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This is a dumb, mostly-from-scratch implementation of read_parquet. It only supports - local and s3 - column selection - grouping partitions when we have fewer columns (+ threads!) - arrow engine/filesystem It is very broken in many ways, but ... - It's only around 100 lines of code - I get 250 MB/s bandwidth on full column reads on an m6i.xlarge (only 50 MB/s when reading columns though) See dask/dask#10602
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fjetter
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| return pa.concat_tables(list(map(read_arrow_table, batch))) | ||
| else: | ||
| with concurrent.futures.ThreadPoolExecutor(len(batch)) as e: | ||
| parts = list(e.map(read_arrow_table, batch)) | ||
| return pa.concat_tables(parts) |
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FYI concat_tables will concat tables zero copy which is great at first glance (It will essentially just append the batch to a ChunkedArray). However, if the tables are heavily fragmented, i.e. there are many batches, many operations operate very slowly on this (e.g. serialization).
calling combine_chunks on the table merges the batches (but copies data) which is sometimes beneficial. I haven't tested this for the arrow->pandas conversion.
This came up in P2P, primarily in the context of writing the table to disk
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This is a dumb, mostly-from-scratch implementation of read_parquet.
It only supports
It is very broken in many ways, but ...
See dask/dask#10602