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author | gatorsmile <gatorsmile@gmail.com> | 2016-02-04 18:37:58 -0800 |
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committer | Reynold Xin <rxin@databricks.com> | 2016-02-04 18:37:58 -0800 |
commit | e3c75c6398b1241500343ff237e9bcf78b5396f9 (patch) | |
tree | a807084a9441b4d8b341f0905790343669c59622 | |
parent | 6dbfc40776514c3a5667161ebe7829f4cc9c7529 (diff) | |
download | spark-e3c75c6398b1241500343ff237e9bcf78b5396f9.tar.gz spark-e3c75c6398b1241500343ff237e9bcf78b5396f9.tar.bz2 spark-e3c75c6398b1241500343ff237e9bcf78b5396f9.zip |
[SPARK-12850][SQL] Support Bucket Pruning (Predicate Pushdown for Bucketed Tables)
JIRA: https://issues.apache.org/jira/browse/SPARK-12850
This PR is to support bucket pruning when the predicates are `EqualTo`, `EqualNullSafe`, `IsNull`, `In`, and `InSet`.
Like HIVE, in this PR, the bucket pruning works when the bucketing key has one and only one column.
So far, I do not find a way to verify how many buckets are actually scanned. However, I did verify it when doing the debug. Could you provide a suggestion how to do it properly? Thank you! cloud-fan yhuai rxin marmbrus
BTW, we can add more cases to support complex predicate including `Or` and `And`. Please let me know if I should do it in this PR.
Maybe we also need to add test cases to verify if bucket pruning works well for each data type.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #10942 from gatorsmile/pruningBuckets.
3 files changed, 245 insertions, 10 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala index da9320ffb6..c24967abeb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala @@ -29,12 +29,14 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning import org.apache.spark.sql.execution.PhysicalRDD.{INPUT_PATHS, PUSHED_FILTERS} import org.apache.spark.sql.execution.SparkPlan import org.apache.spark.sql.sources._ import org.apache.spark.sql.types.{StringType, StructType} import org.apache.spark.unsafe.types.UTF8String import org.apache.spark.util.{SerializableConfiguration, Utils} +import org.apache.spark.util.collection.BitSet /** * A Strategy for planning scans over data sources defined using the sources API. @@ -97,10 +99,15 @@ private[sql] object DataSourceStrategy extends Strategy with Logging { (partitionAndNormalColumnAttrs ++ projects).toSeq } + // Prune the buckets based on the pushed filters that do not contain partitioning key + // since the bucketing key is not allowed to use the columns in partitioning key + val bucketSet = getBuckets(pushedFilters, t.getBucketSpec) + val scan = buildPartitionedTableScan( l, partitionAndNormalColumnProjs, pushedFilters, + bucketSet, t.partitionSpec.partitionColumns, selectedPartitions) @@ -124,11 +131,14 @@ private[sql] object DataSourceStrategy extends Strategy with Logging { val sharedHadoopConf = SparkHadoopUtil.get.conf val confBroadcast = t.sqlContext.sparkContext.broadcast(new SerializableConfiguration(sharedHadoopConf)) + // Prune the buckets based on the filters + val bucketSet = getBuckets(filters, t.getBucketSpec) pruneFilterProject( l, projects, filters, - (a, f) => t.buildInternalScan(a.map(_.name).toArray, f, t.paths, confBroadcast)) :: Nil + (a, f) => + t.buildInternalScan(a.map(_.name).toArray, f, bucketSet, t.paths, confBroadcast)) :: Nil case l @ LogicalRelation(baseRelation: TableScan, _, _) => execution.PhysicalRDD.createFromDataSource( @@ -150,6 +160,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging { logicalRelation: LogicalRelation, projections: Seq[NamedExpression], filters: Seq[Expression], + buckets: Option[BitSet], partitionColumns: StructType, partitions: Array[Partition]): SparkPlan = { val relation = logicalRelation.relation.asInstanceOf[HadoopFsRelation] @@ -174,7 +185,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging { // assuming partition columns data stored in data files are always consistent with those // partition values encoded in partition directory paths. val dataRows = relation.buildInternalScan( - requiredDataColumns.map(_.name).toArray, filters, Array(dir), confBroadcast) + requiredDataColumns.map(_.name).toArray, filters, buckets, Array(dir), confBroadcast) // Merges data values with partition values. mergeWithPartitionValues( @@ -251,6 +262,69 @@ private[sql] object DataSourceStrategy extends Strategy with Logging { } } + // Get the bucket ID based on the bucketing values. + // Restriction: Bucket pruning works iff the bucketing column has one and only one column. + def getBucketId(bucketColumn: Attribute, numBuckets: Int, value: Any): Int = { + val mutableRow = new SpecificMutableRow(Seq(bucketColumn.dataType)) + mutableRow(0) = Cast(Literal(value), bucketColumn.dataType).eval(null) + val bucketIdGeneration = UnsafeProjection.create( + HashPartitioning(bucketColumn :: Nil, numBuckets).partitionIdExpression :: Nil, + bucketColumn :: Nil) + + bucketIdGeneration(mutableRow).getInt(0) + } + + // Get the bucket BitSet by reading the filters that only contains bucketing keys. + // Note: When the returned BitSet is None, no pruning is possible. + // Restriction: Bucket pruning works iff the bucketing column has one and only one column. + private def getBuckets( + filters: Seq[Expression], + bucketSpec: Option[BucketSpec]): Option[BitSet] = { + + if (bucketSpec.isEmpty || + bucketSpec.get.numBuckets == 1 || + bucketSpec.get.bucketColumnNames.length != 1) { + // None means all the buckets need to be scanned + return None + } + + // Just get the first because bucketing pruning only works when the column has one column + val bucketColumnName = bucketSpec.get.bucketColumnNames.head + val numBuckets = bucketSpec.get.numBuckets + val matchedBuckets = new BitSet(numBuckets) + matchedBuckets.clear() + + filters.foreach { + case expressions.EqualTo(a: Attribute, Literal(v, _)) if a.name == bucketColumnName => + matchedBuckets.set(getBucketId(a, numBuckets, v)) + case expressions.EqualTo(Literal(v, _), a: Attribute) if a.name == bucketColumnName => + matchedBuckets.set(getBucketId(a, numBuckets, v)) + case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) if a.name == bucketColumnName => + matchedBuckets.set(getBucketId(a, numBuckets, v)) + case expressions.EqualNullSafe(Literal(v, _), a: Attribute) if a.name == bucketColumnName => + matchedBuckets.set(getBucketId(a, numBuckets, v)) + // Because we only convert In to InSet in Optimizer when there are more than certain + // items. So it is possible we still get an In expression here that needs to be pushed + // down. + case expressions.In(a: Attribute, list) + if list.forall(_.isInstanceOf[Literal]) && a.name == bucketColumnName => + val hSet = list.map(e => e.eval(EmptyRow)) + hSet.foreach(e => matchedBuckets.set(getBucketId(a, numBuckets, e))) + case expressions.IsNull(a: Attribute) if a.name == bucketColumnName => + matchedBuckets.set(getBucketId(a, numBuckets, null)) + case _ => + } + + logInfo { + val selected = matchedBuckets.cardinality() + val percentPruned = (1 - selected.toDouble / numBuckets.toDouble) * 100 + s"Selected $selected buckets out of $numBuckets, pruned $percentPruned% partitions." + } + + // None means all the buckets need to be scanned + if (matchedBuckets.cardinality() == 0) None else Some(matchedBuckets) + } + protected def prunePartitions( predicates: Seq[Expression], partitionSpec: PartitionSpec): Seq[Partition] = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala index 299fc6efbb..737be7dfd1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala @@ -38,6 +38,7 @@ import org.apache.spark.sql.execution.datasources._ import org.apache.spark.sql.execution.streaming.