartitionSpec = partitionSpec.map(extractExpr(_))
+ val newOrderSpec = orderSpec.map { so =>
+ val newChild = extractExpr(so.child)
+ so.copy(child = newChild)
+ }
+ wsc.copy(partitionSpec = newPartitionSpec, orderSpec = newOrderSpec)
+
+ // Extracts AggregateExpression. For example, for SUM(x) - Sum(y) OVER (...),
+ // we need to extract SUM(x).
+ case agg: AggregateExpression =>
+ val withName = Alias(agg, s"_w${extractedExprBuffer.length}")()
+ extractedExprBuffer += withName
+ withName.toAttribute
+ }.asInstanceOf[NamedExpression]
+ }
+
+ (newWindowExpressions, regularExpressions ++ extractedExprBuffer)
+ }
+
+ /**
+ * Adds operators for Window Expressions. Every Window operator handles a single Window Spec.
+ */
+ def addWindow(windowExpressions: Seq[NamedExpression], child: LogicalPlan): LogicalPlan = {
+ // First, we group window expressions based on their Window Spec.
+ val groupedWindowExpression = windowExpressions.groupBy { expr =>
+ val windowExpression = expr.find {
+ case window: WindowExpression => true
+ case other => false
+ }.map(_.asInstanceOf[WindowExpression].windowSpec)
+ windowExpression.getOrElse(
+ failAnalysis(s"$windowExpressions does not have any WindowExpression."))
+ }.toSeq
+
+ // For every Window Spec, we add a Window operator and set currentChild as the child of it.
+ var currentChild = child
+ var i = 0
+ while (i < groupedWindowExpression.size) {
+ val (windowSpec, windowExpressions) = groupedWindowExpression(i)
+ // Set currentChild to the newly created Window operator.
+ currentChild = Window(currentChild.output, windowExpressions, windowSpec, currentChild)
+
+ // Move to next WindowExpression.
+ i += 1
+ }
+
+ // We return the top operator.
+ currentChild
+ }
+
+ // We have to use transformDown at here to make sure the rule of
+ // "Aggregate with Having clause" will be triggered.
+ def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
+ // Lookup WindowSpecDefinitions. This rule works with unresolved children.
+ case WithWindowDefinition(windowDefinitions, child) =>
+ child.transform {
+ case plan => plan.transformExpressions {
+ case UnresolvedWindowExpression(c, WindowSpecReference(windowName)) =>
+ val errorMessage =
+ s"Window specification $windowName is not defined in the WINDOW clause."
+ val windowSpecDefinition =
+ windowDefinitions
+ .get(windowName)
+ .getOrElse(failAnalysis(errorMessage))
+ WindowExpression(c, windowSpecDefinition)
+ }
+ }
+
+ // Aggregate with Having clause. This rule works with an unresolved Aggregate because
+ // a resolved Aggregate will not have Window Functions.
+ case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))
+ if child.resolved &&
+ hasWindowFunction(aggregateExprs) &&
+ !a.expressions.exists(!_.resolved) =>
+ val (windowExpressions, aggregateExpressions) = extract(aggregateExprs)
+ // Create an Aggregate operator to evaluate aggregation functions.
+ val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child)
+ // Add a Filter operator for conditions in the Having clause.
+ val withFilter = Filter(condition, withAggregate)
+ val withWindow = addWindow(windowExpressions, withFilter)
+
+ // Finally, generate output columns according to the original projectList.
+ val finalProjectList = aggregateExprs.map (_.toAttribute)
+ Project(finalProjectList, withWindow)
+
+ case p: LogicalPlan if !p.childrenResolved => p
+
+ // Aggregate without Having clause.
+ case a @ Aggregate(groupingExprs, aggregateExprs, child)
+ if hasWindowFunction(aggregateExprs) &&
+ !a.expressions.exists(!_.resolved) =>
+ val (windowExpressions, aggregateExpressions) = extract(aggregateExprs)
+ // Create an Aggregate operator to evaluate aggregation functions.
+ val withAggregate = Aggregate(groupingExprs, aggregateExpressions, child)
+ // Add Window operators.
+ val withWindow = addWindow(windowExpressions, withAggregate)
+
+ // Finally, generate output columns according to the original projectList.
+ val finalProjectList = aggregateExprs.map (_.toAttribute)
+ Project(finalProjectList, withWindow)
+
+ // We only extract Window Expressions after all expressions of the Project
+ // have been resolved.
+ case p @ Project(projectList, child)
+ if hasWindowFunction(projectList) && !p.expressions.exists(!_.resolved) =>
+ val (windowExpressions, regularExpressions) = extract(projectList)
+ // We add a project to get all needed expressions for window expressions from the child
+ // of the original Project operator.
+ val withProject = Project(regularExpressions, child)
+ // Add Window operators.
+ val withWindow = addWindow(windowExpressions, withProject)
+
+ // Finally, generate output columns according to the original projectList.
+ val finalProjectList = projectList.map (_.toAttribute)
+ Project(finalProjectList, withWindow)
+ }
+ }
}
/**
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 2381689..c8288c6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -70,6 +70,11 @@ trait CheckAnalysis {
failAnalysis(
s"invalid expression ${b.prettyString} " +
s"between ${b.left.simpleString} and ${b.right.simpleString}")
+
+ case w @ WindowExpression(windowFunction, windowSpec) if windowSpec.validate.nonEmpty =>
+ // The window spec is not valid.
+ val reason = windowSpec.validate.get
+ failAnalysis(s"Window specification $windowSpec is not valid because $reason")
}
operator match {
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index c2866cd..8cae548 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -548,3 +548,97 @@ class JoinedRow5 extends Row {
}
}
}
+
+/**
+ * JIT HACK: Replace with macros
+ */
+class JoinedRow6 extends Row {
+ private[this] var row1: Row = _
+ private[this] var row2: Row = _
+
+ def this(left: Row, right: Row) = {
+ this()
+ row1 = left
+ row2 = right
+ }
+
+ /** Updates this JoinedRow to used point at two new base rows. Returns itself. */
+ def apply(r1: Row, r2: Row): Row = {
+ row1 = r1
+ row2 = r2
+ this
+ }
+
+ /** Updates this JoinedRow by updating its left base row. Returns itself. */
+ def withLeft(newLeft: Row): Row = {
+ row1 = newLeft
+ this
+ }
+
+ /** Updates this JoinedRow by updating its right base row. Returns itself. */
+ def withRight(newRight: Row): Row = {
+ row2 = newRight
+ this
+ }
+
+ override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
+
+ override def length: Int = row1.length + row2.length
+
+ override def apply(i: Int): Any =
+ if (i < row1.length) row1(i) else row2(i - row1.length)
+
+ override def isNullAt(i: Int): Boolean =
+ if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length)
+
+ override def getInt(i: Int): Int =
+ if (i < row1.length) row1.getInt(i) else row2.getInt(i - row1.length)
+
+ override def getLong(i: Int): Long =
+ if (i < row1.length) row1.getLong(i) else row2.getLong(i - row1.length)
+
+ override def getDouble(i: Int): Double =
+ if (i < row1.length) row1.getDouble(i) else row2.getDouble(i - row1.length)
+
+ override def getBoolean(i: Int): Boolean =
+ if (i < row1.length) row1.getBoolean(i) else row2.getBoolean(i - row1.length)
+
+ override def getShort(i: Int): Short =
+ if (i < row1.length) row1.getShort(i) else row2.getShort(i - row1.length)
+
+ override def getByte(i: Int): Byte =
+ if (i < row1.length) row1.getByte(i) else row2.getByte(i - row1.length)
+
+ override def getFloat(i: Int): Float =
+ if (i < row1.length) row1.getFloat(i) else row2.getFloat(i - row1.length)
+
+ override def getString(i: Int): String =
+ if (i < row1.length) row1.getString(i) else row2.getString(i - row1.length)
+
+ override def getAs[T](i: Int): T =
+ if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length)
+
+ override def copy(): Row = {
+ val totalSize = row1.length + row2.length
+ val copiedValues = new Array[Any](totalSize)
+ var i = 0
+ while(i < totalSize) {
+ copiedValues(i) = apply(i)
+ i += 1
+ }
+ new GenericRow(copiedValues)
+ }
+
+ override def toString: String = {
+ // Make sure toString never throws NullPointerException.
