reimplement parquet reader using ParquetFile

[rom1504]

https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetFile.html

the whole logic of splitting in small pieces and using our own thread pool make little sense with pyarrow since it has its own pool

try not doing that and instead use directly ParquetFile

think if it still makes sense for small parquet files

[rom1504]

maybe just try to use the current code but cache usage of ParquetFile and close the file when all pieces have been read

[rom1504]

consider using https://arrow.apache.org/docs/python/dataset.html , it's really good

[rom1504]

along with https://arrow.apache.org/docs/python/filesystems.html#filesystem-fsspec

[rom1504]

import fsspec
import pyarrow.dataset as ds
from tqdm import tqdm
fs, p = fsspec.core.url_to_fs("https://mystic.the-eye.eu/public/AI/cah/laion5b/embeddings/laion1B-nolang/laion1B-nolang-metadata")
files = fs.ls(p, detail=False)
d = ds.dataset(files, filesystem=fs)
b = d.to_batches()
for _ in tqdm(b):
    pass

about 1M sample/s eg 200MB/s, saturates the external server connection

[rom1504]

idea:

• use numpy read with the current implementation for numpy
• use pyarrow dataset for parquet
• zip the 2 for the numpy parquet reader

this should fix the parquet speed and will solve the memory issue

[rom1504]

doesn't work due to no support of start/end in pyarrow.dataset

[rom1504]

numpy reader doing 300MB/s from https fsspec numpy parquet is at 30MB/s ... this needs to be improved

[rom1504]

maybe https://arrow.apache.org/docs/python/generated/pyarrow.dataset.Scanner.html#pyarrow.dataset.Scanner can be used to support start stop

[rom1504]

no, can't

[rom1504]

ok best idea is to cache the files

[rom1504]

seems a little bit better but not much

@lru_cache(maxsize=None)
def open_parquet_file(fs, filename):
    return pq.read_table(fs.open(filename, "rb"), use_threads=False)

[rom1504]

actually lru cache doesn't work with multiple threads...

[rom1504]

r = Semaphore(1)

d = {}
def open_parquet_file(fs, filename):
    r.acquire()
    if filename in d:
        r.release()
        return d[filename]
    print(filename)
    t = pq.read_table(fs.open(filename, "rb"), use_threads=False)
    d[filename] = t
    r.release()
    return t

makes things much much faster

[rom1504]

estimated 325min total (laion1B-nolang) with use_threads=True ; probably the same with False

[rom1504]

150min for numpy alone

[rom1504]

1012 without this change

[rom1504]

seems to have solved the memleak too

[rom1504]

https://github.com/rom1504/embedding-reader/pull/21/files

faster but didn't solve memleak

[rom1504]

actually looks like it did. memory usage is a bit high (15GB with these settings), but memleak seems gone

[rom1504]

did a fix here