{Sink, Source} import org.apache.spark.sql.types.{StringType, StructType} import org.apache.spark.util.SerializableConfiguration +import org.apache.spark.util.collection.BitSet /** * ::DeveloperApi:: @@ -722,6 +723,7 @@ abstract class HadoopFsRelation private[sql]( final private[sql] def buildInternalScan( requiredColumns: Array[String], filters: Array[Filter], + bucketSet: Option[BitSet], inputPaths: Array[String], broadcastedConf: Broadcast[SerializableConfiguration]): RDD[InternalRow] = { val inputStatuses = inputPaths.flatMap { input => @@ -743,9 +745,16 @@ abstract class HadoopFsRelation private[sql]( // id from file name. Then read these files into a RDD(use one-partition empty RDD for empty // bucket), and coalesce it to one partition. Finally union all bucket RDDs to one result. val perBucketRows = (0 until maybeBucketSpec.get.numBuckets).map { bucketId => - groupedBucketFiles.get(bucketId).map { inputStatuses => - buildInternalScan(requiredColumns, filters, inputStatuses, broadcastedConf).coalesce(1) - }.getOrElse(sqlContext.emptyResult) + // If the current bucketId is not set in the bucket bitSet, skip scanning it. + if (bucketSet.nonEmpty && !bucketSet.get.get(bucketId)){ + sqlContext.emptyResult + } else { + // When all the buckets need a scan (i.e., bucketSet is equal to None) + // or when the current bucket need a scan (i.e., the bit of bucketId is set to true) + groupedBucketFiles.get(bucketId).map { inputStatuses => + buildInternalScan(requiredColumns, filters, inputStatuses, broadcastedConf).coalesce(1) + }.getOrElse(sqlContext.emptyResult) + } } new UnionRDD(sqlContext.sparkContext, perBucketRows) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala index 150d0c7486..9ba645626f 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala @@ -19,22 +19,28 @@ package org.apache.spark.sql.sources import java.io.File -import org.apache.spark.sql.{Column, DataFrame, DataFrameWriter, QueryTest, SQLConf} -import org.apache.spark.sql.catalyst.expressions.UnsafeProjection +import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning -import org.apache.spark.sql.execution.Exchange -import org.apache.spark.sql.execution.datasources.BucketSpec +import org.apache.spark.sql.execution.{Exchange, PhysicalRDD} +import org.apache.spark.sql.execution.datasources.{BucketSpec, DataSourceStrategy} 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 +import org.apache.spark.util.collection.BitSet class BucketedReadSuite extends QueryTest with SQLTestUtils with TestHiveSingleton { import testImplicits._ + private val df = (0 until 50).map(i => (i % 5, i % 13, i.toString)).toDF("i", "j", "k") + private val nullDF = (for { + i <- 0 to 50 + s <- Seq(null, "a", "b", "c", "d", "e", "f", null, "g") + } yield (i % 5, s, i % 13)).toDF("i", "j", "k") + 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") @@ -59,6 +65,152 @@ class BucketedReadSuite extends QueryTest with SQLTestUtils with TestHiveSinglet } } + // To verify if the bucket pruning works, this function checks two conditions: + // 1) Check if the pruned buckets (before filtering) are empty. + // 2) Verify the final result is the same as the expected one + private def checkPrunedAnswers( + bucketSpec: BucketSpec, + bucketValues: Seq[Integer], + filterCondition: Column, + originalDataFrame: DataFrame): Unit = { + + val bucketedDataFrame = hiveContext.table("bucketed_table").select("i", "j", "k") + val BucketSpec(numBuckets, bucketColumnNames, _) = bucketSpec + // Limit: bucket pruning only works when the bucket column has one and only one