Distributed-Systems on The Insane Techie

Patterns of Distributed Systems | Quick Reference

Wed, 17 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

Quick Reference Summary Table

1. Quick Reference Summary Table Link to heading

#	Pattern	Category	One-Line Problem	Core Idea	Key Systems
1	Write-Ahead Log	Durability	Survive crashes without losing state	Append every change to a sequential log before applying it	PostgreSQL WAL, Kafka, RocksDB
2	Segmented Log	Durability	Single log file grows unbounded	Split log into fixed-size segment files; delete old segments	Kafka, etcd, Bookkeeper
3	Low-Water Mark	Durability	Can’t tell which old log entries are safe to delete	Track the minimum log index confirmed by all followers	Kafka, Raft implementations
4	High-Water Mark	Durability	Followers might expose uncommitted entries to clients	Track the highest log index replicated to a majority	Kafka, Raft, ZooKeeper
5	Versioned Value	Durability	Overwriting loses history; concurrent writes conflict	Store every update with a new version number	etcd, CockroachDB, Cassandra
6	HeartBeat	Coordination	Can’t tell if a remote node is alive or crashed	Periodic messages; silence = failure	ZooKeeper, Kubernetes, Raft
7	Leader and Followers	Coordination	Multiple writers cause conflicting state	Single leader serializes all writes; followers replicate	MySQL, MongoDB, Raft
8	Generation Clock	Coordination	Old leader comes back and causes split-brain	Monotonic epoch counter; reject messages from stale epochs	Raft (term), Paxos (ballot)
9	Emergent Leader	Coordination	Explicit elections are complex	Oldest node in cluster is automatically the leader	Akka Cluster
10	Lease	Coordination	Crash-proof distributed locks needed	Time-limited lock; must be renewed; expires on crash	etcd, ZooKeeper, Chubby
11	Majority Quorum	Consensus	All-or-nothing availability vs. data loss trade-off	Require ⌊N/2⌋+1 confirmations; any two majorities overlap	Raft, Paxos, ZooKeeper
12	Replicated Log	Consensus	Single-node WAL isn’t fault tolerant	Ship WAL entries to followers; commit on majority ack	Raft (etcd), ZooKeeper ZAB
13	Paxos	Consensus	Nodes must agree on a single value despite failures	Two-phase protocol with ballot numbers to prevent conflicting commits	Chubby, ZooKeeper (inspired)
14	Consistent Core	Consensus	Running consensus on every node in a large cluster is expensive	Small 3–5 node strongly-consistent cluster as coordination oracle	ZooKeeper, etcd, Consul
15	Lamport Clock	Ordering	Wall-clock timestamps are unreliable across nodes	Logical counter; max(local, received) + 1 on message receipt	Distributed version numbers
16	Hybrid Clock	Ordering	Lamport clocks aren’t human-readable; wall clocks aren’t safe	Combine physical time + logical counter	CockroachDB, MongoDB, YugabyteDB
17	Clock-Bound Wait	Ordering	Clock uncertainty causes ordering violations in global transactions	Wait `2ε` before committing so all clocks have passed the same point	Google Spanner, CockroachDB
18	Version Vector	Ordering	Need to detect concurrent writes in multi-master systems	Per-node counter vector; concurrent if neither vector dominates	Riak, Amazon Dynamo, CouchDB
19	Fixed Partitions	Partitioning	Data too large for one node; need stable routing	Fixed bucket count (e.g., 1024); reassign buckets, not data	Kafka, Cassandra vnodes, Redis Cluster
20	Key-Range Partitions	Partitioning	Range queries require scanning all nodes	Co-locate sorted key ranges on nodes; split/merge as needed	Bigtable, HBase, CockroachDB
21	Single-Socket Channel	Networking	Messages arrive out of order with multiple connections	One TCP connection per peer pair; TCP guarantees order	ZooKeeper, Raft implementations
22	Request Pipeline	Networking	Waiting for each response limits throughput	Send next request before receiving previous response	Redis pipeline, HTTP/2, Raft replication
23	Request Batch	Networking	Per-request network overhead dominates processing cost	Accumulate multiple requests; send as one payload	Kafka producer, Raft heartbeat
24	Singular Update Queue	Concurrency	Locks and concurrent state mutation cause bugs	Single thread processes all state changes from a queue	etcd, ZooKeeper, Node.js event loop
25	Request Waiting List	Concurrency	Server must track which client waits for which cluster response	Map of in-flight requests → callbacks triggered on quorum ack	Raft leaders, ZooKeeper
26	Idempotent Receiver	Reliability	Client retries on timeout cause duplicate side effects	Unique request ID; server deduplicates using cached results	Stripe, AWS APIs, Kafka EOS
27	Gossip Dissemination	Propagation	Broadcasting cluster state to all N nodes is O(N) bottleneck	Each node randomly picks peers to exchange state; spreads exponentially	Cassandra, Dynamo, Consul, Riak
28	Follower Reads	Scalability	Leader is bottleneck for read-heavy workloads	Serve reads from followers (with bounded staleness)	MySQL replicas, Cassandra, MongoDB
29	State Watch	Observation	Polling for cluster state changes wastes resources	Server pushes notifications to registered watchers on key changes	ZooKeeper, etcd, Kubernetes
30	Two-Phase Commit	Transactions	Atomic updates across multiple nodes are not naturally safe	Coordinator collects votes (prepare), then issues global commit/abort	XA/JTA, CockroachDB, Spanner

