Distributed Systems
25 topics in distributed systems.
This hub groups the most useful distributed systems topics for system design interviews and production architecture reviews. Start with the fundamentals, then open the linked deep dives for diagrams, tradeoffs, failure modes, and implementation notes.
Use these pages to compare design choices, prepare interview explanations, and connect distributed systems decisions with scalability, reliability, latency, cost, and operational complexity.
Heartbeats in Distributed Systems
Failure detection is the foundation of fault tolerance. Without heartbeats, you cannot know when a server has crashed, and failover cannot begin.
Service Discovery
In microservices architectures with dynamic scaling (containers, Kubernetes), services come and go constantly.
Consensus Algorithms: Raft, Paxos and ZAB
Understand consensus algorithms: Raft leader election, Paxos, ZAB, quorum rules, replication safety, tradeoffs, and interview questions.
Distributed Locking
Without distributed locks, concurrent processes can cause data corruption, double-spending, overselling inventory, or duplicate processing.
Gossip Protocol
Gossip protocols enable decentralized failure detection, membership management, and data dissemination without a central coordinator.
Circuit Breaker Pattern
Without circuit breakers, a failing downstream service can cascade failures throughout your system.
Disaster Recovery
Disasters happen: AWS us-east-1 has had multi-hour outages, entire data centers have lost power, and ransomware attacks have encrypted production.
Bulkhead Pattern: Isolate Failures in Distributed Systems
Learn the Bulkhead Pattern for distributed systems: resource isolation, thread pools, connection pools, blast-radius control, and resilience tradeoffs.
Distributed Tracing: Spans, Trace IDs, OpenTelemetry and Debugging
Learn distributed tracing for microservices: trace IDs, spans, context propagation, OpenTelemetry, latency diagnosis, sampling, and observability tradeoffs.
Leader Election
How distributed systems elect a single leader to coordinate work, covering Raft, Bully, and Ring algorithms, along with real-world implementations in.
Retry Patterns
Learn Retry Patterns including exponential backoff with jitter — handle transient failures gracefully in distributed systems without overwhelming.
Timeout Patterns
Learn Timeout Patterns for distributed systems — configure connect, read, and write timeouts to prevent hung requests from consuming resources and.
Load Shedding
Learn Load Shedding in distributed systems — intentionally dropping excess requests to protect system stability and maintain quality of service for.
Observability: Logs, Metrics, Traces and Production Debugging
Learn observability for distributed systems: logs, metrics, traces, SLOs, dashboards, alerting, OpenTelemetry, and debugging tradeoffs.
Logging
Learn structured logging and log levels for distributed systems — capture meaningful context, correlate events across services, and build queryable.
Metrics: Counters, Gauges, Histograms and SLO Monitoring
Learn metrics for distributed systems: counters, gauges, histograms, RED/USE methods, cardinality, dashboards, alerting, and capacity planning.
Correlation IDs
Learn Correlation IDs for request tracing across distributed services — attach unique identifiers to requests so logs, metrics, and traces can be linked.
Monitoring
Learn Monitoring for distributed systems — build dashboards, set SLOs, configure alerts, and establish processes to detect, diagnose, and respond to.
Alerting: SLOs, On-Call, Escalation and Noise Reduction
Design effective alerting for distributed systems: SLO-based alerts, thresholds, escalation policies, alert fatigue, runbooks, and incident response.
Service Mesh
Learn Service Mesh architecture with Istio, Linkerd, and sidecar proxies — handle service-to-service communication, security, observability, and traffic.
Sidecar Pattern
Learn the Sidecar Pattern for distributed systems — deploy companion containers alongside application services to handle cross-cutting concerns like.
Merkle Trees
Learn Merkle Trees — hash-based tree structures that enable efficient data verification, tamper detection, and synchronization in distributed systems and.
MapReduce Explained: Distributed Batch Processing at Scale
Understand MapReduce for system design: map phase, shuffle, reduce phase, fault tolerance, data locality, Hadoop, and batch processing tradeoffs.
Secrets Management
Learn Secrets Management for distributed systems — securely store, distribute, and rotate credentials, API keys, and certificates using tools like.
Erasure Coding
Learn Erasure Coding for distributed storage — achieve fault tolerance with less storage overhead than replication by encoding data into fragments that.