Amazon Web Services (AWS) introduced Aurora in 2014 as a high-performance managed relational database service compatible with MySQL and PostgreSQL. It has quickly become a popular option. In this comprehensive guide, we’ll explore Aurora‘s capabilities, benefits, use cases, and alternatives to help you determine if it’s the right fit for your needs.
Overview of Aurora
Aurora delivers a MySQL and PostgreSQL compatible relational database engine designed to combine the performance and availability of high-end commercial databases such as Oracle or SQL Server with the cost advantages of open-source databases.
Some key capabilities:
- High throughput and scalability
- High availability and durability
- Seamless scaling of storage
- Comprehensive security features
- Fully managed, self-healing infrastructure
As shown below, benchmarks show Aurora offering 5x the performance of MySQL on the same hardware:
Since launch, Aurora has added cross-region read replicas, auto-scaling, and other enterprise-grade functionality through regular updates. It has quickly become one of the most widely used AWS database services.
Key Features and Capabilities
Let‘s explore some of Aurora‘s leading features and capabilities in more detail:
Performance
Aurora uses a purpose-built distributed storage system to enhance performance far beyond what MySQL and PostgreSQL can efficiently sustain on common hardware.
Specific performance enhancements:
- Optimized buffer cache efficiently serves hot data
- Parallel query execution minimizes bottlenecks
- Storage dynamically allocates additional throughput when needed
- Results in 5x faster OLTP performance over MySQL
By tackling storage and IO bottlenecks that constrain most databases, Aurora achieves previously unattainable levels of performance.
Availability and Durability
Aurora guarantees 99.99% uptime, even for single-AZ instances. How?
- 6-way storage replication across 3 AZs means no data loss on instance failure
- Self-healing storage detects and repairs errors automatically
- Control plane services are redundantly deployed for fault tolerance
- Replicas share same highly durable storage as master
Taken together, these create a very resilient database platform. The end result is measured average annual downtime of just 2 minutes for Aurora clusters.
Scalability
Seamless scaling is another Aurora hallmark:
- Start with 10GB, scales to 64TB automatically
- Scales compute up to 32vCPUs and 244GB RAM
- Add up to 15 low-latency read replicas
- Multi-master writes further spreads load
- No downtime for scaling storage or replicas
This auto-scaled performance lets you focus on application functionality rather than database capacity planning.
Security
Aurora has extensive security capabilities including:
- Encryption at rest and in transit
- VPC for network isolation
- IAM authentication and authorization
- Audit logging with CloudTrail integration
- Dedicated instances for extra isolation
Together these satisfy even stringent enterprise security requirements.
Fully Managed
With Aurora, you get a highly automated database platform. Benefits include:
- Automated failure detection and recovery
- Point-in-time restore for backup/recovery
- Automated backups with configurable retention
- Scheduled maintenance procedures
- Health monitoring and diagnostics
The database takes care of these tedious but mission-critical ops tasks, saving your team time and headaches.
Benefits of Using Aurora
Compared to self-managed open source and commercial databases, Aurora offers compelling benefits:
Cost
- Up to 90% lower than commercial database solutions
- No license fees or servers to manage
- Pay-as-you-go pricing, scale up or down anytime
Performance
- 5x MySQL, over 3x PostgreSQL performance
- Consistently low latency for OLTP workloads
Scalability
- Scales from 10GB to 64TB without downtime
- Compute scales seamlessly on demand
- Distributed and parallel architecture
Availability
- 99.99% average uptime with multi-AZ
- No data loss from AZ outages
- Automatic and instantaneous failover
Security
- Encryption, network isolation, access controls
- Integrated threat detection
- Unparalleled skills and experience behind AWS
Management
- Automated provisioning, upgrades, scaling
- Continuous backup and monitoring
- Self-repairing storage and replication
The following chart summarizes how Aurora stacks up to alternatives on key selection criteria:
| Database | Performance | High Availability | Scalability | Cost |
|---|---|---|---|---|
| Aurora | Excellent | Excellent | Excellent | Low |
| MySQL | Fair | Moderate | Moderate | Low |
| PostgreSQL | Good | Moderate | Moderate | Low |
| SQL Server | Very Good | Very Good | Excellent | High |
| Oracle | Excellent | Excellent | Excellent | Very High |
When to Use Aurora
So when should you consider AWS Aurora? Here are the most popular use cases:
High Performance OLTP
With 5x better performance over MySQL and PostgreSQL on the same hardware, Aurora easily tackles demanding OLTP workloads. The autoscaling distributed architecture smoothly handles spikes in transaction volumes.
Read Scale-Out
Up to 15 low-latency read replicas allow scaling out read-intensive workloads for analytics, reporting, etc. Reads can scale independently without affecting write performance.
High Volume Data
Thanks to seamless scalability from 10GB up through 64TB, Aurora‘s performance doesn‘t taper off as data grows exponentially over time as commonly happens with other databases.
Global Applications
Aurora cross-region replication simplifies providing local performance worldwide combined with disaster recovery benefits.
Mission Critical Uptime
Keeping revenue-critical applications continuously available is non-negotiable. Aurora delivers here with a measured availability exceeding five 9s.
