Support for LeftOuter/BuildRight and RightOuter/BuildLeft nested loop joins

docs/configs.md

@@ -338,7 +338,7 @@ Name | Description | Default Value | Notes
 <a name="sql.exec.BroadcastExchangeExec"></a>spark.rapids.sql.exec.BroadcastExchangeExec|The backend for broadcast exchange of data|true|None|
 <a name="sql.exec.ShuffleExchangeExec"></a>spark.rapids.sql.exec.ShuffleExchangeExec|The backend for most data being exchanged between processes|true|None|
 <a name="sql.exec.BroadcastHashJoinExec"></a>spark.rapids.sql.exec.BroadcastHashJoinExec|Implementation of join using broadcast data|true|None|
-<a name="sql.exec.BroadcastNestedLoopJoinExec"></a>spark.rapids.sql.exec.BroadcastNestedLoopJoinExec|Implementation of join using brute force|true|None|
+<a name="sql.exec.BroadcastNestedLoopJoinExec"></a>spark.rapids.sql.exec.BroadcastNestedLoopJoinExec|Implementation of join using brute force. Full outer joins and joins where the broadcast side matches the join side (e.g.: LeftOuter with left broadcast) are not supported. A non-inner join only is supported if the join condition expression can be converted to a GPU AST expression|true|None|
 <a name="sql.exec.CartesianProductExec"></a>spark.rapids.sql.exec.CartesianProductExec|Implementation of join using brute force|true|None|
 <a name="sql.exec.ShuffledHashJoinExec"></a>spark.rapids.sql.exec.ShuffledHashJoinExec|Implementation of join using hashed shuffled data|true|None|
 <a name="sql.exec.SortMergeJoinExec"></a>spark.rapids.sql.exec.SortMergeJoinExec|Sort merge join, replacing with shuffled hash join|true|None|

docs/supported_ops.md

@@ -661,7 +661,7 @@ Accelerator supports are described below.
 </tr>
 <tr>
 <td>BroadcastNestedLoopJoinExec</td>
-<td>Implementation of join using brute force</td>
+<td>Implementation of join using brute force. Full outer joins and joins where the broadcast side matches the join side (e.g.: LeftOuter with left broadcast) are not supported. A non-inner join only is supported if the join condition expression can be converted to a GPU AST expression</td>
 <td>None</td>
 <td>S</td>
 <td>S</td>