+ if ((row1 eq null) && (row2 eq null)) {
+ "[ empty row ]"
+ } else if (row1 eq null) {
+ row2.mkString("[", ",", "]")
+ } else if (row2 eq null) {
+ row1.mkString("[", ",", "]")
+ } else {
+ mkString("[", ",", "]")
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
new file mode 100644
index 0000000..099d67c
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/windowExpressions.scala
@@ -0,0 +1,340 @@
+/*
+ * 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.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.analysis.UnresolvedException
+import org.apache.spark.sql.catalyst.errors.TreeNodeException
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{NumericType, DataType}
+
+/**
+ * The trait of the Window Specification (specified in the OVER clause or WINDOW clause) for
+ * Window Functions.
+ */
+sealed trait WindowSpec
+
+/**
+ * The specification for a window function.
+ * @param partitionSpec It defines the way that input rows are partitioned.
+ * @param orderSpec It defines the ordering of rows in a partition.
+ * @param frameSpecification It defines the window frame in a partition.
+ */
+case class WindowSpecDefinition(
+ partitionSpec: Seq[Expression],
+ orderSpec: Seq[SortOrder],
+ frameSpecification: WindowFrame) extends Expression with WindowSpec {
+
+ def validate: Option[String] = frameSpecification match {
+ case UnspecifiedFrame =>
+ Some("Found a UnspecifiedFrame. It should be converted to a SpecifiedWindowFrame " +
+ "during analysis. Please file a bug report.")
+ case frame: SpecifiedWindowFrame => frame.validate.orElse {
+ def checkValueBasedBoundaryForRangeFrame(): Option[String] = {
+ if (orderSpec.length > 1) {
+ // It is not allowed to have a value-based PRECEDING and FOLLOWING
+ // as the boundary of a Range Window Frame.
+ Some("This Range Window Frame only accepts at most one ORDER BY expression.")
+ } else if (orderSpec.nonEmpty && !orderSpec.head.dataType.isInstanceOf[NumericType]) {
+ Some("The data type of the expression in the ORDER BY clause should be a numeric type.")
+ } else {
+ None
+ }
+ }
+
+ (frame.frameType, frame.frameStart, frame.frameEnd) match {
+ case (RangeFrame, vp: ValuePreceding, _) => checkValueBasedBoundaryForRangeFrame()
+ case (RangeFrame, vf: ValueFollowing, _) => checkValueBasedBoundaryForRangeFrame()
+ case (RangeFrame, _, vp: ValuePreceding) => checkValueBasedBoundaryForRangeFrame()
+ case (RangeFrame, _, vf: ValueFollowing) => checkValueBasedBoundaryForRangeFrame()
+ case (_, _, _) => None
+ }
+ }
+ }
+
+ type EvaluatedType = Any
+
+ override def children: Seq[Expression] = partitionSpec ++ orderSpec
+
+ override lazy val resolved: Boolean =
+ childrenResolved && frameSpecification.isInstanceOf[SpecifiedWindowFrame]
+
+
+ override def toString: String = simpleString
+
+ override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException
+ override def nullable: Boolean = true
+ override def foldable: Boolean = false
+ override def dataType: DataType = throw new UnsupportedOperationException
+}
+
+/**
+ * A Window specification reference that refers to the [[WindowSpecDefinition]] defined
+ * under the name `name`.
+ */
+case class WindowSpecReference(name: String) extends WindowSpec
+
+/**
+ * The trait used to represent the type of a Window Frame.
+ */
+sealed trait FrameType
+
+/**
+ * RowFrame treats rows in a partition individually. When a [[ValuePreceding]]
+ * or a [[ValueFollowing]] is used as its [[FrameBoundary]], the value is considered
+ * as a physical offset.
+ * For example, `ROW BETWEEN 1 PRECEDING AND 1 FOLLOWING` represents a 3-row frame,
+ * from the row precedes the current row to the row follows the current row.
+ */
+case object RowFrame extends FrameType
+
+/**
+ * RangeFrame treats rows in a partition as groups of peers.
+ * All rows having the same `ORDER BY` ordering are considered as peers.
+ * When a [[ValuePreceding]] or a [[ValueFollowing]] is used as its [[FrameBoundary]],
+ * the value is considered as a logical offset.
+ * For example, assuming the value of the current row's `ORDER BY` expression `expr` is `v`,
+ * `RANGE BETWEEN 1 PRECEDING AND 1 FOLLOWING` represents a frame containing rows whose values
+ * `expr` are in the range of [v-1, v+1].
+ *
+ * If `ORDER BY` clause is not defined, all rows in the partition is considered as peers
+ * of the current row.
+ */
+case object RangeFrame extends FrameType
+
+/**
+ * The trait used to represent the type of a Window Frame Boundary.
