Gorilla compression is lossless. This library can be used in three ways: Timestamps only compression. Values only compression (useful for regular time series compression). Timestamp-Value pairs compression (useful for irregular time series compression).

This is Java based implementation of the compression methods described in the paper "Gorilla: A Fast, Scalable, In-Memory Time Series Database". For explanation on how the compression methods work, read the excellent paper.

An effective time-series data compression method based on Facebook's Gorilla. It uses delta-of-delta timestamps and XOR'd floating-points values to reduce the data size. This is because most data points arrived at a fixed interval and the value in …

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Extremely efficient streaming compression algorithm. Our streaming compression algorithm is able to compress real world time series data by over 90%. The delta of delta compression algorithm used by Beringei is also fast - we see that a single machine is able to compress more than 1.5 million datapoints/second.

Package tsz implement the Gorilla Time Series Databasetime-series compression as described in: http://www.vldb.org/pvldb/vol8/p1816-teller.pdf

A .NET implementation of Facebook's Gorilla time series compression algorithm Resources

TSXor, a simple yet effective encoder/decoder for time series that achieves high compression ratios and fast decompression speed. TSXor exploits the redundancy/similarity between close-in-time values through a window that acts as a cache.

This is an implementation (with some adaptations) of the compression algorithm described in **section 4.1** (Time series compression) of [[1]](#1) (read the paper [here](./doc/p1816-teller.pdf)). Gorilla compression is lossless.

Gorilla 🦍 is a port of the Facebook gorilla time-series data compression algorithm to Zig - evanxg852000/gorilla