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Jeremy Longshore
Jeremy Longshore

Posted on • Originally published at startaitools.com

Distributed Systems Architecture Patterns Cheat Sheet

A quick reference guide for distributed systems architecture patterns, covering when to use each pattern and the classic problems they solve.


Distributed Systems Architecture Patterns Cheat Sheet

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Pattern Core Idea When to Use Classic Problems
Caching (cache-aside / write-through / write-back) Keep hot data close to the app Read-heavy workloads, expensive queries, slow upstreams Speed up product pages, session stores, ranking feeds
CDN Push static/streamable assets to edge Global users, large media, static bundles Image/CSS delivery, video streaming, downloads
Load Balancing (L4/L7) Spread traffic across instances Scale stateless services, HA Web/API tier scaling, zero-downtime deploys
Rate Limiting & Throttling Control request volume per key/client Protect downstream services, fair usage Public APIs, login abuse protection
Circuit Breaker Fail fast when a dependency is unhealthy Prevent cascades, degrade gracefully Payment gateway outage, flaky search backend
Backpressure Signal producers to slow down Spiky traffic, limited consumers Upload pipelines, stream processing stability
Retry + Idempotency Safe replays of failed ops Unreliable networks, async workflows Order creation, webhook delivery
Read Replicas Offload reads from primary DB Read-heavy, reporting, geo-reads Analytics pages, timelines, leaderboards
Sharding (Hash/Range/Geo) Split data across nodes Data > single node, parallelism Multi-TB user tables, geo data stores
Replication (Sync/Async) Keep copies for HA & reads Availability, DR, low-latency reads Active-passive failover, follower reads
CQRS Separate read/write models Complex reads + high write throughput Event feeds, denormalized dashboards
Event Sourcing State = log of events Full audit, rebuild state, temporal queries Ledger systems, order state timelines
Message Queue / Stream (SQS/Kafka) Async decoupling via durable logs Spikes, fan-out, ordered pipelines Email/SMS, ETL, clickstream processing
Saga (Orchestration/Choreography) Distributed transaction via steps + compensation Cross-service workflows without 2PC Book-pay-reserve flows, refunds
Search Index (ES/OpenSearch) Inverted index for fast text/filters Full-text, aggregations, relevance Product search, logs explorer
Time-Series DB Append-heavy metrics optimized by time Monitoring, IoT, financial ticks Prometheus/TSDB, sensor data
Write-Optimized Stores (LSM) Fast writes, compaction later High ingest, occasional reads Audit/event logs, analytics ingest
Geo-Replication / Geo-Sharding Place data near users Low latency, data residency Multi-region apps, GDPR residency
Consistency Models (Strong/Eventual) Pick latency vs guarantees Cross-region apps, offline tolerance Cart totals vs likes counters
API Gateway Central entry: auth, routing, limits Many services, uniform policies Public API front door, mTLS termination
Webhooks & Outboxes Reliable external notifications Integrations, third-party callbacks Payment status updates, CRM sync
Blob/Object Storage Cheap infinite files Media, backups, exports User uploads, data lakes
Workflow Orchestrator (Airflow/Temporal) Durable, reliable step with state Long-running jobs, SLAs Report generation, video pipelines
Blue-Green / Canary Deploys Shift traffic gradually Safer releases, quick rollback API rollout, config changes
Feature Flags Runtime on/off % rollouts Experimentation, kill-switches A/B tests, dark launches
Schema Migration Strategy Backward-/forward-compatible changes Zero-downtime DB upgrades Expand-migrate-contract patterns
Distributed Locks / Leader Election Coordinate one active worker Cron uniqueness, shared ownership Single consumer, partition leader
Observability (Logs/Metrics/Traces) See what the system is doing SLOs, debugging, capacity planning P99 latency, error budgets, trace trees
Security: AuthN/AuthZ Verify identity and permissions Multi-tenant products, external APIs OAuth2/OIDC, RBAC/ABAC
Multi-Tenancy (Pool/Bridge/Isolated) Resource & data isolation levels SaaS with many customers Per-tenant DBs vs shared schema
Edge Compute / Functions Run logic near the user Latency-sensitive, light workloads Personalization at edge, AB tests
Rate-Aware DB Patterns Batch, queue, throttle at DB edge Hot partitions, lock contention Bulk imports, ID sequence hot-spot
Pagination Strategies Keyset + Offset for big data Infinite scroll, large tables Feed pagination, admin lists


How to Use This Cheat Sheet

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