cloud-fan commented on a change in pull request #32198:
URL: https://github.com/apache/spark/pull/32198#discussion_r617348669



##########
File path: 
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatDataWriter.scala
##########
@@ -255,25 +320,182 @@ class DynamicPartitionDataWriter(
       }
       if (isBucketed) {
         currentBucketId = nextBucketId
-        statsTrackers.foreach(_.newBucket(currentBucketId.get))
       }
 
       fileCounter = 0
-      newOutputWriter(currentPartitionValues, currentBucketId)
-    } else if (description.maxRecordsPerFile > 0 &&
-      recordsInFile >= description.maxRecordsPerFile) {
-      // Exceeded the threshold in terms of the number of records per file.
-      // Create a new file by increasing the file counter.
-      fileCounter += 1
-      assert(fileCounter < MAX_FILE_COUNTER,
-        s"File counter $fileCounter is beyond max value $MAX_FILE_COUNTER")
+      newOutputWriter(currentPartitionValues, currentBucketId, true)
+    } else {
+      checkRecordsInFile(currentPartitionValues, currentBucketId)
+    }
+    writeRecord(record)
+  }
+}
+
+/**
+ * Dynamic partition writer with concurrent writers, meaning multiple 
concurrent writers are opened
+ * for writing.
+ *
+ * The process has the following steps:
+ *  - Step 1: Maintain a map of output writers per each partition and/or 
bucket columns. Keep all
+ *            writers opened and write rows one by one.
+ *  - Step 2: If number of concurrent writers exceeds limit, sort rest of rows 
on partition and/or
+ *            bucket column(s). Write rows one by one, and eagerly close the 
writer when finishing
+ *            each partition and/or bucket.
+ *
+ * Caller is expected to call `writeWithIterator()` instead of `write()` to 
write records.
+ */
+class DynamicPartitionDataConcurrentWriter(
+    description: WriteJobDescription,
+    taskAttemptContext: TaskAttemptContext,
+    committer: FileCommitProtocol,
+    concurrentOutputWriterSpec: ConcurrentOutputWriterSpec)
+  extends BaseDynamicPartitionDataWriter(description, taskAttemptContext, 
committer) {
+
+  /** Wrapper class to index a unique concurrent output writer. */
+  private case class WriterIndex(
+    var partitionValues: Option[UnsafeRow],
+    var bucketId: Option[Int])
+
+  /** Wrapper class for status of a unique concurrent output writer. */
+  private case class WriterStatus(
+    var outputWriter: OutputWriter,
+    var recordsInFile: Long,
+    var fileCounter: Int,
+    var latestFilePath: String)
 
-      newOutputWriter(currentPartitionValues, currentBucketId)
+  /**
+   * State to indicate if we are falling back to sort-based writer.
+   * Because we first try to use concurrent writers, its initial value is 
false.
+   */
+  private var sortBased: Boolean = false
+  private val concurrentWriters = mutable.HashMap[WriterIndex, WriterStatus]()
+  private val currentWriterId = WriterIndex(None, None)
+
+  /**
+   * Release resources for all concurrent output writers.
+   */
+  override protected def releaseResources(): Unit = {
+    currentWriter = null
+    concurrentWriters.values.foreach(status => {
+      if (status.outputWriter != null) {
+        try {
+          status.outputWriter.close()
+        } finally {
+          status.outputWriter = null
+        }
+      }
+    })
+    concurrentWriters.clear()
+  }
+
+  override def write(record: InternalRow): Unit = {
+    val nextPartitionValues = if (isPartitioned) 
Some(getPartitionValues(record)) else None
+    val nextBucketId = if (isBucketed) Some(getBucketId(record)) else None
+
+    if (currentWriterId.partitionValues != nextPartitionValues ||
+      currentWriterId.bucketId != nextBucketId) {
+      // See a new partition or bucket - write to a new partition dir (or a 
new bucket file).
+      updateCurrentWriterStatusInMap()
+      if (isBucketed) {
+        currentWriterId.bucketId = nextBucketId
+      }
+      if (isPartitioned && currentWriterId.partitionValues != 
nextPartitionValues) {
+        currentWriterId.partitionValues = Some(nextPartitionValues.get.copy())
+        if (!concurrentWriters.contains(currentWriterId)) {
+          
statsTrackers.foreach(_.newPartition(currentWriterId.partitionValues.get))
+        }
+      }
+      retrieveWriterInMap()
     }
-    val outputRow = getOutputRow(record)
-    currentWriter.write(outputRow)
-    statsTrackers.foreach(_.newRow(outputRow))
-    recordsInFile += 1
+
+    checkRecordsInFile(currentWriterId.partitionValues, 
currentWriterId.bucketId)
+    writeRecord(record)
+  }
+
+  /**
+   * Write iterator of records with concurrent writers.
+   */
+  def writeWithIterator(iterator: Iterator[InternalRow]): Unit = {
+    while (iterator.hasNext && !sortBased) {
+      write(iterator.next())
+    }
+
+    if (iterator.hasNext) {
+      clearCurrentWriterStatus()
+      val sorter = concurrentOutputWriterSpec.createSorter()
+      val sortIterator = 
sorter.sort(iterator.asInstanceOf[Iterator[UnsafeRow]])
+      while (sortIterator.hasNext) {
+        write(sortIterator.next())
+      }
+    }
+  }
+
+  /**
+   * Update current writer status when a new writer is needed for writing row.
+   */
+  private def updateCurrentWriterStatusInMap(): Unit = {
+    if (currentWriterId.partitionValues.isDefined || 
currentWriterId.bucketId.isDefined) {
+      if (!sortBased) {
+        // Update writer status in concurrent writers map, because the writer 
is probably needed
+        // again later for writing other rows.

Review comment:
       Is it a performance concern that we only update the writer status when 
it's going to be put back into the map? What if we update the status per row 
after calling `writeRecord`?




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