Foundation
Designing Data-Intensive Applications
Classic database for distributed data and consistency.
Distributed systems appear when one machine is not enough. This gives scale, but complicates everything: data gets scattered, networks break down, and latency becomes unpredictable. This section explains how to think in the world of partial failures and conflicts.
Why do you need knowledge of distributed systems?
Real world = failure
The network is unreliable, nodes fail, packets are lost. Architecture must take this into account from the very beginning.
Consistency vs latency
The stricter the consistency, the higher the delays. You need to be able to choose the right compromises.
State negotiation
Consensus, quorums, and replication are the basic mechanisms for steady state.
Difficulty increases quickly
The system of 1 service simply works. A system of 20 requires a thoughtful architecture.
Key topics in this part
CAP and consistency models
From strong consistency to eventual - what we choose and why.
Reliability and failures
Timeouts, retries, circuit breaker, backpressure.
What will this section give in practice?
- Ability to select a consistency model for the task.
- Understanding when a leader is needed and when quorums are needed.
- Skill in designing protection against network failures and degradation.
- More in-depth answers to the System Design interview.
You can start with CAP theorems And consensus protocols.