The main issue with this approach is that it gets really challenging to dynamically add or remove a database server. The database hotspot problem arises when one shard is accessed more as compared to all other shards and hence, in this case, any benefits of sharding the database are canceled out by the slowdowns and crashes). It is the simplest sharding algorithm and can be used to evenly distribute data among shards and prevent the risk of having a database hotspot. In Hash-based sharding (aka key-based sharding) we take a key value (such as customer Id, client IP address or email id, etc based on criteria we have already decided) from newly inserted data, pass it to the hash function and insert the data into resulting shard number. Considering all this in mind, let’s see what techniques we have to shard databases. Database Sharding Techniquesĭatabase sharding needs to be done in such a way that the incoming data should be inserted into a correct shard, there should not be any data loss and the result queries should not be slow. Also, sharding helps to make our application distributed thus minimizing a single point of failure. Hence it allows us to add more machines to an existing cluster in order to spread out the load, allow more traffic, and faster processing. Each shard has the same schema and columns like that of the original table but data stored in each shard is unique and independent of other shards.ĭatabase sharding is pretty much similar to horizontal scaling(scaling-out). Database sharding is a process of breaking up large tables into multiple smaller tables or chunks called shards and distributing data across multiple machines or clusters.
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