#java #cache #distributed #distributed_locks #executor #hibernate #list #lock #map #mapreduce #queue #redis #redis_client #redis_cluster #scheduler #session #set #spring_cache #tomcat
https://github.com/redisson/redisson
https://github.com/redisson/redisson
GitHub
GitHub - redisson/redisson: Redisson - Valkey & Redis Java client. Real-Time Data Platform. Sync/Async/RxJava/Reactive API. Over…
Redisson - Valkey & Redis Java client. Real-Time Data Platform. Sync/Async/RxJava/Reactive API. Over 50 Valkey and Redis based Java objects and services: Set, Multimap, SortedSet, Map, List...
#cplusplus #database #distributed #kv #namespace #redis #redis_cluster #redis_protocol #rocksdb
https://github.com/apache/incubator-kvrocks
https://github.com/apache/incubator-kvrocks
GitHub
GitHub - apache/kvrocks: Apache Kvrocks is a distributed key value NoSQL database that uses RocksDB as storage engine and is compatible…
Apache Kvrocks is a distributed key value NoSQL database that uses RocksDB as storage engine and is compatible with Redis protocol. - apache/kvrocks
#typescript #nodejs #redis #redis_client #redis_cluster #redis_module #redis_sentinel
https://github.com/luin/ioredis
https://github.com/luin/ioredis
GitHub
GitHub - redis/ioredis: 🚀 A robust, performance-focused, and full-featured Redis client for Node.js.
🚀 A robust, performance-focused, and full-featured Redis client for Node.js. - redis/ioredis
#python #python #redis #redis_client #redis_cluster #redis_py
Redis-py lets you connect your Python programs to Redis, a fast in-memory database, making it easy to store and retrieve data quickly. You can install it with a simple command, and it works with the latest Redis versions. It supports advanced features like connection pools, pipelines for faster operations, and pub/sub for real-time messaging. Using Redis with Python helps your applications run faster, handle more users, and process data in real time, all while reducing the load on your main database[1][3][5].
https://github.com/redis/redis-py
Redis-py lets you connect your Python programs to Redis, a fast in-memory database, making it easy to store and retrieve data quickly. You can install it with a simple command, and it works with the latest Redis versions. It supports advanced features like connection pools, pipelines for faster operations, and pub/sub for real-time messaging. Using Redis with Python helps your applications run faster, handle more users, and process data in real time, all while reducing the load on your main database[1][3][5].
https://github.com/redis/redis-py
GitHub
GitHub - redis/redis-py: Redis Python client
Redis Python client. Contribute to redis/redis-py development by creating an account on GitHub.