Exploring Replication Features in Amazon Aurora PostgreSQL: A Guide by Chanci Turner

In today’s global economy, critical applications such as those in finance, travel, and gaming face stringent demands for availability and disaster recovery. These applications often must withstand region-wide outages, which can complicate decisions regarding performance, availability, cost, and Recovery Point Objectives (RPO) or Recovery Time Objectives (RTO). Moreover, routine database maintenance, including upgrades, can lead to significant downtime issues. Users increasingly require low-latency access to their data from various locations, necessitating uninterrupted service even during planned maintenance.

This article will delve into the various replication options available in the Amazon Aurora PostgreSQL-Compatible Edition, enabling your applications to remain resilient against regional outages and maintain continuous operational functionality.

Amazon Aurora is a cloud-optimized relational database compatible with both MySQL and PostgreSQL. It merges the reliability and performance of traditional enterprise databases with the affordability and ease of open-source alternatives. Database replication plays a crucial role in ensuring consistency across redundant databases, enhancing reliability, fault tolerance, and accessibility. Here, we will explore the replication capabilities specific to Aurora PostgreSQL.

High Availability and Durability

Aurora is engineered to ensure over 99.99% availability by maintaining six copies of your data across three distinct Availability Zones and continuously backing it up to Amazon Simple Storage Service (Amazon S3). The system automatically recovers from both physical storage and instance failures, with the failover to a read replica typically completed in under 30 seconds.

Replication Options in Aurora

Aurora provides a variety of replication methods. By default, each Aurora database cluster creates six copies of data across three Availability Zones at the storage level. When it comes to data replication into or out of an Aurora cluster, you can choose between Aurora features such as the Amazon Aurora global database or the native replication mechanisms for PostgreSQL. Selecting the right method depends on your specific requirements for high availability and performance.

Replication Techniques for Aurora PostgreSQL

For Aurora PostgreSQL, you have several replication options:

Native PostgreSQL Logical Replication
pglogical Extension for Logical Replication
Physical Replication with Aurora Global Database

PostgreSQL Native Logical Replication

Logical replication allows for the replication of data objects and their changes based on their replication identity, typically a primary key. This method contrasts with physical replication, which relies on exact block addresses and byte-by-byte replication. Logical replication operates on a publish/subscribe model, where one or more subscribers can receive data from multiple publications on a publisher node. Subscribers pull data in real-time as it is published, ensuring transactional consistency by applying changes in the same order as they occur on the publisher.

Use Cases for PostgreSQL Native Logical Replication

Here are some common scenarios where logical replication is beneficial:

Migrating data from on-premises or self-managed PostgreSQL databases to Aurora PostgreSQL. For more information, check out this helpful resource on migrating databases.
Replicating data between two Aurora PostgreSQL clusters within the same region for high availability and disaster recovery (HA/DR). In HA/DR situations, the target system remains an exact copy of the source system. If the source fails, the target takes over and starts replicating back.
Facilitating database upgrades and application changes with minimal downtime. AWS Database Migration Service (AWS DMS) employs PostgreSQL logical replication for near real-time data synchronization between major versions.
Implementing role-based access control, allowing various user groups different access levels to replicated data. This strategy can effectively distribute workload and prevent overwhelming the primary database.
Replicating transactional data to a data warehouse (like Amazon Redshift) or a data lake (such as Amazon S3) for advanced analytics, real-time dashboards, and machine learning applications.

Limitations of Logical Replication

Despite its advantages, logical replication has certain limitations, which may be addressed in future Aurora PostgreSQL updates:

Database schema and Data Definition Language (DDL) commands (like CREATE, ALTER, and DROP) are not replicated. The initial schema can be manually copied using pg_dump --schema-only, but future changes must be synced manually.
Sequence data is not replicated. While data in serial or identity columns backed by sequences is replicated, the sequence itself retains its start value on the subscriber. Manual updates are required during failover situations.
TRUNCATE commands are supported from PostgreSQL 11 onward, but caution is warranted when truncating tables linked by foreign keys.
Large objects, particularly those stored as OID types, are not replicated. Only BYTEA types, which are akin to standard character strings, are included in replication.

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