Show HN:The Gorilla in the Room:Exploring RedisTimeSeries Optimizations

I've some remarks regarding the tests and the test data in this survey.

- The data used in the benchmark seems very compressible. However floating point data (also time series) is in general not well losslessly compressible and the Gorilla algorithm is not helping so much.

- TurboPFor Integer Compression [1] has also an improved and faster Gorilla algorithm TurboGorilla (not shown in the benchmark)

- TurboPFor has now a new lz based algorithm "TurboFloat LzX" witch drastically compress time series floating point data (up to 3 times).

- TurboPFor supports lossy compression [2] (TurboRazor) with a point wise relative error bound. The preprocessed data can be further compressed with any floating point compression algorithm.

- TurboPFor supports floating point conversion to integers. The result data can be compressed with any integer compression algorithm.

You can test all this functionality with icapp, a TurboPFor benchmark app. Download the excutable for windows + linux from [3]

See also the time series floating point benchmark [4] "TurboFor: Time Series" showing different szenarios, using 32-bits instead of 64-bits floating point data like in the benchmark above.

[1] https://github.com/powturbo/TurboPFor-Integer-Compression

[2] https://github.com/powturbo/TurboPFor-Integer-Compression/is...

[3] https://github.com/powturbo/TurboPFor-Integer-Compression/re...

[4] https://github.com/powturbo/TurboPFor-Integer-Compression/is...