+ */
+sealed trait FrameBoundary {
+ def notFollows(other: FrameBoundary): Boolean
+}
+
+/** UNBOUNDED PRECEDING boundary. */
+case object UnboundedPreceding extends FrameBoundary {
+ def notFollows(other: FrameBoundary): Boolean = other match {
+ case UnboundedPreceding => true
+ case vp: ValuePreceding => true
+ case CurrentRow => true
+ case vf: ValueFollowing => true
+ case UnboundedFollowing => true
+ }
+
+ override def toString: String = "UNBOUNDED PRECEDING"
+}
+
+/** <value> PRECEDING boundary. */
+case class ValuePreceding(value: Int) extends FrameBoundary {
+ def notFollows(other: FrameBoundary): Boolean = other match {
+ case UnboundedPreceding => false
+ case ValuePreceding(anotherValue) => value >= anotherValue
+ case CurrentRow => true
+ case vf: ValueFollowing => true
+ case UnboundedFollowing => true
+ }
+
+ override def toString: String = s"$value PRECEDING"
+}
+
+/** CURRENT ROW boundary. */
+case object CurrentRow extends FrameBoundary {
+ def notFollows(other: FrameBoundary): Boolean = other match {
+ case UnboundedPreceding => false
+ case vp: ValuePreceding => false
+ case CurrentRow => true
+ case vf: ValueFollowing => true
+ case UnboundedFollowing => true
+ }
+
+ override def toString: String = "CURRENT ROW"
+}
+
+/** <value> FOLLOWING boundary. */
+case class ValueFollowing(value: Int) extends FrameBoundary {
+ def notFollows(other: FrameBoundary): Boolean = other match {
+ case UnboundedPreceding => false
+ case vp: ValuePreceding => false
+ case CurrentRow => false
+ case ValueFollowing(anotherValue) => value <= anotherValue
+ case UnboundedFollowing => true
+ }
+
+ override def toString: String = s"$value FOLLOWING"
+}
+
+/** UNBOUNDED FOLLOWING boundary. */
+case object UnboundedFollowing extends FrameBoundary {
+ def notFollows(other: FrameBoundary): Boolean = other match {
+ case UnboundedPreceding => false
+ case vp: ValuePreceding => false
+ case CurrentRow => false
+ case vf: ValueFollowing => false
+ case UnboundedFollowing => true
+ }
+
+ override def toString: String = "UNBOUNDED FOLLOWING"
+}
+
+/**
+ * The trait used to represent the a Window Frame.
+ */
+sealed trait WindowFrame
+
+/** Used as a place holder when a frame specification is not defined. */
+case object UnspecifiedFrame extends WindowFrame
+
+/** A specified Window Frame. */
+case class SpecifiedWindowFrame(
+ frameType: FrameType,
+ frameStart: FrameBoundary,
+ frameEnd: FrameBoundary) extends WindowFrame {
+
+ /** If this WindowFrame is valid or not. */
+ def validate: Option[String] = (frameType, frameStart, frameEnd) match {
+ case (_, UnboundedFollowing, _) =>
+ Some(s"$UnboundedFollowing is not allowed as the start of a Window Frame.")
+ case (_, _, UnboundedPreceding) =>
+ Some(s"$UnboundedPreceding is not allowed as the end of a Window Frame.")
+ // case (RowFrame, start, end) => ??? RowFrame specific rule
+ // case (RangeFrame, start, end) => ??? RangeFrame specific rule
+ case (_, start, end) =>
+ if (start.notFollows(end)) {
+ None
+ } else {
+ val reason =
+ s"The end of this Window Frame $end is smaller than the start of " +
+ s"this Window Frame $start."
+ Some(reason)
+ }
+ }
+
+ override def toString: String = frameType match {
+ case RowFrame => s"ROWS BETWEEN $frameStart AND $frameEnd"
+ case RangeFrame => s"RANGE BETWEEN $frameStart AND $frameEnd"
+ }
+}
+
+object SpecifiedWindowFrame {
+ /**
+ *
+ * @param hasOrderSpecification If the window spec has order by expressions.
+ * @param acceptWindowFrame If the window function accepts user-specified frame.
+ * @return
+ */
+ def defaultWindowFrame(
+ hasOrderSpecification: Boolean,
+ acceptWindowFrame: Boolean): SpecifiedWindowFrame = {
+ if (hasOrderSpecification && acceptWindowFrame) {
+ // If order spec is defined and the window function supports user specified window frames,
+ // the default frame is RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.
+ SpecifiedWindowFrame(RangeFrame, UnboundedPreceding, CurrentRow)
+ } else {
+ // Otherwise, the default frame is
+ // ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING.
+ SpecifiedWindowFrame(RowFrame, UnboundedPreceding, UnboundedFollowing)
+ }
+ }
+}
+
+/**
+ * Every window function needs to maintain a output buffer for its output.
+ * It should expect that for a n-row window frame, it will be called n times
+ * to retrieve value corresponding with these n rows.
+ */
+trait WindowFunction extends Expression {
+ self: Product =>
+
+ def init(): Unit
+
+ def reset(): Unit
+
+ def prepareInputParameters(input: Row): AnyRef
+
+ def update(input: AnyRef): Unit
+
+ def batchUpdate(inputs: Array[AnyRef]): Unit
+
+ def evaluate(): Unit
+
+ def get(index: Int): Any
+
+ def newInstance(): WindowFunction
+}
+
+case class UnresolvedWindowFunction(
+ name: String,
+ children: Seq[Expression])
+ extends Expression with WindowFunction {
+
+ override def dataType: DataType = throw new UnresolvedException(this, "dataType")
+ override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
+ override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
+ override lazy val resolved = false
+
+ override def init(): Unit =
+ throw new UnresolvedException(this, "init")
+ override def reset(): Unit =
+ throw new UnresolvedException(this, "reset")
+ override def prepareInputParameters(input: Row): AnyRef =
+ throw new UnresolvedException(this, "prepareInputParameters")
+ override def update(input: AnyRef): Unit =
+ throw new UnresolvedException(this, "update")
+ override def batchUpdate(inputs: Array[AnyRef]): Unit =
+ throw new UnresolvedException(this, "batchUpdate")
+ override def evaluate(): Unit =
+ throw new UnresolvedException(this, "evaluate")
+ override def get(index: Int): Any =
+ throw new UnresolvedException(this, "get")
+ // Unresolved functions are transient at compile time and don't get evaluated during execution.
+ override def eval(input: Row = null): EvaluatedType =
+ throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+
+ override def toString: String = s"'$name(${children.mkString(",")})"
+
+ override def newInstance(): WindowFunction =
+ throw new UnresolvedException(this, "newInstance")
+}
+
+case class UnresolvedWindowExpression(
+ child: UnresolvedWindowFunction,
+ windowSpec: WindowSpecReference) extends UnaryExpression {
+ override def dataType: DataType = throw new UnresolvedException(this, "dataType")
+ override def foldable: Boolean = throw new UnresolvedException(this, "foldable")
+ override def nullable: Boolean = throw new UnresolvedException(this, "nullable")
+ override lazy val resolved = false
+
+ // Unresolved functions are transient at compile time and don't get evaluated during execution.
