[xiaodai]

I see. Very nice. So it's a trade-off. I imagine the throughput of these light-weight compression suffers a little. In analytical workloads, it's common to do things like compute the mean of a vector or compute the gradient for this batch of data so random access appear less of an issue here.

[danking00]

We’ll post a blog post soon with specific, benchmarked numbers, but, in this case, you can have your cake and eat it too!

The compression and decompression throughputs of Vortex (and other lightweight compression schemes) are similar or better than Parquet for many common datasets. Unlike Zstd or Blosc, the lightweight encodings are, generally, both computationally simple and SIMD friendly. We’re seeing multiple gibibytes per second on an M2 MacBook Pro on various datasets in the PBI benchmark [1].

The key insight is that most data we all work with has common patterns that don’t require sophisticated, heavyweight compression algorithm. Let’s take advantage of that fact to free up more cycles for compute kernels!

[1] https://github.com/cwida/public_bi_benchmark

[xiaodai]

Cool looking forward to it.