[SOLVED] Is it possible to parallelize bz2's decompression?

Is it possible to parallelize bz2's decompression?

I am using pythons bz2 module to generate (and compress) a large jsonl file (bzip2 compressed 17GB).

However, when I later try to decompress it using pbzip2 it only seems to use one CPU-core for decompression, which is quite slow.

When i compress it with pbzip2 it can leverage multiple cores on decompression. Is there a way to compress within python in the pbzip2-compatible format?

import bz2,sys
from Queue import Empty
#...
compressor = bz2.BZ2Compressor(9)
f = open(path, 'a')

    try:
        while 1:
            m = queue.get(True, 1*60)
            f.write(compressor.compress(m+"\n"))
    except Empty, e:
        pass
    except Exception as e:
        traceback.print_exc()
    finally:
        sys.stderr.write("flushing")
        f.write(compressor.flush())
        f.close()

Solution

A pbzip2 stream is nothing more than the concatenation of multiple bzip2 streams.

An example using the shell:

bzip2 < /usr/share/dict/words > words_x_1.bz2
cat words_x_1.bz2{,,,,,,,,,} > words_x_10.bz2
time bzip2 -d < words_x_10.bz2 > /dev/null
time pbzip2 -d < words_x_10.bz2 > /dev/null

I've never used python's bz2 module, but it should be easy to close/reopen a stream in 'a'ppend mode, every so-many bytes, to get the same result. Note that if BZ2File is constructed from an existing file-like object, closing the BZ2File will not close the underlying stream (which is what you want here).

I haven't measured how many bytes is optimal for chunking, but I would guess every 1-20 megabytes - it definitely needs to be larger than the bzip2 block size (900k) though.

Note also that if you record the compressed and uncompressed offsets of each chunk, you can do fairly efficient random access. This is how the dictzip program works, though that is based on gzip.