prodeezy opened a new pull request #452: V1 Vectorized reader URL: https://github.com/apache/incubator-iceberg/pull/452 **Changes:** - Added a new reader viz. V1VectorizedReader that internally short circuits to using the V1 codepath [3] which does most of the setup and work to perform vectorization. it's exactly what Vanilla Spark's reader does underneath the DSV1 implementation. - It builds an iterator which expects ColumnarBatches from the Objects returned by the resolving iterator. - We re-organized and optimized code while building ReadTask instances which considerably improved task initiation and planning time. - Setting `iceberg.read.enableV1VectorizedReader` to true enables this reader in IcebergSource. - The V1Vectorized reader is an independent class with copied code in some methods as we didn't want to degrade perf due to inheritance/virtual method calls (we noticed degradation when we did try to re-use code). - I'v pushed this code to a separate branch [4] in case others want to give this a try. The Numbers: Flat Data 10 files 10M rows each Benchmark Mode Cnt Score Error Units IcebergSourceFlatParquetDataReadBenchmark.readFileSourceNonVectorized ss 5 63.631 ± 1.300 s/op IcebergSourceFlatParquetDataReadBenchmark.readFileSourceVectorized ss 5 28.322 ± 2.400 s/op IcebergSourceFlatParquetDataReadBenchmark.readIceberg ss 5 65.862 ± 2.480 s/op IcebergSourceFlatParquetDataReadBenchmark.readIcebergV1Vectorized10k ss 5 28.199 ± 1.255 s/op IcebergSourceFlatParquetDataReadBenchmark.readIcebergV1Vectorized20k ss 5 29.822 ± 2.848 s/op IcebergSourceFlatParquetDataReadBenchmark.readIcebergV1Vectorized5k ss 5 27.953 ± 0.949 s/op Flat Data Projections 10 files 10M rows each Benchmark Mode Cnt Score Error Units IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionFileSourceNonVectorized ss 5 11.307 ± 1.791 s/op IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionFileSourceVectorized ss 5 3.480 ± 0.087 s/op IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIceberg ss 5 11.057 ± 0.236 s/op IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIcebergV1Vectorized10k ss 5 3.953 ± 1.592 s/op IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIcebergV1Vectorized20k ss 5 3.619 ± 1.305 s/op IcebergSourceFlatParquetDataReadBenchmark.readWithProjectionIcebergV1Vectorized5k ss 5 4.109 ± 1.734 s/op Filtered Data 500 files 10k rows each Benchmark Mode Cnt Score Error Units IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterFileSourceNonVectorized ss 5 2.139 ± 0.719 s/op IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterFileSourceVectorized ss 5 2.213 ± 0.598 s/op IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIcebergNonVectorized ss 5 0.144 ± 0.029 s/op IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIcebergV1Vectorized100k ss 5 0.179 ± 0.019 s/op IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIcebergV1Vectorized10k ss 5 0.189 ± 0.046 s/op IcebergSourceFlatParquetDataFilterBenchmark.readWithFilterIcebergV1Vectorized5k ss 5 0.195 ± 0.137 s/op **Perf Notes:** - Iceberg V1 Vectorization's real gain (over current Iceberg impl) is in flat data scans. Notice how it's almost exactly same as vanilla spark vectorization. - Projections work equally well. Although we see Nested column projections are still not performing as well as we need to be able to push nested column projections down to Iceberg. - We saw a slight overhead with Iceberg V1 Vectorization over smaller workloads, but this goes away with larger data files.
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