Pattern index
Find the pattern that fits the pressure.
Filter across all 100 patterns by symptom, mechanism, use case, or trade-off.
DATA
Data Storage Patterns
6 patternsPrimary-Replica (Leader-Follower)
Scaling read traffic without sending every query to the write leader.
Sharding (Horizontal Partitioning)
A single database growing beyond one machine’s write or storage limits.
Consistent Hashing
Redistributing too much data when nodes join or leave a cluster.
Write-Ahead Log (WAL)
Recovering durable writes after a crash without corrupting storage.
Event Sourcing
Needing a perfect audit trail and the ability to reconstruct old states.
CQRS (Command Query Responsibility Segregation)
Read needs and write invariants fighting over the same model.
CACHE
Caching Patterns
5 patternsCache-Aside (Lazy Loading)
Avoiding repeated database reads for frequently requested data.
Write-Through
Keeping cache and storage fresh immediately after writes.
Write-Behind (Write-Back)
Absorbing high write volume with low perceived latency.
Read-Through
Keeping application code free of cache-loading logic.
Cache Stampede Prevention
Preventing one expired hot key from stampeding the database.
COMMS
Communication Patterns
7 patternsRequest-Response (Synchronous)
Asking another component for an immediate answer.
Message Queue (Asynchronous)
Decoupling producers from slower or unreliable consumers.
Publish-Subscribe (Pub/Sub)
Letting many services react to the same business event.
Event-Driven Architecture
Reducing direct service coupling across a workflow.
Webhooks
Notifying an external system exactly when something happens.
Server-Sent Events (SSE)
Pushing server updates to browsers without full duplex complexity.
Bidirectional Streaming (WebSockets / gRPC Streaming)
Supporting continuous two-way real-time interaction.
RESILIENCE
Reliability Patterns
7 patternsCircuit Breaker
Stopping a failing dependency from exhausting callers.
Retry with Exponential Backoff
Handling transient failures without giving up immediately.
Bulkhead
Keeping one workload or tenant from sinking the whole service.
Timeout
Preventing slow dependencies from tying up resources forever.
Idempotency
Making retries safe when clients cannot know if a write succeeded.
Dead Letter Queue (DLQ)
Stopping poison messages from blocking the main queue forever.
Graceful Degradation
Serving something useful when a noncritical subsystem breaks.
SCALE
Scaling Patterns
5 patternsHorizontal Scaling
Handling more stateless request volume by adding machines.
Vertical Scaling
Getting more capacity quickly from a single-node component.
Load Balancing
Spreading incoming requests across healthy backends.
Auto-Scaling
Matching capacity to variable traffic without manual intervention.
Database Connection Pooling
Avoiding expensive database connection setup per request.
FLOW
Data Processing Patterns
4 patternsMapReduce
Processing huge datasets that cannot fit on one machine.
Stream Processing
Reacting to data continuously instead of waiting for batch jobs.
Lambda Architecture
Combining accurate batch views with low-latency realtime views.
Change Data Capture (CDC)
Letting other systems react to database changes reliably.
EDGE
API Design Patterns
5 patternsAPI Gateway
Giving clients one stable doorway into many backend services.
Backend for Frontend (BFF)
Serving different client experiences without one bloated api.
Rate Limiting
Protecting services from abusive or accidental request floods.
Pagination (Cursor-Based)
Returning large changing lists without skips or duplicate surprises.
API Versioning
Evolving an api without breaking existing clients.
INFRA
Infrastructure Patterns
4 patternsCDN (Content Delivery Network)
Serving static content with low latency worldwide.
Reverse Proxy
Putting common web concerns in front of application servers.
Service Mesh
Standardizing service-to-service behavior across many teams.
Sidecar Pattern
Adding cross-cutting behavior without modifying the main app.
CONSENSUS
Consistency Patterns
4 patternsTwo-Phase Commit (2PC)
Committing one transaction atomically across multiple participants.
Saga Pattern
Coordinating distributed work without one global transaction.
Quorum
Tuning consistency and availability in replicated systems.
Vector Clocks
Detecting causal ordering without a global clock.
OPS
Observability and Operations Patterns
3 patternsCONSISTENCY
Consistency / Messaging
3 patternsWORKFLOW
Consistency / Workflow
1 patternsQUERY
Data / Query
2 patternsREPAIR
Consistency / Storage
3 patternsCOORD
Coordination
4 patternsEXECUTE
Execution
3 patternsCOMMS
Communication
1 patternsLATENCY
Latency
1 patternsCACHE
Caching / Latency
2 patternsCONTROL
Traffic Control
4 patternsRESILIENCE
Reliability
3 patternsFALLBACK
Reliability / Caching
1 patternsAVAILABLE
Availability
3 patternsCELL
Availability / Scale
1 patternsTENANT
Availability / Multi-Tenant
1 patternsMIGRATE
Migration
3 patternsSHADOW
Migration / Testing
1 patternsRELEASE
Release
3 patternsSCHEMA
Data Evolution
3 patternsLIFECYCLE
Data Lifecycle
2 patternsSECOPS
Security / Operations
2 patternsSECURITY
Security
2 patternsARCH