Everything You Need to Know About Amazon Aurora

If you are looking for a fully-managed relational cloud database with high performance, availability and compatibility with MySQL and PostgreSQL, Amazon Aurora checks all the boxes. This comprehensive guide provides an in-depth look at the capabilities, use cases, migration considerations and alternatives for running production workloads on Aurora.

Introduction to Amazon Aurora

Launched in 2014, Amazon Aurora delivers a MySQL and PostgreSQL compatible relational database engine tuned for the cloud. Key features include:

  • Fully managed service handling provisioning, upgrades, patching, backups
  • Distributed fault-tolerant storage layer for higher availability
  • Up to 5x increase in throughput over MySQL and 3x over PostgreSQL
  • Support for mainstream MySQL and PostgreSQL database workloads
  • Automatic and seamless scaling of storage from 10GB to 128TB
  • Replication across 3 zones for high resilience and HA failovers
  • Continuous incremental backups to enable point-in-time restore
  • Serverless deployment option to reduce costs for spiky workloads
  • Pay-as-you-go pricing without upfront licensing fees

This unique combination of managed service, purpose-built architecture for cloud infrastructure, open source compatibility and enterprise-grade durability, scalability and availability make Aurora a compelling proposition for running production databases cost-effectively.

Diving Deep into Aurora‘s Distributed Architecture

The key breakthrough that enables Aurora‘s industry-leading performance comes from its distributed storage architecture spread across availability zones. Here is a deeper look:

Instance types: Aurora deployments consist of one Primary instance that conducts reads and writes on the storage layer, with up to 15 Secondary Replica instances handling read traffic.

Storage layer: Unlike regular database engines, the Aurora storage system runs on dedicated storage nodes responsible for replicating data for high availability. Log records are continuously streamed rather than using periodic data dumps. The storage layer seamlessly handles replication, failure detection and auto-recovery tasks.

Load distribution: A cluster resource broker directs all client connections to appropriate nodes so you connect using a single endpoint. Read/write splitting happens transparently. Auto scaling modifies the load distribution as nodes scale up or down.

Data durability: Data is replicated 6 ways across storage nodes and 3 zones right out of the box. Crash recovery is near instantaneous by applying redo logs. Backups run continuously to S3 instead of daily snapshots.

Security: Aurora deployments run inside an Amazon VPC for network isolation. Traffic encryption using SSL certificates ensures secure connectivity. Granular access controls are enforced using AWS IAM.

This specially engineered architecture translates into tangible end benefits:

  • 5x increase in throughput over MySQL and over 3x over PostgreSQL
  • Support for upto 15 highly available read replicas vs 5 for typical RDBMS
  • Sub 10 millisecond replication lag between Primary and Secondaries
  • Instant crash recovery and failovers minimizing disruption
  • Point-in-time restore from any time within the backup retention window
  • Peace of mind of data durability despite catastrophic failures

Key Features and Integration

Besides the core relational database capabilities, Aurora comes with enterprise-grade features including:

Monitoring: Key database metrics like connections, queries, load etc. integrated with CloudWatch alarms and dashboards. Aurora can invoke Lambda functions for automated actions.

Notifications: Event-driven alerts via SNS for failures or significant database events to promptly take action.

Security: End-to-end encryption using KMS secured keys. Granular access controls through VPC rules and IAM policies.

Compliance: Detailed database logs available for auditing and meeting regulatory compliance needs.

Integration: Trigger AWS Lambda functions to run custom logic in response to database events. Streaming data to Kinesis. Interoperate with S3, DynamoDB etc.

Backups: Continuous backup to S3 for any point-in-time restore. Automated and manual snapshots for major version restores.

Cloning: Create a new database cluster from a snapshot to set up dev/test environments instantly.

The deep integrations with other AWS infrastructure services and granular security controls make Aurora a versatile platform suitable for use cases ranging from highly dynamic web apps to mission-critical enterprise systems.

Performance Benchmarks

Synthetic benchmarks help quantify the performance advantage of Aurora for relational workloads:

SysBench: Aurora MySQL delivers over 5X higher SysBench throughput compared to MySQL database of similar size running on EC2 instance. Aurora sustains high throughput with lower latency spike despite increasing load.

pgbench: For OLTP workloads, Aurora PostgreSQL processes 150K TPC-C transactions/second with at scale compared to 15K TPS for regular PostgreSQL. 34 Million complex read queries per second is possible with Aurora vs just 0.8 million on PostgreSQL.

Observed performance improvement varies based on type of workload, read/write splits and instance sizes chosen. But most applications see between 3-7X better throughput over MySQL and PostgreSQL.

Applications and Use Cases

Aurora works for any database workload that runs well on MySQL and PostgreSQL. Some popular use cases:

High scale web/mobile apps – Apps that need to handle sudden spikes in traffic, scale up databases on demand. For example, ecommerce apps on Black Friday.

SaaS platforms – Multi-tenant SaaS platforms often use open source LAMP/LEMP stacks which can upgrade to Aurora without changing application code.

