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Amazon RDS for Aurora vs Serverless: What are the differences?
Scalability: A key difference between Amazon RDS for Aurora and Serverless is the scalability options they offer. With Amazon RDS for Aurora, you can scale your database vertically by increasing the instance size or horizontally by adding replicas. On the other hand, Serverless automatically scales your database based on the workload, allowing for seamless handling of variable workloads without manual intervention.
Pricing: The pricing model for Amazon RDS for Aurora and Serverless differs. Amazon RDS for Aurora is billed based on the instance size and storage usage, while Serverless is billed based on actual consumption per second. This makes Serverless a cost-effective option for applications with unpredictable or irregular traffic patterns.
Capacity: In terms of capacity, there is a difference between Amazon RDS for Aurora and Serverless. Amazon RDS for Aurora allows you to choose the instance size that meets your capacity needs, while Serverless automatically scales the capacity to match the workload, providing on-demand resources as needed.
Startup Time: Another important difference is the startup time of the database. With Amazon RDS for Aurora, the database instance needs to be provisioned and started before it can be accessed. In contrast, Serverless has a faster startup time as it doesn't require any upfront provisioning, allowing for quicker access to the database.
Persistent Connections: Amazon RDS for Aurora supports persistent connections, which can help improve performance and reduce connection overhead. In contrast, Serverless doesn't support persistent connections, which may impact certain applications that rely on long-lived connections.
Performance: When it comes to performance, Amazon RDS for Aurora and Serverless have different characteristics. Amazon RDS for Aurora offers consistent performance for steady workloads, while Serverless can handle bursty workloads efficiently by automatically scaling up and down based on demand.
In summary, Amazon RDS for Aurora and Serverless differ in terms of scalability options, pricing model, capacity management, startup time, support for persistent connections, and performance characteristics.
When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:
- Developer Experience trumps everything.
- AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
- If you need geographic spread, AWS is lonely at the top.
Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:
- Pure, uncut, self hosted Kubernetes — way too much complexity
- Managed Kubernetes in various flavors — still too much complexity
- Zeit — Maybe, but no Docker support
- Elastic Beanstalk — Maybe, bit old but does the job
- Heroku
- Lambda
It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?
I chopped that question up into the following categories:
- Developer Experience / DX 🤓
- Ops Experience / OX 🐂 (?)
- Cost 💵
- Lock in 🔐
Read the full post linked below for all details
Pros of Amazon Aurora
- MySQL compatibility14
- Better performance12
- Easy read scalability10
- Speed9
- Low latency read replica7
- High IOPS cost2
- Good cost performance1
Pros of Serverless
- API integration14
- Supports cloud functions for Google, Azure, and IBM7
- Lower cost3
- Auto scale1
- Openwhisk1
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Cons of Amazon Aurora
- Vendor locking2
- Rigid schema1
Cons of Serverless
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What is Amazon Aurora?
What is Serverless?
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What companies use Amazon Aurora?
What companies use Serverless?
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What tools integrate with Amazon Aurora?
What tools integrate with Serverless?
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