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System Design Knowledgebase

Concept-level guides that explain when to use each pattern, where it breaks, and how to defend it in an interview.

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Find patterns by topic, failure mode, or interview area.
20 concepts shown
Backpressure
Control producer rate based on downstream capacity to avoid queue explosions and cascading failures.

Backpressure mechanisms slow, shed, or buffer incoming work when consumers cannot keep up.

reliabilitystreamingload-managementlatency
Bloom Filters
Use memory-efficient probabilistic membership checks to avoid expensive negative lookups.

Bloom filters quickly test possible set membership with false positives but no false negatives.

probabilistic-data-structuresperformancededupcaching
Change Data Capture (CDC)
Stream database changes from logs to downstream systems with low-latency propagation.

CDC reads transactional logs (WAL/binlog/oplog) and emits ordered change events for consumers.

data-pipelinesstreamingschemareplication
Circuit Breaker
Protect services from cascading failures by short-circuiting calls to unhealthy dependencies.

Circuit breakers monitor downstream errors/latency and temporarily block requests when dependency health degrades.

resiliencereliabilitylatencyservice-mesh
Consistent Hashing
Distribute keys across nodes while minimizing remapped keys during node add/remove events.

Consistent hashing places nodes and keys on a hash ring so membership changes move only a bounded subset of keys.

partitioningscalingroutingstate-distribution
Dead-Letter Queue (DLQ)
Isolate repeatedly failing messages for triage without blocking healthy traffic.

A DLQ stores events that exceed retry policy so the main stream can continue while failures are investigated.

messagingfailure-handlingreplayoperations
Event Sourcing
Persist state as an append-only event log and rebuild current state by replay.

Event sourcing stores immutable domain events as source of truth; current state is materialized from the event stream.

event-drivenauditabilityreplaystate-modeling
Exactly-Once Processing (Practical)
Achieve effective exactly-once outcomes via idempotency, transactions, and dedup rather than magic guarantees.

Practical exactly-once combines at-least-once delivery with idempotent writes and dedup/transactional boundaries.

correctnessstream-processingdeduppayments
Fanout-on-Write vs Fanout-on-Read
Choose when feeds/timelines are precomputed at write time versus assembled at read time.

Fanout-on-write pushes updates to follower timelines proactively; fanout-on-read computes results during request.

feedsfanoutlatencycost
Geo-Replication (Active-Active)
Serve traffic from multiple regions simultaneously while synchronizing state across them.

Active-active geo replication allows multiple regions to accept reads/writes with conflict resolution and convergence.

multi-regionavailabilityconsistencyglobal-systems
Idempotency Keys
Guarantee repeated client retries do not create duplicate side effects.

An idempotency key uniquely identifies a client operation so duplicate requests can return the original result.

api-designcorrectnessretriespayments
Leader Election
Select a single coordinator for shared work while preserving failover safety.

Leader election protocols choose one active node to coordinate tasks and transfer leadership on failure.

coordinationcontrol-planeconsensusavailability
Optimistic Concurrency Control (OCC)
Detect write conflicts at commit time using versions instead of locking everything up front.

OCC allows concurrent updates and rejects stale writes when version checks fail.

consistencyapi-designconflictsdata-integrity
Quorum Consistency
Use read/write quorum sizes to balance consistency, availability, and latency in replicated stores.

Quorum consistency requires operations to be acknowledged by a subset of replicas such that read and write quorums overlap.

consistencyreplicationavailabilitydistributed-storage
Read-Through Caching
Cache layer loads missing keys from the backing store on demand to reduce read latency.

Read-through caching serves reads from cache and automatically fetches from the source of truth on cache miss.

cachingreadslatencyhot-keys
Saga Pattern
Coordinate multi-service workflows with local transactions and compensating actions.

Saga pattern breaks distributed transactions into ordered local steps with compensation for failures.

distributed-transactionsmicroservicesconsistencyorchestration
Sharding Strategies
Partition data/work across shards to scale throughput and storage while controlling skew.

Sharding splits datasets and traffic across multiple partitions by hash, range, tenant, or hybrid keys.

partitioningscalabilitymulti-tenantdata-model
Token Bucket Rate Limiting
Allow controlled burst traffic while enforcing long-term request rate limits.

Token bucket refills tokens at fixed rate and permits requests while tokens remain, enabling bursts up to bucket capacity.

rate-limitingedgefairnessabuse-prevention
Transactional Outbox Pattern
Atomically persist business state and event records in one DB transaction, then publish asynchronously.

The outbox pattern writes both domain mutation and event row in the same transaction to avoid dual-write inconsistency.

data-consistencyeventsmicroservicesreliability
Write-Behind Caching
Buffer writes in cache first, then flush asynchronously to the database for higher write throughput.

Write-behind caching accepts writes into a fast cache and persists to durable storage asynchronously in batches.

cachingwritesdurabilitythroughput