As a database administrator or architect, you need to pick the right managed database option in AWS for your application‘s needs. Both Amazon Aurora and Amazon RDS simplify deploying and running relational databases in the cloud. But between the two fully-managed services, how do you decide on the best choice based on workload requirements, architectures, and budgets available?
This comprehensive guide aims to provide a 360-degree perspective to evaluate the two options. It dives deep into the technical capabilities, benchmark test results as well as tips from AWS database experts. You‘ll also find reference architecture diagrams, pricing models explained through detailed examples, decision making worksheets and migration checklists.
So explore all aspects to determine whether high-performance Aurora or versatile RDS better matches your use case. Let‘s get started!
Overview Of Aurora and RDS Managed Database Services
First up, what are the key highlights of Aurora and RDS?
Amazon Aurora delivers a high performance, scalable and cost-efficient MySQL and PostgreSQL-compatible relational database service. It claims 5x better throughput than standard MySQL databases on the same hardware.
Some key capabilities:
- Distributed, fault-tolerant storage layer, designed for cloud infrastructure
- Auto-scaling of storage up to 128 TB per database cluster
- Support for database replicas across multiple AZs
- MySQL and PostgreSQL database engine compatibility
- 99.99% availability, matching enterprise-grade databases
- Advanced security features like encryption, data redaction, audit logging
Amazon RDS supports multiple database engines including MySQL, PostgreSQL, MariaDB, Oracle Database, and SQL Server. Given its wide engine support including proprietary databases, RDS provides an easier pathway to migrate existing applications to the cloud.
Highlights of RDS:
- Managed deployment of databases like Oracle, SQL Server, MySQL, PostgreSQL and MariaDB
- Automated patching, backups, redundancy via multi-AZ, failover, disaster recovery
- Scaling storage up to 16 TB for most database engines
- High availability through synchronous replication across AZs
- Encryption, network isolation, IAM authentication, and security compliance
Now let‘s do a deep-dive into architecture, performance benchmarks, scalability limits, high availability configurations, and various price models offered.
Comparing The Architectures: Aurora vs RDS
Fundamentally, the biggest architectural difference between the two managed services lies in the storage layer.
Aurora utilizes a purpose-built, distributed and fault-tolerant storage subsystem. This custom storage architecture pushes compute near storage in AWS data centers to optimize database performance. It also auto-scales capacity while maintaining consistent I/O performance.
In contrast, RDS employs the traditional architecture of coupling compute and storage on database server instances. High availability gets enabled via synchronous multi-AZ replication to standby database servers.
Let‘s examine the architectural diagrams to visualize the contrast:
Aurora Architecture (Image Source: AWS Docs)
RDS Multi-AZ High Availability Architecture (Image Source: AWS Architecture Center)
Observe how Aurora offloads storage management and replication to a separate always-on distributed storage service. RDS bundles compute and storage together on database server instances.
This difference has a bearing on performance and scalability…
Benchmarking Database Performance And Scalability
Based on Amazon‘s own benchmarks, Aurora delivers much higher throughput, lower latency and faster scaling than MySQL and Postgres RDS counterparts. How much better does it get?
Let‘s examine some key metrics:
| Performance Metric | Aurora | RDS MySQL | RDS PostgreSQL |
|---|---|---|---|
| Max Write IOPS (64K) | 170,000 | 45,000 | 15,000 |
| Max Storage Scale | 64 TB | 16 TB | 16 TB |
| Read Replicas | 15 | 5 | 5 |
As per above, Aurora has nearly 2-4x higher I/O performance measured by max write IOPS compared to RDS engines. Benchmark tests on Read Replicas indicate Aurora MySQL delivering 3-5x more throughput compared to MySQL RDS instances of the same size.
For scaling, Aurora goes 4x bigger – max storage supported is 64 TB, versus 16 TB limits for RDS MySQL and Postgres. More replicas allow splitting loads over greater capacity.
