Amazon Aurora is a relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Aurora is fully compatible with MySQL and PostgreSQL, allowing existing applications and tools to run without requiring modification.
High performance and scalability
Up to 5x the throughput of MySQL and 3x the throughput of PostgreSQL
Testing on standard benchmarks such as SysBench has shown an increase in throughput of up to 5x over stock MySQL and 3x over stock PostgreSQL on similar hardware. Aurora uses a variety of software and hardware techniques to ensure the database engine is able to fully use available compute, memory, and networking. I/O operations use distributed systems techniques, such as quorums to improve performance consistency.
Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Aurora where the database automatically starts up, shuts down, and scales capacity up or down based on your application's needs. With Amazon Aurora Serverless, you can run your database in the cloud without managing any database instances. You can also use Aurora Serverless v2 instances along with provisioned instances in your existing or new database clusters.
Push-button compute scaling
You can use the Amazon Relational Database Service (Amazon RDS) APIs or the AWS Management Console to scale provisioned instances powering your deployment up or down. Compute scaling operations typically complete in a few minutes.
Storage auto scaling
Aurora automatically scales I/O to match the needs of your most demanding applications. It also increases the size of your database volume as your storage needs grow. Your volume expands in increments of 10 GB up to a maximum of 128 TiB. You don't need to provision excess storage for your database to handle future growth. When using the Amazon Aurora I/O-Optimized configuration for your database clusters, Aurora also provides up to 40% cost savings when I/O spend exceeds 25% of your Aurora database spend. To learn more, visit Amazon Aurora storage and reliability.
Low-latency read replicas
You can increase read throughput to support high-volume application requests by creating up to 15 database Amazon Aurora Replicas. Aurora Replicas share the same underlying storage as the source instance, lowering costs and avoiding the need to perform writes at the replica nodes. This frees up more processing power to serve read requests and reduces the replica lag time—often down to single-digit milliseconds. Aurora provides a reader endpoint so the application can connect without having to keep track of replicas as they are added and removed. It also supports auto scaling, automatically adding and removing replicas in response to changes in performance metrics that you specify.
See Aurora supports cross-Region read replicas. Cross-Region replicas provide fast local reads to your users, and each region can have an additional 15 Aurora Replicas to further scale local reads. See Amazon Aurora Global Database for details.
Custom database endpoints
Custom endpoints allow you to distribute and load balance workloads across different sets of database instances. For example, you can provision a set of Aurora Replicas to use an instance type with higher memory capacity in order to run an analytics workload. A custom endpoint can then help you route the workload to these appropriately configured instances while keeping other instances isolated from it.
Amazon Aurora Parallel Query for Aurora MySQL
Amazon Aurora Parallel Query provides faster analytical queries compared to your current data. It can speed up queries by up to two orders of magnitude while maintaining high throughput for your core transaction workload. By pushing query processing down to the Aurora storage layer, it gains a large amount of computing power while reducing network traffic. Use Parallel Query to run transactional and analytical workloads alongside each other in the same Aurora database. Parallel Query is available for Amazon Aurora with MySQL compatibility.
Diagnose and resolve performance bottlenecks with Amazon DevOps Guru for RDS
Amazon DevOps Guru is a cloud operations service powered by machine learning (ML) that helps improve application availability. With Amazon DevOps Guru for RDS, you can use ML-powered insights to help easily detect and diagnose performance-related relational database issues and is designed to resolve them in minutes rather than days. Developers and DevOps engineers can use DevOps Guru for RDS to automatically identify the root cause of performance issues and get intelligent recommendations to help address the issue, without needing help from database experts.
To get started, simply go to the Amazon RDS Management Console and enable Amazon RDS Performance Insights. Once Performance Insights is on, go to the Amazon DevOps Guru Console and enable it for your Amazon Aurora resources, other supported resources, or your entire account.
High availability and durability
Instance monitoring and repair
Amazon RDS continuously monitors the health of your Aurora database and underlying Amazon Elastic Compute Cloud (Amazon EC2) instance. In the event of database failure, Amazon RDS will automatically restart the database and associated processes. Aurora does not require crash recovery replay of database redo logs, which greatly reduces restart times. It also isolates the database buffer cache from database processes, which allows the cache to survive a database restart.
Multi-AZ deployments with Amazon Aurora Replicas
On instance failure, Aurora uses Amazon RDS Multi-AZ technology to automate failover to one of up to 15 Aurora Replicas you have created in any three Availability Zones. If no Aurora Replicas have been provisioned, in the case of a failure, Amazon RDS will automatically attempt to create a new Aurora DB instance for you. Minimize failover time by replacing community MySQL and PostgreSQL drivers with the open-source and drop-in compatible AWS JDBC Driver for MySQL and AWS JDBC Driver for PostgreSQL. You may also use RDS Proxy to reduce failover times and improve availability. When failovers occur, Amazon RDS Proxy routes requests directly to the new database instance, reducing failover times by up to 66% while preserving application connections.
