danking00

    A prvalue of floating-point type can be converted to a prvalue of any integer type. The
    fractional part is truncated, that is, the fractional part is discarded.

    - If the truncated value cannot fit into the destination type, the behavior is undefined (even
      when the destination type is unsigned, modulo arithmetic does not apply).

It’s exciting to see more work on compressed arrays make it out into the world. It would have been nice if a lot of PLINK’s bitfiddling functionality was in an accessible native library rather than wrapped up with the rest of that tool.

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!

In Vortex, we've specifically invested in high throughput compression techniques that admit O(1) random access. These kinds of techniques are also sometimes called "lightweight compression". The DuckDB folks have a good writeup [1] on the common ones.

The last reference indeed argues in favor of too much financialization of housing units but that blog is also tripping a lot of my crackpot alarms.

Can you succinctly explain why you believe the low vacancy rates in major metros (which I think we agree is the cause of high rents / purchase costs) are caused primarily by units which are intentionally held empty despite demand?

In partial defense of your assertion, the FRED data does show that 1/4 to 1/3 of vacant units are for sale or rent at any given time.