Amazon Aurora is a fully managed relational database service that offers high performance, availability, and compatibility with MySQL and PostgreSQL. In this comprehensive guide, we‘ll cover everything you need to know about Aurora.
Overview of Amazon Aurora
Launched in 2014, Aurora delivers up to 5x better performance than standard MySQL and up to 3x than PostgreSQL at one-tenth the cost of commercial databases. Key capabilities include:
- High throughput and low latency
- Scale up to 128 TB storage and 15 read replicas
- Sub 10ms replication between AZs
- Continuous backup to S3 and point-in-time recovery
- Auto healing capability for failures
- Security via encryption, IAM, VPC
- Fully managed service – no admin overhead
These make Aurora a great fit for high scale applications with demanding workloads. It has seen rapid adoption across industries like financial services, SaaS, retail, gaming etc.
Aurora Architecture
Aurora employs a unique distributed, fault-tolerant storage system that replicates data across AZs. Writes go to cluster volume in one AZ while storage nodes in other AZs get updated incrementally.
The log-based storage system detaches the compute layer from storage which allows fast scaling. You can add reader nodes to scale reads while the storage system handles replication, backups etc. Aurora storage dynamically grows from 10GB to 64TB without impacting performance.
Aurora‘s Distributed Storage System – Image Source: AWS
This approach delivers key architectural benefits like:
- High availability – no single point of failure, supports multi-AZ deployments with auto failover
- High throughput – pool of storage resources can parallelize I/O heavy workloads
- Faster scaling – decouples compute and storage, easy to add reader nodes
- Faster recovery – no lengthy crash recovery, restore to any point from continuous backups
In short, Aurora removes various bottlenecks around replica lag, rebuild times, storage scaling etc. that affect legacy databases.
Managed Service Capabilities
As a fully managed service, Aurora makes it easy to set up, operate, scale and secure your databases:
- Get started in minutes with a few clicks. No need to provision servers or storage volumes separately.
- Monitoring, backups, failure detection and recovery, software patching handled automatically.
- Scale up compute or storage on demand without downtime.
- Encryption at rest via AWS KMS. Encryption in transit via SSL.
- Granular access control through AWS IAM. Integrates with AWS VPC.
These capabilities reduce the administrative overheads substantially compared to self-managed open source or commercial databases.
Use Cases for Amazon Aurora
Here are some typical use cases where Aurora delivers value:
Fast growing internet and SaaS companies – Applications that need to scale writes and storage dynamically based on usage. Serverless capability supports microservices needing flexible capacity.
Gaming & Ad-Tech – Applications with unpredictable workloads, traffic spikes that require instant scalability. Low latency access to data is critical.
Digital Payments – Transactional systems needing speed, resilience and ironclad security as they grow.
eCommerce – High volume applications like inventory, orders, fulfillments with complex queries. Scaling seamlessly during events like Black Friday sales surges.
Mobile Apps – Experience millions of users in a short span. Adapt server capacity based on active users.
Across domains, the drivers for adoption include higher performance needs, limitations of existing databases to scale, high costs and lock-in with commercial databases, hunger for innovation and more.
Aurora alleviates the pain points around rigid scaling, complex admin needs, unreliable open source platforms etc. Its managed nature and tight integration with complementary AWS services gives it an edge.
Aurora Serverless Capability
Aurora offers on-demand, auto-scaling configurations called serverless. The database engine automatically starts up, shuts down and scales capacity based on your application‘s needs.
Benefits include:
- No capacity planning – Simply configure max and min capacity, Aurora handles provisioning and scaling
- Sub-second scaling – Go from hundreds of transactions a second to hundreds of thousands instantly
- Cost savings – Pay per second for only what you use with no minimum commitments
This brings cloud-like agility to database usage and pricing. Works great for new apps with volatile workloads, microservices needing independent scaling.