+ override def eval(input: Row = null): EvaluatedType =
+ throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+}
+
+case class WindowExpression(
+ windowFunction: WindowFunction,
+ windowSpec: WindowSpecDefinition) extends Expression {
+ override type EvaluatedType = Any
+
+ override def children: Seq[Expression] =
+ windowFunction :: windowSpec :: Nil
+
+ override def eval(input: Row): EvaluatedType =
+ throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}")
+
+ override def dataType: DataType = windowFunction.dataType
+ override def foldable: Boolean = windowFunction.foldable
+ override def nullable: Boolean = windowFunction.nullable
+
+ override def toString: String = s"$windowFunction $windowSpec"
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 21208c8..ba0abb2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -25,13 +25,14 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend
override def output: Seq[Attribute] = projectList.map(_.toAttribute)
override lazy val resolved: Boolean = {
- val containsAggregatesOrGenerators = projectList.exists ( _.collect {
+ val hasSpecialExpressions = projectList.exists ( _.collect {
case agg: AggregateExpression => agg
case generator: Generator => generator
+ case window: WindowExpression => window
}.nonEmpty
)
- !expressions.exists(!_.resolved) && childrenResolved && !containsAggregatesOrGenerators
+ !expressions.exists(!_.resolved) && childrenResolved && !hasSpecialExpressions
}
}
@@ -170,6 +171,12 @@ case class With(child: LogicalPlan, cteRelations: Map[String, Subquery]) extends
override def output: Seq[Attribute] = child.output
}
+case class WithWindowDefinition(
+ windowDefinitions: Map[String, WindowSpecDefinition],
+ child: LogicalPlan) extends UnaryNode {
+ override def output: Seq[Attribute] = child.output
+}
+
case class WriteToFile(
path: String,
child: LogicalPlan) extends UnaryNode {
@@ -195,9 +202,28 @@ case class Aggregate(
child: LogicalPlan)
extends UnaryNode {
+ override lazy val resolved: Boolean = {
+ val hasWindowExpressions = aggregateExpressions.exists ( _.collect {
+ case window: WindowExpression => window
+ }.nonEmpty
+ )
+
+ !expressions.exists(!_.resolved) && childrenResolved && !hasWindowExpressions
+ }
+
override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute)
}
+case class Window(
+ projectList: Seq[Attribute],
+ windowExpressions: Seq[NamedExpression],
+ windowSpec: WindowSpecDefinition,
+ child: LogicalPlan) extends UnaryNode {
+
+ override def output: Seq[Attribute] =
+ (projectList ++ windowExpressions).map(_.toAttribute)
+}
+
/**
* Apply the all of the GroupExpressions to every input row, hence we will get
* multiple output rows for a input row.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 97502ed..4b93f7d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -72,6 +72,15 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
}
/**
+ * Find the first [[TreeNode]] that satisfies the condition specified by `f`.
+ * The condition is recursively applied to this node and all of its children (pre-order).
+ */
+ def find(f: BaseType => Boolean): Option[BaseType] = f(this) match {
+ case true => Some(this)
+ case false => children.foldLeft(None: Option[BaseType]) { (l, r) => l.orElse(r.find(f)) }
+ }
+
+ /**
* Runs the given function on this node and then recursively on [[children]].
* @param f the function to be applied to each node in the tree.
*/
@@ -151,6 +160,20 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] {
val remainingNewChildren = newChildren.toBuffer
val remainingOldChildren = children.toBuffer
val newArgs = productIterator.map {
+ // This rule is used to handle children is a input argument.
+ case s: Seq[_] => s.map {
+ case arg: TreeNode[_] if children contains arg =>
+ val newChild = remainingNewChildren.remove(0)
+ val oldChild = remainingOldChildren.remove(0)
+ if (newChild fastEquals oldChild) {
+ oldChild
+ } else {
+ changed = true
+ newChild
+ }
+ case nonChild: AnyRef => nonChild
+ case null => null
+ }
case arg: TreeNode[_] if children contains arg =>
val newChild = remainingNewChildren.remove(0)
val oldChild = remainingOldChildren.remove(0)
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
index 6b39332..786ddba 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
import org.scalatest.FunSuite
import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{StringType, NullType}
+import org.apache.spark.sql.types.{IntegerType, StringType, NullType}
case class Dummy(optKey: Option[Expression]) extends Expression {
def children: Seq[Expression] = optKey.toSeq
@@ -129,5 +129,47 @@ class TreeNodeSuite extends FunSuite {
assert(expected === actual)
}
+ test("find") {
+ val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4))))
+ // Find the top node.
+ var actual: Option[Expression] = expression.find {
+ case add: Add => true
+ case other => false
+ }
+ var expected: Option[Expression] =
+ Some(Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4)))))
+ assert(expected === actual)
+
+ // Find the first children.
+ actual = expression.find {
+ case Literal(1, IntegerType) => true
+ case other => false
+ }
+ expected = Some(Literal(1))
+ assert(expected === actual)
+ // Find an internal node (Subtract).
+ actual = expression.find {
+ case sub: Subtract => true
+ case other => false
+ }
+ expected = Some(Subtract(Literal(3), Literal(4)))
+ assert(expected === actual)
+
+ // Find a leaf node.
+ actual = expression.find {
+ case Literal(3, IntegerType) => true
+ case other => false
+ }
+ expected = Some(Literal(3))
+ assert(expected === actual)
+
+ // Find nothing.
+ actual = expression.find {
+ case Literal(100, IntegerType) => true
+ case other => false
+ }
+ expected = None
+ assert(expected === actual)
+ }
}
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
----------------------------------------------------------------------
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 326e8ce..56a4689 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -303,6 +303,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
execution.Expand(projections, output, planLater(child)) :: Nil
case logical.Aggregate(group, agg, child) =>
execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil
+ case logical.Window(projectList, windowExpressions, spec, child) =>
+ execution.Window(projectList, windowExpressions, spec, planLater(child)) :: Nil
case logical.Sample(lb, ub, withReplacement, seed, child) =>
execution.Sample(lb, ub, withReplacement, seed, planLater(child)) :: Nil
case logical.LocalRelation(output, data) =>
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
----------------------------------------------------------------------
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
new file mode 100644
index 0000000..217b559
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -0,0 +1,480 @@
+/*
+ * 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
+
+import java.util
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, Distribution, ClusteredDistribution, Partitioning}
+import org.apache.spark.util.collection.CompactBuffer
+
+/**
+ * :: DeveloperApi ::
+ * For every row, evaluates `windowExpression` containing Window Functions and attaches
+ * the results with other regular expressions (presented by `projectList`).
+ * Evert operator handles a single Window Specification, `windowSpec`.
+ */
+case class Window(
+ projectList: Seq[Attribute],
+ windowExpression: Seq[NamedExpression],
+ windowSpec: WindowSpecDefinition,
+ child: SparkPlan)
+ extends UnaryNode {
+
+ override def output: Seq[Attribute] =
+ (projectList ++ windowExpression).map(_.toAttribute)
+
+ override def requiredChildDistribution: Seq[Distribution] =
+ if (windowSpec.partitionSpec.isEmpty) {
+ // This operator will be very expensive.
+ AllTuples :: Nil
+ } else {
+ ClusteredDistribution(windowSpec.partitionSpec) :: Nil
+ }
+
+ // Since window functions are adding columns to the input rows, the child's outputPartitioning
+ // is preserved.
+ override def outputPartitioning: Partitioning = child.outputPartitioning
+
+ override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
+ // The required child ordering has two parts.
+ // The first part is the expressions in the partition specification.
+ // We add these expressions to the required ordering to make sure input rows are grouped
+ // based on the partition specification. So, we only need to process a single partition
+ // at a time.
+ // The second part is the expressions specified in the ORDER BY cluase.
+ // Basically, we first use sort to group rows based on partition specifications and then sort
+ // Rows in a group based on the order specification.
+ (windowSpec.partitionSpec.map(SortOrder(_, Ascending)) ++ windowSpec.orderSpec) :: Nil
+ }
+
+ // Since window functions basically add columns to input rows, this operator
+ // will not change the ordering of input rows.
