AWS Database Blog
Category: PostgreSQL compatible
Fluent Commerce’s approach to near-zero downtime Amazon Aurora PostgreSQL upgrade at 32 TB scale using snapshots and AWS DMS ongoing replication
Fluent Commerce, an omnichannel commerce platform, offers order management solutions that enable businesses to deliver seamless shopping experiences across various channels. Fluent uses Amazon Aurora PostgreSQL-Compatible Edition as its high-performance OLTP database engine to process their customers’ intricate search queries efficiently. Fluent Commerce strategically combined AWS-based upgrade approaches—including snapshot restores and AWS DMS ongoing replication—to seamlessly upgrade their 32 TB Aurora PostgreSQL databases with minimal downtime. In this post, we explore a pragmatic and cost-effective approach to achieve near-zero downtime during database upgrades. We explore the method of using the snapshot and restore method followed by continuous replication using AWS DMS.
Accelerate SQL Server to Amazon Aurora migrations with a customizable solution
Migrating from SQL Server to Amazon Aurora can significantly reduce database licensing costs and modernize your data infrastructure. To accelerate your migration journey, we have developed a migration solution that offers ease and flexibility. You can use this migration accelerator to achieve fast data migration and minimum downtime while customizing it to meet your specific business requirements. In this post, we showcase the core features of the migration accelerator, demonstrated through a complex use case of consolidating 32 SQL Server databases into a single Amazon Aurora instance with near-zero downtime, while addressing technical debt through refactoring.
Building a job search engine with PostgreSQL’s advanced search features
In today’s employment landscape, job search platforms play a crucial role in connecting employers with potential candidates. Behind these platforms lie complex search engines that must process and analyze vast amounts of structured and unstructured data to deliver relevant results. This post explores how to use PostgreSQL’s search features to build an effective job search engine. We examine each search capability in detail, discuss how they can be combined in PostgreSQL, and offer strategies for optimizing performance as your search engine scales.
Implement a rollback strategy for Amazon Aurora PostgreSQL upgrades using Amazon RDS Blue/Green deployments
Amazon Aurora PostgreSQL-Compatible Edition supports managed blue/green deployments to help reduce downtime and minimize risk during updates. Even with thorough planning and testing in non-production environments, unexpected issues can emerge after a version upgrade. In these cases, having a rollback plan is essential to quickly restore service stability. While the managed Blue/Green deployment feature doesn’t currently include built-in rollback functionality, you can implement alternative solutions for version management. In this post, we show how you can manually set up a rollback cluster using self-managed logical replication to maintain synchronization with the newer version after an Amazon RDS Blue/Green deployment switchover.
How an AWS customer in the learning services industry migrated and modernized SAP ASE to Amazon Aurora PostgreSQL
In this post, we explore how a leading AWS customer in the learning services industry successfully modernized its legacy SAP ASE environment by migrating to Amazon Aurora PostgreSQL-Compatible Edition. Partnering with AWS, the customer engineered a comprehensive migration strategy to transition from a proprietary system to an open source database while providing high availability, performance optimization, and cost-efficiency.
Streamline Amazon Aurora database operations at scale: Introducing the AWS Database Acceleration Toolkit
In this post, we introduce the AWS Database Acceleration Toolkit (DAT), an open source database accelerator. DAT is an infrastructure as code solution using Terraform to simplify and automate initial setup, provisioning, and on-going maintenance activities for Amazon Aurora.
Using the PostgreSQL extension tds_fdw to validate data migration from SQL Server to Amazon Aurora PostgreSQL
Data validation is an important process during data migrations, helping to verify that the migrated data matches the source data. In this post, we present alternatives you can use for data validation when dealing with tables that lack primary keys. We discuss alternative approaches, best practices, and potential solutions to make sure that your data migration process remains thorough and reliable, even in the absence of traditional primary key-based validation methods. Specifically, we demonstrate how to perform data validation after a full load migration from SQL Server to Amazon Aurora PostgreSQL-Compatible Edition using the PostgreSQL tds_fdw extension.
Migrate Google Cloud SQL for PostgreSQL to Amazon RDS and Amazon Aurora using pglogical
In this post, we provide the steps to migrate a PostgreSQL database from Google Cloud SQL to RDS for PostgreSQL and Aurora PostgreSQL using the pglogical extension. We also demonstrate the necessary connection attributes required to support the database migration. The pglogical extension works for the community PostgreSQL version 9.4 and higher, and is supported on RDS for PostgreSQL and Aurora PostgreSQL as of version 12+.
Streamline code conversion and testing from Microsoft SQL Server and Oracle to PostgreSQL with Amazon Bedrock
Organizations are increasingly seeking to modernize their database infrastructure by migrating from legacy database engines such as Microsoft SQL Server and Oracle to more cost-effective and scalable open source alternatives such as PostgreSQL. This transition not only reduces licensing costs but also unlocks the flexibility and innovation offered by PostgreSQL’s rich feature set. In this post, we demonstrate how to convert and test database code from Microsoft SQL Server and Oracle to PostgreSQL using the generative AI capabilities of Amazon Bedrock.
Supercharging vector search performance and relevance with pgvector 0.8.0 on Amazon Aurora PostgreSQL
In this post, we explore how pgvector 0.8.0 on Aurora PostgreSQL-Compatible delivers up to 9x faster query processing and 100x more relevant search results, addressing key scaling challenges that enterprise AI applications face when implementing vector search at scale.