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[DCOS-51453][HADOOP] Add Hadoop 2.9 profile #53
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alembiewski
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Jun 12, 2019
alembiewski
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Aug 19, 2019
* Support for DSCOS_SERVICE_ACCOUNT_CREDENTIAL environment variable in MesosClusterScheduler * File Based Secrets support * [SPARK-723][SPARK-740] Add Metrics to Dispatcher and Driver - Counters: The total number of times that submissions have entered states - Timers: The duration from submit or launch until a submission entered a given state - Histogram: The retry counts at time of retry * Fixes to handling finished drivers - Rename 'failed' case to 'exception' - When a driver is 'finished', record its final MesosTaskState - Fix naming consistency after seeing how they look in practice * Register "finished" counters up-front Otherwise their values are never published. * [SPARK-692] Added spark.mesos.executor.gpus to specify the number of Executor CPUs * [SPARK-23941][MESOS] Mesos task failed on specific spark app name (#33) * [SPARK-23941][MESOS] Mesos task failed on specific spark app name Port from SPARK#21014 ** edit: not a direct port from upstream Spark. Changes were needed because we saw PySpark jobs fail to launch when 1) run with docker and 2) including --py-files ============== * Shell escape only appName, mainClass, default and driverConf Specifically, we do not want to shell-escape the --py-files. What we've seen IRL is that for spark jobs that use docker images coupled w/ python files, the $MESOS_SANDBOX path is escaped and results in FileNotFoundErrors during py4j.SparkSession.getOrCreate * [DCOS-39150][SPARK] Support unique Executor IDs in cluster managers (#36) Using incremental integers as Executor IDs leads to a situation when Spark Executors launched by different Drivers have same IDs. This leads to a situation when Mesos Task IDs for multiple Spark Executors are the same too. This PR prepends UUID unique for a CoarseGrainedSchedulerBackend instance to numeric ID thus allowing to distinguish Executors belonging to different drivers. This PR reverts commit ebe3c7f "[SPARK-12864][YARN] initialize executorIdCounter after ApplicationMaster killed for max n…)" * Upgrade of Hadoop, ZooKeeper, and Jackson libraries to fix CVEs. Updates for JSON-related tests. (#43) List of upgrades for 3rd-party libraries having CVEs: - Hadoop: 2.7.3 -> 2.7.7. Fixes: CVE-2016-6811, CVE-2017-3166, CVE-2017-3162, CVE-2018-8009 - Jackson 2.6.5 -> 2.9.6. Fixes: CVE-2017-15095, CVE-2017-17485, CVE-2017-7525, CVE-2018-7489, CVE-2016-3720 - ZooKeeper 3.4.6 -> 3.4.13 (https://zookeeper.apache.org/doc/r3.4.13/releasenotes.html) # Conflicts: # dev/deps/spark-deps-hadoop-2.6 # dev/deps/spark-deps-hadoop-2.7 # dev/deps/spark-deps-hadoop-3.1 # pom.xml * CNI Support for Docker containerizer, binding to SPARK_LOCAL_IP instead of 0.0.0.0 to properly advertise executors during shuffle (#44) * Spark Dispatcher support for launching applications in the same virtual network by default (#45) * [DCOS-46585] Fix supervised driver retry logic for outdated tasks (#46) This commit fixes a bug where `--supervised` drivers would relaunch after receiving an outdated status update from a restarted/crashed agent even if they had already been relaunched and running elsewhere. In those scenarios, previous logic would cause two identical jobs to be running and ZK state would only have a record of the latest one effectively orphaning the 1st job. * Revert "[SPARK-25088][CORE][MESOS][DOCS] Update Rest Server docs & defaults." This reverts commit 1024875. The change introduced in the reverted commit is breaking: - breaks semantics of `spark.master.rest.enabled` which belongs to Spark Standalone Master only but not to SparkSubmit - reverts the default behavior for Spark Standalone from REST to legacy RPC - contains misleading messages in `require` assertion blocks - prevents users from running jobs without specifying `spark.master.rest.enabled` * [DCOS-49020] Specify user in CommandInfo for Spark Driver launched on Mesos (#49) * [DCOS-40974] Mesos checkpointing support for Spark Drivers (#51) * [DCOS-51158] Improved Task ID assignment for Executor tasks (#52) * [DCOS-51454] Remove irrelevant Mesos REPL test (#54) * [DCOS-51453] Added Hadoop 2.9 profile (#53) * [DCOS-34235] spark.mesos.executor.memoryOverhead equivalent for the Driver when running on Mesos (#55) * Refactoring of metrics naming to add mesos semantics and avoid clashing with existing Spark metrics (#58) * [DCOS-34549] Mesos label NPE fix (#60)
rpalaznik
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Feb 24, 2020
* Support for DSCOS_SERVICE_ACCOUNT_CREDENTIAL environment variable in MesosClusterScheduler * File Based Secrets support * [SPARK-723][SPARK-740] Add Metrics to Dispatcher and Driver - Counters: The total number of times that submissions have entered states - Timers: The duration from submit or launch until a submission entered a given state - Histogram: The retry counts at time of retry * Fixes to handling finished drivers - Rename 'failed' case to 'exception' - When a driver is 'finished', record its final MesosTaskState - Fix naming consistency after seeing how they look in practice * Register "finished" counters up-front Otherwise their values are never published. * [SPARK-692] Added spark.mesos.executor.gpus to specify the number of Executor CPUs * [SPARK-23941][MESOS] Mesos task failed on specific spark app name (#33) * [SPARK-23941][MESOS] Mesos task failed on specific spark app name Port from SPARK#21014 ** edit: not a direct port from upstream Spark. Changes were needed because we saw PySpark jobs fail to launch when 1) run with docker and 2) including --py-files ============== * Shell escape only appName, mainClass, default and driverConf Specifically, we do not want to shell-escape the --py-files. What we've seen IRL is that for spark jobs that use docker images coupled w/ python files, the $MESOS_SANDBOX path is escaped and results in FileNotFoundErrors during py4j.SparkSession.getOrCreate * [DCOS-39150][SPARK] Support unique Executor IDs in cluster managers (#36) Using incremental integers as Executor IDs leads to a situation when Spark Executors launched by different Drivers have same IDs. This leads to a situation when Mesos Task IDs for multiple Spark Executors are the same too. This PR prepends UUID unique for a CoarseGrainedSchedulerBackend instance to numeric ID thus allowing to distinguish Executors belonging to different drivers. This PR reverts commit ebe3c7f "[SPARK-12864][YARN] initialize executorIdCounter after ApplicationMaster killed for max n…)" * Upgrade of Hadoop, ZooKeeper, and Jackson libraries to fix CVEs. Updates for JSON-related tests. (#43) List of upgrades for 3rd-party libraries having CVEs: - Hadoop: 2.7.3 -> 2.7.7. Fixes: CVE-2016-6811, CVE-2017-3166, CVE-2017-3162, CVE-2018-8009 - Jackson 2.6.5 -> 2.9.6. Fixes: CVE-2017-15095, CVE-2017-17485, CVE-2017-7525, CVE-2018-7489, CVE-2016-3720 - ZooKeeper 3.4.6 -> 3.4.13 (https://zookeeper.apache.org/doc/r3.4.13/releasenotes.html) * CNI Support for Docker containerizer, binding to SPARK_LOCAL_IP instead of 0.0.0.0 to properly advertise executors during shuffle (#44) * Spark Dispatcher support for launching applications in the same virtual network by default (#45) * [DCOS-46585] Fix supervised driver retry logic for outdated tasks (#46) This commit fixes a bug where `--supervised` drivers would relaunch after receiving an outdated status update from a restarted/crashed agent even if they had already been relaunched and running elsewhere. In those scenarios, previous logic would cause two identical jobs to be running and ZK state would only have a record of the latest one effectively orphaning the 1st job. * Revert "[SPARK-25088][CORE][MESOS][DOCS] Update Rest Server docs & defaults." This reverts commit 1024875. The change introduced in the reverted commit is breaking: - breaks semantics of `spark.master.rest.enabled` which belongs to Spark Standalone Master only but not to SparkSubmit - reverts the default behavior for Spark Standalone from REST to legacy RPC - contains misleading messages in `require` assertion blocks - prevents users from running jobs without specifying `spark.master.rest.enabled` * [DCOS-49020] Specify user in CommandInfo for Spark Driver launched on Mesos (#49) * [DCOS-40974] Mesos checkpointing support for Spark Drivers (#51) * [DCOS-51158] Improved Task ID assignment for Executor tasks (#52) * [DCOS-51454] Remove irrelevant Mesos REPL test (#54) * [DCOS-51453] Added Hadoop 2.9 profile (#53) * [DCOS-34235] spark.mesos.executor.memoryOverhead equivalent for the Driver when running on Mesos (#55) * Refactoring of metrics naming to add mesos semantics and avoid clashing with existing Spark metrics (#58) * [DCOS-34549] Mesos label NPE fix (#60)
farhan5900
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Aug 7, 2020
* Support for DSCOS_SERVICE_ACCOUNT_CREDENTIAL environment variable in MesosClusterScheduler * File Based Secrets support * [SPARK-723][SPARK-740] Add Metrics to Dispatcher and Driver - Counters: The total number of times that submissions have entered states - Timers: The duration from submit or launch until a submission entered a given state - Histogram: The retry counts at time of retry * Fixes to handling finished drivers - Rename 'failed' case to 'exception' - When a driver is 'finished', record its final MesosTaskState - Fix naming consistency after seeing how they look in practice * Register "finished" counters up-front Otherwise their values are never published. * [SPARK-692] Added spark.mesos.executor.gpus to specify the number of Executor CPUs * [SPARK-23941][MESOS] Mesos task failed on specific spark app name (#33) * [SPARK-23941][MESOS] Mesos task failed on specific spark app name Port from SPARK#21014 ** edit: not a direct port from upstream Spark. Changes were needed because we saw PySpark jobs fail to launch when 1) run with docker and 2) including --py-files ============== * Shell escape only appName, mainClass, default and driverConf Specifically, we do not want to shell-escape the --py-files. What we've seen IRL is that for spark jobs that use docker images coupled w/ python files, the $MESOS_SANDBOX path is escaped and results in FileNotFoundErrors during py4j.SparkSession.getOrCreate * [DCOS-39150][SPARK] Support unique Executor IDs in cluster managers (#36) Using incremental integers as Executor IDs leads to a situation when Spark Executors launched by different Drivers have same IDs. This leads to a situation when Mesos Task IDs for multiple Spark Executors are the same too. This PR prepends UUID unique for a CoarseGrainedSchedulerBackend instance to numeric ID thus allowing to distinguish Executors belonging to different drivers. This PR reverts commit ebe3c7f "[SPARK-12864][YARN] initialize executorIdCounter after ApplicationMaster killed for max n…)" * Upgrade of Hadoop, ZooKeeper, and Jackson libraries to fix CVEs. Updates for JSON-related tests. (#43) List of upgrades for 3rd-party libraries having CVEs: - Hadoop: 2.7.3 -> 2.7.7. Fixes: CVE-2016-6811, CVE-2017-3166, CVE-2017-3162, CVE-2018-8009 - Jackson 2.6.5 -> 2.9.6. Fixes: CVE-2017-15095, CVE-2017-17485, CVE-2017-7525, CVE-2018-7489, CVE-2016-3720 - ZooKeeper 3.4.6 -> 3.4.13 (https://zookeeper.apache.org/doc/r3.4.13/releasenotes.html) * CNI Support for Docker containerizer, binding to SPARK_LOCAL_IP instead of 0.0.0.0 to properly advertise executors during shuffle (#44) * Spark Dispatcher support for launching applications in the same virtual network by default (#45) * [DCOS-46585] Fix supervised driver retry logic for outdated tasks (#46) This commit fixes a bug where `--supervised` drivers would relaunch after receiving an outdated status update from