What Are Distributed Systems?
A distributed system is a collection of independent computers that appears to users as a single coherent system. These systems coordinate through message passing to achieve common goals while handling failures, network partitions, and scalability challenges.
Why Distributed Systems Matter:
- Scalability: Handle growing loads by adding more machines
- Fault Tolerance: Continue operating despite component failures
- Geographic Distribution: Serve users globally with low latency
- Resource Sharing: Efficiently utilize computing resources across organizations
- Performance: Parallel processing enables faster computation
Core Concepts & Principles
Fundamental Properties
Scalability
- Horizontal Scaling: Add more machines to handle increased load
- Vertical Scaling: Upgrade existing hardware (CPU, RAM, storage)
- Load Distribution: Spread work across multiple nodes
Reliability & Fault Tolerance
- Redundancy: Multiple copies of data and services
- Graceful Degradation: System continues functioning with reduced capability
- Failure Detection: Identify and respond to component failures
Consistency Models
- Strong Consistency: All nodes see the same data simultaneously
- Eventual Consistency: Nodes will converge to same state over time
- Weak Consistency: No guarantees about when all nodes will be consistent
CAP Theorem
Core Principle: In presence of network partition, choose between Consistency and Availability
| Property | Description | Trade-offs |
|---|---|---|
| Consistency | All nodes see same data simultaneously | May sacrifice availability during partitions |
| Availability | System remains operational | May serve stale data during partitions |
| Partition Tolerance | System continues despite network failures | Must be handled in distributed systems |
ACID vs BASE
| ACID (Traditional) | BASE (Distributed) |
|---|---|
| Atomicity: All-or-nothing transactions | Basically Available: System available most of the time |
| Consistency: Data integrity maintained | Soft State: Data may change over time |
| Isolation: Transactions don’t interfere | Eventually Consistent: System becomes consistent over time |
| Durability: Committed data persists |
Key Architectures & Patterns
System Architectures
Client-Server
- Traditional request-response model
- Centralized server handles multiple clients
- Simple but creates single point of failure
Peer-to-Peer (P2P)
- All nodes are equal participants
- Decentralized with no single point of failure
- Complex coordination and consistency challenges
Microservices
- Application split into small, independent services
- Each service handles specific business function
- Communicates via APIs (REST, gRPC)
Communication Patterns
| Pattern | Use Case | Pros | Cons |
|---|---|---|---|
| Request-Response | Web APIs, RPC calls | Simple, synchronous | Blocking, tight coupling |
| Publish-Subscribe | Event-driven systems | Loose coupling, scalable | Complex error handling |
| Message Queues | Asynchronous processing | Reliable delivery, buffering | Additional infrastructure |
| Event Streaming | Real-time data processing | High throughput, replay capability | Complexity, ordering challenges |
Consensus Algorithms
Algorithm Comparison
| Algorithm | Fault Tolerance | Performance | Complexity | Best For |
|---|---|---|---|---|
| Paxos | Byzantine faults | Moderate | High | Academic, research |
| Raft | Crash faults | High | Moderate | Production systems |
| PBFT | Byzantine faults | Low | Very High | Blockchain, critical systems |
| Gossip | Node failures | Very High | Low | Large-scale systems |
Raft Consensus Process
- Leader Election: Nodes elect a leader to coordinate
- Log Replication: Leader replicates entries to followers
- Commitment: Entries committed when majority acknowledges
- Consistency: All nodes maintain identical logs
Data Management Strategies
Replication Patterns
Master-Slave Replication
- Single master handles writes, slaves handle reads
- Simple consistency but single point of failure for writes
- Use case: Read-heavy workloads
Master-Master Replication
- Multiple masters accept writes
- Complex conflict resolution required
- Use case: High availability writes
Sharding (Partitioning)
- Split data across multiple nodes by key
- Horizontal scaling but complex queries
- Use case: Large datasets exceeding single node capacity
Consistency Strategies
| Strategy | Description | Trade-offs |
|---|---|---|
| Read Quorum | Require majority of nodes for read | Strong consistency, higher latency |
| Write Quorum | Require majority of nodes for write | Data durability, potential conflicts |
| Vector Clocks | Track causality between events | Complex but handles concurrent updates |
| Merkle Trees | Detect inconsistencies efficiently | Fast comparison, additional storage |
Common Challenges & Solutions
Network Partitions
Challenge: Network splits system into isolated groups
Solutions:
- Partition Detection: Monitor network connectivity and node health
