Working with Aurora PostgreSQL: A Comprehensive Guide 101


Working with Aurora PostgreSQL

Amazon Aurora PostgreSQL is a relational database engine that is fully managed, PostgreSQL-compatible, and ACID-compliant. It combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases.

In this article, you’ll learn how it’s simple to configure Amazon Aurora PostgreSQL, which improves the throughput and efficiency of database operations.

Amazon Aurora PostgreSQL is a PostgreSQL drop-in replacement that makes it simple and cost-effective to set up, run, and scale new and existing PostgreSQL deployments, allowing you to focus on your business and applications.

Amazon RDS handles routine database tasks such as provisioning, patching, backup, recovery, failure detection, and repair for Aurora. Amazon RDS also offers one-click migration tools for converting your existing Amazon RDS for PostgreSQL applications to Amazon Aurora PostgreSQL.

AWS Aurora PostgreSQL is compatible with a wide range of industry standards. Under a completed Business Associate Agreement (BAA) with AWS, for example, you can use Aurora PostgreSQL databases to build HIPAA-compliant applications and store healthcare-related information, including protected health information (PHI).

Table of Contents


  • The database version is 9.4, 9.5, 9.6, or 10.0, and an Amazon Aurora PostgreSQL instance is created.
  • The Amazon Aurora PostgreSQL instance’s Public accessibility option is set to Yes. This ensures that DTS has Internet access to the Amazon Aurora PostgreSQL Instance.

What is Amazon Aurora?

Aurora PostgreSQL - Amazon Aurora logo
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Amazon Aurora is a popular database engine with a robust feature set that can seamlessly import databases from MySQL and PostgreSQL. It provides enterprise-level performance while automating all traditional database tasks. As a result, you won’t have to worry about manually managing processes such as data backups, hardware provisioning, software updates, and others.

Through its multi-deployment tool, Amazon Aurora provides high scalability and can replicate data across multiple zones. This means that users can choose from a variety of hardware specifications to suit their specific use cases. Furthermore, Amazon Aurora’s server-less feature manages database scalability and automatically upscales or downscales storage as needed. This mode only charges you for the time the database is active.

Key Features of Amazon Aurora

The following features contribute to Amazon Aurora’s popularity:

  • Exceptional Performance: Amazon employs software and hardware tweaks that enable the Aurora database engine to take advantage of Amazon’s computing, memory, and network resources. As a result, Aurora outperforms its competitors significantly.
  • Scalability: Amazon Aurora will automatically scale from a minimum of 10 GB storage to 64 TB storage in increments of 10 GB at a time, based on your database usage. This will have no negative impact on its database performance, and you won’t have to worry about allocating storage space as your company grows.
  • Data Backups: Amazon Aurora provides automatic, incremental, and continuous backups that do not impair database performance. This eliminates the need to take data snapshots regularly to maintain data recovery.
  • High Availability and Durability: The health of your Amazon Aurora database and underlying Amazon Elastic Compute Cloud (Amazon EC2) instance is constantly monitored by Amazon RDS. In the event of a database failure, Amazon RDS will restart the database and related activities automatically. Crash recovery replay of database redo logs is not required with Amazon Aurora, dramatically lowering restart times. The database buffer cache is likewise isolated from the database process by Amazon Aurora, allowing it to withstand a database restart.
  • Highly Secure: Aurora is linked with AWS Identity and Access Management (IAM), giving you control over the actions your AWS IAM users and groups can do on individual Aurora resources (e.g., DB Instances, DB Snapshots, DB Parameter Groups, DB Event Subscriptions, DB Options Groups). You may also tag your Aurora resources and limit what actions your IAM users and groups can perform on groups of resources with the same tag (and tag value).
  • Fully Managed: Your database will be kept up to date with the newest patches thanks to Amazon Aurora. DB Engine Version Management allows you to choose whether and when your instance is patched. With a few clicks, you may manually stop and start an Amazon Aurora database. This makes using Aurora for development and testing where the database does not need to be operating all of the time simple and economical. Your data is not deleted when you stop your database.
  • Developer Productivity: Aurora provides machine learning capabilities directly from the database, allowing you to use the traditional SQL programming language to add ML-based predictions to your applications. You may access a wide range of machine learning algorithms without having to construct new integrations or move data around thanks to a straightforward, efficient, and secure integration between Aurora and AWS machine learning services.

