C++ simd-compression Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Well if your integers are sequential you can encode huge numbers of them using diff + RLE in just a few bytes, likely far fewer than 1/2 a byte on average, for the right dataset (in theory you can store 1,2,3,4,5...10_000 in 2 bytes).
But for other integer datasets there's FastPFOR
https://github.com/lemire/FastPFor
The linked papers there will talk about techniques that can be used to store multiple 32bit integers into a single byte, etc. Integer compression is pretty powerful if your data isn't random. The thing with UUIDs is that your data is pretty random - even a UUIDv7 contains a significant amount of random data.
C++ simd-compression related posts
Index
| Project | Stars | |
|---|---|---|
| 1 | FastPFor | 839 |
| 2 | PyFastPFor | 56 |
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