MAPREDUCE-7403. manifest-committer dynamic partitioning support. #4728
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Declares its compatibility with the stream capability "mapreduce.job.committer.dynamic.partitioning" spark will need to cast to StreamCapabilities and then probe. Change-Id: Iafcacc6d2491bb1e7fc2fc033c6d17d5b63b5b4f
…through Change-Id: Icc30bf6251977cfb76211bffcfc5796b1a44989b
* spark-side requirements * why there is risk if you use it at scale. That risk is low because currently spark seems to rename sequentially. if/when it does parallel file rename then throttling may be triggered, with the consequential failure events. Change-Id: I6e442bbdcaa007a3cd2e04ddf8b41d14c51057ff
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Change-Id: I423f052ca48915502f182cb4f1c67cdf04838a99
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🎊 +1 overall
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would be good for some reviews here from @mukund-thakur , @mehakmeet and ideally @sunchao and @dongjoon-hyun -both of whom will be able to review the matching spark-side change, which is simply one of "don't reject attempts to use a PathOutputCommitter for dynamic partition overwrite if the instance created says it is OK" |
attilapiros
approved these changes
Aug 18, 2022
| Assertions.assertThat(committer.hasCapability( | ||
| ManifestCommitterConstants.CAPABILITY_DYNAMIC_PARTITIONING)) | ||
| .describedAs("dynamic partitioning capability in committer %s", | ||
| committer); |
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Suggested change
| committer); | |
| committer).isTrue(); |
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did i just get my asserts wrong. that was bad. thanks!
| Assertions.assertThat(bindingCommitter.hasCapability( | ||
| ManifestCommitterConstants.CAPABILITY_DYNAMIC_PARTITIONING)) | ||
| .describedAs("dynamic partitioning capability in committer %s", | ||
| bindingCommitter); |
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Suggested change
| bindingCommitter); | |
| bindingCommitter).isTrue(); |
mukund-thakur
approved these changes
Aug 19, 2022
Change-Id: I29e98cf4ac607913d59e15babe6180434f665714
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thanks. fixed tests, ran locally, and ran the abfs ITest subclass. all good |
| @@ -29,7 +29,7 @@ | |||
| * <li>Nothing else got through either.</li> | |||
| * </ol> | |||
| */ | |||
| public class AWSStatus500Exception extends AWSServiceIOException { | |||
| public class jAWSStatus500Exception extends AWSServiceIOException { | |||
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I think this is a typo in Intellij.
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And I think Yetus failed beacuse of this only.
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💔 -1 overall
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Change-Id: Ifbe2d1012cbdf2e7467ce84a7d8d93a78e91dcf6
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🎊 +1 overall
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asfgit
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Aug 24, 2022
Declares its compatibility with Spark's dynamic output partitioning by having the stream capability "mapreduce.job.committer.dynamic.partitioning" Requires a Spark release with SPARK-40034, which does the probing before deciding whether to accept/rejecting instantiation with dynamic partition overwrite set This feature can be declared as supported by any other PathOutputCommitter implementations whose algorithm and destination filesystem are compatible. None of the S3A committers are compatible. The classic FileOutputCommitter is, but it does not declare itself as such out of our fear of changing that code. The Spark-side code will automatically infer compatibility if the created committer is of that class or a subclass. Contributed by Steve Loughran.
dongjoon-hyun
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Sep 9, 2022
### What changes were proposed in this pull request? Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. This patch has unit tests but not integration tests; really needs to test the SQL commands through the manifest committer into gcs/abfs, or at least local fs. That would be possible once hadoop 3.3.5 is out... Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. ### Why are the changes needed? Hadoop 3.3.5 adds a new committer in mapreduce-core which works fast and correctly on azure and gcs. (it would also work on hdfs, but its optimised for the cloud stores). The stores and the committer do meet the requirements of Spark SQL Dynamic Partition Overwrite, so it is OK to for spark to work through it. Spark does not know this; MAPREDUCE-7403 adds a way for any PathOutputCommitter to declare that they are compatible; the IntermediateManifestCommitter will do so. (apache/hadoop#4728) ### Does thi 9E88 s PR introduce _any_ user-facing change? No. There is documentation on the feature in the hadoop [manifest committer](https://github.com/apache/hadoop/blob/82372d0d22e696643ad97490bc902fb6d17a6382/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/manifest_committer.md) docs. ### How was this patch tested? 1. Unit tests in hadoop-cloud which work with hadoop versions with/without the matching change. 2. New integration tests in https://github.com/hortonworks-spark/cloud-integration which require spark to be built against hadoop with the manifest committer declaring compatibility Those new integration tests include * spark sql test derived from spark's own [CloudRelationBasicSuite.scala#L212](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/org/apache/spark/sql/sources/CloudRelationBasicSuite.scala#L212) * Dataset tests extended to verify support for/rejection of dynamic partition overwrite [AbstractCommitDataframeSuite.scala#L151](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/com/cloudera/spark/cloud/committers/AbstractCommitDataframeSuite.scala#L151) Tested against azure cardiff with the manifest committer; s3 london (s3a committers reject dynamic partition overwrites) Closes #37468 from steveloughran/SPARK-40034-MAPREDUCE-7403-manifest-committer-partitioning. Authored-by: Steve Loughran <stevel@cloudera.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
a0x8o
