With limited CPU, storage capacity, and memory, query throughput and response times are bound to suffer. A database can be split vertically - storing different table columns in a separate database, or horizontally - storing rows of the same table in multiple database nodes.įigure 1 : Vertical and Horizontal Data Partitioning (Source: Medium) Why Shard a Database?īusiness applications that rely on a monolithic RDBMS hit bottlenecks as they grow. The distinction between horizontal and vertical comes from the traditional tabular view of a database. Sharding is also referred to as horizontal partitioning. The idea is to distribute data that can’t fit on a single node onto a cluster of database nodes. A shard is essentially a horizontal data partition that contains a subset of the total data set, and hence is responsible for serving a portion of the overall workload. Sharding is the process of breaking up large tables into smaller chunks called shards that are spread across multiple servers.
#Database sharding plus#
We will also review the pros and cons of common sharding architectures, plus explore how sharding is implemented in a distributed SQL database like YugabyteDB. In this blog, we will learn what data sharding is and how it can be used to scale a SQL database. One of the most common architectural patterns used to scale an RDBMS is to “shard” the data. However, a monolithic RDBMS tends to quickly get overloaded in such scenarios. The relational database (RDBMS) infrastructure that such applications rely on suddenly needs to support much larger data sizes and transaction volumes. Editor’s note: This post was updated Augto include new sharding features available starting in YugabyteDB 2.2Įnterprises of all sizes are embracing rapid modernization of user-facing applications as part of their broader digital transformation strategy.
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