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[SPARK-22170][SQL] Reduce memory consumption in broadcast joins.
## What changes were proposed in this pull request? This updates the broadcast join code path to lazily decompress pages and iterate through UnsafeRows to prevent all rows from being held in memory while the broadcast table is being built. ## How was this patch tested? Existing tests. Author: Ryan Blue <[email protected]> Closes apache#19394 from rdblue/broadcast-driver-memory.
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/broadcastMode.scala

Lines changed: 6 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -26,6 +26,8 @@ import org.apache.spark.sql.catalyst.InternalRow
2626
trait BroadcastMode {
2727
def transform(rows: Array[InternalRow]): Any
2828

29+
def transform(rows: Iterator[InternalRow], sizeHint: Option[Long]): Any
30+
2931
def canonicalized: BroadcastMode
3032
}
3133

@@ -36,5 +38,9 @@ case object IdentityBroadcastMode extends BroadcastMode {
3638
// TODO: pack the UnsafeRows into single bytes array.
3739
override def transform(rows: Array[InternalRow]): Array[InternalRow] = rows
3840

41+
override def transform(
42+
rows: Iterator[InternalRow],
43+
sizeHint: Option[Long]): Array[InternalRow] = rows.toArray
44+
3945
override def canonicalized: BroadcastMode = this
4046
}

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala

Lines changed: 13 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -223,7 +223,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
223223
* UnsafeRow is highly compressible (at least 8 bytes for any column), the byte array is also
224224
* compressed.
225225
*/
226-
private def getByteArrayRdd(n: Int = -1): RDD[Array[Byte]] = {
226+
private def getByteArrayRdd(n: Int = -1): RDD[(Long, Array[Byte])] = {
227227
execute().mapPartitionsInternal { iter =>
228228
var count = 0
229229
val buffer = new Array[Byte](4 << 10) // 4K
@@ -239,7 +239,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
239239
out.writeInt(-1)
240240
out.flush()
241241
out.close()
242-
Iterator(bos.toByteArray)
242+
Iterator((count, bos.toByteArray))
243243
}
244244
}
245245

@@ -274,19 +274,26 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
274274
val byteArrayRdd = getByteArrayRdd()
275275

276276
val results = ArrayBuffer[InternalRow]()
277-
byteArrayRdd.collect().foreach { bytes =>
278-
decodeUnsafeRows(bytes).foreach(results.+=)
277+
byteArrayRdd.collect().foreach { countAndBytes =>
278+
decodeUnsafeRows(countAndBytes._2).foreach(results.+=)
279279
}
280280
results.toArray
281281
}
282282

283+
private[spark] def executeCollectIterator(): (Long, Iterator[InternalRow]) = {
284+
val countsAndBytes = getByteArrayRdd().collect()
285+
val total = countsAndBytes.map(_._1).sum
286+
val rows = countsAndBytes.iterator.flatMap(countAndBytes => decodeUnsafeRows(countAndBytes._2))
287+
(total, rows)
288+
}
289+
283290
/**
284291
* Runs this query returning the result as an iterator of InternalRow.
285292
*
286293
* @note Triggers multiple jobs (one for each partition).
287294
*/
288295
def executeToIterator(): Iterator[InternalRow] = {
289-
getByteArrayRdd().toLocalIterator.flatMap(decodeUnsafeRows)
296+
getByteArrayRdd().map(_._2).toLocalIterator.flatMap(decodeUnsafeRows)
290297
}
291298

292299
/**
@@ -307,7 +314,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
307314
return new Array[InternalRow](0)
308315
}
309316

310-
val childRDD = getByteArrayRdd(n)
317+
val childRDD = getByteArrayRdd(n).map(_._2)
311318

312319
val buf = new ArrayBuffer[InternalRow]
313320
val totalParts = childRDD.partitions.length

sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala

Lines changed: 21 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -27,8 +27,8 @@ import org.apache.spark.sql.catalyst.InternalRow
2727
import org.apache.spark.sql.catalyst.expressions.UnsafeRow
2828
import org.apache.spark.sql.catalyst.plans.physical.{BroadcastMode, BroadcastPartitioning, Partitioning}
2929
import org.apache.spark.sql.execution.{SparkPlan, SQLExecution}
30+
import org.apache.spark.sql.execution.joins.HashedRelation
3031
import org.apache.spark.sql.execution.metric.SQLMetrics
31-
import org.apache.spark.sql.execution.ui.SparkListenerDriverAccumUpdates
3232
import org.apache.spark.sql.internal.SQLConf
3333
import org.apache.spark.util.ThreadUtils
3434