+ override def outputOrdering: Seq[SortOrder] = child.outputOrdering
+
+ case class ComputedWindow(
+ unbound: WindowExpression,
+ windowFunction: WindowFunction,
+ resultAttribute: AttributeReference)
+
+ // A list of window functions that need to be computed for each group.
+ private[this] val computedWindowExpressions = windowExpression.flatMap { window =>
+ window.collect {
+ case w: WindowExpression =>
+ ComputedWindow(
+ w,
+ BindReferences.bindReference(w.windowFunction, child.output),
+ AttributeReference(s"windowResult:$w", w.dataType, w.nullable)())
+ }
+ }.toArray
+
+ private[this] val windowFrame =
+ windowSpec.frameSpecification.asInstanceOf[SpecifiedWindowFrame]
+
+ // Create window functions.
+ private[this] def windowFunctions(): Array[WindowFunction] = {
+ val functions = new Array[WindowFunction](computedWindowExpressions.length)
+ var i = 0
+ while (i < computedWindowExpressions.length) {
+ functions(i) = computedWindowExpressions(i).windowFunction.newInstance()
+ functions(i).init()
+ i += 1
+ }
+ functions
+ }
+
+ // The schema of the result of all window function evaluations
+ private[this] val computedSchema = computedWindowExpressions.map(_.resultAttribute)
+
+ private[this] val computedResultMap =
+ computedWindowExpressions.map { w => w.unbound -> w.resultAttribute }.toMap
+
+ private[this] val windowExpressionResult = windowExpression.map { window =>
+ window.transform {
+ case w: WindowExpression if computedResultMap.contains(w) => computedResultMap(w)
+ }
+ }
+
+ def execute(): RDD[Row] = {
+ child.execute().mapPartitions { iter =>
+ new Iterator[Row] {
+
+ // Although input rows are grouped based on windowSpec.partitionSpec, we need to
+ // know when we have a new partition.
+ // This is to manually construct an ordering that can be used to compare rows.
+ // TODO: We may want to have a newOrdering that takes BoundReferences.
+ // So, we can take advantave of code gen.
+ private val partitionOrdering: Ordering[Row] =
+ RowOrdering.forSchema(windowSpec.partitionSpec.map(_.dataType))
+
+ // This is used to project expressions for the partition specification.
+ protected val partitionGenerator =
+ newMutableProjection(windowSpec.partitionSpec, child.output)()
+
+ // This is ued to project expressions for the order specification.
+ protected val rowOrderGenerator =
+ newMutableProjection(windowSpec.orderSpec.map(_.child), child.output)()
+
+ // The position of next output row in the inputRowBuffer.
+ var rowPosition: Int = 0
+ // The number of buffered rows in the inputRowBuffer (the size of the current partition).
+ var partitionSize: Int = 0
+ // The buffer used to buffer rows in a partition.
+ var inputRowBuffer: CompactBuffer[Row] = _
+ // The partition key of the current partition.
+ var currentPartitionKey: Row = _
+ // The partition key of next partition.
+ var nextPartitionKey: Row = _
+ // The first row of next partition.
+ var firstRowInNextPartition: Row = _
+ // Indicates if this partition is the last one in the iter.
+ var lastPartition: Boolean = false
+
+ def createBoundaryEvaluator(): () => Unit = {
+ def findPhysicalBoundary(
+ boundary: FrameBoundary): () => Int = boundary match {
+ case UnboundedPreceding => () => 0
+ case UnboundedFollowing => () => partitionSize - 1
+ case CurrentRow => () => rowPosition
+ case ValuePreceding(value) =>
+ () =>
+ val newPosition = rowPosition - value
+ if (newPosition > 0) newPosition else 0
+ case ValueFollowing(value) =>
+ () =>
+ val newPosition = rowPosition + value
+ if (newPosition < partitionSize) newPosition else partitionSize - 1
+ }
+
+ def findLogicalBoundary(
+ boundary: FrameBoundary,
+ searchDirection: Int,
+ evaluator: Expression,
+ joinedRow: JoinedRow): () => Int = boundary match {
+ case UnboundedPreceding => () => 0
+ case UnboundedFollowing => () => partitionSize - 1
+ case other =>
+ () => {
+ // CurrentRow, ValuePreceding, or ValueFollowing.
+ var newPosition = rowPosition + searchDirection
+ var stopSearch = false
+ // rowOrderGenerator is a mutable projection.
+ // We need to make a copy of the returned by rowOrderGenerator since we will
+ // compare searched row with this currentOrderByValue.
+ val currentOrderByValue = rowOrderGenerator(inputRowBuffer(rowPosition)).copy()
+ while (newPosition >= 0 && newPosition < partitionSize && !stopSearch) {
+ val r = rowOrderGenerator(inputRowBuffer(newPosition))
+ stopSearch =
+ !(evaluator.eval(joinedRow(currentOrderByValue, r)).asInstanceOf[Boolean])
+ if (!stopSearch) {
+ newPosition += searchDirection
+ }
+ }
+ newPosition -= searchDirection
+
+ if (newPosition < 0) {
+ 0
+ } else if (newPosition >= partitionSize) {
+ partitionSize - 1
+ } else {
+ newPosition
+ }
+ }
+ }
+
+ windowFrame.frameType match {
+ case RowFrame =>
+ val findStart = findPhysicalBoundary(windowFrame.frameStart)
+ val findEnd = findPhysicalBoundary(windowFrame.frameEnd)
+ () => {
+ frameStart = findStart()
+ frameEnd = findEnd()
+ }
+ case RangeFrame =>
+ val joinedRowForBoundaryEvaluation: JoinedRow = new JoinedRow()
+ val orderByExpr = windowSpec.orderSpec.head
+ val currentRowExpr =
+ BoundReference(0, orderByExpr.dataType, orderByExpr.nullable)
+ val examedRowExpr =
+ BoundReference(1, orderByExpr.dataType, orderByExpr.nullable)
+ val differenceExpr = Abs(Subtract(currentRowExpr, examedRowExpr))
+
+ val frameStartEvaluator = windowFrame.frameStart match {
+ case CurrentRow => EqualTo(currentRowExpr, examedRowExpr)
+ case ValuePreceding(value) =>
+ LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+ case ValueFollowing(value) =>
+ GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+ case o => Literal(true) // This is just a dummy expression, we will not use it.
+ }
+
+ val frameEndEvaluator = windowFrame.frameEnd match {
+ case CurrentRow => EqualTo(currentRowExpr, examedRowExpr)
+ case ValuePreceding(value) =>
+ GreaterThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+ case ValueFollowing(value) =>
+ LessThanOrEqual(differenceExpr, Cast(Literal(value), orderByExpr.dataType))
+ case o => Literal(true) // This is just a dummy expression, we will not use it.
