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

["Alexey Kudinkin (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/PARQUET-2160?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Alexey Kudinkin resolved PARQUET-2160.
--------------------------------------
    Resolution: Fixed

> 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: Bug
>          Components: parquet-format
>    Affects Versions: 1.12.3
>         Environment: Spark 3.1.2 + Iceberg 0.12 + Parquet 1.12.3 + zstd-jni 1.4.9.1 + glibc
>            Reporter: Yujiang Zhong
>            Priority: Blocker
>
> The decompressed stream in HeapBytesDecompressor$decompress 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 maybe we can use ByteArrayBytesInput as decompressed bytes input and close decompressed stream in time to solve this problem:
> {code:java}
> InputStream is = codec.createInputStream(bytes.toInputStream(), decompressor);
> decompressed = BytesInput.from(is, uncompressedSize); {code}
> ->
> {code:java}
> InputStream is = codec.createInputStream(bytes.toInputStream(), decompressor);
> decompressed = BytesInput.copy(BytesInput.from(is, uncompressedSize));
> is.close(); {code}
> 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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