@@ -72,26 +72,39 @@ case class BroadcastExchangeExec(
7272
SQLExecution.withExecutionId(sparkContext, executionId) {
7373
try {
7474
val beforeCollect = System.nanoTime()
75-
// Note that we use .executeCollect() because we don't want to convert data to Scala types
76-
val input: Array[InternalRow] = child.executeCollect()
77-
if (input.length >= 512000000) {
75+
// Use executeCollect/executeCollectIterator to avoid conversion to Scala types
76+
val (numRows, input) = child.executeCollectIterator()
77+
if (numRows >= 512000000) {
7878
throw new SparkException(
79-
s"Cannot broadcast the table with more than 512 millions rows: ${input.length} rows")
79+
s"Cannot broadcast the table with more than 512 millions rows: $numRows rows")
8080
}
81+
8182
val beforeBuild = System.nanoTime()
8283
longMetric("collectTime") += (beforeBuild - beforeCollect) / 1000000
83-
val dataSize = input.map(_.asInstanceOf[UnsafeRow].getSizeInBytes.toLong).sum
84+
85+
// Construct the relation.
86+
val relation = mode.transform(input, Some(numRows))
87+
88+
val dataSize = relation match {
89+
case map: HashedRelation =>
90+
map.estimatedSize
91+
case arr: Array[InternalRow] =>
92+
arr.map(_.asInstanceOf[UnsafeRow].getSizeInBytes.toLong).sum
93+
case _ =>
94+
throw new SparkException("[BUG] BroadcastMode.transform returned unexpected type: " +
95+
relation.getClass.getName)
96+
}
97+
8498
longMetric("dataSize") += dataSize
8599
if (dataSize >= (8L << 30)) {
86100
throw new SparkException(
87101
s"Cannot broadcast the table that is larger than 8GB: ${dataSize >> 30} GB")
88102
}
89103

90-
// Construct and broadcast the relation.
91-
val relation = mode.transform(input)
92104
val beforeBroadcast = System.nanoTime()
93105
longMetric("buildTime") += (beforeBroadcast - beforeBuild) / 1000000
94106

107+
// Broadcast the relation
95108
val broadcasted = sparkContext.broadcast(relation)
96109
longMetric("broadcastTime") += (System.nanoTime() - beforeBroadcast) / 1000000
97110

sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala

Lines changed: 12 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -866,7 +866,18 @@ private[execution] case class HashedRelationBroadcastMode(key: Seq[Expression])
866866
extends BroadcastMode {
867867

868868
override def transform(rows: Array[InternalRow]): HashedRelation = {
869-
HashedRelation(rows.iterator, canonicalized.key, rows.length)
869+
transform(rows.iterator, Some(rows.length))
870+
}
871+
872+
override def transform(
873+
rows: Iterator[InternalRow],
874+
sizeHint: Option[Long]): HashedRelation = {
875+
sizeHint match {
876+
case Some(numRows) =>
877+
HashedRelation(rows, canonicalized.key, numRows.toInt)
878+
case None =>
879+
HashedRelation(rows, canonicalized.key)
880+
}
870881
}
871882

872883
override lazy val canonicalized: HashedRelationBroadcastMode = {

sql/core/src/test/scala/org/apache/spark/sql/ConfigBehaviorSuite.scala

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -58,7 +58,7 @@ class ConfigBehaviorSuite extends QueryTest with SharedSQLContext {
5858
withSQLConf(SQLConf.RANGE_EXCHANGE_SAMPLE_SIZE_PER_PARTITION.key -> "1") {
5959
// If we only sample one point, the range boundaries will be pretty bad and the
6060
// chi-sq value would be very high.
61-
assert(computeChiSquareTest() > 1000)
61+
assert(computeChiSquareTest() > 300)
6262
}
6363
}
6464
}

sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala

Lines changed: 1 addition & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -227,8 +227,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLMetricsTestUtils with Shared
227227
val df = df1.join(broadcast(df2), "key")
228228
testSparkPlanMetrics(df, 2, Map(
229229
1L -> (("BroadcastHashJoin", Map(
230-
"number of output rows" -> 2L,
231-
"avg hash probe (min, med, max)" -> "\n(1, 1, 1)"))))
230+
"number of output rows" -> 2L))))
232231
)
233232
}
234233

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