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https://issues.apache.org/jira/browse/PARQUET-2149?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17541016#comment-17541016
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ASF GitHub Bot commented on PARQUET-2149:
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steveloughran commented on PR #968:
URL: https://github.com/apache/parquet-mr/pull/968#issuecomment-1134843705
1. whose s3 client was used for testing here -if the s3a one, which hadoop
release?
2. the azure abfs and gcs connectors do async prefetching of the next block,
but are simply assuming that code will read sequentially; if there is another
seek/readFully to a new location, those prefetches will be abandoned. there is
work in s3a to do prefetching here with caching, so as to reduce the penalty of
backwards seeks. https://issues.apache.org/jira/browse/HADOOP-18028
hadoop is adding a vectored IO api intended for libraries like orc and
parquet to be able to use, where the application provides an unordered list of
ranges, a bytebuffer supplier and gets back a list of futures to wait for. the
base implementation simply reads using readFully APi. s3a (and later abfs) will
do full async retrieval itself, using the http connection pool.
https://issues.apache.org/jira/browse/HADOOP-18103
both vectored io and s3a prefetching will ship this summer in hadoop 3.4.0.
i don't see this change conflicting with this, though they may obsolete a lot
of it.
have you benchmarked this change with abfs or google gcs connectors to see
what difference it makes there?
> Implement async IO for Parquet file reader
> ------------------------------------------
>
> Key: PARQUET-2149
> URL: https://issues.apache.org/jira/browse/PARQUET-2149
> Project: Parquet
> Issue Type: Improvement
> Components: parquet-mr
> Reporter: Parth Chandra
> Priority: Major
>
> ParquetFileReader's implementation has the following flow (simplified) -
> - For every column -> Read from storage in 8MB blocks -> Read all
> uncompressed pages into output queue
> - From output queues -> (downstream ) decompression + decoding
> This flow is serialized, which means that downstream threads are blocked
> until the data has been read. Because a large part of the time spent is
> waiting for data from storage, threads are idle and CPU utilization is really
> low.
> There is no reason why this cannot be made asynchronous _and_ parallel. So
> For Column _i_ -> reading one chunk until end, from storage -> intermediate
> output queue -> read one uncompressed page until end -> output queue ->
> (downstream ) decompression + decoding
> Note that this can be made completely self contained in ParquetFileReader and
> downstream implementations like Iceberg and Spark will automatically be able
> to take advantage without code change as long as the ParquetFileReader apis
> are not changed.
> In past work with async io [Drill - async page reader
> |https://github.com/apache/drill/blob/master/exec/java-exec/src/main/java/org/apache/drill/exec/store/parquet/columnreaders/AsyncPageReader.java]
> , I have seen 2x-3x improvement in reading speed for Parquet files.
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