Below is a comparison between the provisioned and serverless options on key aspects:
| Factor | Provisioned | Serverless |
|---|---|---|
| Capacity planning | Manual | Fully automated |
| Scaling time | ~Minutes to scale instance class | Sub-second auto-scaling |
| High availability | Multi-AZ with auto failover | Integrated for HA |
| Payments | Pay fixed rates per instance type | Pay per second only for consumed capacity |
| Use cases | Steady state production apps | Variable workloads, new apps |
Serverless allows more fine-grained control without having to manage capacity explicitly. It removes technical and economic barriers to leverage Aurora fully.
Aurora MySQL and PostgreSQL Compatibility
Amazon provides two editions of Aurora – one compatible with MySQL, another with PostgreSQL – without functional differences between the open source versions.
Key aspects:
- SQL Syntax – Fully compatible data types, statements, queries etc. making migration seamless
- Protocol Support – Leverages native MySQL/PostgreSQL communication protocols and network ports
- Client Drivers – Works with existing connectors and ORM frameworks
- Tools – Compatible client tools like MySQL Workbench, pgAdmin with Aurora
You can use Aurora as a drop-in replacement for existing MySQL and PostgreSQL databases. Switch over main production workloads or migrate specific databases incrementally. Some key compatibility considerations around stored procedures, triggers, functions etc. apply so validate thoroughly.
AWS has also launched Aurora Serverless v2 which brings auto-scaling capability to both MySQL and PostgreSQL compatible variants. Earlier it was limited to MySQL only.
Migrating to Amazon Aurora
Since Aurora supports native protocols and tools, migrating existing databases is straightforward. AWS provides a couple of options:
- Backup and Restore – Take a DB snapshot, set up a new Aurora instance, and restore from the backup. Works for large, complex migrations.
- Database Migration Service (DMS) – Incrementally replicate changes from source database to Aurora till switchover. Useful for minimal downtime migrations.
Migration complexity depends on factors like size, workload patterns, replication lags etc. Testing and tuning is recommended before conducting actual switchover. Aurora cloning capability creates instant read replicas to assist migration testing.
Many third party ETL/migration tools also integrate with Aurora. AWS partner network has experts who can help execute complex database transformations.
Some helpful migration tips:
- Start with a small proof-of-concept to validate performance
- Migrate during periods of low workload activity
- Implement in phases for very large databases – migrate most active tables first
- Leverage read replicas to sync data incrementally until final cutover
Reaching out to AWS solutions architects early on for sizing, scaling, migration guidance is highly recommended even for seemingly straightforward migrations.
Scaling Aurora for Growth
To sustain high application growth, Aurora offers multiple dimensions to scale seamlessly:
Storage Auto Scaling
- Starts at 10GB, scales automatically in 10GB increments
- Scales up to 64TB per database instance
- Choose max storage capacity limits per policy
Compute Capacity Scaling
- Switch to higher instance class like moving from db.r4 to db.r5 instance
- Scale up to 32 vCPUs and 244GB RAM
Read Replicas
- Create up to 15 low latency read replicas
- Auto scaling option available
Multi-Master Writes
- Scale writes across master nodes up to 16 nodes
- Active-active clustering
The separate compute and storage architecture makes scaling seamless and independent across layers. Aurora global database feature spreads data globally across multiple regions. You can enable fast local reads and disaster recovery via simple checkboxes.
Tools like auto scaling policies, usage metrics and dashboard provide fine grained visibility into database load.
Amazon Aurora Security
Database security aspects revolve around encryption, identity and access control, network isolation and auditing. Aurora provides multiple capabilities:
Encryption
- Encryption at rest via AWS KMS
- Encryption in transit via SSL
Access Control
- IAM policies and rules to control database access
- SQL users and permissions
Network Security
- VPC network isolation including VPC endpoints
- Security groups to restrict network traffic
Auditing
- Audit trail including connection attempts
- Database activity monitoring
Additionally, features like transparent data encryption, data masking can be implemented at the application layer. Tools like database firewalls, host based intrusion detection extend security deeper.
