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https://issues.apache.org/jira/browse/SPARK-2650?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14084397#comment-14084397
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Cheng Lian commented on SPARK-2650:
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Did some experiments and came to some conclusions:
# Needless to say, the {{10 * 1024 * 104}} is definitely a typo, but it's not
related to the OOMs. More reasonable initial buffer sizes don't help solving
these OOMs.
# The OOMs are also not related to whether the table size is larger than
available memory. The cause is that the process of building in-memory columnar
buffers is memory consuming, and multiple tasks building buffers in parallel
eat too much memory altogether.
# According to 2, reducing parallelism or increasing executor memory can
workaround this issue. For example, a {{HiveThriftServer2}} started with
default executor memory (512MB) and {{--total-executor-cores=1}} could cache a
1.7GB table.
# Shark performs better than Spark SQL in this case, but still OOMs when the
table gets larger: caching a 1.8GB table with default Shark configurations
makes Shark OOM too.
I'm investigating why Spark SQL consumes more memory than Shark when building
in-memory columnar buffers.
> Wrong initial sizes for in-memory column buffers
> ------------------------------------------------
>
> Key: SPARK-2650
> URL: https://issues.apache.org/jira/browse/SPARK-2650
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.0.0, 1.0.1
> Reporter: Michael Armbrust
> Assignee: Cheng Lian
> Priority: Critical
>
> The logic for setting up the initial column buffers is different for Spark
> SQL compared to Shark and I'm seeing OOMs when caching tables that are larger
> than available memory (where shark was okay).
> Two suspicious things: the intialSize is always set to 0 so we always go with
> the default. The default looks like it was copied from code like 10 * 1024 *
> 1024... but in Spark SQL its 10 * 102 * 1024.
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