What is PostgreSQL?

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PostgreSQL is well-known in the market for its ability to handle complex, high-volume data operations. PostgreSQL has a few more features and is extensible when compared to other Database Management Systems. It allows you to define index types, data types, and functional languages in addition to storing information in the form of tables and columns.

PostgreSQL is unique in that it is Object-Relational, ACID-compliant supports NoSQL and is highly concurrent. Let’s take a look at some additional features that make PostgreSQL a viable Database Management System for your requirements.

Key Features of PostgreSQL

Here are a few notable key features of PostgreSQL:

  • Customizable: PostgreSQL can be customized by creating plugins to make the Database Management System meet your needs. PostgreSQL also allows you to include custom functions written in other programming languages such as Java, C, C++, and others.
  • Long Tradition: PostgreSQL has been around for over 30 years, having first been released in 1988.
  • MVCC Characteristics: PostgreSQL was the first database management system to include Multi-Version Concurrency Control (MVCC) capabilities.
  • A Community That Cares: A dedicated community is always available to you. Private, third-party support services are also available. The PostgreSQL Global Development Group is where the community updates the PostgreSQL platform.
  • Open-Source: An Object-Relational Database Management System is what this is (ORDBMS). PostgreSQL can now provide Object-Oriented and Relational Database functionality.

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Working with Amazon Aurora PostgreSQL

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AWS Aurora PostgreSQL is backwards compatible with PostgreSQL 9.6 and PostgreSQL 10. It is extremely simple to configure PostgreSQL with Amazon Aurora, which can improve the throughput and efficiency of database operations.

Here are some practices that meet with AWS Aurora PostgreSQL:

Amazon Aurora PostgreSQL Security

AWS Identity and Access Management are used to control who can perform Amazon RDS management actions on Aurora PostgreSQL DB clusters and DB instances (IAM). When you use IAM credentials to connect to AWS, your AWS account must have IAM policies that grant the permissions required to perform Amazon RDS management operations.

Data Migration to Amazon Aurora using PostgreSQL Compatibility

  • Using a snapshot to migrate an RDS for PostgreSQL DB instance: Data can be directly migrated from an RDS for PostgreSQL DB snapshot to an Aurora PostgreSQL DB cluster.
  • Using an Amazon Aurora read replica to migrate an RDS for PostgreSQL DB instance: You can also migrate from an RDS for PostgreSQL DB instance by creating an AWS Aurora PostgreSQL read replica of that instance. You can stop replication when the replica lag between the RDS for the PostgreSQL DB instance and the Aurora PostgreSQL read replica is zero. You can now make the Aurora read replica a standalone AWS Aurora PostgreSQL DB cluster for reading and writing.
  • Importing data from S3 into AWS Aurora PostgreSQL: Data can be moved by importing it from Amazon S3 into a table in an Aurora PostgreSQL DB cluster.

Amazon Aurora PostgreSQL Integration with other AWS services

AWS Aurora PostgreSQL with other AWS services, allowing you to extend your Aurora PostgreSQL DB cluster to take advantage of additional AWS Cloud capabilities. AWS services can be used by your Aurora PostgreSQL DB cluster to do the following:

