Distributed Systems
25 topics in distributed systems.
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.