Distributed Systems

23 chapters

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Why are distributed systems and consistency needed?easy

Introductory chapter on reasoning about invariants, consistency boundaries, coordination, and partial failures in distributed systems.

CAP theoremmedium

Foundational chapter on the CAP theorem: network partitions, the consistency-versus-availability choice, common misconceptions, and the relationship to ACID and BASE.

PACELC theoremhard

Extending CAP with the normal-operation trade-off between latency and consistency, plus the partition-time choice between availability and consistency. System classes: PA/EL, PC/EC, PA/EC, PC/EL.

Clock Synchronization in Distributed Systemsmedium

Physical and logical time in distributed systems: NTP/PTP, clock skew and drift, and architectural safeguards for timeouts, leases, and event ordering.

Leader Election: patterns and implementationsmedium

How to design leader election: leases, quorum-based promotion, failover timing, split-brain protection, stale-leader handling, and practical implementations in Raft, ZooKeeper, etcd, and Kubernetes.

Consensus: Paxos and Raftexpert

How systems choose one agreed value: quorums, replicated logs, Paxos, Multi-Paxos, Raft, and the practical cost of consensus.

Leslie Lamport: Causality, Paxos and Engineering Thinkinghard

How Lamport's ideas on causality, logical time, Paxos, TLA+, invariants, and correctness proofs became foundational for distributed systems.

Distributed Transactions: 2PC and 3PChard

A practical look at distributed transactions: coordinator and participants, two-phase and three-phase commit, blocking, partial failures, and alternatives via Saga and transactional outbox.

Jepsen and consistency modelsexpert

How Jepsen validates distributed-system guarantees: operation histories, Nemesis, network partitions, linearizability, serializability, and real-world anomalies.

Testing Distributed Systemshard

A practical approach to validating distributed systems: contract and integration testing, chaos engineering, fault injection, SLOs, and rollback readiness.

Designing Data-Intensive Applications, 2nd Edition (short summary)hard

A summary of DDIA's second edition: architecture trade-offs, data models, storage, sharding, consistency, streams, derived state, and data privacy.

Distributed Systems, 4th Edition (short summary)expert

Tanenbaum and van Steen's foundational textbook: distribution transparency, architectures, communication, coordination, replication, fault tolerance, and security.

Google Global Network: Evolution and Architectural Principles for the AI Erahard

Evolution of Google’s global backbone: inter-region traffic, network sharding, Protective ReRoute, traffic engineering, and autonomous operations for the AI era.

Streaming Data (short summary)hard

Andrew Psaltis on streaming architecture: message queues, delivery semantics, event time, windowing, stream state, and historical backfill.

Kafka: The Definitive Guide, 2nd Edition (short summary)medium

Practical guide to Kafka as a broker and partitioned log: producers, consumer groups, replication, delivery guarantees, Kafka Connect, Kafka Streams, and cluster operations.

Kappa Architecture: stream-first alternative to Lambdahard

A single streaming path over an event log: materialized views, historical replay, backfill, and a practical comparison with Lambda.

Data Pipeline / ETL / ELT Architecturemedium

How to design data pipelines: ETL and ELT, batch and stream processing, orchestration, data quality, failure recovery, cost governance, and operational reliability.

Apache Iceberg: table architecture for data lakeshard

Practical guide to Apache Iceberg: open table formats, snapshots, manifest files, ACID over data lakes, schema evolution, hidden partitioning, time travel, and Tableflow's role in streaming pipelines.

Big Data (short summary)hard

Nathan Marz on Lambda Architecture: batch, serving, and speed layers, immutable event history, batch and realtime views, HyperLogLog, and the cost of complexity.

Data Mesh in Actionhard

A practical guide to Data Mesh: domain responsibility for data, data products, self-serve data platforms, federated governance, and MVP adoption.

Brief overview of the T-Bank data platformhard

Evolution of T-Bank's data platform from DWH to lakehouse architecture, covering ingestion, storage, processing, governance, scale, and operating lessons.

Data platforms in 2025: interview with Nikolay Golovhard

Research Insights Made Simple #6 on data platforms in 2025: centralization and federation, Data Mesh, OLTP/MPP, operating models, governance, cost, and platform evolution.

Local-First Software: Taking Back Control of Dataeasy

A short documentary about local-first software: local storage, offline mode, synchronization, conflicts, data privacy, and user control.