In Search of an Understandable Consensus Algorithm (Raft 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.

Replication

Leader/follower, multi-leader and leaderless replication.

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

• Databases Deep Dive · 12 minReplication topologies and multi-region databasesSingle-leader, multi-leader and leaderless replication, how they handle conflicts and lag, and what changes across regions.Read hereClose

  Why replicate

  Replication keeps copies of data on several machines for three reasons: availability (survive a node failure), read scaling (serve reads from replicas) and latency (put data near users). The hard part is keeping copies consistent while writes continue. (Basics are in the replication and sharding lesson of the foundations course; this lesson compares the topologies.)

  Single-leader (primary and replicas)

  One node, the leader (primary or master), accepts writes and ships its changes to followers (replicas), which serve reads.

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  Figure SDA-DDV-04-1 · Single-leader replication · © forcsguy.com

  • Synchronous replication waits for a follower before acknowledging: safer, slower, and a stalled follower can block writes. Asynchronous is faster but a failover can lose the latest writes. A common compromise: one synchronous follower, others asynchronous.
  • Failover must detect failure, choose the most up-to-date replica, redirect clients and ensure the old leader does not come back believing it still leads (split brain).
  • Replication lag causes stale reads. Techniques: read-your-writes (route a user's reads to the leader shortly after they write), monotonic reads (keep a user on one replica), and checking replica position.

  Multi-leader

  Several nodes accept writes (often one per region) and replicate to each other. Benefits: local write latency and continued operation if a region is cut off. Cost: write conflicts.

  Conflict handling options:

  • Avoid them: route all writes for one record to one leader (for example by user or region).
  • Last write wins by timestamp: simple, but silently drops one of two concurrent updates and depends on clocks.
  • Merge values (union of sets, counters) using mergeable data types (CRDTs; see the collaborative editing lesson).
  • Custom resolution in application code, or ask the user.

  Multi-leader also appears in offline-capable apps (each device is a leader) and collaborative editing.

  Before: single leader

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  After: multi-leader across regions

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  What changed

  • Users in each region write to a nearby database
  • Concurrent edits to the same row need conflict handling
  • More moving parts to run and monitor

  Leaderless (Dynamo-style)

  Any replica accepts writes; clients write to several replicas and read from several, using quorums (W + R > N) to get fresh data (see the key-value store lesson). Background anti-entropy and read repair fix stale copies; versions or vector clocks detect conflicts. It favours availability and is typical of Cassandra and DynamoDB-style stores.

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  Figure SDA-DDV-04-2 · Choosing a replication model · © forcsguy.com

  Multi-region designs

  • Active-passive: one region serves writes; another is a warm standby. Simple; failover loses a little data (the replication lag) and takes minutes.
  • Active-active: several regions serve traffic. Requires either partitioning data by home region (each record has one home, cross-region reads are replicas) or true multi-leader conflict handling.
  • Global databases use consensus across regions (see the consensus lesson) for strong consistency at the cost of cross-region write latency; some offer tunable consistency per query.
  • Latency numbers matter: a cross-ocean round trip is around 100 ms or more, so synchronous cross-region replication adds that to every write.
  • Data residency laws may require keeping certain users' data in a region.

  Partitioning and replication together

  Real systems shard data into partitions and replicate each partition (a leader per partition spreads write load across nodes). Rebalancing, hot partitions and cross-partition transactions are the new problems (see sharding and the consistent-hashing lesson).

  Master/slave naming

  You will see "master/slave", "primary/replica" and "leader/follower" for the same single-leader idea; most current documentation prefers the last two.

  Checklist

  • What happens to writes if the leader or a region is lost, and how long?
  • How much data can we lose (RPO) and how long can we be down (RTO)?
  • How do we handle stale reads from replicas?
  • Who resolves conflicts, and how?
  • Have we practised failover?

  Real-world reading

  Related reading

  • Active-Active for Multi-Regional ResiliencyNetflix Engineering · NetflixCase Studies
  • Designing Data-Intensive ApplicationsMartin Kleppmann · O'ReillyBooks
  • Read Consistency with Database Replicas at ShopifyShopify engineers · ShopifyCase Studies
  • Uber Schemaless: A Scalable MySQL DatastoreUber Engineering · UberCase Studies

  Show 6 moreShow fewer

  • Mitigating MySQL Replication Lag and Reducing Read Load at GithubGithub engineers · GithubCase Studies
  • Cassandra architecture overviewApache Cassandra · Apache Software FoundationDocumentation
  • How Discord Stores Trillions of MessagesBo Ingram · DiscordBlogs
  • Spanner: Google's Globally Distributed Database (paper)Corbett et al. · GooglePapers
  • Scaling Multitenant Architecture Across Multiple Data Centres at Shopify - Weingarten, Engineering Lead at ShopifyShopifyYouTube
  • MIT 6.824: Distributed SystemsRobert Morris · MITCourses

