[SPARK-1331] Added graceful shutdown to Spark Streaming #247
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
Add a lifecycle interface is a good idea? |
|
Build triggered. |
|
Build started. |
|
Build finished. |
|
One or more automated tests failed |
|
Jenkins, test this again. |
| @@ -158,6 +158,15 @@ class StreamingContext private[streaming] ( | |||
|
|
|||
| private[streaming] val waiter = new ContextWaiter | |||
|
|
|||
| /** Enumeration to identify current state of the StreamingContext */ | |||
| private[streaming] object ContextState extends Enumeration { | |||
There was a problem hiding this comment.
minor: but how would you feel about calling this StreamingContextState? I found it a bit confusing when looking at it directly. Then you could probably even remove the doc because it would be totally clear.
There was a problem hiding this comment.
Changed. But keeping the doc in. Extra doc do no harm. ;)
|
Hey TD - Did a pass. Had some questions and surface level comment. One thing that came up a few was the use of a busy-wait/Thread.sleep along with interruption. Ideally rather than calling sleep in a loop you would sleep on a condition variable and notify it in the other thread. With the current approach you anyways have to keep a pointer to the thread so it knows what to interrupt. Why not just have a CV? |
|
Build triggered. |
|
Build started. |
…tdown Conflicts: streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala
|
Merged build triggered. |
|
Merged build started. |
|
Merged build triggered. |
|
Merged build started. |
|
Merged build finished. |
|
Refer to this link for build results: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/13865/ |
|
Merged build finished. All automated tests passed. |
|
All automated tests passed. |
|
Looks good TD, merging this. |
asfgit
pushed a commit
that referenced
this pull request
Apr 22, 2014
…eam API [WIP] The current Network Receiver API makes it slightly complicated to right a new receiver as one needs to create an instance of BlockGenerator as shown in SocketReceiver https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala#L51 Exposing the BlockGenerator interface has made it harder to improve the receiving process. The API of NetworkReceiver (which was not a very stable API anyways) needs to be change if we are to ensure future stability. Additionally, the functions like streamingContext.socketStream that create input streams, return DStream objects. That makes it hard to expose functionality (say, rate limits) unique to input dstreams. They should return InputDStream or NetworkInputDStream. This is still not yet implemented. This PR is blocked on the graceful shutdown PR #247 Author: Tathagata Das <[email protected]> Closes #300 from tdas/network-receiver-api and squashes the following commits: ea27b38 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into network-receiver-api 3a4777c [Tathagata Das] Renamed NetworkInputDStream to ReceiverInputDStream, and ActorReceiver related stuff. 838dd39 [Tathagata Das] Added more events to the StreamingListener to report errors and stopped receivers. a75c7a6 [Tathagata Das] Address some PR comments and fixed other issues. 91bfa72 [Tathagata Das] Fixed bugs. 8533094 [Tathagata Das] Scala style fixes. 028bde6 [Tathagata Das] Further refactored receiver to allow restarting of a receiver. 43f5290 [Tathagata Das] Made functions that create input streams return InputDStream and NetworkInputDStream, for both Scala and Java. 2c94579 [Tathagata Das] Fixed graceful shutdown by removing interrupts on receiving thread. 9e37a0b [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into network-receiver-api 3223e95 [Tathagata Das] Refactored the code that runs the NetworkReceiver into further classes and traits to make them more testable. a36cc48 [Tathagata Das] Refactored the NetworkReceiver API for future stability. (cherry picked from commit 04c37b6) Signed-off-by: Patrick Wendell <[email protected]>
asfgit
pushed a commit
that referenced
this pull request
Apr 22, 2014
…eam API [WIP] The current Network Receiver API makes it slightly complicated to right a new receiver as one needs to create an instance of BlockGenerator as shown in SocketReceiver https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala#L51 Exposing the BlockGenerator interface has made it harder to improve the receiving process. The API of NetworkReceiver (which was not a very stable API anyways) needs to be change if we are to ensure future stability. Additionally, the functions like streamingContext.socketStream that create input streams, return DStream objects. That makes it hard to expose functionality (say, rate limits) unique to input dstreams. They should return InputDStream or NetworkInputDStream. This is still not yet implemented. This PR is blocked on the graceful shutdown PR #247 Author: Tathagata Das <[email protected]> Closes #300 from tdas/network-receiver-api and squashes the following commits: ea27b38 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into network-receiver-api 3a4777c [Tathagata Das] Renamed NetworkInputDStream to ReceiverInputDStream, and ActorReceiver related stuff. 838dd39 [Tathagata Das] Added more events to the StreamingListener to report errors and stopped receivers. a75c7a6 [Tathagata Das] Address some PR comments and fixed other issues. 91bfa72 [Tathagata Das] Fixed bugs. 8533094 [Tathagata Das] Scala style fixes. 028bde6 [Tathagata Das] Further refactored receiver to allow restarting of a receiver. 43f5290 [Tathagata Das] Made functions that create input streams return InputDStream and NetworkInputDStream, for both Scala and Java. 2c94579 [Tathagata Das] Fixed graceful shutdown by removing interrupts on receiving thread. 9e37a0b [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into network-receiver-api 3223e95 [Tathagata Das] Refactored the code that runs the NetworkReceiver into further classes and traits to make them more testable. a36cc48 [Tathagata Das] Refactored the NetworkReceiver API for future stability.
