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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. -- This message was sent by Atlassian Jira (v8.20.7#820007)