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  1. Feb 01, 2017
  2. Jan 30, 2017
  3. Jan 25, 2017
    • aokolnychyi's avatar
      [SPARK-16046][DOCS] Aggregations in the Spark SQL programming guide · e2f77392
      aokolnychyi authored
      ## What changes were proposed in this pull request?
      
      - A separate subsection for Aggregations under “Getting Started” in the Spark SQL programming guide. It mentions which aggregate functions are predefined and how users can create their own.
      - Examples of using the `UserDefinedAggregateFunction` abstract class for untyped aggregations in Java and Scala.
      - Examples of using the `Aggregator` abstract class for type-safe aggregations in Java and Scala.
      - Python is not covered.
      - The PR might not resolve the ticket since I do not know what exactly was planned by the author.
      
      In total, there are four new standalone examples that can be executed via `spark-submit` or `run-example`. The updated Spark SQL programming guide references to these examples and does not contain hard-coded snippets.
      
      ## How was this patch tested?
      
      The patch was tested locally by building the docs. The examples were run as well.
      
      ![image](https://cloud.githubusercontent.com/assets/6235869/21292915/04d9d084-c515-11e6-811a-999d598dffba.png
      
      )
      
      Author: aokolnychyi <okolnychyyanton@gmail.com>
      
      Closes #16329 from aokolnychyi/SPARK-16046.
      
      (cherry picked from commit 3fdce814)
      Signed-off-by: default avatargatorsmile <gatorsmile@gmail.com>
      e2f77392
  4. Jan 10, 2017
  5. Jan 07, 2017
  6. Jan 06, 2017
  7. Jan 02, 2017
  8. Dec 30, 2016
    • Cheng Lian's avatar
      [SPARK-19016][SQL][DOC] Document scalable partition handling · 20ae1172
      Cheng Lian authored
      
      This PR documents the scalable partition handling feature in the body of the programming guide.
      
      Before this PR, we only mention it in the migration guide. It's not super clear that external datasource tables require an extra `MSCK REPAIR TABLE` command is to have per-partition information persisted since 2.1.
      
      N/A.
      
      Author: Cheng Lian <lian@databricks.com>
      
      Closes #16424 from liancheng/scalable-partition-handling-doc.
      
      (cherry picked from commit 871f6114)
      Signed-off-by: default avatarCheng Lian <lian@databricks.com>
      20ae1172
  9. Dec 29, 2016
    • adesharatushar's avatar
      [SPARK-19003][DOCS] Add Java example in Spark Streaming Guide, section Design... · 47ab4afe
      adesharatushar authored
      [SPARK-19003][DOCS] Add Java example in Spark Streaming Guide, section Design Patterns for using foreachRDD
      
      ## What changes were proposed in this pull request?
      
      Added missing Java example under section "Design Patterns for using foreachRDD". Now this section has examples in all 3 languages, improving consistency of documentation.
      
      ## How was this patch tested?
      
      Manual.
      Generated docs using command "SKIP_API=1 jekyll build" and verified generated HTML page manually.
      
      The syntax of example has been tested for correctness using sample code on Java1.7 and Spark 2.2.0-SNAPSHOT.
      
      Author: adesharatushar <tushar_adeshara@persistent.com>
      
      Closes #16408 from adesharatushar/streaming-doc-fix.
      
      (cherry picked from commit dba81e1d)
      Signed-off-by: default avatarSean Owen <sowen@cloudera.com>
      47ab4afe
  10. Dec 28, 2016
  11. Dec 20, 2016
    • Josh Rosen's avatar
      [SPARK-18761][CORE] Introduce "task reaper" to oversee task killing in executors · 2971ae56
      Josh Rosen authored
      ## What changes were proposed in this pull request?
      
      Spark's current task cancellation / task killing mechanism is "best effort" because some tasks may not be interruptible or may not respond to their "killed" flags being set. If a significant fraction of a cluster's task slots are occupied by tasks that have been marked as killed but remain running then this can lead to a situation where new jobs and tasks are starved of resources that are being used by these zombie tasks.
      
      This patch aims to address this problem by adding a "task reaper" mechanism to executors. At a high-level, task killing now launches a new thread which attempts to kill the task and then watches the task and periodically checks whether it has been killed. The TaskReaper will periodically re-attempt to call `TaskRunner.kill()` and will log warnings if the task keeps running. I modified TaskRunner to rename its thread at the start of the task, allowing TaskReaper to take a thread dump and filter it in order to log stacktraces from the exact task thread that we are waiting to finish. If the task has not stopped after a configurable timeout then the TaskReaper will throw an exception to trigger executor JVM death, thereby forcibly freeing any resources consumed by the zombie tasks.
      
      This feature is flagged off by default and is controlled by four new configurations under the `spark.task.reaper.*` namespace. See the updated `configuration.md` doc for details.
      
      ## How was this patch tested?
      
      Tested via a new test case in `JobCancellationSuite`, plus manual testing.
      