Pattern Selection Guide Link to heading

If you need…	Consider these patterns
Crash recovery for a single node	Write-Ahead Log, Segmented Log, Low-Water Mark
Fault-tolerant data replication	Replicated Log, Leader and Followers, Majority Quorum
Leader election	Leader and Followers, Generation Clock, Emergent Leader, Consistent Core
Ordering events across nodes	Lamport Clock, Hybrid Clock, Version Vector
Handling clock skew in global transactions	Clock-Bound Wait, Hybrid Clock
Conflict detection in multi-master systems	Version Vector, Versioned Value
Scaling read throughput	Follower Reads
Efficient cluster information spread	Gossip Dissemination
Reliable distributed locking	Lease, Consistent Core
Safe client retries	Idempotent Receiver
Atomic multi-node updates	Two-Phase Commit
Reactive updates to cluster state	State Watch
High-throughput messaging	Request Pipeline, Request Batch, Single-Socket Channel
Concurrent state management	Singular Update Queue, Request Waiting List
Distributing data across nodes	Fixed Partitions, Key-Range Partitions

Patterns of Distributed Systems | A Layered View

Tue, 16 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

How Patterns Fit Together — A Layered View

1. How Patterns Fit Together — A Layered View Link to heading

The patterns are not independent. They compose into complete systems:

Patterns of Distributed Systems | Data Propagation & Observation

Sun, 14 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

Group G — Data Propagation & Observation

Group G — Data Propagation & Observation Link to heading

These patterns handle how information spreads through a cluster and how clients track cluster state changes.

Patterns of Distributed Systems | Network Communication & Request Handling

Sat, 13 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

Group F — Network Communication & Request Handling

Group F — Network Communication & Request Handling Link to heading

Reliable data structures and leader election aren’t enough. Nodes must communicate efficiently and correctly over unreliable networks.

Patterns of Distributed Systems | Data Distribution & Partitioning

Fri, 12 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

Group E — Data Distribution & Partitioning
- Fixed Partitions
- Key-Range Partitions

Group E — Data Distribution & Partitioning Link to heading

A single node cannot hold all data for a large-scale system. Partitioning distributes data across nodes. These two patterns cover the main approaches.

Patterns of Distributed Systems | Replication & Consensus

Thu, 04 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

Group C — Replication & Consensus

Group C — Replication & Consensus Link to heading

Individual nodes can now store data and elect a leader. The next challenge is keeping data consistent across multiple nodes when failures occur.

Patterns of Distributed Systems | Cluster Coordination & Leadership

Wed, 03 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

Group B — Cluster Coordination & Leadership

Group B — Cluster Coordination & Leadership Link to heading

Once individual nodes can store state reliably, you need them to act as a team. These patterns manage cluster membership, detect failures, and elect a single coordinator.

Patterns of Distributed Systems | Durability & Storage

Tue, 02 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

Group A — Durability & Storage

Group A — Durability & Storage Link to heading

These are the foundational patterns. Before nodes can coordinate, each individual node must be able to survive a crash and recover its own state reliably.

Patterns of Distributed Systems

Mon, 01 Jun 2026 14:10:00 +0530

Patterns of Distributed Systems Link to heading

Based on: Catalog of Patterns of Distributed Systems by Unmesh Joshi (published on martinfowler.com, Nov 2023)

Table of Contents Link to heading

1. Why Distributed Systems Are Hard Link to heading

A distributed system is a collection of independent computers that work together and appear to users as a single coherent system. Your bank, your email, your cloud storage — all of these are distributed systems.