  Open this lesson with its quiz and exercise

• System Design Foundations · 11 minReplication and shardingTwo ways to spread data over many machines: copy it for safety and read speed, or split it for capacity.Read hereClose

  Replication: keep copies

  Replication keeps the same data on more than one machine. Reasons:

  • Availability: if one machine dies, another has the data.
  • Read capacity: reads can go to any copy.
  • Locality: place a copy near users in another region.

  The most common arrangement is leader and followers. All writes go to the leader, which sends the changes to followers. Reads can use followers.

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  Figure SDA-FND-05-1 · Leader and follower replication · © forcsguy.com

  Many teams hide the split between the primary and the replicas behind a small router, so the application does not need to know:

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  Figure SDA-FND-05-2 · Read and write splitting through a router · © forcsguy.com

  Synchronous or asynchronous

  • Synchronous: the leader waits for followers to confirm before saying "done". Safer, but slower, and a down follower can block writes.
  • Asynchronous: the leader confirms immediately and followers catch up. Fast, but a follower can be behind, and a leader crash can lose the most recent writes.

  Most systems use asynchronous replication for most followers, sometimes with one synchronous follower as a safety net.

  Replication lag

  Because followers can lag, a user may write something and then read an older value from a follower. Fixes include reading the user's own recent writes from the leader, or tracking how fresh each replica is. This is the first taste of a bigger idea, consistency, covered in lesson 7.

  When the leader fails

  A follower must be promoted (failover). That needs failure detection and agreement on who the new leader is, so that two machines do not both believe they are the leader (a split brain). This is why teams rely on tested tooling or managed databases for failover rather than hand-written scripts.

  Read-after-write consistency

  With asynchronous replicas, a user can save something and then not see it: the write went to the primary, the next read hit a replica that is a few seconds behind. Three common fixes:

  • Send reads that must be fresh (the user's own profile, an order just placed) to the primary.
  • For a short window after a user writes, route that user's reads to the primary.
  • Let the router check whether a replica has caught up to the user's last write; if not, read from the primary or wait.

  A database proxy in front of the cluster can do this routing so application code stays simple, at the cost of one more hop and one more component that must itself be highly available.

  Before: one database

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  After: primary with read replicas

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  What changed

  • Reads are spread across copies
  • A copy can be promoted if the primary fails
  • Replicas may lag slightly behind the primary

  Sharding: split the data

  Replication does not help when the data or the writes no longer fit on one machine. Sharding (partitioning) divides the data so each shard holds a part. Each shard is usually replicated too.

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  Figure SDA-FND-05-3 · Sharding by key range · © forcsguy.com

  Choosing a shard key

  The shard key decides which shard owns a row. A good key:

  • Spreads data evenly, so no shard is much bigger than the others.
  • Spreads traffic evenly, so no shard is a hot spot.
  • Matches your queries, so most queries touch one shard.

  Common schemes:

  Scheme	How	Watch out for
  Range	key ranges per shard	hot spots, e.g. newest dates
  Hash	hash(key) mod N	adding shards remaps most keys
  Directory	a lookup table says where each key lives	the directory is another thing to run and scale
  Consistent hashing	keys and nodes placed on a ring	needs virtual nodes to balance well

  Consistent hashing in one picture

  Place both servers and keys on a ring. A key belongs to the first server clockwise from it. Adding a server only takes keys from its neighbour; the rest do not move.

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  Figure SDA-FND-05-4 · Consistent hashing ring · © forcsguy.com

  Horizontal and vertical partitioning

  Sharding is horizontal partitioning: the rows of a table are split across machines, each shard holding the same columns for a different set of rows. Vertical partitioning splits by columns or by feature instead: rarely used or large columns (a profile photo, a long description) move to a separate table or store, or whole groups of tables go to different databases (users on one, orders on another). It is simpler than sharding and keeps frequently read data small and fast, but it does not help when a single table's rows outgrow one machine.

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  Figure SDA-FND-05-5 · Splitting a table by rows and by columns · © forcsguy.com

  What sharding costs you

  • Cross-shard queries and joins become slow or need extra work.
  • Transactions across shards are hard (lesson 8 touches on how systems cope).
  • Rebalancing when a shard gets too big is operational work.
  • More moving parts to monitor.

