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https://issues.apache.org/jira/browse/HADOOP-18296?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17836272#comment-17836272
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Mukund Thakur commented on HADOOP-18296:
----------------------------------------
Yes, it is. Although direct buffers are not used in Orc/Parquet. thinking if
we should throw an Exception if the user is calling readVectored on direct
buffers something like
{code:java}
class ChecksumFSInputChecker {
...
...
@Override
public void readVectored(List<? extends FileRange> ranges,
IntFunction<ByteBuffer> allocate) throws IOException {
if (allocate.apply(0).isDirect()) {
throw new UnsupportedOperationException("Direct buffer is not supported");
}
}
}{code}
cc [[email protected]]
> Memory fragmentation in ChecksumFileSystem Vectored IO implementation.
> ----------------------------------------------------------------------
>
> Key: HADOOP-18296
> URL: https://issues.apache.org/jira/browse/HADOOP-18296
> Project: Hadoop Common
> Issue Type: Sub-task
> Components: common
> Affects Versions: 3.4.0
> Reporter: Mukund Thakur
> Priority: Minor
> Labels: fs
>
> As we have implemented merging of ranges in the ChecksumFSInputChecker
> implementation of vectored IO api, it can lead to memory fragmentation. Let
> me explain by example.
>
> Suppose client requests for 3 ranges.
> 0-500, 700-1000 and 1200-1500.
> Now because of merging, all the above ranges will get merged into one and we
> will allocate a big byte buffer of 0-1500 size but return sliced byte buffers
> for the desired ranges.
> Now once the client is done reading all the ranges, it will only be able to
> free the memory for requested ranges and memory of the gaps will never be
> released for eg here (500-700 and 1000-1200).
>
> Note this only happens for direct byte buffers.
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