Category: PostgreSQL compatible

Migrating SQL Server Full-Text Search to Babelfish for Amazon Aurora PostgreSQL is one of the trickier parts of a migration. This post presents a six-checkpoint decision tree to assess whether your Full-Text Search workload can move to Babelfish, what migrates directly, what needs a custom function, and what requires an alternative approach.

AWS today announced the general availability of AMD-based R8a and M8a database instances for Amazon Aurora and Amazon RDS, powered by 5th generation AMD EPYC processors. R8a is a memory-optimized instance family available on both Aurora and RDS. M8a is a general-purpose instance family available on RDS.

Replication lag during a change data capture (CDC) migration can be counterintuitive: the source is busy, yet replication falls behind and WAL piles up. This post shows how to resolve PostgreSQL replication lag with heartbeat tables using AWS DMS or Debezium, and how to monitor replication slot health with Amazon CloudWatch and SQL diagnostics.

Learn how to use AWS DMS and a Db2 gateway server on Amazon EC2 to migrate and replicate data from an on-premises IBM Db2 database on z/OS to Amazon Aurora PostgreSQL. This post covers configuring the gateway, creating DMS resources, running a full load with periodic full-load refresh, and validating the migration.

In this post, we show you how to build a fully managed, event-driven change data capture (CDC) pipeline. It streams row-level changes from Amazon RDS for PostgreSQL or Amazon Aurora PostgreSQL to Amazon S3 in near real time. You deploy the entire pipeline with a single AWS CloudFormation template, and it can run in private subnets with no internet gateway without exposing resources to the public internet.

This post shows how to compose a Bloom filter with an exact-match cache and a relational source of truth into a three-tier, correctness-safe membership lookup using Amazon ElastiCache for Valkey and Amazon Aurora PostgreSQL, serving sub-millisecond decisions at peak throughput without false-positive risk.

How CORTO scaled Amazon Aurora PostgreSQL with pgvector to 7.6 billion vectors and 2.5 billion documents in production, delivering sub-second legal search for 10,000+ law firms at 75% lower storage cost.

Migrating full-text search from SQL Server to PostgreSQL can silently change results because the engines handle text, linguistics, and accents differently. This post shows how to reproduce SQL Server full-text search on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL, covering collation, tokenization, accent-insensitive search, and synonyms.

PostgreSQL out-of-memory (OOM) events and excessive disk spilling are among the most common production incidents on Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL. Learn how PostgreSQL allocates and consumes memory, how to identify memory-intensive queries, and how to diagnose, prevent, and recover from OOM events on both engines.

Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best.