Amazon Aurora Global Database
For globally distributed applications, you can use an Aurora Global Database, where a single Aurora database can span multiple AWS Regions to enable fast local reads and quick disaster recovery. An Aurora Global Database uses storage-based replication to replicate a database across multiple Regions, with typical latency of less than one second. You can use a secondary Region as a backup option in case you need to quickly recover from a regional degradation or outage. A database in a secondary Region can be promoted to full read/write capabilities in less than 1 minute.
Fault-tolerant and self-healing storage
Aurora's database storage volume is segmented in 10 GiB chunks and replicated across three Availability Zones, with each Availability Zone persisting 2 copies of each write. Amazon Aurora storage is fault-tolerant, transparently handling the loss of up to two copies of data without affecting database write availability and up to three copies without affecting read availability. Amazon Aurora storage is also self-healing; data blocks and disks are continuously scanned for errors and replaced automatically.
Automatic, continuous, incremental backups and point-in-time restore
Amazon Aurora's backup capability enables point-in-time recovery for your instance. This allows you to restore your database to any second during your retention period, up to the last 5 minutes. Your automatic backup retention period can be configured up to 35 days. Automated backups are stored in Amazon Simple Storage Service (Amazon S3), which is designed for 99.999999999% durability. Amazon Aurora backups are automatic, incremental, and continuous and have no impact on database performance.
DB snapshots are user-initiated backups of your instance stored in Amazon S3 that will be kept until you explicitly delete them. They leverage the automated incremental snapshots to reduce the time and storage required. You can create a new instance from a DB snapshot whenever you desire.
Backtrack for Aurora MySQL
Backtrack lets you quickly move a database to a prior point in time without needing to restore data from a backup. This lets you quickly recover from user errors, such as dropping the wrong table or deleting the wrong row. When you enable Backtrack, Aurora will retain data records for the specified Backtrack duration. For example, you could set up Backtrack to allow you to move your database up to 72 hours back. Backtrack completes in seconds, even for large databases, because no data records need to be copied. You can go backwards and forwards to find the point just before the error occurred.
Backtrack is also useful for development and test, particularly in situations where your test deletes or otherwise invalidates the data. Simply backtrack to the original database state, and you're ready for another test run. You can create a script that calls Backtrack through an API and then runs the test, for simple integration into your test framework. Backtrack is available for Amazon Aurora with MySQL compatibility.
Amazon Aurora runs in Amazon Virtual Private Cloud (VPC), which helps you isolate your database in your own virtual network and connect to your on-premises IT infrastructure using industry-standard encrypted IPsec VPNs. To learn more about Amazon Relational Database Service (RDS) in Amazon VPC, refer to the Amazon RDS User Guide. Also, when using Amazon RDS, you can configure firewall settings and control network access to your DB instances.
Aurora is integrated with AWS Identity and Access Management (IAM) and provides you the ability to control the actions that your IAM users and groups can take on specific Aurora resources (for example, DB instances, DB snapshots, DB parameter groups, DB event subscriptions, DB option groups). Also, you can tag your Aurora resources and control the actions that your IAM users and groups can take on groups of resources that have the same tag (and tag value). For more information about IAM integration, see the IAM database authentication documentation.
Aurora helps you encrypt your databases using keys you create and control through AWS Key Management Service (KMS). On a database instance running with Aurora encryption, data stored at rest in the underlying storage is encrypted, as are the automated backups, snapshots, and replicas in the same cluster. Aurora uses SSL (AES-256) to secure data in transit.
Aurora helps you log database events with minimal impact on database performance. Logs can later be analyzed for database management, security, governance, regulatory compliance, and other purposes. You can also monitor activity by sending audit logs to Amazon CloudWatch.
Aurora is integrated with Amazon GuardDuty to help you identify potential threats to data stored in Aurora databases. GuardDuty RDS Protection profiles and monitors login activity to existing and new databases in your account and uses tailored ML models to accurately detect suspicious logins to Aurora databases. If a potential threat is detected, GuardDuty generates a security finding that includes database details and rich contextual information on the suspicious activity. Aurora integration with GuardDuty gives direct access to database event logs without requiring you to modify your databases and is designed not to have an impact on database performance.
Pay only for what you use
There is no upfront commitment with Aurora. You pay an hourly charge for each instance that you launch, and when you’re finished with an Amazon Aurora DB instance, you can delete it. You do not need to overprovision storage as a safety margin, and you only pay for the storage you actually consume. To see more details, visit the Amazon Aurora pricing page.
