Documentation - Redise Pack

A guide to Redise Pack installation, operation and administration

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9.2 Sharded structures

Sharding is a well-known technique that has been used to help many different databases scale to larger data storage and processing loads. Basically, sharding takes your data, partitions it into smaller pieces based on some simple rules, and then sends the data to different locations depending on which partition the data had been assigned to.

In this section, we’ll talk about applying the concept of sharding to HASHes, SETs, and ZSETs to support a subset of their standard functionality, while still letting us use the small structures from section 9.1 to reduce memory use. Generally, instead of storing value X in key Y, we’ll store X in key Y:<shardid>.

SHARDING LISTSSharding LISTs without the use of Lua scripting is difficult, which is why we omit it here. When we introduce scripting with Lua in chapter 11, we’ll build a sharded LIST implementation that supports blocking and nonblocking pushes and pops from both ends.

SHARDING ZSETSUnlike sharded HASHes and SETs, where essentially all operations can be supported with a moderate amount of work (or even LISTs with Lua scripting), commands like ZRANGEZRANGEBYSCOREZRANKZCOUNTZREMRANGEZREMRANGEBYSCORE, and more require operating on all of the shards of a ZSET to calculate their final result. Because these operations on sharded ZSETs violate almost all of the expectations about how quickly a ZSET should perform with those operations, sharding a ZSET isn’t necessarily that useful, which is why we essentially omit it here.

If you need to keep full information for a large ZSET, but you only really perform queries against the top- or bottom-scoring X, you can shard your ZSET in the same way we shard HASHes in section 9.2.1: keeping auxiliary top/bottom scoring ZSETs, which you can update with ZADD/ZREMRANGEBYRANK to keep limited (as we’ve done previously in chapters 2 and 4–8).

You could also use sharded ZSETs as a way of reducing single-command latencies if you have large search indexes, though discovering the final highestand lowest-scoring items would take a potentially long series of ZUNIONSTORE/ ZREMRANGEBYRANK pairs.

When sharding structures, we can make a decision to either support all of the functionality of a single structure or only a subset of the standard functionality. For the sake of simplicity, when we shard structures in this book, we’ll only implement a subset of the functionality offered by the standard structures, because to implement the full functionality can be overwhelming (from both computational and code-volume perspectives). Even though we only implement a subset of the functionality, we’ll use these sharded structures to offer memory reductions to existing problems, or to solve new problems more efficiently than would otherwise be possible.

The first structure we’ll talk about sharding is the HASH.