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Amazon Aurora

Amazon Aurora Features: MySQL-Compatible Edition

Overview

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. The MySQL-compatible edition of Aurora delivers more throughput of standard MySQL running on the same hardware, and enables existing MySQL applications and tools to run without requiring modification.
The official MySQL logo featuring the word 'MySQL' and a dolphin graphic, representing the popular open-source relational database management system.

Page topics

High performance and scalability

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Amazon Aurora uses a variety of software and hardware techniques to ensure the database engine is able to fully leverage available compute, memory and networking. I/O operations use distributed systems techniques such as quorums to improve performance consistency.

Using the Amazon RDS APIs or with a few clicks in the Amazon Web Services Management Console, you can scale the compute and memory resources powering your deployment up or down. Compute scaling operations typically complete in a few minutes.

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.

Increase read throughput to support high-volume application requests by creating up to 15 database Aurora replicas. Amazon 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. Aurora also supports auto-scaling, where it automatically adds and removes replicas in response to changes in performance metrics that you specify.

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. You can set up your own binlog replication with external MySQL databases.

Aurora Serverless is an on-demand, auto-scaling configuration for Aurora where the database will automatically start-up, shut down, and scale up or down capacity based on your application's needs. Aurora Serverless enables you to run your database in the cloud without managing any database instances.

Custom endpoints allow you to distribute and load balance workloads across different sets of database instances. For example, you may 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 analytics workload to these appropriately-configured instances, while keeping other instances isolated from this workload.

Amazon Aurora Parallel Query provides faster analytical queries over your current data. It can speed up queries by up to 2 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.

High availability and durability

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Amazon RDS continuously monitors the health of your Amazon Aurora database and underlying EC2 instance. In the event of database failure, Amazon RDS will automatically restart the database and associated processes. Amazon Aurora does not require crash recovery replay of database redo logs, greatly reducing restart times. It also isolates the database buffer cache from database processes, allowing the cache to survive a database restart.

On instance failure, Amazon Aurora uses  RDS Multi-AZ technology to automate failover to one of up to 15 Amazon Aurora Replicas you have created in any of three Availability Zones. If no Amazon Aurora Replicas have been provisioned, in the case of a failure, Amazon RDS will attempt to create a new Amazon Aurora DB instance for you automatically. Minimize failover time with the  Amazon Web Services JDBC Driver for MySQL, an open source driver that can be used as a drop-in replacement for the MySQL Connector/J driver.

For globally distributed applications you can use Global Database, where a single Aurora database can span Amazon Web Services China Regions to enable fast local reads and quick disaster recovery. Global Database uses storage-based replication to replicate a database across Amazon Web Services China Regions, with typical latency of less than 1 second. You can use a secondary region as a backup option in case you need to recover quickly 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.

Each 10GB chunk of your database volume is replicated six ways, across three Availability Zones. 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.

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 five minutes. Your automatic backup retention period can be configured up to thirty-five days. Automated backups are stored in 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 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 & 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 via an API and then runs the test, for simple integration into your test framework.

Highly secure

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Amazon Aurora runs in Amazon VPC, which allows you to 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 RDS in VPC, refer to the Amazon RDS User Guide. In addition, using Amazon RDS, you can configure firewall settings and control network access to your DB Instances.

Aurora is integrated with Amazon Identity and Access Management (IAM) and provides you the ability to control the actions that your Amazon IAM users and groups can take on specific Aurora resources (e.g., DB Instances, DB Snapshots, DB Parameter Groups, DB Event Subscriptions, DB Options Groups). In addition, 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.

Amazon Aurora allows you to encrypt your databases using keys you create and control through  Amazon Key Management Service (KMS). On a database instance running with Amazon Aurora encryption, data stored at rest in the underlying storage is encrypted, as are the automated backups, snapshots, and replicas in the same cluster. Amazon Aurora uses SSL (AES-256) to secure data in transit.

Amazon Aurora allows you to 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.

Fully managed

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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 pre-configured 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.

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.

Aurora provides multiple options for monitoring and optimizing database performance, including Amazon CloudWatch, Enhanced Monitoring, and RDS Performance Insights.

Amazon CloudWatch metrics for Aurora allow you to track key operational metrics, such as compute, memory, and storage, query throughput, cache hit ratio, and active connections via the Amazon Web Services Management Console. With Amazon CloudWatch Alarms you can set alarms for specific metrics over a specified time period, and perform actions based on customizable thresholds. 

Amazon CloudWatch Database Insights consolidates logs and metrics from your applications, your databases, and the operating systems on which they run into a unified view in the console. Using its pre-built dashboards, recommended alarms, and automated telemetry collection, you can monitor the health of your database fleets and use a guided troubleshooting experience to drill down to individual instances for root-cause analysis.

Application developers can correlate application performance with database performance by drilling down from the context of their application performance view in Amazon CloudWatch Application Signals to the specific dependent database in CloudWatch Database Insights. CloudWatch Database Insights inherits all the features of Amazon RDS Performance Insights along with additional features such as fleet-level monitoring, integration with application performance monitoring and correlation of database metrics with logs and events.

Enhanced Monitoring provides metrics in real time from the operating system instance running your database. You can view all the system metrics and process information for your RDS DB instances on the console. Aurora delivers the metrics from Enhanced Monitoring to your Amazon CloudWatch Logs account. You can create metrics filters in CloudWatch from CloudWatch Logs and display the graphs on the CloudWatch dashboard.

Amazon Aurora will keep your database up-to-date with the latest patches. You can control if and when your instance is patched via 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 (5 second or so) drop in throughput.

Amazon Aurora can notify you via email or SMS of important database events such as an automated failover. You can use the Amazon Web Services 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 with just a few clicks, 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 with just a few clicks. 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.

Zero-ETL integrations

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Amazon Aurora zero-ETL integration with Amazon Redshift enables near real-time analytics and ML using Amazon Redshift on petabytes of transactional data from Aurora by removing the need for you to build and maintain complex data pipelines that perform extract, transform, and load (ETL) operations. Transactional data is automatically and continuously replicated within seconds of being written in Aurora and is seamlessly made available in Amazon Redshift.

Once data is available in Amazon Redshift, you can start analyzing it immediately and apply advanced features like data sharing, materialized views, and Amazon Redshift ML to get holistic and predictive insights. You can consolidate multiple tables from various Aurora database clusters and replicate your data into one Amazon Redshift data warehouse to run unified analytics across multiple applications and data sources. When using both Aurora Serverless and Amazon Redshift Serverless, you can generate near real-time analytics on transactional data without having to manage any infrastructure for data pipelines. Read our documentation on working with Aurora zero-ETL integrations with Amazon Redshift.

Migration support

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Amazon Aurora combines enterprise-grade security, performance, high availability and durability with the low cost and ease of use of MySQL. This makes it an good migration target when moving workloads from expensive commercial databases to Amazon Web Services. The capabilities of MySQL make it an optimal database for a wide range of database workloads, from simple transactional applications to complex OLTP and OLAP workloads with complicated SQL and stored procedures.

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.

You can also set up binlog-based replication between an Aurora MySQL database and an external MySQL database running inside or outside of Amazon Web Services. 

Cost-effectiveness

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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.

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.

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 Amazon Web Services 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.

Developer productivity

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Aurora offers machine learning capabilities directly from the database, enabling you to add ML-based predictions to your applications via the familiar SQL programming language. With a simple, optimized, and secure integration between Aurora and Amazon Web Services machine learning services, you have access to a wide selection of ML algorithms without having to build custom integrations or move data around.

Aurora can work 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 Amazon IAM and Amazon Secrets Manager.