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Posted to dev@parquet.apache.org by "Yujiang Zhong (Jira)" <ji...@apache.org> on 2022/06/16 13:21:00 UTC

[jira] [Commented] (PARQUET-2160) Close decompression stream to free off-heap memory in time

    [ https://issues.apache.org/jira/browse/PARQUET-2160?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17555082#comment-17555082 ] 

Yujiang Zhong commented on PARQUET-2160:
----------------------------------------

[~shangxinli] [~dongjoon] Can you please take a look at this?

> Close decompression stream to free off-heap memory in time
> ----------------------------------------------------------
>
>                 Key: PARQUET-2160
>                 URL: https://issues.apache.org/jira/browse/PARQUET-2160
>             Project: Parquet
>          Issue Type: Improvement
>         Environment: Spark 3.1.2 + Iceberg 0.12 + Parquet 1.12.3 + zstd-jni 1.4.9.1 + glibc
>            Reporter: Yujiang Zhong
>            Priority: Major
>
> The decompressed stream now relies on the JVM GC to close. When reading parquet in zstd compressed format, sometimes I ran into OOM cause high off-heap usage. I think the reason is that the GC is not timely and causes off-heap memory fragmentation. I had to set  lower MALLOC_TRIM_THRESHOLD_ to make glibc give back memory to system quickly. There is a [thread|[https://apache-iceberg.slack.com/archives/C025PH0G1D4/p1650928750269869?thread_ts=1650927062.590789&cid=C025PH0G1D4]] of this zstd parquet issus in Iceberg community slack:  some people had the same problem. 
> I think we can close decompressed stream mannually in time to solve this problem:
>  
> InputStream is = codec.createInputStream(bytes.toInputStream(), decompressor);
> decompressed = BytesInput.from(is, uncompressedSize);
> ->
> InputStream is = codec.createInputStream(bytes.toInputStream(), decompressor);
> decompressed = BytesInput.{_}copy{_}(BytesInput.{_}from{_}(is, uncompressedSize));
> is.close();
>  
> After I made this change to decompress, I found off-heap memory is significantly reduced (with same query on same data).



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