Subtracks & Tasks
Advanced Paradigms
Implement MapReduce
Implement MapReduce: Map emits (key, value) pairs, shuffle groups by key, Reduce aggregates. Build word count as example....
Build Distributed Hash Table (Chord)
Build Chord DHT: nodes on ring, finger tables for routing. Achieve O(log n) lookups in P2P network....
Implement Byzantine Fault Tolerance
Implement PBFT: tolerates f Byzantine faults with 3f+1 nodes. Three phases: pre-prepare, prepare, commit....
Build Stream Processing Pipeline
Build stream processor with windowing. Support tumbling and sliding windows with event-time processing....
Implement CRDTs
Build CRDTs for conflict-free replication: G-Counter (grow-only counter), G-Set, OR-Set....
Interview Prep
Common interview questions for Distributed Systems Engineer roles that map directly to what you build in this track. Click any question to reveal the model answer.
Questions are representative of real interview patterns. Model answers are starting points — adapt them with your own experience and the specific context of the interview.
Common Mistakes
The top 5 mistakes builders make in this track — and exactly how to fix them. Click any mistake to see the root cause and the correct approach.
Comparison Mode
Side-by-side comparisons of the approaches, algorithms, and trade-offs you encounter in this track. Expand any comparison to see a detailed breakdown.
Concepts Covered
Prerequisites
It is recommended to complete the previous tracks before starting this one. Concepts build progressively throughout the curriculum.
Rabbit Holes
For when you want to go deeper. Curated papers, posts, and talks beyond what this track covers.
MapReduce: Simplified Data Processing on Large Clusters
Dean and Ghemawat, 2004. The paper that kicked off the big data era. The programming model is simple; the engineering required to make it fault-tolerant at Google scale is what the paper actually teaches.
The Google File System
Ghemawat, Gobioff, and Leung, 2003. GFS makes explicit design choices that seem wrong until you understand the failure model: append-mostly workloads, relaxed consistency, and giant chunks. These choices make sense for the MapReduce workloads it was designed to serve.
Dapper, a Large-Scale Distributed Systems Tracing Infrastructure
Google's distributed tracing system. This is the paper that Zipkin, Jaeger, and OpenTelemetry are all based on. After building complex multi-node systems, you will want tracing. This is where to start.
The Tail at Scale
Dean and Barroso, 2013. Why latency tail percentiles matter more than averages at scale. The hedged request and tied request techniques are still the state of the art for latency-sensitive distributed systems.