The Chubby Lock Service (paper)

Ideas you will meet

Consensus

Raft, Paxos and how replicas agree on one order.

Distributed Systems

Consensus, time, failure and the fundamentals of scale-out.

What we already know about this

Our own lessons on these ideas. Read them here before you start, so it makes sense.

• Advanced Distributed Systems · 12 minConsensus: Raft and PaxosHow a group of machines agrees on one order of operations even when some crash or messages are delayed, and why a majority is the magic number.Read hereClose

  The problem consensus solves

  Several machines must agree on one sequence of operations (who is the leader, what is the next log entry, who owns a lock) even though machines crash and messages are delayed or lost. Without a protocol, replicas drift apart and two nodes may both believe they are the leader.

  Systems that rely on consensus include coordination services (etcd, ZooKeeper), many databases for their metadata or their data, and Kubernetes (through etcd).

  Why a majority

  A decision is valid when a majority (quorum) of nodes accepts it. Any two majorities share at least one node, so a later decision always meets someone who remembers the earlier one. A cluster of n nodes tolerates floor((n-1)/2) failures: 3 nodes tolerate 1, 5 nodes tolerate 2. That is why clusters have an odd number of nodes; a 4th node adds cost but no extra fault tolerance over 3.

  Raft in three parts

  Raft splits the problem so it can be understood:

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  Figure SDA-ADS-01-1 · Raft roles and elections · © forcsguy.com

  1. Leader election. Time is divided into numbered terms. A node that hears nothing becomes a candidate and requests votes. A node votes at most once per term, and only for a candidate whose log is at least as up to date as its own. Randomised timeouts make split votes rare.
  2. Log replication. The leader appends a client command to its log and sends it to followers. When a majority has stored the entry, it is committed; the leader applies it and replies to the client. Followers apply committed entries in order, so every replica runs the same sequence.
  3. Safety. The vote rule guarantees that any new leader already has every committed entry, so committed entries are never lost or reordered.

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  Figure SDA-ADS-01-2 · Replicating one write · © forcsguy.com

  Paxos, briefly

  Paxos reaches the same guarantee with two phases (prepare/promise, then accept/accepted) over a majority. It is famous for being hard to implement and explain, and for being the basis of many systems. Multi-Paxos repeats it for a log of decisions. Raft is essentially a clearer arrangement of the same ideas with a strong leader, which is why most new systems choose it.

  What consensus costs and what it does not give

  • Latency: every committed write waits for a majority round trip, so cross-region clusters are slow. Keep consensus groups within a region or use few, small groups for metadata.
  • Throughput: one leader handles all writes in a group. Large databases run many small groups (one per data range) to spread load.
  • Availability under partition: the minority side cannot make progress. This is the CAP trade-off made concrete: consensus systems are consistent first.
  • It does not remove all problems: slow nodes, clock assumptions, and reconfiguration (adding or removing nodes safely) need care.

  Where you will meet it

  Leader election for databases and queues, distributed locks and configuration stores, metadata for storage systems, and cluster state in Kubernetes. When a design needs "exactly one leader" or "all replicas agree on this order", you are describing consensus. See also the distributed-locks lesson in the core-concepts course.

  Real-world reading

  Related reading

  • In Search of an Understandable Consensus Algorithm (Raft paper)Ongaro and Ousterhout · Stanford UniversityPapers
  • The Raft Consensus Algorithm (site and visualisation)Diego Ongaro and community · raft.github.ioDocumentation
  • ZippyDB: Facebook's key-value storeMeta Engineering · MetaCase Studies
  • How Ringpop from Uber Engineering Helps Distribute Your ApplicationUber Engineering · UberCase Studies

  Show 6 moreShow fewer

  • etcd Raft libraryetcd · CNCF / etcdGitHub
  • Google SRE Book: Managing Critical State (Distributed Consensus)Google SRE team · GoogleBlogs
  • Paxos Made Simple (paper)Leslie Lamport · Microsoft ResearchPapers
  • HashiCorp RaftHashiCorp · GitHubGitHub
  • Distributed Systems (4th edition)Maarten van Steen and Andrew Tanenbaum · distributed-systems.netBooks
  • Scaling Hadoop YARN cluster beyond 10,000 nodes at LinkedInLinkedIn engineers · LinkedInCase Studies

  Open this lesson with its quiz and exercise

• Core Concepts: How Things Actually Work · 8 minProcesses, threads and concurrencyThe difference between a process and a thread, and the classic bugs that appear when work runs in parallel.Read hereClose

  Process versus thread

  • A process is a running program with its own memory space. Processes are isolated: one crashing does not corrupt another. Communicating between them needs explicit channels (pipes, sockets, shared memory).
  • A thread is a unit of execution inside a process. Threads share the process's memory, so they communicate cheaply, but they can also corrupt each other's data.
  • A context switch (the CPU changing what it runs) is cheaper between threads than between processes.

  	Process	Thread
  Memory	separate	shared
  Creation cost	higher	lower
  Isolation	strong	weak
  Crash impact	contained	can take down the process

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  Figure SDA-CORE-11-1 · From a program on disk to threads · © forcsguy.com

  Concurrency versus parallelism

  • Concurrency: dealing with many things at once (interleaving tasks).
  • Parallelism: actually running at the same time on several CPU cores.

  A single core can be concurrent (switching between tasks) but not parallel.