  • With Amazon RDS Performance Insights, you can quickly collect, view, and assess performance for your Aurora PostgreSQL DB instances. Performance Insights enhances existing Amazon RDS monitoring features by displaying your database’s performance and assisting you in analyzing any issues that affect it.
  • Aurora Auto Scaling allows you to automatically add or remove Aurora Replicas.
  • Import data from an Amazon S3 bucket into an Aurora PostgreSQL DB cluster, or export data from an Aurora PostgreSQL DB cluster back to an Amazon S3 bucket.
  • Using the SQL language, incorporate Machine Learning-based predictions into database applications. Amazon Aurora Machine Learning makes use of a highly optimized integration between the Aurora database and the Amazon Web Services (AWS) Machine Learning (ML) services SageMaker and Amazon Comprehend.
  • The PostgreSQL oracle few extension, which provides a foreign-data wrapper, can be used to provide easy and efficient access to Oracle databases for Aurora PostgreSQL.
  • Integrates Amazon Redshift and AWS Aurora PostgreSQL queries.

Using AWS Aurora PostgreSQL and Machine Learning (ML)

You can incorporate Machine Learning-based predictions into your existing database applications. You do not need to create custom integrations or learn new tools. You can incorporate machine learning processing directly into your SQL query as function calls.

The ML Integration is a quick way to give ML services access to transactional data. To perform Machine Learning operations, you do not need to move the data out of the database. To use the results of Machine Learning operations in your database application, you do not need to convert or reimport them.

You can control who has access to the underlying data and the generated insights using your existing governance policies.

AWS Machine Learning services are managed services that you configure and run in your production environment. Amazon Aurora Machine Learning currently integrates with Amazon Comprehend for sentiment analysis and SageMaker for a wide range of machine learning algorithms.

Using the pg_partman extension to manage PostgreSQL Partitions

Table partitioning in PostgreSQL provides a framework for high-performance data input and reporting. Partitioning is useful for databases that require large amounts of data to be entered quickly.

Partitioning also speeds up queries on large tables. Partitioning allows data to be maintained without affecting the database instance because it requires fewer I/O resources.

To enable the pg_partman extension for a specific database, first, create the partition maintenance schema, then the pg_partman extension, as shown below.


Amazon Aurora PostgreSQL with Kerberos

When users connect to your PostgreSQL-powered database cluster, you can use Kerberos Authentication to authenticate them. In this case, your DB instance communicates with AWS Directory Service for Microsoft Active Directory to enable Kerberos Authentication.

AWS Directory Service for Microsoft Active Directory, also known as AWS Managed Microsoft AD, is a cloud-based directory service for Microsoft Active Directory.

Benefits of Amazon Aurora PostgreSQL

Here are a few benefits of AWS Aurora PostgreSQL:

  • Amazon Aurora was created specifically for the cloud and has improved Scalability and Speed, which is three times faster than a standard Postgres database.
  • Data is automatically replicated across three AWS availability zones, ensuring high availability.
  • It also provides fault-tolerant and self-healing storage, instance monitoring, and database recovery in the event of a failure.
  • There is no pricing or resource lock-in, the database will scale up and down as needed, and you will only ever pay for what you use.

Amazon Aurora PostgreSQL: Best practices

  • Set TCP keepalives aggressively to ensure that longer-running queries that are awaiting a server response are terminated before the read timeout expires if the connection fails.
  • To ensure that the Aurora read-only endpoint may properly cycle across read-only nodes on successive connection attempts, increase the Java DNS caching timeouts.
  • Set the JDBC connection string’s timeout variables as low as possible. For a brief and long-running queries, use different connection objects.
  • To connect to the cluster, use the read and write Aurora endpoints that are supplied.
  • To test application response for server-side failures, utilize RDS APIs, and to test application response for client-side failures, use a packet dropping tool.


The article introduced you to Amazon Aurora and PostgreSQL, as well as their key features. Furthermore, the characteristics of both have been explained in this article. The article also listed the major advantages that they can bring to your company.

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Share your understanding of AWS Aurora PostgreSQL in the comments below!

Davor DSouza
Research Analyst, Hevo Data

Davor is a data analyst at heart with a passion for data, software architecture, and writing technical content. He has experience writing more than 100 articles on data integration and infrastructure.

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