  A worked example: sizing replicas and shards

  Replicas for reads. A social app has 20,000 reads and 1,000 writes per second. One database node can do about 5,000 simple operations per second. A primary alone is hopeless. With writes of 1,000 per second on the primary, you have capacity left for about 4,000 reads, so you need at least four replicas to serve 20,000 reads (16,000 on replicas plus 4,000 on the primary) with no headroom, so six in practice. Adding replicas scales reads almost linearly. It does nothing for the 1,000 writes, which still all go to one primary.

  Shards for writes. If writes grow to 12,000 per second, no single primary can take them. Split the data into shards, each with its own primary: with 4 shards each takes about 3,000 writes per second. Each shard then has its own replicas for reads. Your final count is shards x (1 primary + replicas), for example 4 x (1 + 2) = 12 database nodes.

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  Figure SDA-FND-05-6 · Replicas scale reads, shards scale writes · © forcsguy.com

  Walking through a failover

  Replication is only useful if you know what happens when a primary dies. With asynchronous replication:

  1. The primary acknowledges writes 100 to 140, but the follower has only received up to 135 (replication lag).
  2. The primary crashes. Writes 136 to 140 exist only on the dead machine.
  3. A monitor notices missing heartbeats, usually after several seconds, and promotes the most up-to-date follower to primary.
  4. Clients are redirected. Writes 136 to 140 are lost (or recoverable only when the old primary returns).
  5. When the old primary comes back it must not accept writes as if nothing happened (that would create two primaries, "split brain"); it rejoins as a follower and discards or reconciles what the new primary does not have.

  With synchronous replication to at least one follower, step 2 loses nothing, but every write waits for that follower, adding latency and making writes fail if the follower is unavailable. Many systems choose a compromise: wait for one follower to confirm (semi-synchronous), accepting a little latency to avoid losing acknowledged writes.

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  Figure SDA-FND-05-7 · What an asynchronous failover can lose · © forcsguy.com

  Choosing a shard key: three examples

  • User id for a messaging app. Almost every query is "this user's messages", so a hash of the user id keeps each user's data together and spreads users evenly. The pain: a query across all users (an admin search) must ask every shard.
  • Order date for an orders table. Range sharding by date makes "last month's orders" cheap, but all new writes land on the newest shard, a hot spot, while old shards sit idle. A better key adds a hash of the customer id.
  • Country for a multi-region product. Good for keeping data near users and meeting data-residency rules, but shards are uneven: one country may hold half your users.

  A good key has high cardinality (many distinct values), even load and matches your most common query. Changing it later means moving most of your data, so test it against a sample of real traffic first.

  Mistakes people make

  • Reading from a replica right after writing. The replica may not have the write yet, so users do not see their own change. Read your own writes from the primary for a short time, or route that user to the primary.
  • Sharding too early. Sharding multiplies operational work: backups, schema changes, cross-shard queries and transactions all get harder. First try indexes, caching, replicas and a bigger machine.
  • Counting on replicas as backups. A mistaken delete is replicated within milliseconds. Keep real backups and test restoring them.
  • Forgetting to rebalance. Shards grow unevenly. Plan how to split a shard, ideally with consistent hashing or many small virtual shards that you can move between machines.

  Try it yourself

  In the System Builder, open "The database goes down". Run it with no replicas and see reads and writes fail, then add replicas and a write queue and compare. For hashing itself, build the consistent hash ring lab and measure how many keys move when you add a node. To place replicas and a queue yourself, open the database-outage design on the free canvas and kill the database.

  More on this site

  Related reading

  • How Discord Stores Trillions of MessagesBo Ingram · DiscordBlogs
  • MongoDB: Shard KeysMongoDB · MongoDB Inc.Documentation
  • Uber Schemaless: A Scalable MySQL DatastoreUber Engineering · UberCase Studies
  • How Discord Stores Billions of MessagesDiscordCase Studies

  Show 6 moreShow fewer

  • Consistent Hashing and Random Trees (paper)Karger et al. · MITPapers
  • Amazon Dynamo: Highly Available Key-Value StoreWerner Vogels · AmazonCase Studies
  • Mitigating MySQL Replication Lag and Reducing Read Load at GithubGithub engineers · GithubCase Studies
  • MongoDB at Baidu: Multi-Tenant Cluster Storing 200+ Billion Documents across 160 ShardsMongodb engineers · MongodbCase Studies
  • Shopify EngineeringShopify engineers · ShopifyBlogs
  • Sharding MySQL at QuoraQuora engineers · QuoraCase Studies

  Open this lesson with its quiz and exercise