Category: RDS for PostgreSQL

In this post, we focus on working with the features of mysql_fdw PostgreSQL extension on Amazon RDS for PostgreSQL to help manage a large set of data that on an external database scenarios. It enables you to interact with your MySQL database for importing individual/large/selectively number of objects at the schema level and simplifying how we get information about the MySQL/MariaDB schema, to make it easier to ultimately read/write data. We will also provide an introduction to understand query performance on foreign tables.

There are a variety of different techniques available to support data masking in databases, each with their trade-offs. In this post, we explore dynamic data masking, a technique that returns anonymized data from a query without modifying the underlying data. In this post, we discuss a dynamic data masking technique based on dynamic masking views. These views mask personally identifiable information (PII) columns for unauthorized users. This post discusses how to implement this technique in Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL including Babelfish for Aurora PostgreSQL.

Choosing the right storage configuration that meets performance requirements is a common challenge when creating and managing database instances. In this post, we provide an end-to-end guide for what storage class to choose depending on your use case. In addition, we compare the performance of different storage volumes on open source engines supported by Amazon RDS, to validate them from a database-centric perspective.

Recently, Amazon RDS launched the ability to shrink storage volumes using Amazon RDS Blue/Green Deployments – a nice addition to the list of new use cases that Blue/Green Deployments now supports. In this post, we cover how to use the new storage volume shrink feature in Amazon RDS Blue/Green Deployments to minimize the downtime required to perform the storage size reduction operation. We also review various mechanisms to monitor the progress of storage shrink and best practices on how to arrive at the optimal storage size for your shrink storage task.

With the recent addition of physical replication as an option for RDS Blue/Green Deployments, you can overcome most of the limitations of logical replication. This makes physical replication particularly well-suited for use cases like minor version upgrades, schema changes (DDL operations) in the blue environment, and storage adjustments. In this post, we delve into the advantages of using physical replication in RDS for PostgreSQL blue/green deployments to simplify database operations and scale with application demands. We explore the key benefits of physical replication and provide a step-by-step guide to help you get started with this new capability.

In this post, we demonstrate improvements in planned failover downtime of Multi-AZ instance deployment with Amazon RDS Proxy, a result of several optimizations made by RDS. In the event of a failure, Amazon RDS automatically switches the roles of the primary and standby instances and updates the IP address associated with the database’s DNS (hostname). This allows client applications to maintain their connection settings during failover. This process, known as DNS propagation, can take up to 35 seconds to complete. RDS Proxy eliminates the 35 seconds of DNS propagation delay by continuously monitoring both instances, allowing it to bypass DNS propagation. This allows RDS Proxy to deliver a faster failover response for client applications, maximizing availability during failovers.

In this post, we introduce postgres_get_av_diag(), a new function available in RDS for PostgreSQL to monitor aggressive autovacuum blockers. By using this function, you can identify and address performance and availability risks through actionable insights provided by postgres_get_av_diag().

In this post, we demonstrate how to set up an event-driven workflow to run stored procedures for Amazon RDS for PostgreSQL with AWS Lambda to bridge this gap by securely connecting to an Aurora PostgreSQL database using AWS Secrets Manager, making sure that stored procedures can be managed in the cloud. We explore the step-by-step process, discuss the advantages of this approach, and address the limitations of invoking stored procedures from Lambda functions.

PostgreSQL’s ability to handle concurrent access while maintaining data consistency relies heavily on its locking mechanisms, particularly at the row level. When multiple transactions attempt to lock the same row simultaneously, PostgreSQL turns to a specialized structure called MultiXact IDs. While MultiXacts provide an efficient way to manage multiple locks on a single row, they […]

In this post, we guide you through the process of benchmarking the performance of Amazon RDS for PostgreSQL using the Dedicated Log Volume (DLV) feature. To do this, we use pgbench – a tool for running benchmark tests on PostgreSQL databases, pgbench repeatedly executes a defined sequence of SQL commands across multiple concurrent database sessions. Through our benchmarking, you’ll learn how to quantify the performance improvements delivered by DLV.