pdeyhim
pushed a commit
to pdeyhim/spark-1
that referenced
this pull request
Jun 25, 2014
Current version of StreamingContext.stop() directly kills all the data receivers (NetworkReceiver) without waiting for the data already received to be persisted and processed. This PR provides the fix. Now, when the StreamingContext.stop() is called, the following sequence of steps will happen. 1. The driver will send a stop signal to all the active receivers. 2. Each receiver, when it gets a stop signal from the driver, first stop receiving more data, then waits for the thread that persists data blocks to BlockManager to finish persisting all receive data, and finally quits. 3. After all the receivers have stopped, the driver will wait for the Job Generator and Job Scheduler to finish processing all the received data. It also fixes the semantics of StreamingContext.start and stop. It will throw appropriate errors and warnings if stop() is called before start(), stop() is called twice, etc. Author: Tathagata Das <[email protected]> Closes apache#247 from tdas/graceful-shutdown and squashes the following commits: 61c0016 [Tathagata Das] Updated MIMA binary check excludes. ae1d39b [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into graceful-shutdown 6b59cfc [Tathagata Das] Minor changes based on Andrew's comment on PR. d0b8d65 [Tathagata Das] Reduced time taken by graceful shutdown unit test. f55bc67 [Tathagata Das] Fix scalastyle c69b3a7 [Tathagata Das] Updates based on Patrick's comments. c43b8ae [Tathagata Das] Added graceful shutdown to Spark Streaming.
pdeyhim
pushed a commit
to pdeyhim/spark-1
that referenced
this pull request
Jun 25, 2014
…eam API [WIP] The current Network Receiver API makes it slightly complicated to right a new receiver as one needs to create an instance of BlockGenerator as shown in SocketReceiver https://github.com/apache/spark/blob/master/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala#L51 Exposing the BlockGenerator interface has made it harder to improve the receiving process. The API of NetworkReceiver (which was not a very stable API anyways) needs to be change if we are to ensure future stability. Additionally, the functions like streamingContext.socketStream that create input streams, return DStream objects. That makes it hard to expose functionality (say, rate limits) unique to input dstreams. They should return InputDStream or NetworkInputDStream. This is still not yet implemented. This PR is blocked on the graceful shutdown PR apache#247 Author: Tathagata Das <[email protected]> Closes apache#300 from tdas/network-receiver-api and squashes the following commits: ea27b38 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into network-receiver-api 3a4777c [Tathagata Das] Renamed NetworkInputDStream to ReceiverInputDStream, and ActorReceiver related stuff. 838dd39 [Tathagata Das] Added more events to the StreamingListener to report errors and stopped receivers. a75c7a6 [Tathagata Das] Address some PR comments and fixed other issues. 91bfa72 [Tathagata Das] Fixed bugs. 8533094 [Tathagata Das] Scala style fixes. 028bde6 [Tathagata Das] Further refactored receiver to allow restarting of a receiver. 43f5290 [Tathagata Das] Made functions that create input streams return InputDStream and NetworkInputDStream, for both Scala and Java. 2c94579 [Tathagata Das] Fixed graceful shutdown by removing interrupts on receiving thread. 9e37a0b [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into network-receiver-api 3223e95 [Tathagata Das] Refactored the code that runs the NetworkReceiver into further classes and traits to make them more testable. a36cc48 [Tathagata Das] Refactored the NetworkReceiver API for future stability.
