[SPARK-17876] Write StructuredStreaming WAL to a stream instead of materializing all at once #15437
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Test build #66754 has finished for PR 15437 at commit
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zsxwing
reviewed
Oct 12, 2016
| import org.apache.hadoop.conf.Configuration | ||
| import org.apache.hadoop.fs._ | ||
| import org.apache.hadoop.fs.permission.FsPermission | ||
| import org.apache.hadoop.io.IOUtils |
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nit: use org.apache.commons.io.IOUtils instead. Hadoop's IOUtils is @InterfaceStability.Evolving which can break compatibility at minor release
zsxwing
reviewed
Oct 12, 2016
| val lines = new String(bytes, UTF_8).split("\n") | ||
| if (lines.length == 0) { | ||
| override def deserialize(in: InputStream): Array[T] = { | ||
| val lines = IOUtils.lineIterator(in, UTF_8).asScala |
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nit: why not use Source.getLines since this is Scala.
zsxwing
reviewed
Oct 12, 2016
| out.write('\n') | ||
| out.write(serializeData(data).getBytes(UTF_8)) | ||
| } | ||
| out.flush() |
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nit: no need to flush since it will be closed at once.
zsxwing
reviewed
Oct 12, 2016
| @@ -17,6 +17,8 @@ | |||
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| package org.apache.spark.sql.execution.streaming | |||
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| import java.io.OutputStream | |||
zsxwing
reviewed
Oct 12, 2016
| @@ -17,6 +17,7 @@ | |||
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| package org.apache.spark.sql.execution.streaming | |||
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| import java.io.OutputStream | |||
zsxwing
requested changes
Oct 12, 2016
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Thanks @zsxwing addressed your comments |
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Test build #66855 has finished for PR 15437 at commit
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Test build #66856 has finished for PR 15437 at commit
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LGTM. Thanks! Merging to master and 2.0. |
asfgit
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Oct 13, 2016
…terializing all at once ## What changes were proposed in this pull request? 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: ``` 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) ``` The safer way is to write to an output stream so that we don't have to materialize a huge string. ## How was this patch tested? Existing unit tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #15437 from brkyvz/ser-to-stream. (cherry picked from commit edeb51a) Signed-off-by: Shixiong Zhu <shixiong@databricks.com>
uzadude
pushed a commit
to uzadude/spark
that referenced
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Jan 27, 2017
…terializing all at once ## What changes were proposed in this pull request? 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: ``` 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) ``` The safer way is to write to an output stream so that we don't have to materialize a huge string. ## How was this patch tested? Existing unit tests Author: Burak Yavuz <brkyvz@gmail.com> Closes apache#15437 from brkyvz/ser-to-stream.
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What changes were proposed in this pull request?
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:
The safer way is to write to an output stream so that we don't have to materialize a huge string.
How was this patch tested?
Existing unit tests