Distributed Consensus Patterns
Understand and implement distributed consensus algorithms and patterns, crucial for maintaining consistency in distributed systems.
Agreeing in a Distributed World
Imagine multiple computers (nodes) needing to agree on a single outcome, even if some nodes fail or messages get lost. This challenge is called Distributed Consensus.
It's vital for maintaining data consistency and ensuring all parts of a system see the same "truth". Without it, your system might end up in a confused, inconsistent state.
The Hard Problem of Coordination
Achieving consensus is difficult because:
- Network Delays: Messages don't arrive instantly or in order.
- Node Failures: A computer might crash at any moment.
- Message Loss: Messages can be dropped by the network.
How do you ensure everyone agrees when communication is unreliable and participants can vanish?
All lessons in this course
- Designing for High Availability
- Distributed Consensus Patterns
- Erlang OTP Case Studies
- Backpressure & Load Regulation Patterns