sql-plugin/src/main/scala/com/nvidia/spark/rapids/AbstractGpuJoinIterator.scala

@@ -0,0 +1,311 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed 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 com.nvidia.spark.rapids
+
+import scala.collection.mutable
+
+import ai.rapids.cudf.{GatherMap, NvtxColor}
+import com.nvidia.spark.rapids.RapidsBuffer.SpillCallback
+
+import org.apache.spark.TaskContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.vectorized.ColumnarBatch
+
+/**
+ * Base class for iterators producing the results of a join.
+ * @param gatherNvtxName name to use for the NVTX range when producing the join gather maps
+ * @param targetSize configured target batch size in bytes
+ * @param joinTime metric to record GPU time spent in join
+ * @param totalTime metric to record total time in the iterator
+ */
+abstract class AbstractGpuJoinIterator(
+    gatherNvtxName: String,
+    targetSize: Long,
+    joinTime: GpuMetric,
+    totalTime: GpuMetric) extends Iterator[ColumnarBatch] with Arm with AutoCloseable {
+  private[this] var nextCb: Option[ColumnarBatch] = None
+  private[this] var gathererStore: Option[JoinGatherer] = None
+
+  protected[this] var closed = false
+
+  TaskContext.get().addTaskCompletionListener[Unit](_ => close())
+
+  /** Returns whether there are any more batches on the stream side of the join */
+  protected def hasNextStreamBatch: Boolean
+
+  /**
+   * Called to setup the next join gatherer instance when the previous instance is done or
+   * there is no previous instance.
+   * @param startNanoTime system nanoseconds timestamp at the top of the iterator loop, useful for
+   *                      calculating the time spent producing the next stream batch
+   * @return some gatherer to use next or None if there is no next gatherer or the loop should try
+   *         to build the gatherer again (e.g.: to skip a degenerate join result batch)
+   */
+  protected def setupNextGatherer(startNanoTime: Long): Option[JoinGatherer]
+
+  override def hasNext: Boolean = {
+    if (closed) {
+      return false
+    }
+    var mayContinue = true
+    while (nextCb.isEmpty && mayContinue) {
+      val startNanoTime = System.nanoTime()
+      if (gathererStore.exists(!_.isDone)) {
+        nextCb = nextCbFromGatherer()
+      } else if (hasNextStreamBatch) {
+        // Need to refill the gatherer
+        gathererStore.foreach(_.close())
+        gathererStore = None
+        gathererStore = setupNextGatherer(startNanoTime)
+        nextCb = nextCbFromGatherer()
+      } else {
+        mayContinue = false
+      }
+      totalTime += (System.nanoTime() - startNanoTime)
+    }
+    if (nextCb.isEmpty) {
+      // Nothing is left to return so close ASAP.
+      close()
+    }
+    nextCb.isDefined
+  }
+
+  override def next(): ColumnarBatch = {
+    if (!hasNext) {
+      throw new NoSuchElementException()
+    }
+    val ret = nextCb.get
+    nextCb = None
+    ret
+  }
+
+  override def close(): Unit = {
+    if (!closed) {
+      nextCb.foreach(_.close())
+      nextCb = None
+      gathererStore.foreach(_.close())
+      gathererStore = None
+      closed = true
+    }
+  }
+
+  private def nextCbFromGatherer(): Option[ColumnarBatch] = {
+    withResource(new NvtxWithMetrics(gatherNvtxName, NvtxColor.DARK_GREEN, joinTime)) { _ =>
+      val ret = gathererStore.map { gather =>
+        val nextRows = JoinGatherer.getRowsInNextBatch(gather, targetSize)
+        gather.gatherNext(nextRows)
+      }
+      if (gathererStore.exists(_.isDone)) {
+        gathererStore.foreach(_.close())
+        gathererStore = None
+      }
+
+      if (ret.isDefined) {
+        // We are about to return something. We got everything we need from it so now let it spill
+        // if there is more to be gathered later on.
+        gathererStore.foreach(_.allowSpilling())
+      }
+      ret
+    }
+  }
+}
+
+/**
+ * Base class for join iterators that split and spill batches to avoid GPU OOM errors.
+ * @param gatherNvtxName name to use for the NVTX range when producing the join gather maps
+ * @param stream iterator to produce the batches for the streaming side input of the join
+ * @param streamAttributes attributes corresponding to the streaming side input
+ * @param builtBatch batch for the built side input of the join
+ * @param targetSize configured target batch size in bytes
+ * @param spillCallback callback to use when spilling
+ * @param joinTime metric to record GPU time spent in join
+ * @param streamTime metric to record time spent producing streaming side batches
+ * @param totalTime metric to record total time in the iterator
+ */
+abstract class SplittableJoinIterator(
+    gatherNvtxName: String,
+    stream: Iterator[LazySpillableColumnarBatch],
+    streamAttributes: Seq[Attribute],
+    builtBatch: LazySpillableColumnarBatch,
+    targetSize: Long,
+    spillCallback: SpillCallback,
+    joinTime: GpuMetric,
+    streamTime: GpuMetric,
+    totalTime: GpuMetric)
+    extends AbstractGpuJoinIterator(
+      gatherNvtxName,
+      targetSize,
+      joinTime = joinTime,
+      totalTime = totalTime) with Logging {
+  // For some join types even if there is no stream data we might output something
+  private var isInitialJoin = true
+  // If the join explodes this holds batches from the stream side split into smaller pieces.
+  private val pendingSplits = mutable.Queue[SpillableColumnarBatch]()
+
+  protected def computeNumJoinRows(cb: ColumnarBatch): Long
+
+  /**
+   * Create a join gatherer.
+   * @param cb next column batch from the streaming side of the join