+ }
+
+ val findStart =
+ findLogicalBoundary(
+ boundary = windowFrame.frameStart,
+ searchDirection = -1,
+ evaluator = frameStartEvaluator,
+ joinedRow = joinedRowForBoundaryEvaluation)
+ val findEnd =
+ findLogicalBoundary(
+ boundary = windowFrame.frameEnd,
+ searchDirection = 1,
+ evaluator = frameEndEvaluator,
+ joinedRow = joinedRowForBoundaryEvaluation)
+ () => {
+ frameStart = findStart()
+ frameEnd = findEnd()
+ }
+ }
+ }
+
+ val boundaryEvaluator = createBoundaryEvaluator()
+ // Indicates if we the specified window frame requires us to maintain a sliding frame
+ // (e.g. RANGES BETWEEN 1 PRECEDING AND CURRENT ROW) or the window frame
+ // is the entire partition (e.g. ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING).
+ val requireUpdateFrame: Boolean = {
+ def requireUpdateBoundary(boundary: FrameBoundary): Boolean = boundary match {
+ case UnboundedPreceding => false
+ case UnboundedFollowing => false
+ case _ => true
+ }
+
+ requireUpdateBoundary(windowFrame.frameStart) ||
+ requireUpdateBoundary(windowFrame.frameEnd)
+ }
+ // The start position of the current frame in the partition.
+ var frameStart: Int = 0
+ // The end position of the current frame in the partition.
+ var frameEnd: Int = -1
+ // Window functions.
+ val functions: Array[WindowFunction] = windowFunctions()
+ // Buffers used to store input parameters for window functions. Because we may need to
+ // maintain a sliding frame, we use this buffer to avoid evaluate the parameters from
+ // the same row multiple times.
+ val windowFunctionParameterBuffers: Array[util.LinkedList[AnyRef]] =
+ functions.map(_ => new util.LinkedList[AnyRef]())
+
+ // The projection used to generate the final result rows of this operator.
+ private[this] val resultProjection =
+ newMutableProjection(
+ projectList ++ windowExpressionResult,
+ projectList ++ computedSchema)()
+
+ // The row used to hold results of window functions.
+ private[this] val windowExpressionResultRow =
+ new GenericMutableRow(computedSchema.length)
+
+ private[this] val joinedRow = new JoinedRow6
+
+ // Initialize this iterator.
+ initialize()
+
+ private def initialize(): Unit = {
+ if (iter.hasNext) {
+ val currentRow = iter.next().copy()
+ // partitionGenerator is a mutable projection. Since we need to track nextPartitionKey,
+ // we are making a copy of the returned partitionKey at here.
+ nextPartitionKey = partitionGenerator(currentRow).copy()
+ firstRowInNextPartition = currentRow
+ fetchNextPartition()
+ } else {
+ // The iter is an empty one. So, we set all of the following variables
+ // to make sure hasNext will return false.
+ lastPartition = true
+ rowPosition = 0
+ partitionSize = 0
+ }
+ }
+
+ // Indicates if we will have new output row.
+ override final def hasNext: Boolean = {
+ !lastPartition || (rowPosition < partitionSize)
+ }
+
+ override final def next(): Row = {
+ if (hasNext) {
+ if (rowPosition == partitionSize) {
+ // All rows of this buffer have been consumed.
+ // We will move to next partition.
+ fetchNextPartition()
+ }
+ // Get the input row for the current output row.
+ val inputRow = inputRowBuffer(rowPosition)
+ // Get all results of the window functions for this output row.
+ var i = 0
+ while (i < functions.length) {
+ windowExpressionResultRow.update(i, functions(i).get(rowPosition))
+ i += 1
+ }
+
+ // Construct the output row.
+ val outputRow = resultProjection(joinedRow(inputRow, windowExpressionResultRow))
+ // We will move to the next one.
+ rowPosition += 1
+ if (requireUpdateFrame && rowPosition < partitionSize) {
+ // If we need to maintain a sliding frame and
+ // we will still work on this partition when next is called next time, do the update.
+ updateFrame()
+ }
+
+ // Return the output row.
+ outputRow
+ } else {
+ // no more result
+ throw new NoSuchElementException
+ }
+ }
+
+ // Fetch the next partition.
+ private def fetchNextPartition(): Unit = {
+ // Create a new buffer for input rows.
+ inputRowBuffer = new CompactBuffer[Row]()
+ // We already have the first row for this partition
+ // (recorded in firstRowInNextPartition). Add it back.
+ inputRowBuffer += firstRowInNextPartition
+ // Set the current partition key.
+ currentPartitionKey = nextPartitionKey
+ // Now, we will start to find all rows belonging to this partition.
+ // Create a variable to track if we see the next partition.
+ var findNextPartition = false
+ // The search will stop when we see the next partition or there is no
+ // input row left in the iter.
+ while (iter.hasNext && !findNextPartition) {
+ // Make a copy of the input row since we will put it in the buffer.
+ val currentRow = iter.next().copy()
+ // Get the partition key based on the partition specification.
+ // For the below compare method, we do not need to make a copy of partitionKey.
+ val partitionKey = partitionGenerator(currentRow)
+ // Check if the current row belongs the current input row.
+ val comparing = partitionOrdering.compare(currentPartitionKey, partitionKey)
+ if (comparing == 0) {
+ // This row is still in the current partition.
+ inputRowBuffer += currentRow
+ } else {
+ // The current input row is in a different partition.
+ findNextPartition = true
+ // partitionGenerator is a mutable projection.
+ // Since we need to track nextPartitionKey and we determine that it should be set
+ // as partitionKey, we are making a copy of the partitionKey at here.
+ nextPartitionKey = partitionKey.copy()
+ firstRowInNextPartition = currentRow
+ }
+ }
+
+ // We have not seen a new partition. It means that there is no new row in the
+ // iter. The current partition is the last partition of the iter.
+ if (!findNextPartition) {
+ lastPartition = true
+ }
+
+ // We have got all rows for the current partition.
+ // Set rowPosition to 0 (the next output row will be based on the first
+ // input row of this partition).
+ rowPosition = 0
+ // The size of this partition.
+ partitionSize = inputRowBuffer.size
+ // Reset all parameter buffers of window functions.
+ var i = 0
+ while (i < windowFunctionParameterBuffers.length) {
+ windowFunctionParameterBuffers(i).clear()
+ i += 1
+ }
+ frameStart = 0
+ frameEnd = -1
+ // Create the first window frame for this partition.
+ // If we do not need to maintain a sliding frame, this frame will
+ // have the entire partition.
+ updateFrame()
+ }
+
+ /** The function used to maintain the sliding frame. */
+ private def updateFrame(): Unit = {
+ // Based on the difference between the new frame and old frame,
+ // updates the buffers holding input parameters of window functions.
+ // We will start to prepare input parameters starting from the row
+ // indicated by offset in the input row buffer.
+ def updateWindowFunctionParameterBuffers(
+ numToRemove: Int,
+ numToAdd: Int,
+ offset: Int): Unit = {
+ // First, remove unneeded entries from the head of every buffer.
+ var i = 0
+ while (i < numToRemove) {
+ var j = 0
+ while (j < windowFunctionParameterBuffers.length) {
+ windowFunctionParameterBuffers(j).remove()
+ j += 1
+ }
+ i += 1
+ }
+ // Then, add needed entries to the tail of every buffer.
+ i = 0
+ while (i < numToAdd) {
+ var j = 0
+ while (j < windowFunctionParameterBuffers.length) {
+ // Ask the function to prepare the input parameters.
