The Gorilla in the Room: Exploring RedisTimeSeries Performance Optimizations

RedisTimeSeries is a time-series database module that stores large amounts of streaming data in-memory, allowing for fast processing thanks to the Gorilla compression/decompression algorithm. The algorithm achieves a 10X compression ratio and was developed by Meta for its large-scale and distributed Time-Series Database (TSDB). Intel and Redis are collaborating to investigate potential performance optimizations for the RedisTimeSeries database. They have explored vectorizing the compression/decompression computation, but found that it would not pay off in terms of performance and code complexity. Instead, they focused on optimizing the decompression routine for floating-point values and discovered that using a dictionary and/or run-length encoding could lead to better performance and/or compression ratio. The team also considered alternative approaches, such as storing data as integers instead of floating points, but concluded that Gorilla remains the best compression ratio option.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.