+   * @param numJoinRows if present, the number of join output rows computed for this batch
+   * @return some gatherer to use next or None if there is no next gatherer or the loop should try
+   *         to build the gatherer again (e.g.: to skip a degenerate join result batch)
+   */
+  protected def createGatherer(cb: ColumnarBatch, numJoinRows: Option[Long]): Option[JoinGatherer]
+
+  override def hasNextStreamBatch: Boolean = {
+    isInitialJoin || pendingSplits.nonEmpty || stream.hasNext
+  }
+
+  override def setupNextGatherer(startNanoTime: Long): Option[JoinGatherer] = {
+    val wasInitialJoin = isInitialJoin
+    isInitialJoin = false
+    if (pendingSplits.nonEmpty || stream.hasNext) {
+      val cb = if (pendingSplits.nonEmpty) {
+        withResource(pendingSplits.dequeue()) {
+          _.getColumnarBatch()
+        }
+      } else {
+        val batch = withResource(stream.next()) { lazyBatch =>
+          // TODO: Worth having a releaseBatch method for LazySpillableBatch?
+          GpuColumnVector.incRefCounts(lazyBatch.getBatch)
+        }
+        streamTime += (System.nanoTime() - startNanoTime)
+        batch
+      }
+      withResource(cb) { cb =>
+        val numJoinRows = computeNumJoinRows(cb)
+
+        // We want the gather maps size to be around the target size. There are two gather maps
+        // that are made up of ints, so compute how many rows on the stream side will produce the
+        // desired gather maps size.
+        val maxJoinRows = Math.max(1, targetSize / (2 * Integer.BYTES))
+        if (numJoinRows > maxJoinRows && cb.numRows() > 1) {
+          // Need to split the batch to reduce the gather maps size. This takes a simplistic
+          // approach of assuming the data is uniformly distributed in the stream table.
+          val numSplits = Math.min(cb.numRows(),
+            Math.ceil(numJoinRows.toDouble / maxJoinRows).toInt)
+          splitAndSave(cb, numSplits)
+
+          // Return no gatherer so the outer loop will try again
+          return None
+        }
+
+        createGatherer(cb, Some(numJoinRows))
+      }
+    } else {
+      assert(wasInitialJoin)
+      import scala.collection.JavaConverters._
+      withResource(GpuColumnVector.emptyBatch(streamAttributes.asJava)) { cb =>
+        createGatherer(cb, None)
+      }
+    }
+  }
+
+  override def close(): Unit = {
+    if (!closed) {
+      super.close()
+      builtBatch.close()
+      pendingSplits.foreach(_.close())
+      pendingSplits.clear()
+    }
+  }
+
+  /**
+   * Split a stream-side input batch, making all splits spillable, and replacing this batch with
+   * the splits in the stream-side input
+   * @param cb stream-side input batch to split
+   * @param numBatches number of splits to produce with approximately the same number of rows each
+   * @param oom a prior OOM exception that this will try to recover from by splitting
+   */
+  protected def splitAndSave(
+      cb: ColumnarBatch,
+      numBatches: Int,
+      oom: Option[OutOfMemoryError] = None): Unit = {
+    val batchSize = cb.numRows() / numBatches
+    if (oom.isDefined && batchSize < 100) {
+      // We just need some kind of cutoff to not get stuck in a loop if the batches get to be too
+      // small but we want to at least give it a chance to work (mostly for tests where the
+      // targetSize can be set really small)
+      throw oom.get
+    }
+    val msg = s"Split stream batch into $numBatches batches of about $batchSize rows"
+    if (oom.isDefined) {
+      logWarning(s"OOM Encountered: $msg")
+    } else {
+      logInfo(msg)
+    }
+    val splits = withResource(GpuColumnVector.from(cb)) { tab =>
+      val splitIndexes = (1 until numBatches).map(num => num * batchSize)
+      tab.contiguousSplit(splitIndexes: _*)
+    }
+    withResource(splits) { splits =>
+      val schema = GpuColumnVector.extractTypes(cb)
+      pendingSplits ++= splits.map { ct =>
+        SpillableColumnarBatch(ct, schema,
+          SpillPriorities.ACTIVE_ON_DECK_PRIORITY, spillCallback)
+      }
+    }
+  }
+
+  /**
+   * Create a join gatherer from gather maps.
+   * @param maps gather maps produced from a cudf join
+   * @param leftData batch corresponding to the left table in the join
+   * @param rightData batch corresponding to the right table in the join
+   * @return some gatherer or None if the are no rows to gather in this join batch
+   */
+  protected def makeGatherer(
+      maps: Array[GatherMap],
+      leftData: LazySpillableColumnarBatch,
+      rightData: LazySpillableColumnarBatch): Option[JoinGatherer] = {
+    assert(maps.length > 0 && maps.length <= 2)
+    try {
+      val leftMap = maps.head
+      val rightMap = if (maps.length > 1) {
+        if (rightData.numCols == 0) {
+          // No data so don't bother with it
+          None
+        } else {
+          Some(maps(1))
+        }
+      } else {
+        None
+      }
+
+      val lazyLeftMap = LazySpillableGatherMap(leftMap, spillCallback, "left_map")
+      val gatherer = rightMap match {
+        case None =>
+          rightData.close()
+          JoinGatherer(lazyLeftMap, leftData)
+        case Some(right) =>
+          val lazyRightMap = LazySpillableGatherMap(right, spillCallback, "right_map")
+          JoinGatherer(lazyLeftMap, leftData, lazyRightMap, rightData)
+      }
+      if (gatherer.isDone) {
+        // Nothing matched...
+        gatherer.close()
+        None
+      } else {
+        Some(gatherer)
+      }
+    } finally {
+      maps.foreach(_.close())
+    }
+  }
+}

