PostgreSQL Sharding: Scaling Your Database Horizontally
Sharding is a database scaling technique that distributes data across multiple servers. PostgreSQL supports sharding through extensions like Citus and manual partitioning, enabling horizontal scalability for applications with large datasets.
1. What is Sharding?
Sharding divides large datasets into smaller, manageable pieces (shards) stored across multiple nodes. Each shard contains a subset of data based on defined criteria, like a range or hash.
2. When to Use Sharding?
3. PostgreSQL Sharding Approaches
A. Manual Sharding
Split tables into multiple smaller tables manually, distribute them across nodes, and manage connections in the application logic.
B. Citus Extension
Citus transforms PostgreSQL into a distributed database by automatically sharding data across nodes.
4. Setting Up Sharding with Citus
Install Citus:
sudo apt install postgresql-15-citus
Enable Citus Extension:
CREATE EXTENSION citus;
Distribute a Table:
SELECT create_distributed_table('my_table', 'distribution_column');
5. Example Sharding Query with Citus
Assume you have a table orders with order_id as the distribution column:
SELECT create_distributed_table('orders', 'order_id');
INSERT INTO orders (order_id, customer_id, amount) VALUES (1, 1001, 250.00);
6. Advantages of Sharding in PostgreSQL
Benefit Description Horizontal Scaling Spread data across multiple servers. Reduced Latency Queries execute on smaller data chunks. Fault Tolerance Node failure impacts only specific shards.7. Challenges of Sharding
8. Sharding Best Practices
Use Case Example
Large E-Commerce Platform
For platforms handling millions of transactions daily, sharding enables scalability by splitting orders and customers tables across multiple nodes.
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