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Sep 9, 2022
### What changes were proposed in this pull request? Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. This patch has unit tests but not integration tests; really needs to test the SQL commands through the manifest committer into gcs/abfs, or at least local fs. That would be possible once hadoop 3.3.5 is out... Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. ### Why are the changes needed? Hadoop 3.3.5 adds a new committer in mapreduce-core which works fast and correctly on azure and gcs. (it would also work on hdfs, but its optimised for the cloud stores). The stores and the committer do meet the requirements of Spark SQL Dynamic Partition Overwrite, so it is OK to for spark to work through it. Spark does not know this; MAPREDUCE-7403 adds a way for any PathOutputCommitter to declare that they are compatible; the IntermediateManifestCommitter will do so. (apache/hadoop#4728) ### Does this PR introduce _any_ user-facing change? No. There is documentation on the feature in the hadoop [manifest committer](https://github.com/apache/hadoop/blob/82372d0d22e696643ad97490bc902fb6d17a6382/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/manifest_committer.md) docs. ### How was this patch tested? 1. Unit tests in hadoop-cloud which work with hadoop versions with/without the matching change. 2. New integration tests in https://github.com/hortonworks-spark/cloud-integration which require spark to be built against hadoop with the manifest committer declaring compatibility Those new integration tests include * spark sql test derived from spark's own [CloudRelationBasicSuite.scala#L212](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/org/apache/spark/sql/sources/CloudRelationBasicSuite.scala#L212) * Dataset tests extended to verify support for/rejection of dynamic partition overwrite [AbstractCommitDataframeSuite.scala#L151](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/com/cloudera/spark/cloud/committers/AbstractCommitDataframeSuite.scala#L151) Tested against azure cardiff with the manifest committer; s3 london (s3a committers reject dynamic partition overwrites) Closes #37468 from steveloughran/SPARK-40034-MAPREDUCE-7403-manifest-committer-partitioning. Authored-by: Steve Loughran <stevel@cloudera.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
HarshitGupta11
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Nov 28, 2022
…che#4728) Declares its compatibility with Spark's dynamic output partitioning by having the stream capability "mapreduce.job.committer.dynamic.partitioning" Requires a Spark release with SPARK-40034, which does the probing before deciding whether to accept/rejecting instantiation with dynamic partition overwrite set This feature can be declared as supported by any other PathOutputCommitter implementations whose algorithm and destination filesystem are compatible. None of the S3A committers are compatible. The classic FileOutputCommitter is, but it does not declare itself as such out of our fear of changing that code. The Spark-side code will automatically infer compatibility if the created committer is of that class or a subclass. Contributed by Steve Loughran.
a0x8o
added a commit
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Dec 30, 2022
### What changes were proposed in this pull request? Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. This patch has unit tests but not integration tests; really needs to test the SQL commands through the manifest committer into gcs/abfs, or at least local fs. That would be possible once hadoop 3.3.5 is out... Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. ### Why are the changes needed? Hadoop 3.3.5 adds a new committer in mapreduce-core which works fast and correctly on azure and gcs. (it would also work on hdfs, but its optimised for the cloud stores). The stores and the committer do meet the requirements of Spark SQL Dynamic Partition Overwrite, so it is OK to for spark to work through it. Spark does not know this; MAPREDUCE-7403 adds a way for any PathOutputCommitter to declare that they are compatible; the IntermediateManifestCommitter will do so. (apache/hadoop#4728) ### Does this PR introduce _any_ user-facing change? No. There is documentation on the feature in the hadoop [manifest committer](https://github.com/apache/hadoop/blob/82372d0d22e696643ad97490bc902fb6d17a6382/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/manifest_committer.md) docs. ### How was this patch tested? 1. Unit tests in hadoop-cloud which work with hadoop versions with/without the matching change. 2. New integration tests in https://github.com/hortonworks-spark/cloud-integration which require spark to be built against hadoop with the manifest committer declaring compatibility Those new integration tests include * spark sql test derived from spark's own [CloudRelationBasicSuite.scala#L212](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/org/apache/spark/sql/sources/CloudRelationBasicSuite.scala#L212) * Dataset tests extended to verify support for/rejection of dynamic partition overwrite [AbstractCommitDataframeSuite.scala#L151](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/com/cloudera/spark/cloud/committers/AbstractCommitDataframeSuite.scala#L151) Tested against azure cardiff with the manifest committer; s3 london (s3a committers reject dynamic partition overwrites) Closes #37468 from steveloughran/SPARK-40034-MAPREDUCE-7403-manifest-committer-partitioning. Authored-by: Steve Loughran <stevel@cloudera.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
a0x8o