liancheng
pushed a commit
to liancheng/spark
that referenced
this pull request
Mar 17, 2017
…ckage ## What changes were proposed in this pull request? As part of the effort of clearly separating out Edge functionality, we're hereby moving all code related to the Directory Commit protocol to its own package. A few things about this change are not ideal - had to: - import com.databricks in `PartitioningAwareFileIndex` - open up `DirectoryAtomicReadProtocol.testingFs` (for testig hack) - write ugly code for getting configs from `SparkEnv.conf`, because of not having access to `ConfigEntry` - duplicate a bunch of utility classes: `Clock`, `ManualClock`, `SystemClock`, `ThreadUtils` ... but most of these (except the last) should hopefully be resolved by [SC-5838](https://databricks.atlassian.net/browse/SC-5838). ## How was this patch tested? spark-sql tests Author: Adrian Ionescu <[email protected]> Closes apache#247 from adrian-ionescu/SC-5835.
mccheah
pushed a commit
to mccheah/spark
that referenced
this pull request
Oct 12, 2017
[NOSQUASH] Resync from Apache
Igosuki
pushed a commit
to Adikteev/spark
that referenced
this pull request
Jul 31, 2018
bzhaoopenstack
pushed a commit
to bzhaoopenstack/spark
that referenced
this pull request
Sep 11, 2019
arjunshroff
pushed a commit
to arjunshroff/spark
that referenced
this pull request
Nov 24, 2020
cloud-fan
pushed a commit
that referenced
this pull request
Apr 20, 2022
### What changes were proposed in this pull request? This PR adds a new optimizer rule `MergeScalarSubqueries` to merge multiple non-correlated `ScalarSubquery`s to compute multiple scalar values once. E.g. the following query: ``` SELECT (SELECT avg(a) FROM t), (SELECT sum(b) FROM t) ``` is optimized from: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [] AS scalarsubquery()#253, scalar-subquery#243 [] AS scalarsubquery()#254L] : :- Aggregate [avg(a#244) AS avg(a)#247] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Aggregate [sum(a#251) AS sum(a)#250L] : +- Project [a#251] : +- Relation default.t[a#251,b#252] parquet +- OneRowRelation ``` to: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [].avg(a) AS scalarsubquery()#253, scalar-subquery#243 [].sum(a) AS scalarsubquery()#254L] : :- Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] : : +- Aggregate [avg(a#244) AS avg(a)#247, sum(a#244) AS sum(a)#250L] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] : +- Aggregate [avg(a#244) AS avg(a)#247, sum(a#244) AS sum(a)#250L] : +- Project [a#244] : +- Relation default.t[a#244,b#245] parquet +- OneRowRelation ``` and in the physical plan subqueries are reused: ``` == Physical Plan == AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(1) Project [Subquery subquery#242, [id=#113].avg(a) AS scalarsubquery()#253, ReusedSubquery Subquery subquery#242, [id=#113].sum(a) AS scalarsubquery()#254L] : :- Subquery subquery#242, [id=#113] : : +- AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(2) Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] +- *(2) HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)#247, sum(a)#250L]) +- ShuffleQueryStage 0 +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#158] +- *(1) HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- *(1) ColumnarToRow +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> +- == Initial Plan == Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] +- HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)#247, sum(a)#250L]) +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#110] +- HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> : +- ReusedSubquery Subquery subquery#242, [id=#113] +- *(1) Scan OneRowRelation[] +- == Initial Plan == ... ``` Please note that the above simple example could be easily optimized into a common select expression without reuse node, but this PR can handle more complex queries as well. ### Why are the changes needed? Performance improvement. ``` [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9 - MergeScalarSubqueries off 50798 52521 1423 0.0 Infinity 1.0X [info] q9 - MergeScalarSubqueries on 19484 19675 226 0.0 Infinity 2.6X [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9b - MergeScalarSubqueries off 15430 17803 NaN 0.0 Infinity 1.0X [info] q9b - MergeScalarSubqueries on 3862 4002 196 0.0 Infinity 4.0X ``` Please find `q9b` in the description of SPARK-34079. It is a variant of [q9.sql](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q9.sql) using CTE. The performance improvement in case of `q9` comes from merging 15 subqueries into 5 and in case of `q9b` it comes from merging 5 subqueries into 1. ### Does this PR introduce _any_ user-facing change? No. But this optimization can be disabled with `spark.sql.optimizer.excludedRules` config. ### How was this patch tested? Existing and new UTs. Closes #32298 from peter-toth/SPARK-34079-multi-column-scalar-subquery. Lead-authored-by: Peter Toth <[email protected]> Co-authored-by: attilapiros <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
cloud-fan