Third party solutions on AWS Marketplace complement the built-in capabilities across compliance, access controls, activity monitoring etc. Enterprise grade needs get fulfilled between Aurora security constructs and integrations.
Aurora Database Services Compared
Beyond core relational database capabilities, Aurora offers additional database engines for specific needs:
Amazon Aurora – Default MySQL and PostgreSQL compatible relational database engine. Use cases covered earlier apply here.
Amazon Aurora Serverless – On-demand auto-scaling capacity. Ideal for variable/unpredictable workloads.
Amazon Aurora Global Database – Low latency globally distributed database system. Active-active multi-master replication.
Amazon Neptune – Fully managed graph database allowing highly connected dataset queries.
Amazon Quantum Ledger Database – Fully managed ledger database providing centralized query capability across replicated data.
Amazon Timestream – Time series database for IoT and operational monitoring data needing high performance queries.
Each engine targets specific data models and workload patterns. But as relational databases, both Aurora MySQL and PostgreSQL provide broad coverage for mainstream use cases.
Aurora vs DynamoDB Comparison
Both Aurora and DynamoDB are fully managed AWS database services. How do they compare?
| Factor | Aurora | DynamoDB |
|---|---|---|
| Database Type | Relational | NoSQL |
| Data Model | Tables with relationships | Denormalized nested data |
| Data Processing | SQL based | Key-value lookups |
| Scaling | Read/write splitting | Auto-scaling partitions |
| Failover | AZ failover, replicas | Replicated redundancy |
| Throughput | High queries/transactions | High reads/writes |
| Latency | Sub 10 ms | Single digit ms |
Some key pointers:
- Aurora allows complex relational data models ideal for transaction systems
- DynamoDB suits large scale key-value workloads with simpler access patterns
- Aurora has higher query flexibility including joins, analytics
- DynamoDB offers lower latency for basic create, read type apps
In essence, relational capabilities make Aurora a better fit for mainstream business applications while DynamoDB suits high scale cloud native apps needing flexible schemas.
Amazon Aurora Pricing and Cost Management
As a managed service, Aurora pricing is easy to model and forecast based on configured capacity. The pay-as-you-go model allows optimizing costs for cloud elasticity needs.
Relational Database Pricing
- Instance usage – Per second rate for database instance class selected
- Storage – Per GB per month rate based on utilized storage
- I/O – Per request rate beyond included I/O quota
- Backups – Per GB per month for any backup storage
Serverless Pricing
- ACUs – Per Aurora capacity unit (ACU) per hour
- Storage – Per GB per month rate
- I/O – Per million request rate
- Backups – Per GB per month rate
Aurora optimizes costs in multiple ways:
- Compute and storage scaling independent of each other unlike commercial DB licenses
- Significantly lower than enterprise grade databases for equivalent workload needs
- No overprovisioning of capacity compared to on-prem or self-managed databases
- Serverless removes need for manual guesswork around capacity planning
Cost Explorer tools allow analysis across services, resources, tags and time periods. You can visualize key trends and identify optimization areas.
Third party cost management solutions further help benchmark workloads, identify unused capacity, detect runaway spending, and size for future needs.
Overall, Aurora with its cloud-native architecture brings simpler, predictable and affordable pricing for scaling databases.
Amazon Aurora delivers an enterprise-grade database experience combining performance, availability, and compatibility benefits in a fully managed package. The distributed storage system and separation of compute allows unprecedented scaling. Unique capabilities around auto healing, fault isolation and time travel enhance robustness further.
Over the last few years, Aurora adoption has grown vastly to become one of the most popular AWS database services. It caters to a spectrum of needs from lean start-ups to large regulated enterprises equally well. With capabilities maturing rapidly, Aurora promises to be the default choice for future application database needs across scales.
