MySQL Partition vs. Sharding: When to Use Each and How They Differ

1. What are MySQL sharding and partitioning

Sharding (splitting a table into multiple independent tables) and partitioning (dividing a single table’s data into multiple physical blocks) are two ways to handle large data sets in MySQL.

2. Differences between sharding and partitioning

2.1 Implementation

Sharding creates separate tables, each with its own .MYD, .MYI, and .frm files. Example file list for a merged‑engine sharding setup:

alluser.MRG

alluser.frm

user1.MYD

user1.MYI

user1.frm

user2.MYD

user2.MYI

user2.frm

The .MRG file stores the relationship between the master table (alluser) and its child tables (user1, user2) and the insert method.

Partitioning keeps a single logical table but creates multiple data and index files for each partition, plus a .par file that records partition metadata. Example file list for a partitioned table:

aa#P#p1.MYD

aa#P#p1.MYI

aa#P#p3.MYD

aa#P#p3.MYI

aa.frm

aa.par

2.2 Data handling

With sharding, queries are routed to the appropriate child tables; for example, SELECT * FROM alluser WHERE id='12' actually accesses the underlying shard tables.

With partitioning, the table remains a single logical entity; the engine automatically reads from the correct partition files.

2.3 Performance impact

Sharding reduces contention by spreading reads/writes across multiple smaller tables and improves disk I/O because each .MYD file is smaller.

Partitioning aims to break the I/O bottleneck by placing partition files on different disks, enhancing read/write throughput.

2.4 Implementation difficulty

Sharding using the MERGE storage engine is straightforward and transparent to application code; other sharding methods can be more complex.

Partitioning is also simple to set up—creating a partitioned table is similar to creating a regular table and is transparent to the application.

3. Relationship between sharding and partitioning

Both techniques can improve MySQL performance under high concurrency and large data volumes. They are not mutually exclusive; they can be combined—for example, sharding a heavily accessed table and then partitioning each shard for further I/O optimization.