Category: PostgreSQL compatible

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 demonstrate how you can use Amazon CloudWatch Database Insights to analyze your SQL execution plan to troubleshoot and optimize your SQL query performance in an Aurora PostgreSQL cluster.

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.

In this post, we explore how you can use Vercel’s v0 generative UI to build applications with a modern UI for AWS purpose-built databases such as Amazon Aurora, Amazon DynamoDB, Amazon Neptune, and Amazon ElastiCache.

Wiz, a leading cloud security company, identifies and removes risks across major cloud platforms. Our agent-less scanner processes tens of billions of daily cloud resource metadata entries. This demands high-performance, low-latency processing, making our Amazon Aurora PostgreSQL-Compatible Edition database, serving hundreds of microservices at scale, a critical component of our architecture. In this post, we share how we upgraded our Aurora PostgreSQL database from version 14 to 16 with near-zero downtime using Amazon Aurora Blue/Green Deployments.

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 […]