Struggling to choose between KeyDB and Redis? Both products offer unique advantages, making it a tough decision.
KeyDB is a Development solution with tags like opensource, redis, keyvalue, performance.
It boasts features such as In-memory key-value store, Supports data structures like Strings, Hashes, Lists, Sets, Sorted Sets and Streams, Built-in replication and clustering, Supports Lua scripting, Persistence - RDB and AOF, Transactions and pros including Faster performance than Redis, Additional data structures like Sorted Sets and Streams, Modular architecture, Compatible with Redis clients and ecosystem, Active development.
On the other hand, Redis is a Development product tagged with caching, inmemory, keyvalue-store.
Its standout features include In-memory data structure store, Supports various data structures (strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, geospatial indexes, streams), Used as a database, cache, and message broker, Provides high performance and low latency, Supports replication, clustering, and high availability, Supports a wide range of programming languages, Provides a rich set of commands and APIs, Supports data persistence (RDB and AOF), and it shines with pros like High performance and low latency, Flexible and versatile data structures, Supports a wide range of use cases, Easy to set up and configure, Scalable and highly available, Open-source and free to use.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
KeyDB is an open source, high performance fork of Redis that supports additional data structures like Sorted Sets and Streams. It aims to be a faster, more modular alternative to Redis while maintaining compatibility.
Redis is an open-source, in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes and streams.