Category: RDS for PostgreSQL

In this post, we show you how to migrate spatial (geospatial) data from self-managed PostgreSQL, Amazon RDS for PostgreSQL, or Amazon Aurora PostgreSQL-Compatible Edition to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL using AWS DMS. Spatial data is useful for applications such as mapping, routing, asset tracking, and geographic visualization. We walk through setting up your environment, configuring AWS DMS, and validating the successful migration of spatial datasets.

In this post, we discuss configuring AWS DMS tasks to migrate HierarchyID columns from SQL Server to Aurora PostgreSQL-Compatible efficiently.

In this post, we help you plan your upgrade from PostgreSQL version 13 before standard support ends on February 28, 2026. We discuss the key benefits of upgrading, breaking changes to consider, and multiple upgrade strategies to choose from.

In 2023, Tradeshift migrated one of its core PostgreSQL databases from self-managed Amazon Elastic Compute Cloud (Amazon EC2) instances to Amazon Relational Database Service (Amazon RDS) for PostgreSQL. The decision followed mounting operational risks and performance limits that made the existing setup increasingly unsustainable. Tradeshift needed a managed solution that could reduce downtime risk, improve observability, and simplify ongoing operations. Amazon RDS met those requirements. In this post, we explain why we migrated to Amazon RDS, how we executed the migration, and highlight the invaluable benefits it delivered in terms of safety, flexibility, and audit compliance.

In this post, we explore the use cases for delayed replication, the recovery procedures, and best practices for managing delayed replicas to help ensure your database recovery strategy is both robust and efficient.

This post shows you how to use PostgreSQL to store and search JSON data effectively. You’ll learn when to use JSON versus JSONB, how to create the right indexes, and how to write queries that perform well at scale.

In this post, we explore an artificial intelligence and machine learning (AI/ML)-powered database monitoring tool for PostgreSQL, using a self-managed or managed database service such as Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL.

Database migrations from Oracle to PostgreSQL are becoming increasingly common as organizations seek to optimize database costs while leveraging the benefits of open-source database solutions. However, these migrations present specific challenges, particularly when dealing with an Oracle-specific feature such as reference partitioning, which doesn’t have a direct equivalent in PostgreSQL. In this post, we show you how to migrate Oracle reference-partitioned tables to Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition using AWS DMS.

In this post, we explore the challenges of OID exhaustion in PostgreSQL, focusing on its impact on TOAST tables and how it leads to performance issues. We will cover how to identify the problem by reviewing wait events, session activity, and table usage. Additionally, we discuss practical solutions, from cleaning up data to more advanced strategies such as partitioning.

In this post, we show you how to migrate full-text search in Microsoft SQL Server to Amazon Aurora PostgreSQL using text searching data types tsvector and tsquery. We also show you how to implement FTS using pg_trgm and pg_bigm extensions.