      Author: Josh Rosen <joshrosen@databricks.com>
      
      Closes #16189 from JoshRosen/cancellation.
      2971ae56
  12. Dec 18, 2016
  13. Dec 17, 2016
  14. Dec 15, 2016
  15. Dec 14, 2016
  16. Dec 12, 2016
  17. Dec 10, 2016
  18. Dec 09, 2016
    • Xiangrui Meng's avatar
      [SPARK-18812][MLLIB] explain "Spark ML" · e45345d9
      Xiangrui Meng authored
      ## What changes were proposed in this pull request?
      
      There has been some confusion around "Spark ML" vs. "MLlib". This PR adds some FAQ-like entries to the MLlib user guide to explain "Spark ML" and reduce the confusion.
      
      I check the [Spark FAQ page](http://spark.apache.org/faq.html
      
      ), which seems too high-level for the content here. So I added it to the MLlib user guide instead.
      
      cc: mateiz
      
      Author: Xiangrui Meng <meng@databricks.com>
      
      Closes #16241 from mengxr/SPARK-18812.
      
      (cherry picked from commit d2493a20)
      Signed-off-by: default avatarXiangrui Meng <meng@databricks.com>
      e45345d9
    • Jacek Laskowski's avatar
      [MINOR][CORE][SQL][DOCS] Typo fixes · b226f10e
      Jacek Laskowski authored
      
      ## What changes were proposed in this pull request?
      
      Typo fixes
      
      ## How was this patch tested?
      
      Local build. Awaiting the official build.
      
      Author: Jacek Laskowski <jacek@japila.pl>
      
      Closes #16144 from jaceklaskowski/typo-fixes.
      
      (cherry picked from commit b162cc0c)
      Signed-off-by: default avatarSean Owen <sowen@cloudera.com>
      b226f10e
  19. Dec 08, 2016
    • Yanbo Liang's avatar
      [SPARK-18325][SPARKR][ML] SparkR ML wrappers example code and user guide · 9095c152
      Yanbo Liang authored
      
      ## What changes were proposed in this pull request?
      * Add all R examples for ML wrappers which were added during 2.1 release cycle.
      * Split the whole ```ml.R``` example file into individual example for each algorithm, which will be convenient for users to rerun them.
      * Add corresponding examples to ML user guide.
      * Update ML section of SparkR user guide.
      
      Note: MLlib Scala/Java/Python examples will be consistent, however, SparkR examples may different from them, since R users may use the algorithms in a different way, for example, using R ```formula``` to specify ```featuresCol``` and ```labelCol```.
      
      ## How was this patch tested?
      Run all examples manually.
      
      Author: Yanbo Liang <ybliang8@gmail.com>
      
      Closes #16148 from yanboliang/spark-18325.
      
      (cherry picked from commit 9bf8f3cd)
      Signed-off-by: default avatarYanbo Liang <ybliang8@gmail.com>
      9095c152
    • Patrick Wendell's avatar
      48aa6775
    • Patrick Wendell's avatar
      Preparing Spark release v2.1.0-rc2 · 08071749
      Patrick Wendell authored
      08071749
  20. Dec 07, 2016
  21. Dec 05, 2016
  22. Dec 04, 2016
    • Felix Cheung's avatar
      [SPARK-18643][SPARKR] SparkR hangs at session start when installed as a package without Spark · c13c2939
      Felix Cheung authored
      
      ## What changes were proposed in this pull request?
      
      If SparkR is running as a package and it has previously downloaded Spark Jar it should be able to run as before without having to set SPARK_HOME. Basically with this bug the auto install Spark will only work in the first session.
      
      This seems to be a regression on the earlier behavior.
      
      Fix is to always try to install or check for the cached Spark if running in an interactive session.
      As discussed before, we should probably only install Spark iff running in an interactive session (R shell, RStudio etc)
      
      ## How was this patch tested?
      
      Manually
      
      Author: Felix Cheung <felixcheung_m@hotmail.com>
      
      Closes #16077 from felixcheung/rsessioninteractive.
      
      (cherry picked from commit b019b3a8)
      Signed-off-by: default avatarShivaram Venkataraman <shivaram@cs.berkeley.edu>
      c13c2939
  23. Dec 03, 2016
    • Yunni's avatar
      [SPARK-18081][ML][DOCS] Add user guide for Locality Sensitive Hashing(LSH) · 28f698b4
      Yunni authored
      
      ## What changes were proposed in this pull request?
      The user guide for LSH is added to ml-features.md, with several scala/java examples in spark-examples.
      
      ## How was this patch tested?
      Doc has been generated through Jekyll, and checked through manual inspection.
      
      Author: Yunni <Euler57721@gmail.com>
      Author: Yun Ni <yunn@uber.com>
      Author: Joseph K. Bradley <joseph@databricks.com>
      Author: Yun Ni <Euler57721@gmail.com>
      
      Closes #15795 from Yunni/SPARK-18081-lsh-guide.
      
      (cherry picked from commit 34777184)
      Signed-off-by: default avatarJoseph K. Bradley <joseph@databricks.com>
      28f698b4
  24. Dec 02, 2016
  25. Nov 30, 2016
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