System Design Replication and Failover: Keep Services Alive When a Primary Dies
Scenario context
Replication means keeping multiple copies of your data so the system can survive machine, process, or availability-zone failures.
🎯 System Scenario & Requirements
System Goal
Replication means keeping multiple copies of your data so the system can survive machine, process, or availability-zone failures.
Actors & Components
- Application or API tier
- Core service under design
- Operator or SRE observer
Functional Requirements
- Replication means keeping multiple copies of your data so the system can survive machine, process, or availability-zone failures.
- Failover is the coordinated act of promoting a healthy replica, rerouting traffic, and recovering without corrupting data.
- TLDR : If one database going down takes your product down, replication is the next design step.
Constraints & Assumptions
- The source does not isolate a separate failure mode.
- Limits that matter for the scenario are called out in the stage decisions above.
⚡ Execution Stages & Decision Trace
Stage 1 — End-to-end outcome
After working through the stages, you should be able to explain: - - Replication and failover are separate ideas: one copies data, the other restores authority.
- - Read replicas help scale reads, but they do not guarantee fresh data.
Deep Dive
Scenario context
Replication means keeping multiple copies of your data so the system can survive machine, process, or availability-zone failures. Failover is the coordinated act of promoting a healthy replica, rerouting traffic, and recovering without corrupting data.
TLDR: If one database going down takes your product down, replication is the next design step. Failover is how you make that redundancy usable.
**How it……
This walkthrough focuses on Scenario context.
Before making a design choice, clarify:
- Replication means keeping multiple copies of your data so the system can survive machine, process, or availability-zone failures.
- Failover is the coordinated act of promoting a healthy replica, rerouting traffic, and recovering without corrupting data.
- TLDR : If one database going down takes your product down, replication is the next design step.
Concepts in play: consistency-replication, system-design-hld-architectures, transaction-isolation-distributed-acid
Stage 1 — End-to-end outcome
- After working through the stages, you should be able to explain: - - Replication and failover are separate ideas: one copies data, the other restores authority.
- Read replicas help scale reads, but they do not guarantee fresh data.
Observable outcome: - - Read replicas help scale reads, but they do not guarantee fresh data.
End-to-end outcome
Replication means keeping multiple copies of your data so the system can survive machine, process, or availability-zone failures.
Alternatives considered
Alternatives considered
The source focuses on one primary design.
Failure and degraded modes
Failure and degraded modes
- Detection: The source does not isolate a separate failure mode.
- User or operator impact: The source does not isolate a separate failure mode.
Key learnings
- After working through the stages, you should be able to explain: - - Replication and failover are separate ideas: one copies data, the other restores authority.