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Maarten Breddels commented on ARROW-9458: ----------------------------------------- let me know if you want to do the honors yourself, otherwise, I can open a PR tonight > [Python] Dataset singlethreaded only > ------------------------------------ > > Key: ARROW-9458 > URL: https://issues.apache.org/jira/browse/ARROW-9458 > Project: Apache Arrow > Issue Type: Bug > Components: Python > Reporter: Maarten Breddels > Priority: Major > Attachments: image-2020-07-14-14-31-29-943.png, > image-2020-07-14-14-38-16-767.png > > > I'm not sure this is a misunderstanding, or a compilation issue (flags?) or > an issue in the C++ layer. > I have 1000 parquet files with a total of 1 billion rows (1 million rows each > file, ~20 columns). I wanted to see if I could go through all rows 1 of 2 > columns efficiently (vaex use case). > > {code:java} > import pyarrow.parquet > import pyarrow as pa > import pyarrow.dataset as ds > import glob > ds = pa.dataset.dataset(glob.glob('/data/taxi_parquet/data_*.parquet')) > scanned = 0 > for scan_task in ds.scan(batch_size=1_000_000, columns=['passenger_count'], > use_threads=True): > for record_batch in scan_task.execute(): > scanned += record_batch.num_rows > scanned > {code} > This only seems to use 1 cpu. > Using a threadpool from Python: > {code:java} > # %%timeit > import concurrent.futures > pool = concurrent.futures.ThreadPoolExecutor() > ds = pa.dataset.dataset(glob.glob('/data/taxi_parquet/data_*.parquet')) > def process(scan_task): > scan_count = 0 > for record_batch in scan_task.execute(): > scan_count += len(record_batch) > return scan_count > sum(pool.map(process, ds.scan(batch_size=1_000_000, > columns=['passenger_count'], use_threads=False))) > {code} > Gives me a similar performance, again, only 100% cpu usage (=1 core/cpu). > py-spy (profiler for Python) shows no GIL, so this might be something at the > C++ layer. > Am I 'holding it wrong' or could this be a bug? Note that IO speed is not a > problem on this system (it actually all comes from OS cache, no disk read > observed) > -- This message was sent by Atlassian Jira (v8.3.4#803005)