[WIP] Implement filesystem="arrow" in dask_cudf.read_parquet
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rjzamora
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Aug 30, 2024
Adds new benchmark for parquet read performance using a `LocalCUDACluster`. The user can pass in `--key` and `--secret` options to specify S3 credentials. E.g. ``` $ python ./local_read_parquet.py --devs 0,1,2,3,4,5,6,7 --filesystem fsspec --type gpu --file-count 48 --aggregate-files Parquet read benchmark -------------------------------------------------------------------------------- Path | s3://dask-cudf-parquet-testing/dedup_parquet Columns | None Backend | cudf Filesystem | fsspec Blocksize | 244.14 MiB Aggregate files | True Row count | 372066 Size on disk | 1.03 GiB Number of workers | 8 ================================================================================ Wall clock | Throughput -------------------------------------------------------------------------------- 36.75 s | 28.78 MiB/s 21.29 s | 49.67 MiB/s 17.91 s | 59.05 MiB/s ================================================================================ Throughput | 41.77 MiB/s +/- 7.81 MiB/s Bandwidth | 0 B/s +/- 0 B/s Wall clock | 25.32 s +/- 8.20 s ================================================================================ ... ``` **Notes**: - S3 Performance generally scales with the number of workers (multiplied the number of threads per worker) - The example shown above was not executed from an EC2 instance - The example shown above *should* perform better after rapidsai/cudf#16657 - Using `--filesystem arrow` together with `--type gpu` performs well, but depends on rapidsai/cudf#16684 Authors: - Richard (Rick) Zamora (https://github.com/rjzamora) Approvers: - Mads R. B. Kristensen (https://github.com/madsbk) - Peter Andreas Entschev (https://github.com/pentschev) URL: #1371
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Closes rapidsai#14537. Authors: - Matthew Murray (https://github.com/Matt711) - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - GALI PREM SAGAR (https://github.com/galipremsagar) - Matthew Roeschke (https://github.com/mroeschke) - Vyas Ramasubramani (https://github.com/vyasr) URL: rapidsai#16601
…#16574) Improves performance of wide strings (avg > 64 bytes) when using `cudf::strings::slice_strings`. Addresses some concerns from issue rapidsai#15924 Authors: - David Wendt (https://github.com/davidwendt) Approvers: - Bradley Dice (https://github.com/bdice) - Muhammad Haseeb (https://github.com/mhaseeb123) URL: rapidsai#16574
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Description
This PR piggybacks on the existing CPU/Arrow Parquet infrastructure in dask-expr. With this PR,
will produce a
cudf-backed collection using PyArrow for IO (i.e. disk->pa.Table->cudf.DataFrame). Before this PR, passingfilesystem="arrow"will simply result in an error.Although this code path is not ideal for fast/local storage, it can be very efficient for remote storage (e.g. S3).
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