Code Library

Two features make Redis stand out: it keeps the entire database in RAM, and its values can be complex data structures. Though the entire dataset is kept in memory, it’s also backed up on disk periodically, so you can use it as a persistent database. This approach does offer fast and predictable performance, but speed falls off a cliff if the size of your data expands beyond available memory and the operating system starts paging virtual memory to handle accesses. This won’t be a problem if you have small or predictably sized storage needs, but it does require a bit of forward planning as you’re developing applications. You can deal with larger data sets by clustering multiple machines together, but the sharding is currently handled at the client level. There is an experimental branch of the code under active development that supports clustering at the server level.

The support for complex data structures is impressive, with a large number of list and set operations handled quickly on the server side. It makes it easy to do things like appending to the end of a value that’s a list, and then trim the list so that it only holds the most recent 100 items. These capabilities do make it easier to limit the growth of your data than it would be in most systems, as well as making life easier for application developers.

Source of Information : Big data Glossary