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Redis

Redis supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster. You can run atomic operations on these types, like appending to a string; incrementing the value in a hash; pushing an element to a list; computing set intersection, union, and difference; or getting the member with highest ranking in a sorted set. In order to achieve its outstanding performance, Redis works with an in-memory dataset. Depending on your use case, you can persist it either by dumping the dataset to disk every once in a while or by appending each command to a log. Persistence can be optionally disabled if you just need a feature-rich, networked, in-memory cache. Redis also supports trivial-to-setup master-slave asynchronous replication, with very fast non-blocking first synchronization, auto-reconnection with partial resynchronization on net split. Other features include:
  • Transactions
  • Pub/Sub
  • Lua scripting
  • Keys with a limited time-to-live
  • LRU eviction of keys
  • Automatic failover
You can use Redis from most programming languages out there. Redis is written in ANSI C and works in most POSIX systems like Linux, *BSD, OS X without external dependencies. Linux and OS X are the two operating systems where Redis is developed and tested the most, and we recommend using Linux for deploying. Redis may work in Solaris-derived systems like SmartOS, but the support is the best effort. There is no official support for Windows builds.
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The ROI4CIO Product Catalog is a database of business software, hardware, and IT services. Using filters, select IT products by category, supplier or vendor, business tasks, problems, availability of ROI calculator or price calculator. Find the right business solutions by using a neural network search based on the results of deployment products in other companies.