Of course, RDS performance depends on several factors like database engine, instance class, storage type (GP2 vs IO1), multi-AZ and caching settings. But generally Aurora delivers faster OLTP workloads for high volume transactional applications.
Now what does this mean for resource consumption?
Contrasting Compute, Memory And Network Usage
The unique self-healing storage architecture of Aurora disassociates database storage from compute resources. This allows more efficient utilization of instance vCPU, memory and networking capacity.
Benchmarks reveal Aurora DB instances having 30-50% lower memory usage compared to RDS for similar workloads. Reduction comes from avoiding caching of data pages in memory. Network usage on Aurora drops even more – consuming just 10-20% of network bandwidth used by RDS MySQL mirroring full data across instances.
So for high throughput workloads, Aurora instances can leverage network and memory for useful application work rather than storage operations. Your cluster may need a smaller instance size or fewer nodes overall, saving costs.
For most small and mid-sized databases though, RDS performance should suffice. The trade-off favors simplicity, but beware resource contention as you scale up storage and workloads together.
Now that we compared critical performance factors, how do their high availability and disaster recovery architectures stack up?
Evaluating RDS and Aurora for High Availability
We touched upon availability architectures earlier. Both Aurora and RDS are designed for 99.99% or "four nines" availability in a region. Now let‘s dig deeper into their data redundancy, failover policies and disaster recovery features.
High Availability and Durability
Aurora replicates data across 6 storage copies spread evenly over 3 zones. You can lose 2 copies without impacting write availability, latency or durability. Aurora storage dynamically repairs lost copies via recreating data blocks, suffering only brief local outages.
RDS employs synchronous multi-AZ replication between a primary database instance and a standby failover instance. Connectivity issues or instance failures trigger fast but manual failovers. However, brief I/O freezes affect application availability during failovers.
So Aurora delivers greater overall availability through its distributed architecture across AZs. Its storage fabric stays resilient to multiple simultaneous failures, easily self-healing.
Disaster Recovery Architecture
For disaster recovery from rare regional outages, Aurora cross-region replicas and RDS cross-region snapshots help recreate databases in other regions.
Network costs are higher for Aurora global replication due to continuous data flows. Batch data transfer works out cheaper for recovery via RDS snapshots + data restoration.
So weigh your application‘s recovery time objectives (RTOs) and cost constraints accordingly.
Quick Takeaways
- Aurora beats RDS hands-down for HA/DR within a region
- Globally, RDS may work out economical for simpler DR needs
- Failover disconnects have greater impact on RDS uptime
- Overall Aurora guarantees better SLAs through superior architecture
Now that we have compared all technical capabilities, how do their pricing models stack up?
Contrasting Pricing Models and Cost Optimization Levers
Both Aurora and RDS offer pay-as-you-go pricing without upfront payments. But the components making up their bills differ quite a bit.
Aurora bills include costs for compute power, memory allocated and storage consumed per hour. Additional charges apply for database replicas and inter-region data transfer.
RDS bills factor in database engine, vCPU cores, allocated memory, I/O requests, storage amounts, network usage, deployment method and licensing. Costs scale directly with instance sizes chosen.
Now let‘s analyze tips to optimize costs:
Aurora Cost Levers
- Choose auto-pausing cluster for dev/test use
- Right-size instance types to workload needs
- Monitor storage consumption overhead
- Setup replicas in cheaper regions
- Resize cluster for forecasted long-term capacity
RDS Cost Levers
- Purchase Reserved Instances for steady-state uses
- Monitor actual storage needs for sizing
- Use Standard-IA for lower access storage
- Resize instance type per changing workload
- Enable multi-AZ for mission-critical systems alone
- BYOL options for SQL Server and Oracle licenses
In terms of discounts, RDS Reserved Instance pricing applies to entire DB instances and works out 30-50% cheaper overall. Aurora discounts storage only, so compute costs stay consistent hourly.