added a commit
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Dec 30, 2022
### What changes were proposed in this pull request? Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. This patch has unit tests but not integration tests; really needs to test the SQL commands through the manifest committer into gcs/abfs, or at least local fs. That would be possible once hadoop 3.3.5 is out... Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. ### Why are the changes needed? Hadoop 3.3.5 adds a new committer in mapreduce-core which works fast and correctly on azure and gcs. (it would also work on hdfs, but its optimised for the cloud stores). The stores and the committer do meet the requirements of Spark SQL Dynamic Partition Overwrite, so it is OK to for spark to work through it. Spark does not know this; MAPREDUCE-7403 adds a way for any PathOutputCommitter to declare that they are compatible; the IntermediateManifestCommitter will do so. (apache/hadoop#4728) ### Does this PR introduce _any_ user-facing change? No. There is documentation on the feature in the hadoop [manifest committer](https://github.com/apache/hadoop/blob/82372d0d22e696643ad97490bc902fb6d17a6382/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/manifest_committer.md) docs. ### How was this patch tested? 1. Unit tests in hadoop-cloud which work with hadoop versions with/without the matching change. 2. New integration tests in https://github.com/hortonworks-spark/cloud-integration which require spark to be built against hadoop with the manifest committer declaring compatibility Those new integration tests include * spark sql test derived from spark's own [CloudRelationBasicSuite.scala#L212](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/org/apache/spark/sql/sources/CloudRelationBasicSuite.scala#L212) * Dataset tests extended to verify support for/rejection of dynamic partition overwrite [AbstractCommitDataframeSuite.scala#L151](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/com/cloudera/spark/cloud/committers/AbstractCommitDataframeSuite.scala#L151) Tested against azure cardiff with the manifest committer; s3 london (s3a committers reject dynamic partition overwrites) Closes #37468 from steveloughran/SPARK-40034-MAPREDUCE-7403-manifest-committer-partitioning. Authored-by: Steve Loughran <stevel@cloudera.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
peter-toth
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Nov 26, 2024
Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. This patch has unit tests but not integration tests; really needs to test the SQL commands through the manifest committer into gcs/abfs, or at least local fs. That would be possible once hadoop 3.3.5 is out... Uses the StreamCapabilities probe in MAPREDUCE-7403 to identify when a PathOutputCommitter is compatible with dynamic partition overwrite. Hadoop 3.3.5 adds a new committer in mapreduce-core which works fast and correctly on azure and gcs. (it would also work on hdfs, but its optimised for the cloud stores). The stores and the committer do meet the requirements of Spark SQL Dynamic Partition Overwrite, so it is OK to for spark to work through it. Spark does not know this; MAPREDUCE-7403 adds a way for any PathOutputCommitter to declare that they are compatible; the IntermediateManifestCommitter will do so. (apache/hadoop#4728) No. There is documentation on the feature in the hadoop [manifest committer](https://github.com/apache/hadoop/blob/82372d0d22e696643ad97490bc902fb6d17a6382/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/manifest_committer.md) docs. 1. Unit tests in hadoop-cloud which work with hadoop versions with/without the matching change. 2. New integration tests in https://github.com/hortonworks-spark/cloud-integration which require spark to be built against hadoop with the manifest committer declaring compatibility Those new integration tests include * spark sql test derived from spark's own [CloudRelationBasicSuite.scala#L212](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/org/apache/spark/sql/sources/CloudRelationBasicSuite.scala#L212) * Dataset tests extended to verify support for/rejection of dynamic partition overwrite [AbstractCommitDataframeSuite.scala#L151](https://github.com/hortonworks-spark/cloud-integration/blob/master/cloud-examples/src/test/scala/com/cloudera/spark/cloud/committers/AbstractCommitDataframeSuite.scala#L151) Tested against azure cardiff with the manifest committer; s3 london (s3a committers reject dynamic partition overwrites) Closes apache#37468 from steveloughran/SPARK-40034-MAPREDUCE-7403-manifest-committer-partitioning. Authored-by: Steve Loughran <stevel@cloudera.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org> Change-Id: Ibd45ef53f828acf39ceeaaea81f7f149a0eb6f23
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Description of PR
Declares its compatibility with the stream capability
"mapreduce.job.committer.dynamic.partitioning"
spark will need to cast to StreamCapabilities and then probe.
How was this patch tested?
SPARK-40034 has a PR with patch matching changes in the spark code; plus unit tests to verify that it's not an error to ask for dynamic partition if the committer's hasCapability holds.
apache/spark#37468
Testing
all the abfs tests against azure cardiff. one transient failure; one new JIRA (HADOOP-18405
abfs testReadAndWriteWithDifferentBufferSizesAndSeek failure)
unit tests of the spark code in to check for the capability and reject if missing CommitterBindingSuite.scala#L162
new integration tests in https://github.com/hortonworks-spark/cloud-integration
those new integration tests include
Tested through a spark build with the matching patch against s3 london, azure cardiff.
GCS test setup failing with oauth problems the way they were not on friday. assuming unrelated.
For code changes:
LICENSE,LICENSE-binary,NOTICE-binaryfiles?