RedisBloom RedisBloom is a Redis module providing four probabilistic datatypes in Redis: RedisAI RedisAI serves models from within Redis. Tensorflow & PyTorch models are supported. RedisAI version 1.0.0 was released in April of 2019. View the RedisAI announcement here. Thanks to Luca Antiga and Sherin Thomas of TensorWerk, and Itamar Haber of Redis Labs for creating and maintaining RedisAI. RedisJSON RedisJSON is a Redis module that implements ECMA-404 (The JSON Data Interchange Standard) as a native data type in Redis. It allows storing, updating and fetching JSON values from Redis keys (documents). RedisJSON version 1.0.0 was first released in March of 2017. Read the RedisJSON announcement here. Thanks to Itamar Haber, Guy Korland and everyone involved for creating and maintaining it. RedisTimeSeries RedisTimeSeries is a Redis Module adding a Time Series data structure to Redis. RedisTimeSeries version 1.0.0 was released in June of 2019. Read the RedisTimeSeries announcement here. And an InfoQ summary here. RedisGraph RedisGraph is a property graph database provided as a Redis module. It implements the Cypher query language, and uses GraphBLAS to model common graph operations as linear algebra problems. Version 1.0.0 was released in November of 2018. View the RedisGraph announcement here. Thanks to Roi Lipman, Jeffrey Lovitz, Dvir Volk and everyone involved for creating and maintaining RedisGraph. RediSearch RediSearch is an in-memory search provided as a Redis module. This forum is meant for discussing use cases and asking general questions about RediSearch. If you think you’ve found a bug, please submit an issue on our issue tracker. RedisGears RedisGears is a pipes-like computing system in a Redis module. It provides high-level APIs using Python, and a low level api using C. It’s end-goal is to enable developers to build an operations pipe (OPP) which Redis keys will pass through. The output from the first operation will pass as input to the second operation, The output from the second operation will pass as input to the third operation, and so on. The pipe builds using a python script and then runs in background thread. When finished, the output from the last operation will pass to the user as a reply.