Why Strong Consistency?
15 days ago
- #scaling
- #database
- #consistency
- AWS initially used MySQL databases with statement-based replication for EC2's control plane, facing issues with costly operations and eventual consistency.
- Managed databases like Aurora MySQL have simplified operations, but eventual consistency remains a challenge in scaling reads.
- Eventual consistency causes problems like 'time moving backwards' for application programmers, leading to complex and inefficient workarounds.
- Application builders face similar issues, with eventual consistency introducing bugs in workflows and reducing the effectiveness of read replicas.
- Eventual consistency complicates scaling by making read replicas less effective for read-modify-write operations.
- Aurora DSQL ensures strong consistency in all reads by using a journal-based system to track updates and synchronize replicas.
- Strong consistency in DSQL simplifies application development by removing the need to handle eventual consistency complexities.
- While eventual consistency has its place, it is often not suitable for services or APIs due to the complexity it introduces.