[WIP] Reduce HDFS NameNode RPC on vectorized Parquet reader #50765
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pan3793
commented
Apr 30, 2025
| ParquetFileReader fileReader; | ||
| if (fileFooter.isDefined()) { | ||
| fileReader = new ParquetFileReader(configuration, file, fileFooter.get()); |
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This constructor internally calls HadoopInputFile.fromPath(file, configuration), which produces an unnecessary GetFileInfo RPC
public static HadoopInputFile fromPath(Path path, Configuration conf) throws IOException {
FileSystem fs = path.getFileSystem(conf);
return new HadoopInputFile(fs, fs.getFileStatus(path), conf);
}
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What changes were proposed in this pull request?
On a busy Hadoop cluster, the
GetFileInfoandGetBlockLocationscontribute the most RPCs to the HDFS NameNode. After investigating the Spark Parquet vectorized reader, I think 3/4 RPCs can be reduced.Currently, the Parquet vectorized reader produces 4 NameNode RPCs on reading each file (or split):
GetFileInfoand oneGetBlockLocationsGetFileInfoand oneGetBlockLocationsThe key idea of this PR is:
FileStatusfor each Parquet file during the planning phase, we can transfer theFileStatusfrom the driver to the executor viaPartitionFile, so that the task doesn't need to ask the NameNode again, this saves twoGetFileInfoRPCs.SeekableInputStreamon reading footer and row groups, this saves oneGetBlockLocationsRPC.TODO: The PR currently requires some changes on Parquet side first.
Why are the changes needed?
Reduce unnecessary RPCs of NameNode to improve performance and stability for large Hadoop clusters.
Does this PR introduce any user-facing change?
No.
How was this patch tested?
Manually tested on a Hadoop cluster, the test uses TPC-H Q4, based on sf3000 Parquet tables.
HDFS NameNode metrics (master VS. this PR)
HDFS NameNode audit logs:
Taking file
part-01027-419c80f3-8921-4ed3-b31a-0fe72b9c6732-c000.zstd.parquetas example, the file is supposed to be split into 3 splitsBefore
After
Was this patch authored or co-authored using generative AI tooling?
No.