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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2016/10/11 18:28:20 UTC
[jira] [Assigned] (SPARK-17876) Write StructuredStreaming WAL to a
stream instead of materializing all at once
[ https://issues.apache.org/jira/browse/SPARK-17876?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-17876:
------------------------------------
Assignee: Apache Spark
> Write StructuredStreaming WAL to a stream instead of materializing all at once
> ------------------------------------------------------------------------------
>
> Key: SPARK-17876
> URL: https://issues.apache.org/jira/browse/SPARK-17876
> Project: Spark
> Issue Type: Bug
> Components: SQL, Streaming
> Affects Versions: 2.0.0, 2.0.1
> Reporter: Burak Yavuz
> Assignee: Apache Spark
>
> The CompactibleFileStreamLog materializes the whole metadata log in memory as a String. This can cause issues when there are lots of files that are being committed, especially during a compaction batch.
> You may come across stacktraces that look like:
> {code}
> java.lang.OutOfMemoryError: Requested array size exceeds VM limit
> at java.lang.StringCoding.encode(StringCoding.java:350)
> at java.lang.String.getBytes(String.java:941)
> at org.apache.spark.sql.execution.streaming.FileStreamSinkLog.serialize(FileStreamSinkLog.scala:127)
> at
> {code}
> The safer way is to write to an output stream so that we don't have to materialize a huge string.
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