Price predictability at any scale
Aurora offers the flexibility to optimize your database spend by choosing between two configuration options based on your price-performance and price-predictability needs, regardless of the I/O consumption of your application. The two configuration options are Aurora I/O-Optimized and Aurora Standard. Neither option requires upfront I/O or storage provisioning and both can scale I/O to support your most demanding applications.
Aurora I/O-Optimized is a database cluster configuration. It delivers improved price performance for customers with I/O-intensive workloads such as payment processing systems, ecommerce systems, and financial applications. If your I/O spend exceeds 25% of your total Aurora database spend, you can save up to 40% on costs for I/O-intensive workloads with Aurora I/O-Optimized. With Aurora I/O-Optimized you pay for database instances and storage. There are no charges for read and write I/O operations, providing price predictability for all applications regardless of I/O variability.
Aurora Standard is a database cluster configuration that offers cost-effective pricing for the vast majority of applications with low to moderate I/O usage. With Aurora Standard you pay for database instances, storage, and pay-per-request I/O.
Optimize I/O costs
For a heavily analytical application, I/O costs are typically the largest contributor to the database cost. I/O operations are performed by the Aurora database engine against its SSD-based virtualized storage layer. Every database page read operation counts as one I/O. The Aurora database engine issues reads against the storage layer to fetch database pages not present in the buffer cache. Each database page is 8 KB in Aurora with PostgreSQL compatibility and 16 KB in Aurora with MySQL compatibility.
Aurora was designed to eliminate unnecessary I/O operations to reduce costs and ensure resources are available for serving read/write traffic. Write I/O operations are only consumed when pushing transaction log records to the storage layer for the purpose of making writes durable. Write I/O operations are counted in 4 KB units. For example, a transaction log record that is 1,024 bytes counts as one I/O operation. However, concurrent write operations whose transaction log is less than 4 KB can be batched together by the Aurora database engine to optimize I/O consumption. Unlike traditional database engines Aurora never pushes modified database pages to the storage layer, resulting in further I/O consumption savings.
You can see how many I/O operations your Aurora instance is consuming by going to the AWS Management Console. To find your I/O consumption, go to the RDS section of the console, look at your list of instances, select your Aurora instances, then look for the “Billed read operations” and “Billed write operations” metrics in the monitoring section.
You are charged for read and write I/O operations when you configure your database clusters to the Aurora Standard configuration. You are not charged for read and write I/O operations when you configure your database clusters to Aurora I/O-Optimized. For more information on the pricing of I/O operations, visit Amazon Aurora Pricing page.
Easy to use
Getting started with Amazon Aurora is easy. Just launch a new Amazon Aurora DB instance using the Amazon RDS Management Console or a single API call or CLI. Amazon Aurora DB instances are preconfigured with parameters and settings appropriate for the DB instance class you have selected. You can launch a DB instance and connect your application within minutes without additional configuration. DB parameter groups provide granular control and fine-tuning of your database.
Monitoring and metrics
Amazon Aurora provides Amazon CloudWatch metrics for your DB instances at no additional charge. You can use the AWS Management Console to view over 20 key operational metrics for your database instances, including compute, memory, storage, query throughput, cache hit ratio, and active connections. In addition, you can use Enhanced Monitoring to gather metrics from the operating system instance that your database runs on. You can use Amazon RDS Performance Insights, a database monitoring tool that makes it easy to detect database performance problems and take corrective action with an easy-to-understand dashboard that visualizes database load. Finally, you also can use Amazon DevOps Guru for RDS to easily detect performance issues, automatically identify the root cause of performance issues, and get intelligent recommendations to help address the issue without needing help from database experts.
Amazon RDS Blue/Green Deployments
Amazon RDS Blue/Green Deployments allow you to make safer, simpler, and faster database updates with zero data loss on Amazon Aurora MySQL-Compatible Edition. In a few steps, Blue/Green Deployments creates a staging environment that mirrors the production environment and keeps the two environments in sync using logical replication. You can make changes—such as major/minor version upgrades, schema modifications, and parameter setting changes—without impacting your production workload.
When promoting your staging environment, Blue/Green Deployments blocks writes to both the blue and green environments until switchover is complete. Blue/Green Deployments uses built-in switchover guardrails that time out promotion if it exceeds your maximum tolerable downtime, detects replication errors, checks instance health, and more.
Automatic software patching
Amazon Aurora will keep your database up-to-date with the latest patches. You can control if and when your instance is patched through DB Engine Version Management. Aurora uses zero-downtime patching when possible: if a suitable time window appears, the instance is updated in place, application sessions are preserved and the database engine restarts while the patch is in progress, leading to only a transient (five-second or so) drop in throughput.
DB event notifications
Amazon Aurora can notify you by email or SMS of important database events such as an automated failover. You can use the AWS Management Console or the Amazon RDS APIs to subscribe to over 40 different DB events associated with your Amazon Aurora databases.