pushed a commit
that referenced
this pull request
Apr 20, 2022
### What changes were proposed in this pull request? This PR adds a new optimizer rule `MergeScalarSubqueries` to merge multiple non-correlated `ScalarSubquery`s to compute multiple scalar values once. E.g. the following query: ``` SELECT (SELECT avg(a) FROM t), (SELECT sum(b) FROM t) ``` is optimized from: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [] AS scalarsubquery()#253, scalar-subquery#243 [] AS scalarsubquery()#254L] : :- Aggregate [avg(a#244) AS avg(a)#247] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Aggregate [sum(a#251) AS sum(a)#250L] : +- Project [a#251] : +- Relation default.t[a#251,b#252] parquet +- OneRowRelation ``` to: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [].avg(a) AS scalarsubquery()#253, scalar-subquery#243 [].sum(a) AS scalarsubquery()#254L] : :- Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] : : +- Aggregate [avg(a#244) AS avg(a)#247, sum(a#244) AS sum(a)#250L] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] : +- Aggregate [avg(a#244) AS avg(a)#247, sum(a#244) AS sum(a)#250L] : +- Project [a#244] : +- Relation default.t[a#244,b#245] parquet +- OneRowRelation ``` and in the physical plan subqueries are reused: ``` == Physical Plan == AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(1) Project [Subquery subquery#242, [id=#113].avg(a) AS scalarsubquery()#253, ReusedSubquery Subquery subquery#242, [id=#113].sum(a) AS scalarsubquery()#254L] : :- Subquery subquery#242, [id=#113] : : +- AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(2) Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] +- *(2) HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)#247, sum(a)#250L]) +- ShuffleQueryStage 0 +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#158] +- *(1) HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- *(1) ColumnarToRow +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> +- == Initial Plan == Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] +- HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)#247, sum(a)#250L]) +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#110] +- HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> : +- ReusedSubquery Subquery subquery#242, [id=#113] +- *(1) Scan OneRowRelation[] +- == Initial Plan == ... ``` Please note that the above simple example could be easily optimized into a common select expression without reuse node, but this PR can handle more complex queries as well. ### Why are the changes needed? Performance improvement. ``` [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9 - MergeScalarSubqueries off 50798 52521 1423 0.0 Infinity 1.0X [info] q9 - MergeScalarSubqueries on 19484 19675 226 0.0 Infinity 2.6X [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9b - MergeScalarSubqueries off 15430 17803 NaN 0.0 Infinity 1.0X [info] q9b - MergeScalarSubqueries on 3862 4002 196 0.0 Infinity 4.0X ``` Please find `q9b` in the description of SPARK-34079. It is a variant of [q9.sql](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q9.sql) using CTE. The performance improvement in case of `q9` comes from merging 15 subqueries into 5 and in case of `q9b` it comes from merging 5 subqueries into 1. ### Does this PR introduce _any_ user-facing change? No. But this optimization can be disabled with `spark.sql.optimizer.excludedRules` config. ### How was this patch tested? Existing and new UTs. Closes #32298 from peter-toth/SPARK-34079-multi-column-scalar-subquery. Lead-authored-by: Peter Toth <[email protected]> Co-authored-by: attilapiros <[email protected]> Signed-off-by: Wenchen Fan <[email protected]> (cherry picked from commit e00b81e) Signed-off-by: Wenchen Fan <[email protected]>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Current version of StreamingContext.stop() directly kills all the data receivers (NetworkReceiver) without waiting for the data already received to be persisted and processed. This PR provides the fix. Now, when the StreamingContext.stop() is called, the following sequence of steps will happen.
It also fixes the semantics of StreamingContext.start and stop. It will throw appropriate errors and warnings if stop() is called before start(), stop() is called twice, etc.