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Amazon Aurora

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Amazon RDS for Aurora vs Serverless: What are the differences?

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Decisions about Amazon Aurora and Serverless

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.
The setup

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

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Pros of Amazon Aurora
Pros of Serverless
  • 14
    MySQL compatibility
  • 12
    Better performance
  • 10
    Easy read scalability
  • 9
    Speed
  • 7
    Low latency read replica
  • 2
    High IOPS cost
  • 1
    Good cost performance
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    3. Simplified Management for developers to focus on cod
  • 1
    Auto scale
  • 1
    5. Built-in Redundancy and Availability:
  • 1
    Openwhisk

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Cons of Amazon Aurora
Cons of Serverless
  • 2
    Vendor locking
  • 1
    Rigid schema
    Be the first to leave a con

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    What is Amazon Aurora?

    Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

    What is Serverless?

    Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

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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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    Blog Posts

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    What are some alternatives to Amazon Aurora and Serverless?
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    PostgreSQL
    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
    MongoDB
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    Redis
    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
    Amazon S3
    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
    See all alternatives