SPARK-1305: Support persisting RDD's directly to Tachyon #158

RongGu · 2014-03-16T14:59:25Z

Move the PR#468 of apache-incubator-spark to the apache-spark
"Adding an option to persist Spark RDD blocks into Tachyon."

RongGu · 2014-03-16T15:00:34Z

The PR is moved here, and the left commits will be push to this site then.

AmplabJenkins · 2014-03-16T15:42:17Z

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… PR#468 of apache-incubator-spark to the apache-spark

AmplabJenkins · 2014-03-17T09:43:26Z

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nicklan · 2014-03-18T17:13:58Z

core/src/main/scala/org/apache/spark/storage/BlockManager.scala

@@ -97,6 +101,23 @@ private[spark] class BlockManager(
  var asyncReregisterTask: Future[Unit] = null
  val asyncReregisterLock = new Object

+  private def tachyonStore : TachyonStore = synchronized {


If you make this a lazy val, it will have the same effect, be more efficient, and be less code

Makes sense, here it's fine to use lazy val.

mateiz · 2014-03-19T00:34:30Z

This seems to be failing tests, have you run them locally?

pwendell · 2014-03-21T04:13:52Z

Hey @RongGu we've made some changes to the block store that I think will require up-merging this patch to master. We're interested in merging this soon. Any chance you could take a look at this and bring it up to date? Thanks!

RongGu · 2014-03-21T04:32:03Z

Hey @mateiz . Yes, I verified the whole thing last night. I set up a tachyon locally and the example I added using tachyonstore works well. Also, the BlockManagerSuite with my added test passed here.
BTW, the whole test never passed here on my laptop from the very beginning to now. Some test failed here because of my environment problem,such as OOM "Test org.apache.spark.JavaAPISuite.sparkContextUnion failed: java.lang.OutOfMemoryError: unable to create new native thread".
I would like to re-test the current version on the AmplabJenkins in order to check out current logs. The previous log link https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/13209/ seems expired.

RongGu · 2014-03-21T04:49:59Z

Hey, @pwendell . Thanks for your notification. I've been busy with the school stuff here since this semester started. There is still a bit work need to done in this PR. I guess several other PRs will be merged into trunk in next a few days. Thus, I would like to perform the up-merging work when I have done with the feature in this PR and before you think it's okay to merge this PR. In a word, I would like to perform the up-merging work at last. Is this plan work for you?
I will continue to work on this PR in the next weeks in a part-time way. I believe the code/features merged into the Apahe Spark should have high quality.

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haoyuan · 2014-03-21T19:29:08Z

Jenkins, test this please

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mateiz · 2014-03-21T21:01:25Z

core/src/main/java/org/apache/spark/api/java/StorageLevels.java

+    boolean useTachyon,
+    boolean deserialized,
+    int replication) {
+    return StorageLevel.apply(useDisk, useMemory, useTachyon, deserialized, replication);


Don't just delete the old method -- add a second version that takes the new useTachyon argument. Some user code will be using the old method and there's no reason to break it here.

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haoyuan · 2014-04-05T00:59:04Z

@mateiz Merged the master branch.

mateiz · 2014-04-05T01:24:10Z

HY, it still doesn't merge. I see CONFLICT (content): Merge conflict in core/src/main/scala/org/apache/spark/util/Utils.scala.

Maybe you didn't update to the very latest one? Or maybe you got unlucky.

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haoyuan · 2014-04-05T02:47:09Z

@mateiz See if it works now :)

pwendell · 2014-04-05T03:39:00Z

Merging cleanly now. I merged this - thanks!

RongGu · 2014-04-05T05:25:58Z

It seems that I just missed our last part. I am so happy this PR is merged. Thank @pwendell, @mateiz and @aarondav for your valuable comments all the way. Also, I appreciate @haoyuan 's leading and helping on this PR.

mateiz · 2014-04-05T06:01:10Z

No worries, thanks a lot to both you and Haoyuan for putting this together! This will be a great feature to have.

