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
| 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
- Problem First: Start with your problem in the “Classic Problems” column
- Match Pattern: Find patterns that address similar challenges
- Validate Fit: Check if “When to Use” matches your context
- Combine Patterns: Most systems use multiple patterns together
Key Considerations
- No Silver Bullets: Each pattern has trade-offs
- Start Simple: Don’t over-engineer; add patterns as you scale
- Measure First: Use metrics to justify architectural changes
- Team Knowledge: Consider your team’s expertise with each pattern
Related Resources
This cheat sheet is a living document. As distributed systems evolve, new patterns emerge and existing ones adapt. Use it as a starting point for deeper exploration.