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
Without consensus, distributed systems cannot reliably replicate data, elect leaders, or coordinate actions.
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
Learn the Bulkhead Pattern for isolating failures in distributed systems — prevent cascading outages by partitioning resources into independent.
Distributed Tracing
In a microservices system, a single user request may pass through 10+ services. When something is slow or fails, you need to see the entire chain to find.
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
Learn Observability in distributed systems — understand how logs, metrics, and traces work together to provide deep insight into system behavior and.
Logging
Learn structured logging and log levels for distributed systems — capture meaningful context, correlate events across services, and build queryable.
Metrics
Learn about metrics in distributed systems — counters, gauges, and histograms that enable real-time dashboards, alerting, and capacity planning for.
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
Learn Alerting for distributed systems — design on-call rotations, configure escalation policies, set meaningful thresholds, and reduce alert fatigue with.
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
Learn MapReduce — the parallel data processing paradigm that splits computation across distributed nodes using map and reduce phases, pioneered by Google.
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.