Category: RDS for PostgreSQL

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.

AWS DMS accommodates a broad range of source and target data repositories, such as relational databases, data warehouses, and NoSQL databases. Proper preparation and design are vital for a successful migration process, especially when it comes to optimizing performance and addressing potential delay issues. In this blog post, we offer guidance about recognizing potential root causes of complete load and CDC delays early in the process and provide suggestions for optimally clustering tables to achieve the best performance for an AWS DMS task.

Organizations rely on real-time analytics to gain insights into their core business drivers, enhance operational efficiency, and maintain a competitive edge. Traditionally, this has involved the use of complex extract, transform, and load (ETL) pipelines. ETL is the process of combining, cleaning, and normalizing data from different sources to prepare it for analytics, AI, and […]

Are your database read operations unexpectedly slowing down as your workload scales? Many organizations running PostgreSQL-based systems encounter performance bottlenecks that aren’t immediately obvious. When many concurrent read operations access tables with numerous partitions or indexes, they can even exhaust PostgreSQL’s fast path locking mechanism, forcing the system to use shared memory locks. The switch […]

In this post, we discuss different ways you can use volatility classification with functions in PostgreSQL and provide best practices to help you keep your database optimized and develop efficient and reliable database applications.

In November 2024, AWS introduced the latest evolution of its custom-designed ARM-based processors with Graviton4, delivering significant performance and efficiency improvements for Amazon RDS for PostgreSQL, MySQL, and MariaDB and Amazon Aurora. In this post, we focus on Amazon RDS for PostgreSQL and compare the performance of the new Graviton4 instances to both Graviton3 and Graviton2. Using benchmarks, we evaluate throughput, latency, and price-performance, showcasing the advantages of Graviton4 for modern database workloads.