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Redis supports strings, hashes, lists, sets, sorted sets with range queries,
bitmaps, hyperloglogs and geospatial indexes with radius queries.
These data structures can be accessed through multiple commands and Lua scripting.
Redis' data structures allow objects to be stored in their original format and in an optimized structure (i.e. sorted, filtered, etc.). It enables data to be processed on the database level rather than the application level. An application can retrieve only discrete elements from the object, as opposed to other K/V datastores that require the application to retrieve an entire object value (blob), and de-serialize and parse it in order to get the desired part. This unique architecture results in cleaner, more elegant code with fewer lines, faster execution time, better application performance and better CPU, I/O and network utilization.
By adding more shards to your database you can utilize all your cores in an optimal manner, and easily process over one million ops/sec at sub‑millisecond latencies, even when running Redis on a standard cloud instance.
Redis enables use cases like in-database real-time analytics, high-speed transactions, data ingestion, social functionality, job management, message queues and caching. According to a recent user survey, 62% of respondents said they use Redis because of its "super high versatility." Redis is also known for its time-series capabilities, which make it extremely suitable for modern financial and IoT applications.