Why Redis SortedSet uses Skip List instead of Balanced Tree?

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The Redis document said as below :

ZSETs are ordered sets using two data structures to hold the same elements in order to get O(log(N)) INSERT and REMOVE operations into a sorted data structure.

The elements are added to a hash table mapping Redis objects to scores. At the same time the elements are added to a skip list mapping scores to Redis objects (so objects are sorted by scores in this "view").

I can not understand very much. Could someone give me a detailed explanation?

sortedset redis skip-lists data-structures
2 Answers

Antirez said, see in https://news.ycombinator.com/item?id=1171423

There are a few reasons:

  • They are not very memory intensive. It's up to you basically. Changing parameters about the probability of a node to have a given number of levels will make then less memory intensive than btrees.
  • A sorted set is often target of many ZRANGE or ZREVRANGE operations, that is, traversing the skip list as a linked list. With this operation the cache locality of skip lists is at least as good as with other kind of balanced trees.
  • They are simpler to implement, debug, and so forth. For instance thanks to the skip list simplicity I received a patch (already in Redis master) with augmented skip lists implementing ZRANK in O(log(N)). It required little changes to the code.

About the Append Only durability & speed, I don't think it is a good idea to optimize Redis at cost of more code and more complexity for a use case that IMHO should be rare for the Redis target (fsync() at every command). Almost no one is using this feature even with ACID SQL databases, as the performance hint is big anyway.

About threads: our experience shows that Redis is mostly I/O bound. I'm using threads to serve things from Virtual Memory. The long term solution to exploit all the cores, assuming your link is so fast that you can saturate a single core, is running multiple instances of Redis (no locks, almost fully scalable linearly with number of cores), and using the "Redis Cluster" solution that I plan to develop in the future.

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