Question: How can you implement partitioning across multiple servers in PostgreSQL?

Answer

Partitioning in PostgreSQL is primarily designed to enhance performance and manageability for large tables within a single database server. It enables you to split a large table into smaller, more manageable pieces, called partitions, which can improve query performance and maintenance tasks. However, the concept of partitioning across multiple servers extends beyond PostgreSQL's built-in partitioning capabilities and ventures into the territory of distributed databases or using additional tools for managing data across servers.

Using Built-in Partitioning Features

PostgreSQL's built-in partitioning features do not directly support partitioning data across multiple servers. Instead, it focuses on dividing tables into partitions within the same database instance, based on certain keys such as range or list. Here is an example of creating a range-partitioned table:

CREATE TABLE measurement (
    city_id int not null,
    logdate date not null,
    peaktemp int,
    unitsales int
) PARTITION BY RANGE (logdate);

CREATE TABLE measurement_y2020 PARTITION OF measurement
    FOR VALUES FROM ('2020-01-01') TO ('2021-01-01');

CREATE TABLE measurement_y2021 PARTITION OF measurement
    FOR VALUES FROM ('2021-01-01') TO ('2022-01-01');

This code snippet creates a parent table measurement and two partitions for different years. Each partition stores data for its specified range of dates.

Extending Partitioning Across Multiple Servers

To extend the concept of partitioning across multiple servers, you would typically look into solutions like:

1. Foreign Data Wrappers (FDW): PostgreSQL includes Foreign Data Wrappers, which allow you to define foreign tables that actually reside in other PostgreSQL servers or even completely different types of databases. With FDWs, you could manually distribute your data across servers and use these foreign tables as partitions. This requires careful planning and manual management of data distribution and queries.

2. PostgreSQL Extensions and Tools: Extensions like Citus expand PostgreSQL to support distributed databases, effectively allowing horizontal scaling across multiple servers. Citus achieves this by transparently distributing your data and queries across a cluster of multiple nodes.

3. Custom Sharding Solutions: Implementing a custom sharding solution involves manually partitioning your data and distributing it across multiple PostgreSQL instances. Queries and data management become more complex as you will need to handle the routing of queries to the correct shard, as well as consistency and replication between shards.

4. Third-party Solutions: There are third-party solutions and platforms that provide distributed SQL databases with horizontal scaling, such as CockroachDB or YugabyteDB, which are inspired by Google Spanner. These solutions offer SQL interfaces and aim to simplify the complexities involved in managing distributed data.

While PostgreSQL's built-in partitioning does not natively support multi-server partitioning, there are ways to achieve this through creative use of FDWs, extensions like Citus, or external tools. Each approach has its own set of trade-offs in terms of complexity, performance, and maintainability.