Amazon Aurora supports quick, efficient cloning operations, where entire multi-terabyte database clusters can be cloned in minutes. Cloning is useful for a number of purposes including application development, testing, database updates, and running analytical queries. Immediate availability of data can significantly accelerate your software development and upgrade projects, and make analytics more accurate.
You can clone an Amazon Aurora database in only a few steps, and you don't incur any storage charges, except if you use additional space to store data changes.
You can manually stop and start an Amazon Aurora database in only a few steps. This makes it easy and affordable to use Aurora for development and test purposes, where the database is not required to be running all of the time. Stopping your database doesn't delete your data. See the start/stop documentation for more details.
MySQL database migrations
Standard MySQL import and export tools work with Amazon Aurora. You can also easily create a new Amazon Aurora database from an Amazon RDS for MySQL DB snapshot. Migration operations based on DB snapshots typically complete in under an hour, but will vary based on the amount and format of data being migrated.
Alternatively, AWS Database Migration Service (AWS DMS) offers built-in native tooling from within the DMS Console for a seamless migration. With no replication instances to provision or scale, you can initiate a database migration with a few simple clicks, and only pay on an hourly basis for the time used.
You can also set up binlog-based replication between an Aurora MySQL-Compatible Edition database and an external MySQL database running inside or outside of AWS.
PostgreSQL database migrations
Standard PostgreSQL import and export tools work with Amazon Aurora, including pg_dump and pg_restore. Amazon Aurora also supports snapshot import from Amazon RDS for PostgreSQL, and replication with AWS Database Migration Service (AWS DMS).
Commercial database migrations
Amazon Aurora provides an ideal environment for moving database workloads off of commercial databases. Aurora has functional capabilities which are a close match to those of commercial database engines, and delivers the enterprise-grade performance, durability, and high availability required by most enterprise database workloads. AWS Database Migration Service (AWS DMS) can help accelerate database migrations to Amazon Aurora with managed features like DMS Schema Conversion and DMS Serverless. DMS Schema Conversion will automatically assess and convert schemas and source objects to be compatible with the target Aurora cluster. Meanwhile, DMS Serverless automates provisioning, monitoring, and scaling of migration resources.
Babelfish for Aurora PostgreSQL
Babelfish for Aurora PostgreSQL is a new capability for Amazon Aurora PostgreSQL-Compatible Edition that enables Aurora to understand commands from applications written for Microsoft SQL Server. With Babelfish, Aurora PostgreSQL now understands T-SQL, Microsoft SQL Server's proprietary SQL dialect, and supports the same communications protocol, so your apps that were originally written for SQL Server can now work with Aurora with fewer code changes. As a result, the effort required to modify and move applications running on SQL Server 2005 or newer to Aurora is reduced, leading to faster, lower-risk, and more cost-effective migrations. Babelfish is a built-in capability of Amazon Aurora, and does not have an additional cost. You can enable Babelfish on your Amazon Aurora cluster in only a few steps in the RDS console.
Trusted Language Extensions for PostgreSQL
Amazon Aurora machine learning
Aurora offers machine learning capabilities directly from the database, enabling you to add ML-based predictions to your applications through the familiar SQL programming language. With a simple, optimized, and secure integration between Aurora and AWS machine learning services, you have access to a wide selection of ML algorithms without having to build custom integrations or move data around. Learn more about Aurora machine learning.
Amazon RDS Proxy support
Aurora works in conjunction with Amazon RDS Proxy, a fully managed, highly available database proxy that makes applications more scalable, more resilient to database failures, and more secure. RDS Proxy allows applications to pool and share connections established with the database, improving database efficiency and application scalability. It reduces failover times by automatically connecting to a new database instance while preserving application connections. It enhances security through integrations with AWS IAM and AWS Secrets Manager.
Aurora offers capabilities to enable machine learning (ML) and generative artificial intelligence (AI) models to work with data stored in Aurora in real-time and without moving the data. With Amazon Aurora PostgreSQL-Compatible Edition, you can access vector database capabilities to store, search, index, and query ML embeddings.
A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video. Generative AI and other AI/ML systems use embeddings to capture the semantic meaning of this content input into a large language model (LLM). You can store embeddings from ML and AI models, such as those from Amazon Bedrock (limited preview), and Amazon SageMaker in your Aurora PostgreSQL database. Read our documentation on extensions versions for Amazon Aurora PostgreSQL.
Aurora machine learning also simplifies adding generative AI model predictions and embeddings to your Aurora database. Aurora ML exposes ML models as SQL functions, allowing you to use standard SQL to call ML models, pass data to them, and return predictions or embeddings as query results. With Aurora ML, you can make the process of adding new embeddings to your database real-time via periodic calls to the SageMaker model which returns the latest, up-to-date embeddings .