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https://issues.apache.org/jira/browse/PARQUET-2149?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17538495#comment-17538495
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ASF GitHub Bot commented on PARQUET-2149:
-----------------------------------------
parthchandra commented on PR #968:
URL: https://github.com/apache/parquet-mr/pull/968#issuecomment-1129363998
I have some numbers from an internal benchmark using Spark. I didn't see any
benchmarks in the Parquet codebase that I could reuse.
Here are the numbers from my own benchmark -
- 10 runs, each run reads all columns from store_sales (the largest table)
in the TPC-DS (100G) dataset
`spark.sql("select * from store_sales")`
- Sync reader with default 8MB buffer size, Async reader with 1MB buffer
size (achieves better pipelining)
- Run on Macbook Pro, reading from S3. Spark has 6 cores.
- All times in seconds
| Run | Async | Sync | Async (w/o outliers)| Sync (w/o outliers) |
| ---:| ---:| ---:| ---:| ---:|
|1| 84| 102| - | - |
|2| 90| 366| 90| 366|
|3| 78| 156| - | 156|
|4| 84| 128| 84| - |
|5| 108|402| - | - |
|6| 90| 432| 90| - |
|7| 84| 378| 84| 378|
|8| 108|324| - | 324|
|9| 90| 318| 90| 318|
|10|90| 282| 90| 282|
|Average| 90.6| 288.8| 88| 304|
|Median| 90| 321| **90**| **321**|
|StdDev| 9.98| 119.
After removing the two highest and two lowest runs for each case, and taking
the median value:
Async: 90 sec
Sync: 321 sec
> 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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