column + assert(bucketColumnNames.length == 1) + val bucketColumnIndex = bucketedDataFrame.schema.fieldIndex(bucketColumnNames.head) + val bucketColumn = bucketedDataFrame.schema.toAttributes(bucketColumnIndex) + val matchedBuckets = new BitSet(numBuckets) + bucketValues.foreach { value => + matchedBuckets.set(DataSourceStrategy.getBucketId(bucketColumn, numBuckets, value)) + } + + // Filter could hide the bug in bucket pruning. Thus, skipping all the filters + val rdd = bucketedDataFrame.filter(filterCondition).queryExecution.executedPlan + .find(_.isInstanceOf[PhysicalRDD]) + assert(rdd.isDefined) + + val checkedResult = rdd.get.execute().mapPartitionsWithIndex { case (index, iter) => + if (matchedBuckets.get(index % numBuckets)) Iterator(true) else Iterator(iter.isEmpty) + } + // checking if all the pruned buckets are empty + assert(checkedResult.collect().forall(_ == true)) + + checkAnswer( + bucketedDataFrame.filter(filterCondition).orderBy("i", "j", "k"), + originalDataFrame.filter(filterCondition).orderBy("i", "j", "k")) + } + + test("read partitioning bucketed tables with bucket pruning filters") { + withTable("bucketed_table") { + val numBuckets = 8 + val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil) + // json does not support predicate push-down, and thus json is used here + df.write + .format("json") + .partitionBy("i") + .bucketBy(numBuckets, "j") + .saveAsTable("bucketed_table") + + for (j <- 0 until 13) { + // Case 1: EqualTo + checkPrunedAnswers( + bucketSpec, + bucketValues = j :: Nil, + filterCondition = $"j" === j, + df) + + // Case 2: EqualNullSafe + checkPrunedAnswers( + bucketSpec, + bucketValues = j :: Nil, + filterCondition = $"j" <=> j, + df) + + // Case 3: In + checkPrunedAnswers( + bucketSpec, + bucketValues = Seq(j, j + 1, j + 2, j + 3), + filterCondition = $"j".isin(j, j + 1, j + 2, j + 3), + df) + } + } + } + + test("read non-partitioning bucketed tables with bucket pruning filters") { + withTable("bucketed_table") { + val numBuckets = 8 + val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil) + // json does not support predicate push-down, and thus json is used here + df.write + .format("json") + .bucketBy(numBuckets, "j") + .saveAsTable("bucketed_table") + + for (j <- 0 until 13) { + checkPrunedAnswers( + bucketSpec, + bucketValues = j :: Nil, + filterCondition = $"j" === j, + df) + } + } + } + + test("read partitioning bucketed tables having null in bucketing key") { + withTable("bucketed_table") { + val numBuckets = 8 + val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil) + // json does not support predicate push-down, and thus json is used here + nullDF.write + .format("json") + .partitionBy("i") + .bucketBy(numBuckets, "j") + .saveAsTable("bucketed_table") + + // Case 1: isNull + checkPrunedAnswers( + bucketSpec, + bucketValues = null :: Nil, + filterCondition = $"j".isNull, + nullDF) + + // Case 2: <=> null + checkPrunedAnswers( + bucketSpec, + bucketValues = null :: Nil, + filterCondition = $"j" <=> null, + nullDF) + } + } + + test("read partitioning bucketed tables having composite filters") { + withTable("bucketed_table") { + val numBuckets = 8 + val bucketSpec = BucketSpec(numBuckets, Seq("j"), Nil) + // json does not support predicate push-down, and thus json is used here + df.write + .format("json") + .partitionBy("i") + .bucketBy(numBuckets, "j") + .saveAsTable("bucketed_table") + + for (j <- 0 until 13) { + checkPrunedAnswers( + bucketSpec, + bucketValues = j :: Nil, + filterCondition = $"j" === j && $"k" > $"j", + df) + + checkPrunedAnswers( + bucketSpec, + bucketValues = j :: Nil, + filterCondition = $"j" === j && $"i" > j % 5, + df) + } + } + } + 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") |