Gaming platforms – Online gaming backends support millions of concurrent users with fast data access, implemented efficiently on Aurora.

Open source modernization – Shifting apps from self-managed complex MySQL farms to Aurora maintains compatibility while improving availability.

Enterprise data warehousing – Migrating data warehouses from legacy databases like Oracle Exadata to save significant infrastructure and licensing costs.

Hybrid cloud bursting – Deploying Aurora as the primary transactional database while offloading analytic workloads to the cloud as needed provides the best of both worlds.

Here is a size to performance guide for Aurora instance sizing:

Instance Class vCPU RAM (GiB) Use Cases
db.t3.small 2 2 Dev/test environments
db.r6g.large 2 16 Entry production workloads
db.r5.xlarge 4 32 High performance web apps
db.r5.4xlarge 16 128 Mission critical systems
db.r5.12xlarge 48 384 Very large databases

Migrating to Aurora

The fully managed deployment model, MySQL compatibility and purpose-built cloud architecture make migration to Aurora relatively smooth.

For MySQL, standard tools like mysqldump can migrate data. Most frameworks and drivers compatible with MySQL 5.7+ also work seamlessly with Aurora. This allows migrating apps without system downtime or code changes.

For PostgreSQL, the AWS Schema Conversion Tool helps convert schemas from other databases. pg_dump and pg_restore handle data import/export. Certain advanced PostgreSQL features like partitions may not be supported currently.

Migration is easiest for apps using InnoDB tables, ACID transactions and SQL statements compatible with Aurora. NoSQL style databases using MongoDB or other proprietary syntax require more effort.

Here are some best practices to follow:

  • Use current Aurora MySQL or Postgres versions for maximum compatibility
  • Redirect client connection strings to new endpoint
  • Load test performance after migration to validate improvement
  • Check CloudWatch metrics during first week of usage
  • Tune autoscaling rules once regular usage patterns are established

Following these will ensure a smooth journey to leverage Aurora benefits.

Cost Model Deep Dive

Aurora uses a pay-as-you-go model with hourly cost and monthly discounts depending on usage. The pricing is customized for memory-intensive database workloads on cloud infrastructure:

Compute: Hourly rate charged based on Aurora Capacity Units (ACUs) which maps instance type to vCPU, memory and performance capability. Includes EBS storage for buffer cache.

Storage: Per GB per month change for used storage including replicas and backups. Minimum 100 GB per month reserved if used storage lower.

I/O: Per million requests for data load, updates, deletes etc. Subsequent IOPS become cheaper at higher volumes due to discounted pricing tiers.

Replications: Free for up to 15 reader replicas available by default. Useful for scaling read traffic.

Comparing equivalent workloads, Aurora provides upto 90% cost savings over commercial grade databases like Oracle, SQL Server etc by avoiding license fees and matching cloud infrastructure efficiency. The serverless deployment option automatically scales compute capacity based on traffic levels resulting in further savings.

How Aurora Compares to Other AWS Database Services

AWS offers purpose-built database engines for various use cases. How does Aurora fit?

Aurora vs DynamoDB: Highly distributed NoSQL apps are better suited for DynamoDB whereas complex queries and relationships benefit from Aurora.

Aurora vs RDS: For standard MySQL/Postgres needs RDS might suffice but Aurora adds considerable throughput, availability and scale benefits for mission-critical systems.

Aurora vs Redshift: Analytics/business intelligence workloads are better served by Redshift data warehouses whereas Aurora focuses on online transactional processing.

Based on the type of application, data model and access patterns, customers mix and match AWS purpose-built databases. For lifting and shifting production applications from enterprise MySQL and Postgres environments, Aurora makes an obvious choice.

Limitations and Challenges

While Aurora improves vastly upon first generation RDBMS systems, there are some limitations:

  • Vendor lock-in: Migrating from cloud-native Aurora back to self-managed deployment requires significant re-engineering.

  • Cost at massive scale: While extremely cost-efficient for mainstream use, costs compound at very high scale (1M+ requests/sec).

  • Learning curve: Optimal configuration and scaling of Aurora clusters takes getting used to for teams accustomed to tuning regular databases.

  • Immature ecosystem: Fewer supportive tools and deep expertise available, especially for PostgreSQL workloads.

Despite these, the productivity and performance gains outweigh the drawbacks for a wide variety of relational workloads.


Amazon Aurora delivers an enterprise-grade MySQL and PostgreSQL compatible relational database service combining outstanding performance, availability, durability and compatibility benefits at a fraction of commercial database costs.

The distributed fault-tolerant storage architecture, continuous backups and mile-high scalability enables Aurora to run production workloads with requirements beyond what regular databases can deliver.

We recommend standardizing on Amazon Aurora to run business-critical, transactional SQL-based applications that require high throughput, low latency, on-demand scalability and maximum uptime. Following AWS best practices around instance sizing, storage scaling and monitoring helps realize the full platform benefits.

Over time, the gap between open source and commercial grade databases will continue to shrink thanks to innovations like Aurora. This guide offered a 360 degree overview of everything needed to assess if Aurora is the right fit to take your production databases to the next level.