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Posted to reviews@spark.apache.org by jerryshao <gi...@git.apache.org> on 2015/01/06 09:02:29 UTC
[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
GitHub user jerryshao opened a pull request:
https://github.com/apache/spark/pull/3906
[SPARK-4999][Streaming] Change storeInBlockManager to false by default
Currently WAL-backed block is read out from HDFS and put into BlockManger with storage level MEMORY_ONLY_SER by default, since WAL-backed block is already materialized in HDFS with fault-tolerance, no need to put into BlockManger again by default.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/jerryshao/apache-spark SPARK-4999
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/3906.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #3906
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commit b95f95e6af0dcbd6489f26969afc88e027408350
Author: jerryshao <sa...@intel.com>
Date: 2015-01-06T07:36:19Z
Change storeInBlockManager to false by default
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by xiajunluan <gi...@git.apache.org>.
Github user xiajunluan commented on the pull request:
https://github.com/apache/spark/pull/3906#issuecomment-68840845
Good catch！
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/3906#issuecomment-68842492
Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/25092/
Test PASSed.
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by jerryshao <gi...@git.apache.org>.
Github user jerryshao commented on the pull request:
https://github.com/apache/spark/pull/3906#issuecomment-68848565
I'm not sure if I understand correctly, in WindowedDStream we already called persist to cache the parent DStream, internally it will call RDD's persist. This will cache the RDD into BM when current interval's job is finished, if we move to the next batch in this window, because RDD is already cached, no matter it is from HDFS or others, we can directly use it without recalculate.
That's my understanding, I'm not sure if it is correct, sorry for my misunderstanding.
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by asfgit <gi...@git.apache.org>.
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/3906
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/3906#issuecomment-69645446
I think I agree with you are saying. Good observation. I am merging this.
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/3906#issuecomment-68837031
[Test build #25092 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/25092/consoleFull) for PR 3906 at commit [`b95f95e`](https://github.com/apache/spark/commit/b95f95e6af0dcbd6489f26969afc88e027408350).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/3906#issuecomment-68843161
If you are using a window operations, then previous batches data may need to be access multiple times. If we dont put the data in WAL back in memory, the system will have to read the data multiple times from the WAL. That's going to be very slow, isnt it.
A smarter thing to do is to figure (based on the transformations) whether the data is going to required multiple times or not, and accordingly store the data in BM. Just turning it blindly setting it to false will cause a performance regression.
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[GitHub] spark pull request: [SPARK-4999][Streaming] Change storeInBlockMan...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/3906#issuecomment-68842485
[Test build #25092 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/25092/consoleFull) for PR 3906 at commit [`b95f95e`](https://github.com/apache/spark/commit/b95f95e6af0dcbd6489f26969afc88e027408350).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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