  Models you will meet

  • Thread per request: simple, but memory and switching costs limit how many you can run.
  • Event loop (Node.js, Redis, Nginx): one thread handles many connections using non-blocking I/O. Efficient for I/O-bound work, but one slow task blocks everyone.
  • Coroutines / async-await and lightweight threads (goroutines, virtual threads): cheap concurrency that reads like sequential code.
  • Worker pool: a fixed number of workers pull jobs from a queue, bounding resource use.

  Classic concurrency bugs

  • Race condition: result depends on timing. Cure with locks, atomic operations or by not sharing state.
  • Deadlock: threads wait on each other's locks forever. Cure with a consistent lock order and timeouts.
  • Starvation: a thread never gets its turn.
  • Visibility problems: one thread's write is not seen by another without proper synchronisation (memory barriers).

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  Figure SDA-CORE-11-2 · Classic concurrency bugs · © forcsguy.com

  Practical rules

  • Prefer immutable data and message passing over shared mutable state.
  • Keep critical sections small.
  • Use thread-safe data structures from the standard library.
  • Bound everything: thread pools, queues and connection pools, so overload degrades gracefully.

  Real-world reading

  Related reading

  • Memory Optimization in Redis at WattpadWattpad engineers · WattpadCase Studies
  • Distributed Locks using Redis at GoSquaredGoSquared engineers · GoSquaredCase Studies
  • The Chubby Lock Service (paper)Mike Burrows · GooglePapers
  • Reduce Memcached Memory Usage by 50% at TrivagoTrivago engineers · TrivagoCase Studies

  Show 6 moreShow fewer

  • Chubby: Lock Service for Loosely Coupled Distributed Systems at GoogleGoogle engineers · GoogleCase Studies
  • Eventually Consistent - RevisitedWerner Vogels · AmazonBlogs
  • Black-Box Auditing: Verifying End-to-End Replication Integrity between MySQL and Redshift at YelpYelp engineers · YelpCase Studies
  • Debugging Java Memory Leaks at AllegroAllegro engineers · AllegroCase Studies
  • Trivago Engineering BlogTrivago engineers · TrivagoBlogs
  • Walmart BlogWalmart engineers · WalmartBlogs

  Open this lesson with its quiz and exercise

• Core Concepts: How Things Actually Work · 8 minTime, clocks and ordering of eventsWhy you cannot trust timestamps across machines, and how logical clocks give a safe ordering.Read hereClose

  Why time is hard

  On one machine, order is easy: the clock only goes forward (mostly). Across machines, there is no shared clock.

  • Each machine's clock drifts, and synchronisation (NTP) is only accurate to some milliseconds, sometimes much worse.
  • Clocks can jump (corrections, leap seconds, virtual machine pauses).
  • Messages take unpredictable time, so "A happened before B" cannot be read from two clocks.

  So never use wall-clock timestamps from different machines to order important events (or to decide who wins a conflict, or to measure the timeout of a lock).

  Image: National Institute of Standards and Technology (NIST) · Public domain · Source · Wikimedia Commons: NIST-F2 cesium fountain atomic clock

  What we can know: happened-before

  Event A happened before B if A could have caused B: same process in order, or a message sent at A and received at B (and transitivity). Events with no such chain are concurrent.

  Logical clocks

  Lamport clock

  Each process keeps a counter. Increase it on each event; attach it to messages; on receiving, set it to max(local, received) + 1. Guarantee: if A happened before B, then clock(A) < clock(B). The reverse does not hold, so equal-looking order does not prove causality.

  Vector clock

  Each process keeps a vector of counters, one per process. Comparing two vectors tells you whether one event happened before the other or whether they are concurrent. Used by Dynamo-style stores to detect conflicting writes.

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  Figure SDA-CORE-17-1 · Vector clock · © forcsguy.com

  Practical choices

  Need	Tool
  A total order of events	A single leader, or consensus (Raft, Paxos), or a log such as Kafka partitions
  Detect concurrent updates	Version vectors, or version numbers with conditional writes
  Merge concurrent edits	CRDTs, operational transformation, application-level merge
  Unique, roughly time-ordered ids	Snowflake-style ids (accepting small skew)
  Real-time bounds	Databases that use atomic clocks and wait out uncertainty (such as Spanner)

  Rules of thumb

  • Use monotonic clocks (not wall time) to measure durations on one machine.
  • Treat timestamps as approximate and for humans; use versions for correctness.
  • Make operations idempotent and commutative where possible, so arrival order matters less.

  More on this site

  Related reading

  • Time, Clocks, and the Ordering of Events in a Distributed System (paper)Leslie Lamport · Microsoft ResearchPapers
  • Spanner: Google's Globally Distributed Database (paper)Corbett et al. · GooglePapers
  • The Raft Consensus Algorithm (site and visualisation)Diego Ongaro and community · raft.github.ioDocumentation
  • In Search of an Understandable Consensus Algorithm (Raft paper)Ongaro and Ousterhout · Stanford UniversityPapers

  Show 6 moreShow fewer

  • How Ringpop from Uber Engineering Helps Distribute Your ApplicationUber Engineering · UberCase Studies
  • Google SRE Book: Managing Critical State (Distributed Consensus)Google SRE team · GoogleBlogs
  • The Chubby Lock Service (paper)Mike Burrows · GooglePapers
  • Clock Skew when Syncing Password Payloads at DrobboxDrobbox engineers · DrobboxCase Studies
  • Airflow at GrabGrab engineers · GrabCase Studies
  • OpenTable BlogOpenTable engineers · OpenTableBlogs

  Open this lesson with its quiz and exercise