What is data partitioning?

Data partitioning is a technique to split a large database into multiple smaller, distinct, and independent parts; across multiple machines; to improve manageability, availability and performance.

What are the key benefits of data partitioning?

Following are the key benefits of data partitioning.

1. Improve Scalability - Scalability improves since you can scale up by adding new machines.

2. Improve Performance - Performance improves since you will query from a smaller subset of data.

3. Improve Availability - Availability improves since having multiple machines avoids a single pount of failure.

4. Improve Security - Security improves since you can separate secure data from un-secure data.

What are the different strategies of data partitioning?

Following are the key strategies of data partitioning.

1. Horizontal partitioning (Sharding) - In this strategy data is split into partitions, called shards. Each shard contains a subset of the data, based on a shard key. Example - a system can store orders for each state in a different partition.

2. Vertical partitioning - In this strategy data is split into partiotions based on the pattern of use. Example - an application can store frequently ordered items in one partition, and less frequently ordered items in another partiton.

3. Functional partitioning (Federation) - In this strategy data is split into partitions based on its functional use. For example an application can store inventory data in partition and order data in another partition.

What are the different partitioning criteria?

Following are the key strategies of data partitioning.

1. Range partitioning

2. List partitioning

3. Round-robin partitioning

4. Hash partitioning

5. Composite partitioning