Distributed Systems
Master consensus algorithms, leader election, fault tolerance, and distributed transactions.
In distributed systems, faults and partial failures are the norm, not edge cases. Designs that assume a single machine or a reliable network eventually collapse into non-deterministic, hard-to-reproduce failures.
These topics help you build a rigorous mental model of consensus, replication, transactions, and eventual consistency—how failures propagate, where latency and partitions surface, and which trade-offs are unavoidable. You'll learn to design, reason about, and diagnose distributed behavior under real constraints, and explain those decisions clearly in interviews.
Topics in this category
Consensus Algorithms (Raft, Paxos)
Read →Learn how distributed systems achieve consensus among nodes using Raft and Paxos algorithms.
Clock Synchronization (NTP, Lamport)
Read →Learn how distributed systems synchronize clocks and order events using NTP and Lamport clocks.
Distributed Logging
Read →Learn how to collect, aggregate, and analyze logs from distributed systems.