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https://issues.apache.org/jira/browse/SPARK-23145?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-23145.
-------------------------------
Resolution: Invalid
Fix Version/s: (was: 2.1.1)
Please post these to the mailing list or StackOverflow
> How to set Apache Spark Executor memory
> ---------------------------------------
>
> Key: SPARK-23145
> URL: https://issues.apache.org/jira/browse/SPARK-23145
> Project: Spark
> Issue Type: Question
> Components: EC2
> Affects Versions: 2.1.0
> Reporter: Azharuddin
> Priority: Major
> Original Estimate: 954h
> Remaining Estimate: 954h
>
> How can I increase the memory available for Apache spark executor nodes?
> I have a 2 GB file that is suitable to loading in to [Apache
> Spark|https://mindmajix.com/apache-spark-training]. I am running apache spark
> for the moment on 1 machine, so the driver and executor are on the same
> machine. The machine has 8 GB of memory.
> When I try count the lines of the file after setting the file to be cached in
> memory I get these errors:
> {\{2014-10-25 22:25:12 WARN CacheManager:71 - Not enough space to cache
> partition rdd_1_1 in memory! Free memory is 278099801 bytes. }}
> I looked at the documentation
> [here|http://spark.apache.org/docs/latest/configuration.html] and set
> {{spark.executor.memory}} to {{4g}} in
> {{$SPARK_HOME/conf/spark-defaults.conf}}
> The UI shows this variable is set in the Spark Environment. You can find
> screenshot
> [here|https://drive.google.com/file/d/0B0B_O5bxDDlsc3JmY0xfbjFtN0k/view?usp=sharing]
> However when I go to the [Executor
> tab|https://drive.google.com/file/d/0B0B_O5bxDDlsV25ka2lHd1ZFNzA/view?usp=sharing]
> the memory limit for my single Executor is still set to 265.4 MB. I also
> still get the same error.
> I tried various things mentioned
> [here|https://stackoverflow.com/questions/24242060/how-to-change-memory-per-node-for-apache-spark-worker]
> but I still get the error and don't have a clear idea where I should change
> the setting.
> I am running my code interactively from the spark-shell
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