a restarted/crashed agent even if they had already been relaunched and running elsewhere. In those scenarios, previous logic would cause two identical jobs to be running and ZK state would only have a record of the latest one effectively orphaning the 1st job. * Revert "[SPARK-25088][CORE][MESOS][DOCS] Update Rest Server docs & defaults." This reverts commit 1024875. The change introduced in the reverted commit is breaking: - breaks semantics of `spark.master.rest.enabled` which belongs to Spark Standalone Master only but not to SparkSubmit - reverts the default behavior for Spark Standalone from REST to legacy RPC - contains misleading messages in `require` assertion blocks - prevents users from running jobs without specifying `spark.master.rest.enabled` * [DCOS-49020] Specify user in CommandInfo for Spark Driver launched on Mesos (#49) * [DCOS-40974] Mesos checkpointing support for Spark Drivers (#51) * [DCOS-51158] Improved Task ID assignment for Executor tasks (#52) * [DCOS-51454] Remove irrelevant Mesos REPL test (#54) * [DCOS-51453] Added Hadoop 2.9 profile (#53) * [DCOS-34235] spark.mesos.executor.memoryOverhead equivalent for the Driver when running on Mesos (#55) * Refactoring of metrics naming to add mesos semantics and avoid clashing with existing Spark metrics (#58) * [DCOS-34549] Mesos label NPE fix (#60)
farhan5900
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Oct 2, 2020
…in optimizations <!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> This is a followup of apache#26434 This PR use one special shuffle reader for skew join, so that we only have one join after optimization. In order to do that, this PR 1. add a very general `CustomShuffledRowRDD` which support all kind of partition arrangement. 2. move the logic of coalescing shuffle partitions to a util function, and call it during skew join optimization, to totally decouple with the `ReduceNumShufflePartitions` rule. It's too complicated to interfere skew join with `ReduceNumShufflePartitions`, as you need to consider the size of split partitions which don't respect target size already. ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> The current skew join optimization has a serious performance issue: the size of the query plan depends on the number and size of skewed partitions. ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> no ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> existing tests test UI manually:  explain output ``` AdaptiveSparkPlan(isFinalPlan=true) +- OverwriteByExpression org.apache.spark.sql.execution.datasources.noop.NoopTable$403a2ed5, [AlwaysTrue()], org.apache.spark.sql.util.CaseInsensitiveStringMap1f +- *(5) SortMergeJoin(skew=true) [key1#2L], [key2#6L], Inner :- *(3) Sort [key1#2L ASC NULLS FIRST], false, 0 : +- SkewJoinShuffleReader 2 skewed partitions with size(max=5 KB, min=5 KB, avg=5 KB) : +- ShuffleQueryStage 0 : +- Exchange hashpartitioning(key1#2L, 200), true, [id=#53] : +- *(1) Project [(id#0L % 2) AS key1#2L] : +- *(1) Filter isnotnull((id#0L % 2)) : +- *(1) Range (0, 100000, step=1, splits=6) +- *(4) Sort [key2#6L ASC NULLS FIRST], false, 0 +- SkewJoinShuffleReader 2 skewed partitions with size(max=5 KB, min=5 KB, avg=5 KB) +- ShuffleQueryStage 1 +- Exchange hashpartitioning(key2#6L, 200), true, [id=#64] +- *(2) Project [((id#4L % 2) + 1) AS key2#6L] +- *(2) Filter isnotnull(((id#4L % 2) + 1)) +- *(2) Range (0, 100000, step=1, splits=6) ``` Closes apache#27493 from cloud-fan/aqe. Authored-by: Wenchen Fan <wenchen@databricks.com> Signed-off-by: herman <herman@databricks.com> (cherry picked from commit a4ceea6) Signed-off-by: herman <herman@databricks.com>
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What changes were proposed in this pull request?
hadoop-2.9Maven profile to Spark build so it fits well alongside withhadoop-2.7andhadoop-3.1How was this patch tested?
-Phadoop-2.9profile enabled (spark-build-log-hadoop-2.9-20190408.log)-Phadoop-2.9profile enabled