[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1020130165
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -144,36 +144,10 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
.toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId)
}
-val signal = new Object
-val partitions = collection.mutable.Map.empty[Int, Array[Batch]]
-
-val processPartition = (iter: Iterator[Batch]) => iter.toArray
-
// This callback is executed by the DAGScheduler thread.
-// After fetching a partition, it inserts the partition into the Map,
and then
-// wakes up the main thread.
-val resultHandler = (partitionId: Int, partition: Array[Batch]) => {
- signal.synchronized {
-partitions(partitionId) = partition
-signal.notify()
- }
- ()
-}
-
-spark.sparkContext.runJob(batches, processPartition, resultHandler)
-
-// The man thread will wait until 0-th partition is available,
-// then send it to client and wait for next partition.
-var currentPartitionId = 0
-while (currentPartitionId < numPartitions) {
- val partition = signal.synchronized {
-while (!partitions.contains(currentPartitionId)) {
- signal.wait()
-}
-partitions.remove(currentPartitionId).get
- }
-
- partition.foreach { case (bytes, count) =>
+def writeBatches(arrowBatches: Array[Batch]): Unit = {
Review Comment:
Sorry I missed your comment when I opened this PR. BTW, this is actually
what PySpark's implementation doing, and was thinking that it's better to match
how they work, dedup, and improve together. It should work fine most cases -
PySpark implementation has been running in production many years, and I haven't
yet heard complaints related to this.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1020130165
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -144,36 +144,10 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
.toArrowBatchIterator(iter, schema, maxRecordsPerBatch, timeZoneId)
}
-val signal = new Object
-val partitions = collection.mutable.Map.empty[Int, Array[Batch]]
-
-val processPartition = (iter: Iterator[Batch]) => iter.toArray
-
// This callback is executed by the DAGScheduler thread.
-// After fetching a partition, it inserts the partition into the Map,
and then
-// wakes up the main thread.
-val resultHandler = (partitionId: Int, partition: Array[Batch]) => {
- signal.synchronized {
-partitions(partitionId) = partition
-signal.notify()
- }
- ()
-}
-
-spark.sparkContext.runJob(batches, processPartition, resultHandler)
-
-// The man thread will wait until 0-th partition is available,
-// then send it to client and wait for next partition.
-var currentPartitionId = 0
-while (currentPartitionId < numPartitions) {
- val partition = signal.synchronized {
-while (!partitions.contains(currentPartitionId)) {
- signal.wait()
-}
-partitions.remove(currentPartitionId).get
- }
-
- partition.foreach { case (bytes, count) =>
+def writeBatches(arrowBatches: Array[Batch]): Unit = {
Review Comment:
Sorry I missed your comment when I opened this PR. BTW, this is actually
what PySpark's implementation doing, and was thinking that it's better to match
how they work, dedup, and improve together. It should work fine most cases -
PySpark implementation has been running in production many years, and I haven't
yet heard complaints related to this.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1020127951
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -56,7 +56,7 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
try {
processAsArrowBatches(request.getClientId, dataframe)
} catch {
- case e: Exception =>
+ case e: Throwable =>
Review Comment:
```suggestion
case e: Exception =>
```
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1020127788
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -56,7 +56,7 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
try {
processAsArrowBatches(request.getClientId, dataframe)
} catch {
- case e: Exception =>
+ case e: Throwable =>
Review Comment:
Yeah, let me revert this for now. We should remove this JSON fallback anyway.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1019833127
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -184,9 +158,30 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
responseObserver.onNext(response.build())
numSent += 1
}
+}
+
+// Store collection results for worst case of 1 to N-1 partitions
+val results = new Array[Array[Batch]](numPartitions - 1)
+var lastIndex = -1 // index of last partition written
- currentPartitionId += 1
+// Handler to eagerly write partitions in order
+val resultHandler = (partitionId: Int, partition: Array[Batch]) => {
+ // If result is from next partition in order
+ if (partitionId - 1 == lastIndex) {
+writeBatches(partition)
+lastIndex += 1
+// Write stored partitions that come next in order
+while (lastIndex < results.length && results(lastIndex) != null) {
+ writeBatches(results(lastIndex))
+ results(lastIndex) = null
+ lastIndex += 1
+}
+ } else {
+// Store partitions received out of order
+results(partitionId - 1) = partition
+ }
}
+spark.sparkContext.runJob(batches, (iter: Iterator[Batch]) =>
iter.toArray, resultHandler)
Review Comment:
Hm, I just noticed the review comment. I believe this is matched with our
current implementation in PySpark. If we should improve, let's improve both
paths together. I would prefer to match them and deduplicate the logic first.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1019831985
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -184,9 +158,30 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
responseObserver.onNext(response.build())
numSent += 1
}
+}
+
+// Store collection results for worst case of 1 to N-1 partitions
+val results = new Array[Array[Batch]](numPartitions - 1)
+var lastIndex = -1 // index of last partition written
- currentPartitionId += 1
+// Handler to eagerly write partitions in order
+val resultHandler = (partitionId: Int, partition: Array[Batch]) => {
Review Comment:
Nope, it doesn't (because it's guided by the index). This approach is
actually from the initial ordered implementation of collect with Arrow (that
were in production for very long time),
https://github.com/apache/spark/commit/82c18c240a6913a917df3b55cc5e22649561c4dd#diff-459628811d7786c705fbb2b7a381ecd2b88f183f44ab607d43b3d33ea48d390fR3282-R3318.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1019831985
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -184,9 +158,30 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
responseObserver.onNext(response.build())
numSent += 1
}
+}
+
+// Store collection results for worst case of 1 to N-1 partitions
+val results = new Array[Array[Batch]](numPartitions - 1)
+var lastIndex = -1 // index of last partition written
- currentPartitionId += 1
+// Handler to eagerly write partitions in order
+val resultHandler = (partitionId: Int, partition: Array[Batch]) => {
Review Comment:
Nope, it doesn't (because it's guided by the index). This approach is
actually from the initial ordered implementation of collect with Arrow (that
has been running in production for very long time),
https://github.com/apache/spark/commit/82c18c240a6913a917df3b55cc5e22649561c4dd#diff-459628811d7786c705fbb2b7a381ecd2b88f183f44ab607d43b3d33ea48d390fR3282-R3318.
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38613: [SPARK-41005][CONNECT][PYTHON][FOLLOW-UP] Fetch/send partitions in parallel for Arrow based collect
HyukjinKwon commented on code in PR #38613:
URL: https://github.com/apache/spark/pull/38613#discussion_r1019826331
##
connector/connect/src/main/scala/org/apache/spark/sql/connect/service/SparkConnectStreamHandler.scala:
##
@@ -56,7 +56,7 @@ class SparkConnectStreamHandler(responseObserver:
StreamObserver[Response]) exte
try {
processAsArrowBatches(request.getClientId, dataframe)
} catch {
- case e: Exception =>
+ case e: Throwable =>
Review Comment:
Hm, it's a fallback so should better work in any case .. (?). I think we
should remove this fallback anyway. I can revert this change for now too. (see
https://github.com/apache/spark/pull/38468#discussion_r1013555606
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