Now let’s insert some test values and check them: postgres=# INSERT INTO customers (id, name, registered) VALUES (1, 'James', '') Now let’s create the table customers partitioned by registered date: postgres=# CREATE TABLE customers (Īnd the following partitions: postgres=# CREATE TABLE customers_2021 To enable the FDW extension, you just need to run the following command in your main server, in this case, Shard1: postgres=# CREATE EXTENSION postgres_fdw It is an extension available by default in the common PostgreSQL installation. This functionality allows PostgreSQL to access data stored in other servers. Now we will configure Sharding using PostgreSQL Partitions and Foreign Data Wrapper (FDW). Repeat the process to have at least two separate PostgreSQL clusters to configure Sharding, which is the next step. Now that you have your cluster created, you can perform several tasks on it like adding a load balancer (HAProxy), connection pooler (pgBouncer), or a new replica. It is useful when queries tend to return only a subset of columns of the data.
Vertical Sharding: Each new table has a schema that is a subset of the original table’s schema. It is useful when queries tend to return a subset of rows that are often grouped together. Horizontal Sharding: Each new table has the same schema as the big table but unique rows. The main difference is that sharding implies the data is spread across multiple computers while partitioning is about grouping subsets of data within a single database instance. Partitioning and Sharding are similar concepts. Smaller tables are Shards (or partitions). Sharding is the action of optimizing a database by separating data from a big table into multiple small ones.
#Postgresql sharding how to
In this blog, we will see what Sharding is and how to configure it in PostgreSQL using ClusterControl to simplify the task. There are different ways to scale your PostgreSQL databases and one of them is Sharding. Depending on the size, this amount of data could affect the performance of your systems and you will probably need to scale your databases or find a way to fix this. Sometimes it is hard to manage a large amount of data in a company, especially with the exponential increment of Data Analytics and IoT usage.
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