When creating a sharded architecture, the goal is to have many small shards to create an even distribution of data across the nodes. More than one logical shard can be located on the same physical shard, but a logical shard can’t be split between physical shards. 1.1 How sharding worksĮach row is assigned a shard key that maps it to the logical shard it can be found on. Then we’ll cover when to use and when not to use sharding. We’ll go over how it’s implemented with shard keys, and the importance of denormalizing data. There are a couple of key features a sharded architecture needs to be efficient. The goal of sharding is to increase database capacity, but another effect is that the database can handle more traffic because any one server in the cluster only has to respond to a fraction of the total requests. This is in contrast to a vertical partition, where partitions are made between columns.
A shard is a horizontal partition, meaning the database table is split up by drawing a horizontal line between rows. The alternative is splitting up data across a cluster of database servers.ĭatabase sharding splits up data in a particular way so that data access patterns can remain as efficient as possible. The capacity of the database server can be increased, but eventually runs into a physical limit.
#Database sharding software
Modern software services increasingly collect and use more data than can fit on a single machine. This is just a high-level definition, so to fill in the gaps we’ve put together the below guide that will walk you through sharding and how you should approach it during a system design interview. Sharding is essentially the horizontal scaling of a database system that is accomplished by breaking the database up into smaller “shards”, which are separate database servers that all contain a subset of the overall dataset.
If you want to succeed in system design interviews for a tech role, then you’ll probably need to understand sharding and when to use it within the context of a larger system.
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