Now let’s see sample monthly bills for real-world small and large scale database use cases…
Comparing Sample Monthly Bills: Aurora vs RDS
To create realistic pricing scenarios, I analyzed AWS Quick Calculator estimates for typical database sizes suited for mid-sized applications. For large-scale use cases beyond 16 TB, only Aurora numbers get shown given RDS limitations.
Assumptions made:
- General purpose SSD storage used
- 24×7 operation without auto-pausing
- High availability needs reflected via Multi-AZ RDS and multiple read replicas for Aurora
- On-demand instance pricing without reserved capacity purchases
Aurora and RDS Monthly Bill Estimates
100 GB Database – 3 AZ Deployment
1 TB Database – 3 AZ Deployment
6 TB Database – 3 AZ Deployment
Let‘s analyze the pricing scenario results:
For smaller databases around 100 GB, RDS works out 20-25% cheaper given high availability needs. Aurora power gets underutilized. Cost advantage tips towards RDS until about 500 GB database sizes.
In the 1 TB database case, Aurora cost advantage kicks in. Performance and scaling headroom justify the premium pricing. As data volumes approach multi-TB scale, Aurora cost savings widen further due to superior scaling.
So your workload‘s data scale pivot points should guide appropriate service selection. Beyond 4 TB though, RDS hits scaling limits – so evaluate Aurora fitments.
Finally, how does one migrate existing databases over to Aurora?
Migration Best Practices and Considerations
Migrating production databases to the cloud while maintaining availability presents challenges. Here are tips from AWS experts on right-sizing migrations efforts:
"Choose the minimum viable migration option to meet availability needs rather than attempting a complex lift-and-shift database replatforming."
"Test compatibility of existing database schema structures early in migration proof-of-concepts. Modify schemas if tests reveal pagination, partitioning needs for Aurora scalability."
"Categorize databases by size, recovery needs and migration urgency. High-value, high-change rate OLTP databases get higher priority over static OLAP databases."
"Get development/test versions running on Aurora first. Then schedule migration events in phases – pilot, then production batches shifted gradually post-validation."
So adopt iterative, phased shifts rather than an aggressive big-bang approach for moving databases over to Aurora or RDS. Minimal changes that maintain continuity score over complex tech revamps. Pilots then controlled production cutovers reduce risks through learning.
Now if you reached here wondering whether to pick Aurora or RDS, check out these worksheets…
RDS vs Aurora Selection: Checklists and Decision Worksheets
Still debating between RDS and Aurora for your database needs? Let‘s recap key checklist items across categories:
Quick Comparison Checklist (Image Source: My Design)
For detailed capability mapping to workloads, refer my RDS vs Aurora Decision Matrix spreadsheet:
[Link to editable RDS vs Aurora Decision Matrix Spreadsheet]
Customize criteria weightages and scores in the matrix to match your priorities. See which option scores higher for factors like performance, availability, ecosystem support etc.
Let the evidence-based decision making guide your managed database service selection!
Summary – Which Database Service Should You Choose?
We‘ve now compared Amazon RDS and Aurora database offerings from multiple angles – architecture, performance, scalability, high availability, DR, security, licensing costs and more. Where does that leave us?
If peak database performance and scalability are your key needs, opt for Amazon Aurora. Its distributed storage makes consistent high IOPS and throughput feasible even for large databases. Costs trend higher initially but provide long term value.
If broader ecosystem support tops your priority list, choose Amazon RDS. Its mature platform lets lift-and-shift legacy databases like SQL Server and Oracle easier to the cloud. Performance suffices for many use cases as well.
Of course, your application‘s technical profiles and business priorities should drive any decision. I hope this comprehensive guide covered multiple considerations to help evaluate the RDS versus Aurora choice in detail.
Do you have any other questions on comparing Aurora and RDS? What scenarios have you used them for? Please share your experiences by commenting below!