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala

@@ -3075,7 +3075,10 @@ object GpuOverrides {
         TypeSig.DECIMAL_64 + TypeSig.STRUCT), TypeSig.all),
       (exchange, conf, p, r) => new GpuBroadcastMeta(exchange, conf, p, r)),
     exec[BroadcastNestedLoopJoinExec](
-      "Implementation of join using brute force",
+      "Implementation of join using brute force. Full outer joins and joins where the " +
+          "broadcast side matches the join side (e.g.: LeftOuter with left broadcast) are not " +
+          "supported. A non-inner join only is supported if the join condition expression can " +
+          "be converted to a GPU AST expression",
       ExecChecks(TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL_64 +
           TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL_64),
         TypeSig.all),

sql-plugin/src/main/scala/org/apache/spark/sql/rapids/GpuCartesianProductExec.scala

@@ -21,7 +21,6 @@ import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
 import scala.collection.mutable
 
 import ai.rapids.cudf.{JCudfSerialization, NvtxColor, NvtxRange}
-import ai.rapids.cudf.ast
 import com.nvidia.spark.rapids.{Arm, GpuBindReferences, GpuBuildLeft, GpuColumnVector, GpuExec, GpuExpression, GpuMetric, GpuSemaphore, LazySpillableColumnarBatch, MetricsLevel}
 import com.nvidia.spark.rapids.RapidsBuffer.SpillCallback
 import com.nvidia.spark.rapids.RapidsPluginImplicits._
@@ -115,6 +114,7 @@ class GpuCartesianRDD(
     sc: SparkContext,
     boundCondition: Option[GpuExpression],
     numFirstTableColumns: Int,
+    streamAttributes: Seq[Attribute],
     spillCallback: SpillCallback,
     targetSize: Long,
     joinTime: GpuMetric,
@@ -186,9 +186,13 @@ class GpuCartesianRDD(
       }
 
       GpuBroadcastNestedLoopJoinExecBase.nestedLoopJoin(
-        Cross, numFirstTableColumns, batch, streamIterator, targetSize, GpuBuildLeft,
-        boundCondition, spillCallback, numOutputRows, joinOutputRows, numOutputBatches,
-        joinTime, totalTime)
+        Cross, GpuBuildLeft, numFirstTableColumns, batch, streamIterator, streamAttributes,
+        targetSize, boundCondition, spillCallback,
+        numOutputRows = numOutputRows,
+        joinOutputRows = joinOutputRows,
+        numOutputBatches = numOutputBatches,
+        joinTime = joinTime,
+        totalTime = totalTime)
     }
   }
 
@@ -273,6 +277,7 @@ case class GpuCartesianProductExec(
       new GpuCartesianRDD(sparkContext,
         boundCondition,
         numFirstTableColumns,
+        right.output,
         spillCallback,
         targetSizeBytes,
         joinTime,