Here is the underlying implementation of every Redis data type.
- Strings are implemented using a C dynamic string library so that we don’t pay (asymptotically speaking) for allocations in append operations. This way we have O(N) appends, for instance, instead of having quadratic behavior.
- Lists are implemented with linked lists.
- Sets and Hashes are implemented with hash tables.
- Sorted sets are implemented with skip lists (a peculiar type of balanced trees).
But when lists, sets, and sorted sets are small in number of items and size of the largest values, a different, much more compact encoding is used. This encoding differs for different types, but has the feature that it is a compact blob of data that often forces an O(N) scan for every operation. Since we use this format only for small objects this is not an issue; scanning a small O(N) blob is cache oblivious so practically speaking it is very fast, and when there are too many elements the encoding is automatically switched to the native encoding (linked list, hash, and so forth).
This is the base type of all the types. It’s one of the four types but is also the base type of the complex types, because a List is a list of strings, a Set is a set of strings, and so forth.
A Redis string is a good idea in all the obvious scenarios where you want to store an HTML page, but also when you want to avoid converting your already encoded data. So for instance, if you have JSON or MessagePack you may just store objects as strings. In Redis 2.6 you can even manipulate this kind of object server side using Lua scripts.
Lists are good when you are likely to touch only the extremes of the list: near tail, or near head. Lists are not very good to paginate stuff, because random access is slow, O(N). So good uses of lists are plain queues and stacks, or processing items in a loop using RPOPLPUSH with same source and destination to “rotate” a ring of items.
Lists are also good when we want just to create a capped collection of N items where usually we access just the top or bottom items, or when N is small.
Sets are an unordered data collection, so they are good every time you have a collection of items and it is very important to check for existence or size of the collection in a very fast way. Another cool thing about sets is support for peeking or popping random elements (SRANDMEMBER and SPOP commands).
Sets are also good to represent relations, e.g., “What are friends of user X?” and so forth. But other good data structures for this kind of stuff are sorted sets as we’ll see.
Sets support complex operations like intersections, unions, and so forth, so this is a good data structure for using Redis in a “computational” manner, when you have data and you want to perform transformations on that data to obtain some output.
Small sets are encoded in a very efficient way.
Hashes are the perfect data structure to represent objects, composed of fields and values. Fields of hashes can also be atomically incremented using HINCRBY. When you have objects such as users, blog posts, or some other kind of item, hashes are likely the way to go if you don’t want to use your own encoding like JSON or similar.
However, keep in mind that small hashes are encoded very efficiently by Redis, and you can ask Redis to atomically GET, SET or increment individual fields in a very fast fashion.
Sorted sets are the only other data structures, besides lists, to maintain ordered elements. You can do a number of cool stuff with sorted sets. For instance, you can have all kinds of Top Something lists in your web application. Top users by score, top posts by pageviews, top whatever, but a single Redis instance will support tons of insertion and get-top-elements operations per second.
Sorted sets, like regular sets, can be used to describe relations, but they also allow you to paginate the list of items and to remember the ordering. For instance, if I remember friends of user X with a sorted set I can easily remember them in order of accepted friendship.
Sorted sets are good for priority queues.
Sorted sets are like more powerful lists where inserting, removing, or getting ranges from the the middle of the list is always fast. But they use more memory, and are O(log(N)) data structures.