Sharding vs Partition

This is the final blog in the series. In my previous two blogs, I explained sharding and partition in detail.

In this blog, I’ll compare sharding and partitioning side by side.

Sharding vs paritition

Partitioning and sharding are not mutually exclusive. In fact, many systems use both: Partitioned tables inside each shard of a sharded architecture. Understanding when and how to use them can make or break your system’s scalability. Choose wisely based on traffic patterns, data volume, query complexity, and operational goals.

	Sharding	Partition
What	divide the table into multiple shards across multiple servers	split single table into multiple partition at same server
Where	across multiple servers	at same server
Use Cases	handling global-scale systems. Distributed stystem	managing large local tables. Not looking for distrbuted system.
Advantage	enable scalability & distribution	improve query performance
Complixity	requires careful planning	relatively simple
Cross-partition queries	Complex	Easy
Management	Hard to manage	Easy to manage
Examples	MongoDB	MySQL

There are databases which can use both sharding and partition eg: Cassandra is built on sharding and partitioning with a distributed architecture. PostgreSQL is native table partitioning; sharding via Citus extension.

When to use?

use partitioning when:

1 . You’re on a single-node setup but your table size is growing.

2 . You want to speed up queries, manage archival better.

3 . Your DB supports native partitioning (e.g., PostgreSQL, MySQL).

use sharding when:

1 . Your dataset is too big for one server.

2 . You need horizontal scaling across machines.

3 . Your system is write-heavy or globally distributed.

Happy Learning!!