Might be specific to the Python driver, but it was slower for queries because most of the cputime was being spent decoding column structure/metadata and on type conversions. We also tested msgpack blobs, iirc, but the xsv format was the winner for query perf and for compactness on disk.
Would have gladly gone into more detail, but at 4,000+ words for the blog post already... :)
Hmm, so I tried converting a table we had from a standard table to one with COMPACT STORAGE. The space saving wasn't all that great (100GB -> 80GB), not near 30x.
I stand by my point that the mistake was using CQL maps when you should have just used a defined schema.
Comments
Might be specific to the Python driver, but it was slower for queries because most of the cputime was being spent decoding column structure/metadata and on type conversions. We also tested msgpack blobs, iirc, but the xsv format was the winner for query perf and for compactness on disk.
Would have gladly gone into more detail, but at 4,000+ words for the blog post already... :)
Hmm, so I tried converting a table we had from a standard table to one with COMPACT STORAGE. The space saving wasn't all that great (100GB -> 80GB), not near 30x.
I stand by my point that the mistake was using CQL maps when you should have just used a defined schema.