Move the PR#468 of apache-incubator-spark to the apache-spark "Adding an option to persist Spark RDD blocks into Tachyon." Author: Haoyuan Li <[email protected]> Author: RongGu <[email protected]> Closes apache#158 from RongGu/master and squashes the following commits: 72b7768 [Haoyuan Li] merge master 9f7fa1b [Haoyuan Li] fix code style ae7834b [Haoyuan Li] minor cleanup a8b3ec6 [Haoyuan Li] merge master branch e0f4891 [Haoyuan Li] better check offheap. 55b5918 [RongGu] address matei's comment on the replication of offHeap storagelevel 7cd4600 [RongGu] remove some logic code for tachyonstore's replication 51149e7 [RongGu] address aaron's comment on returning value of the remove() function in tachyonstore 8adfcfa [RongGu] address arron's comment on inTachyonSize 120e48a [RongGu] changed the root-level dir name in Tachyon 5cc041c [Haoyuan Li] address aaron's comments 9b97935 [Haoyuan Li] address aaron's comments d9a6438 [Haoyuan Li] fix for pspark 77d2703 [Haoyuan Li] change python api.git status 3dcace4 [Haoyuan Li] address matei's comments 91fa09d [Haoyuan Li] address patrick's comments 589eafe [Haoyuan Li] use TRY_CACHE instead of MUST_CACHE 64348b2 [Haoyuan Li] update conf docs. ed73e19 [Haoyuan Li] Merge branch 'master' of github.com:RongGu/spark-1 619a9a8 [RongGu] set number of directories in TachyonStore back to 64; added a TODO tag for duplicated code from the DiskStore be79d77 [RongGu] find a way to clean up some unnecessay metods and classed to make the code simpler 49cc724 [Haoyuan Li] update docs with off_headp option 4572f9f [RongGu] reserving the old apply function API of StorageLevel 04301d3 [RongGu] rename StorageLevel.TACHYON to Storage.OFF_HEAP c9aeabf [RongGu] rename the StorgeLevel.TACHYON as StorageLevel.OFF_HEAP 76805aa [RongGu] unifies the config properties name prefix; add the configs into docs/configuration.md e700d9c [RongGu] add the SparkTachyonHdfsLR example and some comments fd84156 [RongGu] use randomUUID to generate sparkapp directory name on tachyon;minor code style fix 939e467 [Haoyuan Li] 0.4.1-thrift from maven central 86a2eab [Haoyuan Li] tachyon 0.4.1-thrift is in the staging repo. but jenkins failed to download it. temporarily revert it back to 0.4.1 16c5798 [RongGu] make the dependency on tachyon as tachyon-0.4.1-thrift eacb2e8 [RongGu] Merge branch 'master' of https://github.com/RongGu/spark-1 bbeb4de [RongGu] fix the JsonProtocolSuite test failure problem 6adb58f [RongGu] Merge branch 'master' of https://github.com/RongGu/spark-1 d827250 [RongGu] fix JsonProtocolSuie test failure 716e93b [Haoyuan Li] revert the version ca14469 [Haoyuan Li] bump tachyon version to 0.4.1-thrift 2825a13 [RongGu] up-merging to the current master branch of the apache spark 6a22c1a [Haoyuan Li] fix scalastyle 8968b67 [Haoyuan Li] exclude more libraries from tachyon dependency to be the same as referencing tachyon-client. 77be7e8 [RongGu] address mateiz's comment about the temp folder name problem. The implementation followed mateiz's advice. 1dcadf9 [Haoyuan Li] typo bf278fa [Haoyuan Li] fix python tests e82909c [Haoyuan Li] minor cleanup 776a56c [Haoyuan Li] address patrick's and ali's comments from the previous PR 8859371 [Haoyuan Li] various minor fixes and clean up e3ddbba [Haoyuan Li] add doc to use Tachyon cache mode. fcaeab2 [Haoyuan Li] address Aaron's comment e554b1e [Haoyuan Li] add python code 47304b3 [Haoyuan Li] make tachyonStore in BlockMananger lazy val; add more comments StorageLevels. dc8ef24 [Haoyuan Li] add old storelevel constructor e01a271 [Haoyuan Li] update tachyon 0.4.1 8011a96 [RongGu] fix a brought-in mistake in StorageLevel 70ca182 [RongGu] a bit change in comment 556978b [RongGu] fix the scalastyle errors 791189b [RongGu] "Adding an option to persist Spark RDD blocks into Tachyon." move the PR#468 of apache-incubator-spark to the apache-spark

## What changes were proposed in this pull request? The ACL Thrift end to end test currently fail because they do not set the `spark.databricks.acl.enabled` flag. This PR fixes that. ## How was this patch tested? This is a test. Author: Herman van Hovell <[email protected]> Closes apache#158 from hvanhovell/SC-5365-thrift.

### 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]>

### 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]>

"Adding an option to persist Spark RDD blocks into Tachyon." move the…

791189b

… PR#468 of apache-incubator-spark to the apache-spark

fix the scalastyle errors

556978b

nicklan reviewed Mar 18, 2014
View reviewed changes

RongGu added 2 commits March 21, 2014 20:19

a bit change in comment

70ca182

fix a brought-in mistake in StorageLevel

8011a96

mateiz reviewed Mar 21, 2014
View reviewed changes

fix code style

9f7fa1b

merge master

72b7768

asfgit closed this in b50ddfd Apr 5, 2014

mccheah pushed a commit to mccheah/spark that referenced this pull request Oct 12, 2017

Bring back hive (apache#158)

2bd3c33

jlopezmalla pushed a commit to jlopezmalla/spark that referenced this pull request Feb 27, 2018

[QATM-960]Change bdt version (apache#158)

b5ed7a7

JoshRosen mentioned this pull request Jun 30, 2022

[SPARK-39636][CORE][UI] Fix multiple bugs in JsonProtocol, impacting off heap StorageLevels and Task/Executor ResourceRequests #37027

Closed

SPARK-1305: Support persisting RDD's directly to Tachyon #158

SPARK-1305: Support persisting RDD's directly to Tachyon #158

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