Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/10604#discussion_r49398766
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala ---
@@ -0,0 +1,158 @@
+/*
+ * 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.sources
+
+import org.apache.spark.sql.{Column, DataFrame, DataFrameWriter,
QueryTest, SQLConf}
+import org.apache.spark.sql.catalyst.expressions.{Murmur3Hash,
UnsafeProjection}
+import org.apache.spark.sql.execution.Exchange
+import org.apache.spark.sql.execution.joins.SortMergeJoin
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.util.Utils
+
+class BucketedReadSuite extends QueryTest with SQLTestUtils with
TestHiveSingleton {
+ import testImplicits._
+
+ test("read bucketed data") {
+ val df = (0 until 50).map(i => (i % 5, i % 13, i.toString)).toDF("i",
"j", "k")
+ withTable("bucketed_table") {
+ df.write
+ .format("parquet")
+ .partitionBy("i")
+ .bucketBy(8, "j", "k")
+ .saveAsTable("bucketed_table")
+
+ for (i <- 0 until 5) {
+ val rdd = hiveContext.table("bucketed_table").filter($"i" ===
i).queryExecution.toRdd
+ assert(rdd.partitions.length == 8)
+
+ val attrs = df.select("j", "k").schema.toAttributes
+ val checkBucketId = rdd.mapPartitionsWithIndex((index, rows) => {
+ val projection = UnsafeProjection.create(new Murmur3Hash(attrs)
:: Nil, attrs)
+ rows.map(row => Utils.nonNegativeMod(projection(row).getInt(0),
8) == index)
+ })
+
+ assert(checkBucketId.collect().reduce(_ && _))
+ }
+ }
+ }
+
+ private val df1 = (0 until 50).map(i => (i % 5, i % 13,
i.toString)).toDF("i", "j", "k").as("df1")
+ private val df2 = (0 until 50).map(i => (i % 7, i % 11,
i.toString)).toDF("i", "j", "k").as("df2")
+
+ private def testBucketing(
+ bucketing1: DataFrameWriter => DataFrameWriter,
+ bucketing2: DataFrameWriter => DataFrameWriter,
+ joinColumns: Seq[String],
+ shuffleLeft: Boolean,
+ shuffleRight: Boolean): Unit = {
+ withTable("bucketed_table1", "bucketed_table2") {
+
bucketing1(df1.write.format("parquet")).saveAsTable("bucketed_table1")
+
bucketing2(df2.write.format("parquet")).saveAsTable("bucketed_table2")
+
+ withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "0") {
+ val t1 = hiveContext.table("bucketed_table1")
+ val t2 = hiveContext.table("bucketed_table2")
+ val joined = t1.join(t2, joinCondition(t1, t2, joinColumns))
+
+ // First check the result is corrected.
+ checkAnswer(
+ joined.sort("bucketed_table1.k", "bucketed_table2.k"),
+ df1.join(df2, joinCondition(df1, df2,
joinColumns)).sort("df1.k", "df2.k"))
+
+
assert(joined.queryExecution.executedPlan.isInstanceOf[SortMergeJoin])
+ val joinOperator =
joined.queryExecution.executedPlan.asInstanceOf[SortMergeJoin]
+ assert(joinOperator.left.find(_.isInstanceOf[Exchange]).isDefined
== shuffleLeft)
+ assert(joinOperator.right.find(_.isInstanceOf[Exchange]).isDefined
== shuffleRight)
+ }
+ }
+ }
+
+ private def joinCondition(left: DataFrame, right: DataFrame, joinCols:
Seq[String]): Column = {
+ joinCols.map(col => left(col) === right(col)).reduce(_ && _)
+ }
+
+ test("avoid shuffle when join 2 bucketed tables") {
+ val bucketing = (writer: DataFrameWriter) => writer.bucketBy(8, "i",
"j")
+ testBucketing(bucketing, bucketing, Seq("i", "j"), shuffleLeft =
false, shuffleRight = false)
+ }
+
+ // Enable it after fix https://issues.apache.org/jira/browse/SPARK-12704
+ ignore("avoid shuffle when join keys are a super-set of bucket keys") {
+ val bucketing = (writer: DataFrameWriter) => writer.bucketBy(8, "i")
+ testBucketing(bucketing, bucketing, Seq("i", "j"), shuffleLeft =
false, shuffleRight = false)
+ }
+
+ ignore("only shuffle one side when join bucketed table and non-bucketed
table") {
+ val bucketing = (writer: DataFrameWriter) => writer.bucketBy(8, "i",
"j")
+ testBucketing(bucketing, identity, Seq("i", "j"), shuffleLeft = false,
shuffleRight = true)
+ }
+
+ ignore("only shuffle one side when 2 bucketed tables have different
bucket number") {
+ val bucketing1 = (writer: DataFrameWriter) => writer.bucketBy(8, "i",
"j")
+ val bucketing2 = (writer: DataFrameWriter) => writer.bucketBy(5, "i",
"j")
+ testBucketing(bucketing1, bucketing2, Seq("i", "j"), shuffleLeft =
false, shuffleRight = true)
+ }
+
+ ignore("only shuffle one side when 2 bucketed tables have different
bucket keys") {
--- End diff --
all "shuffle one side" tests are ignored, waiting for
https://github.com/apache/spark/pull/10703
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