Skip to content
This repository has been archived by the owner on Aug 5, 2024. It is now read-only.

Latest commit

 

History

History
42 lines (36 loc) · 1.94 KB

3-amazon-aurora.md

File metadata and controls

42 lines (36 loc) · 1.94 KB

Amazon Aurora

  • Amazon Aurora is a MySQL and PostgreSQL compatible relational database engine

  • Combines speed and availability of commerical database with the cost-effectiveness of open-source databases

  • 5x better performance with MySQL and 3x better performance than PostgreSQL at a much lower pricepoint

  • Propietary Amazon database

  • Starts in 10GB size, scales to 128TB in 10GB increments (Storage Auto Scaling)

  • Compute resources can scale up to 96vCPUs and 768GB of memory

  • Two copies of your data are contained in each availabity zone, with a minimum of 3 availability zones (6 copies of your data)

  • Aurora is designed to handle the loss of up to 2 copies of data without affecting database write availability and up to 3 copies without affecting read availability

  • Aurora storage is self-healing. Data blocks and disks are continously scanned for errors and repaired automatically

  • Three different types of Aurora replicas available

    • Aurora Replicas
      • 15 Read replicas
      • In region replica location
      • Automated failover
      • No data loss at failover
      • No support for different data or schema vs primary
    • MySQL Replicas
      • 5 read replicas
      • Cross-region replica location
      • No automated failover
      • Support for different data or schema vs primary
    • PostgreSQL replicas
      • 5 read replicas
  • Backups

    • Automated backups are always enabled with Aurora DB instances
    • Backups do not impact performance
    • You can take snapshots
      • Snapshots do not impact performance
      • Aurora snapshots can be shared with other AWS accounts
  • Aurora Serverless

    • On-demand, auto-scalling configuration for the MySQL and PostgreSQL compatible versions of Aurora

    • Aurora serverless DB clusters startup, shutdown, scale up/down based on application needs

    • Only pay for what you use

    • Use Cases

      • Infrequent, intermittent, or unpredictable workloads