+ val parameters = functions(j).prepareInputParameters(inputRowBuffer(i + offset))
+ windowFunctionParameterBuffers(j).add(parameters)
+ j += 1
+ }
+ i += 1
+ }
+ }
+
+ // Record the current frame start point and end point before
+ // we update them.
+ val previousFrameStart = frameStart
+ val previousFrameEnd = frameEnd
+ boundaryEvaluator()
+ updateWindowFunctionParameterBuffers(
+ frameStart - previousFrameStart,
+ frameEnd - previousFrameEnd,
+ previousFrameEnd + 1)
+ // Evaluate the current frame.
+ evaluateCurrentFrame()
+ }
+
+ /** Evaluate the current window frame. */
+ private def evaluateCurrentFrame(): Unit = {
+ var i = 0
+ while (i < functions.length) {
+ // Reset the state of the window function.
+ functions(i).reset()
+ // Get all buffered input parameters based on rows of this window frame.
+ val inputParameters = windowFunctionParameterBuffers(i).toArray()
+ // Send these input parameters to the window function.
+ functions(i).batchUpdate(inputParameters)
+ // Ask the function to evaluate based on this window frame.
+ functions(i).evaluate()
+ i += 1
+ }
+ }
+ }
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
----------------------------------------------------------------------
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 81ee48e..5e411c2 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -185,7 +185,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
// Hive does not support buckets.
".*bucket.*",
- // No window support yet
+ // We have our own tests based on these query files.
".*window.*",
// Fails in hive with authorization errors.
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
----------------------------------------------------------------------
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 1d8d0b5..f25723e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -252,6 +252,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
catalog.CreateTables ::
catalog.PreInsertionCasts ::
ExtractPythonUdfs ::
+ ResolveHiveWindowFunction ::
sources.PreInsertCastAndRename ::
Nil
}
http://git-wip-us.apache.org/repos/asf/spark/blob/f2c47082/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
----------------------------------------------------------------------
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 63a8c05..8a0686a 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -421,16 +421,16 @@ private[hive] object HiveQl {
}
/**
- * SELECT MAX(value) FROM src GROUP BY k1, k2, k3 GROUPING SETS((k1, k2), (k2))
- * is equivalent to
+ * SELECT MAX(value) FROM src GROUP BY k1, k2, k3 GROUPING SETS((k1, k2), (k2))
+ * is equivalent to
* SELECT MAX(value) FROM src GROUP BY k1, k2 UNION SELECT MAX(value) FROM src GROUP BY k2
* Check the following link for details.
- *
+ *
https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup
*
* The bitmask denotes the grouping expressions validity for a grouping set,
* the bitmask also be called as grouping id (`GROUPING__ID`, the virtual column in Hive)
- * e.g. In superset (k1, k2, k3), (bit 0: k1, bit 1: k2, and bit 2: k3), the grouping id of
+ * e.g. In superset (k1, k2, k3), (bit 0: k1, bit 1: k2, and bit 2: k3), the grouping id of
* GROUPING SETS (k1, k2) and (k2) should be 3 and 2 respectively.
*/
protected def extractGroupingSet(children: Seq[ASTNode]): (Seq[Expression], Seq[Int]) = {
@@ -444,7 +444,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
val bitmasks: Seq[Int] = setASTs.map(set => set match {
case Token("TOK_GROUPING_SETS_EXPRESSION", null) => 0
- case Token("TOK_GROUPING_SETS_EXPRESSION", children) =>
+ case Token("TOK_GROUPING_SETS_EXPRESSION", children) =>
children.foldLeft(0)((bitmap, col) => {
val colString = col.asInstanceOf[ASTNode].toStringTree()
require(keyMap.contains(colString), s"$colString doens't show up in the GROUP BY list")
@@ -613,7 +613,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
clusterByClause ::
distributeByClause ::
limitClause ::
- lateralViewClause :: Nil) = {
+ lateralViewClause ::
+ windowClause :: Nil) = {
getClauses(
Seq(
"TOK_INSERT_INTO",
@@ -631,15 +632,16 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
"TOK_CLUSTERBY",
"TOK_DISTRIBUTEBY",
"TOK_LIMIT",
- "TOK_LATERAL_VIEW"),
+ "TOK_LATERAL_VIEW",
+ "WINDOW"),
singleInsert)
}
-
+
val relations = fromClause match {
case Some(f) => nodeToRelation(f)
case None => OneRowRelation
}
-
+
val withWhere = whereClause.map { whereNode =>
val Seq(whereExpr) = whereNode.getChildren.toSeq
Filter(nodeToExpr(whereExpr), relations)
@@ -691,7 +693,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
val serdeProps = propsClause.map {
case Token("TOK_TABLEPROPERTY", Token(name, Nil) :: Token(value, Nil) :: Nil) =>
(name, value)
- }
+ }
(Nil, serdeClass, serdeProps)
case Nil => (Nil, "", Nil)
@@ -736,7 +738,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
// The projection of the query can either be a normal projection, an aggregation
// (if there is a group by) or a script transformation.
val withProject: LogicalPlan = transformation.getOrElse {
- val selectExpressions =
+ val selectExpressions =
nameExpressions(select.getChildren.flatMap(selExprNodeToExpr).toSeq)
Seq(
groupByClause.map(e => e match {
@@ -764,31 +766,34 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
Some(Project(selectExpressions, withLateralView))).flatten.head
}
- val withDistinct =
- if (selectDistinctClause.isDefined) Distinct(withProject) else withProject
-
+ // Handle HAVING clause.
val withHaving = havingClause.map { h =>
val havingExpr = h.getChildren.toSeq match { case Seq(hexpr) => nodeToExpr(hexpr) }
// Note that we added a cast to boolean. If the expression itself is already boolean,
// the optimizer will get rid of the unnecessary cast.
- Filter(Cast(havingExpr, BooleanType), withDistinct)
- }.getOrElse(withDistinct)
+ Filter(Cast(havingExpr, BooleanType), withProject)
+ }.getOrElse(withProject)
+
+ // Handle SELECT DISTINCT
+ val withDistinct =
+ if (selectDistinctClause.isDefined) Distinct(withHaving) else withHaving
+ // Handle ORDER BY, SORT BY, DISTRIBETU BY, and CLUSTER BY clause.
val withSort =
(orderByClause, sortByClause, distributeByClause, clusterByClause) match {
case (Some(totalOrdering), None, None, None) =>
- Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withHaving)
+ Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withDistinct)
case (None, Some(perPartitionOrdering), None, None) =>
- Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withHaving)
+ Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withDistinct)
case (None, None, Some(partitionExprs), None) =>
- RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving)
+ RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct)
case (None, Some(perPartitionOrdering), Some(partitionExprs), None) =>
Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false,
- RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving))
+ RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct))
case (None, None, None, Some(clusterExprs)) =>
Sort(clusterExprs.getChildren.map(nodeToExpr).map(SortOrder(_, Ascending)), false,
- RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withHaving))
- case (None, None, None, None) => withHaving
+ RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withDistinct))
+ case (None, None, None, None) => withDistinct
case _ => sys.error("Unsupported set of ordering / distribution clauses.")