Any application where performance, scalability, uptime, and operational simplicity are higher priorities than squeezing the lowest possible database licensing costs is a good fit for Aurora.
Real-World Use Cases
Leading companies now using AWS Aurora include:
Sony‘sscalable IoT platform
Over 3 million sensors generate 5 billion data points daily across Sony facilities. By leveraging Aurora for sensor data ingestion and analytics, they have scaled IoT deployments that would overwhelm most databases.
Fanatics handles 5x sales spikes
Sports merchandise retailer Fanatics uses Aurora to easily handle 5x traffic spikes driven by major sporting events. Previously they struggled with scaling MySQL.
WD robust analytics pipeline
600 billion read/writes per month are aggregated in Aurora for analysis to drive Western Digital product improvements and supply chain insights. Custom analytics queries have sped time-to-insight 5-10x.
Skyscanner booking surges
1.6 million visitors search flights through Skyscanner daily. Aurora effortlessly scales to handle surges from holiday travel planning spikes.
In all these cases, Aurora‘s performance, scalability, and operational simplicity were pivotal to success.
Getting Started with Aurora
It‘s easy to get started. I‘ll walk through the key steps:
1. Login to AWS console
Go to AWS console and navigate to RDS service.
2. Create Aurora DB cluster
Provision a new Aurora DB cluster, choosing PostgreSQL or MySQL compatibility:
3. Configure settings
Select instance size, enable multi-AZ, configure backups etc:
4. Create database
Connect using preferred client to create database schema, tables, etc. Popular tools include:
- MySQL Workbench
- pgAdmin
- DBBeaver
5. Connect application
Update database connection strings to point your application to the Aurora cluster endpoint.
And that‘s it! Within minutes you can have a high performance managed database ready for your application.
Migrating Existing Databases
Since Aurora supports MySQL and PostgreSQL, most existing relational databases can migrate with minimal effort.
Here is a step-by-step guide to migrate from MySQL to Aurora:
1. Use AWS SCT for schema conversion
The AWS Schema Conversion Tool auto-converts 99% of schema objects and code to ensure compatibility:
2. Set up replication
Configure native MySQL replication between source and Aurora replicas to sync data:
MASTER_HOST=mysqlhost
MASTER_USER=repluser
MASTER_PASSWORD=password
mysql> CREATE REPLICATION SLAVE ON DATABASE
FOR CHANNEL `source`
CONNECTION_NAME src
HOST $MASTER_HOST
USER $MASTER_USER
PASSWORD $MASTER_PASSWORD;3. Redirect application
Point application connection strings from old database to Aurora replica:
# BEFORE
DB_URL="mysql://user:password@oldhost/database
# AFTER
DB_URL="mysql://user:password@aurora-replica/database4. Cut over
When ready, promote replica to be primary database instance. This can be near-instant with minimal downtime.
For more detail, see the AWS Aurora MySQL migration guide.
Aurora Alternatives
While Aurora has many advantages, it isn‘t necessarily the ideal choice for all use cases. The following alternatives merit consideration.
MongoDB Atlas
MongoDB Atlas provides managed MongoDB clusters, a popular document database. Benefits include:
- Flexible document data model
- Tunable consistency and isolation
- Native horizontal scaling and sharding
- Integrations for popular languages
Drawbacks compared to Aurora are potential DBA complexity tuning performance and less mature ecosystem. Overall MongoDB fits modern applications using diverse, dynamic data very well.
Google Cloud SQL
Part of Google Cloud Platform, Google Cloud SQL offers managed MySQL and PostgreSQL instances. Pros are:
- Tight integration with Google Cloud services
- High availability configurations with regional failover replicas
- Automated security patching, upgrades, and backups
Limitations primarily relate to fewer high availability regions currently than Aurora globally. But otherwise an excellent managed relational database option combining open source technology with enterprise capabilities.
Microsoft Azure SQL
Azure SQL from Microsoft Azure offers SQL Server instances and a SQL Server-compatible managed service called Azure SQL Managed Instance. Benefits include:
- Broadest SQL Server ecosystem support
- Business intelligence and machine learning integration
- Hybrid cloud supporting on-premises connectivity
Downsides mainly consist of higher complexity than Aurora and higher costs at scale. But makes an attractive option for SQL Server-centric organizations.
The following table summarizes how the alternatives compare on key criteria:
| Database | Performance | High Availability | Scalability | Cost |
|---|---|---|---|---|
| Aurora | Excellent | Excellent | Excellent | Low |
| MongoDB Atlas | Very Good | Very Good | Excellent | Moderate |
| Google Cloud SQL | Good | Very Good | Moderate | Moderate |
| Azure SQL | Excellent | Excellent | Very Good | High |
Conclusion
I hope this guide has provided you a comprehensive overview of AWS Aurora and clarified how it may be a great fit for your relational database needs. Aurora delivers an enterprise-grade database that combines extreme performance, availability, and scalability at costs far lower than commercial engines like Oracle. Migrating to and operating Aurora is also straightforward thanks to native MySQL and PostgreSQL compatibility.
If you found this helpful or still have questions, post them in the comments section below! I‘m happy to help further.