}
@@ -797,6 +802,27 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
.map(Limit(_, withSort))
.getOrElse(withSort)
+ // Collect all window specifications defined in the WINDOW clause.
+ val windowDefinitions = windowClause.map(_.getChildren.toSeq.collect {
+ case Token("TOK_WINDOWDEF",
+ Token(windowName, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) =>
+ windowName -> nodesToWindowSpecification(spec)
+ }.toMap)
+ // Handle cases like
+ // window w1 as (partition by p_mfgr order by p_name
+ // range between 2 preceding and 2 following),
+ // w2 as w1
+ val resolvedCrossReference = windowDefinitions.map {
+ windowDefMap => windowDefMap.map {
+ case (windowName, WindowSpecReference(other)) =>
+ (windowName, windowDefMap(other).asInstanceOf[WindowSpecDefinition])
+ case o => o.asInstanceOf[(String, WindowSpecDefinition)]
+ }
+ }
+
+ val withWindowDefinitions =
+ resolvedCrossReference.map(WithWindowDefinition(_, withLimit)).getOrElse(withLimit)
+
// TOK_INSERT_INTO means to add files to the table.
// TOK_DESTINATION means to overwrite the table.
val resultDestination =
@@ -804,7 +830,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
val overwrite = intoClause.isEmpty
nodeToDest(
resultDestination,
- withLimit,
+ withWindowDefinitions,
overwrite)
}
@@ -1053,7 +1079,6 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
}
-
protected val escapedIdentifier = "`([^`]+)`".r
/** Strips backticks from ident if present */
protected def cleanIdentifier(ident: String): String = ident match {
@@ -1250,6 +1275,25 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length))
case Token("TOK_FUNCTION", Token(COALESCE(), Nil) :: list) => Coalesce(list.map(nodeToExpr))
+ /* Window Functions */
+ case Token("TOK_FUNCTION", Token(name, Nil) +: args :+ Token("TOK_WINDOWSPEC", spec)) =>
+ val function = UnresolvedWindowFunction(name, args.map(nodeToExpr))
+ nodesToWindowSpecification(spec) match {
+ case reference: WindowSpecReference =>
+ UnresolvedWindowExpression(function, reference)
+ case definition: WindowSpecDefinition =>
+ WindowExpression(function, definition)
+ }
+ case Token("TOK_FUNCTIONSTAR", Token(name, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) =>
+ // Safe to use Literal(1)?
+ val function = UnresolvedWindowFunction(name, Literal(1) :: Nil)
+ nodesToWindowSpecification(spec) match {
+ case reference: WindowSpecReference =>
+ UnresolvedWindowExpression(function, reference)
+ case definition: WindowSpecDefinition =>
+ WindowExpression(function, definition)
+ }
+
/* UDFs - Must be last otherwise will preempt built in functions */
case Token("TOK_FUNCTION", Token(name, Nil) :: args) =>
UnresolvedFunction(name, args.map(nodeToExpr))
@@ -1312,6 +1356,89 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
""".stripMargin)
}
+ def nodesToWindowSpecification(nodes: Seq[ASTNode]): WindowSpec = nodes match {
+ case Token(windowName, Nil) :: Nil =>
+ // Refer to a window spec defined in the window clause.
+ WindowSpecReference(windowName)
+ case Nil =>
+ // OVER()
+ WindowSpecDefinition(
+ partitionSpec = Nil,
+ orderSpec = Nil,
+ frameSpecification = UnspecifiedFrame)
+ case spec =>
+ val (partitionClause :: rowFrame :: rangeFrame :: Nil) =
+ getClauses(
+ Seq(
+ "TOK_PARTITIONINGSPEC",
+ "TOK_WINDOWRANGE",
+ "TOK_WINDOWVALUES"),
+ spec)
+
+ // Handle Partition By and Order By.
+ val (partitionSpec, orderSpec) = partitionClause.map { partitionAndOrdering =>
+ val (partitionByClause :: orderByClause :: sortByClause :: clusterByClause :: Nil) =
+ getClauses(
+ Seq("TOK_DISTRIBUTEBY", "TOK_ORDERBY", "TOK_SORTBY", "TOK_CLUSTERBY"),
+ partitionAndOrdering.getChildren.toSeq.asInstanceOf[Seq[ASTNode]])
+
+ (partitionByClause, orderByClause.orElse(sortByClause), clusterByClause) match {
+ case (Some(partitionByExpr), Some(orderByExpr), None) =>
+ (partitionByExpr.getChildren.map(nodeToExpr),
+ orderByExpr.getChildren.map(nodeToSortOrder))
+ case (Some(partitionByExpr), None, None) =>
+ (partitionByExpr.getChildren.map(nodeToExpr), Nil)
+ case (None, Some(orderByExpr), None) =>
+ (Nil, orderByExpr.getChildren.map(nodeToSortOrder))
+ case (None, None, Some(clusterByExpr)) =>
+ val expressions = clusterByExpr.getChildren.map(nodeToExpr)
+ (expressions, expressions.map(SortOrder(_, Ascending)))
+ case _ =>
+ throw new NotImplementedError(
+ s"""No parse rules for Node ${partitionAndOrdering.getName}
+ """.stripMargin)
+ }
+ }.getOrElse {
+ (Nil, Nil)
+ }
+
+ // Handle Window Frame
+ val windowFrame =
+ if (rowFrame.isEmpty && rangeFrame.isEmpty) {
+ UnspecifiedFrame
+ } else {
+ val frameType = rowFrame.map(_ => RowFrame).getOrElse(RangeFrame)
+ def nodeToBoundary(node: Node): FrameBoundary = node match {
+ case Token("preceding", Token(count, Nil) :: Nil) =>
+ if (count == "unbounded") UnboundedPreceding else ValuePreceding(count.toInt)
+ case Token("following", Token(count, Nil) :: Nil) =>
+ if (count == "unbounded") UnboundedFollowing else ValueFollowing(count.toInt)
+ case Token("current", Nil) => CurrentRow
+ case _ =>
+ throw new NotImplementedError(
+ s"""No parse rules for the Window Frame Boundary based on Node ${node.getName}
+ """.stripMargin)
+ }
+
+ rowFrame.orElse(rangeFrame).map { frame =>
+ frame.getChildren.toList match {
+ case precedingNode :: followingNode :: Nil =>
+ SpecifiedWindowFrame(
+ frameType,
+ nodeToBoundary(precedingNode),
+ nodeToBoundary(followingNode))
+ case precedingNode :: Nil =>
+ SpecifiedWindowFrame(frameType, nodeToBoundary(precedingNode), CurrentRow)
+ case _ =>
+ throw new NotImplementedError(
+ s"""No parse rules for the Window Frame based on Node ${frame.getName}
+ """.stripMargin)
+ }
+ }.getOrElse(sys.error(s"If you see this, please file a bug report with your query."))
+ }
+
+ WindowSpecDefinition(partitionSpec, orderSpec, windowFrame)
+ }
val explode = "(?i)explode".r
def nodesToGenerator(nodes: Seq[Node]): (Generator, Seq[String]) = {
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