- Split-Brain Prevention: Use odd number of nodes or external arbitrator
- Graceful Degradation: Maintain read-only or limited functionality
Distributed Transactions
Challenge: Ensuring atomicity across multiple nodes
Solutions:
- Two-Phase Commit (2PC): Coordinator ensures all nodes commit or abort
- Saga Pattern: Chain of local transactions with compensating actions
- Event Sourcing: Store events instead of current state
Service Discovery
Challenge: Services need to find and communicate with each other
Solutions:
- Service Registry: Centralized directory of available services
- DNS-Based Discovery: Use DNS for service location
- Sidecar Pattern: Proxy handles service discovery and communication
Load Management
Challenge: Distributing work efficiently across nodes
Solutions:
- Load Balancers: Distribute requests across healthy nodes
- Circuit Breakers: Prevent cascade failures by stopping failed calls
- Rate Limiting: Control request rates to prevent overload
- Bulkhead Pattern: Isolate resources to contain failures
Essential Tools & Technologies
Message Brokers
| Tool | Type | Best For | Key Features |
|---|---|---|---|
| Apache Kafka | Event Streaming | High-throughput, real-time | Partitioning, persistence, replay |
| RabbitMQ | Message Queue | Reliable messaging | Routing, clustering, plugins |
| Apache Pulsar | Pub-Sub | Multi-tenancy | Geo-replication, schema registry |
| Redis Streams | In-memory | Low latency | Persistence, clustering |
Coordination Services
Apache Zookeeper
- Configuration management and service discovery
- Distributed synchronization and group membership
- Used by Kafka, HBase, and other distributed systems
etcd
- Distributed key-value store for configuration
- Strong consistency using Raft consensus
- Used by Kubernetes and other cloud-native systems
Consul
- Service discovery and configuration
- Health checking and key-value storage
- Multi-datacenter support
Databases
| Database | Type | Consistency | Best For |
|---|---|---|---|
| Cassandra | NoSQL | Eventual | Write-heavy, high availability |
| MongoDB | Document | Strong/Eventual | Flexible schema, rapid development |
| CockroachDB | SQL | Strong | ACID transactions, geo-distribution |
| DynamoDB | NoSQL | Eventual | Serverless, auto-scaling |
Best Practices
Design Principles
Design for Failure
- Assume components will fail and plan accordingly
- Implement health checks and monitoring
- Use timeouts and retries with exponential backoff
- Design idempotent operations
Loose Coupling
- Minimize dependencies between services
- Use asynchronous communication when possible
- Implement proper service contracts and versioning
- Apply circuit breaker pattern for external dependencies
Observability
- Implement comprehensive logging across all services
- Use distributed tracing for request flows
- Monitor key metrics (latency, throughput, error rates)
- Set up alerting for critical issues
Development Guidelines
API Design
- Use RESTful principles or gRPC for service communication
- Implement proper error handling and status codes
- Version APIs to maintain backward compatibility
- Document APIs thoroughly
Data Management
- Choose appropriate consistency model for each use case
- Implement proper data validation and sanitization
- Use database transactions judiciously
- Plan for data migration and schema evolution
Security
- Implement authentication and authorization at service boundaries
- Use TLS for all network communication
- Apply principle of least privilege
- Regular security audits and vulnerability assessments
Operational Excellence
Deployment Strategies
- Use blue-green or canary deployments
- Implement proper rollback procedures
- Automate deployment pipelines
- Use infrastructure as code
Monitoring & Alerting
- Monitor SLIs (Service Level Indicators)
- Define SLOs (Service Level Objectives)
- Implement effective alerting to reduce noise
- Use dashboards for system visibility
Performance Optimization
Caching Strategies
| Strategy | Description | Best For |
|---|---|---|
| Cache-Aside | Application manages cache | Read-heavy workloads |
| Write-Through | Write to cache and database | Data consistency priority |
| Write-Behind | Async write to database | High write throughput |
| Refresh-Ahead | Proactive cache refresh | Predictable access patterns |
Scalability Patterns
Horizontal Partitioning
- Distribute data across multiple databases
- Use consistent hashing for even distribution
- Consider cross-shard queries complexity
Read Replicas
- Separate read and write operations
- Scale read capacity independently
- Handle replication lag appropriately
Auto-Scaling
- Scale resources based on demand
- Use predictive scaling when possible
- Implement proper metrics for scaling decisions
Troubleshooting Guide
Common Issues
Split-Brain Scenarios
- Symptoms: Multiple nodes think they’re the leader
- Solutions: Implement proper quorum mechanisms, use external arbitrator
Network Partitions
- Symptoms: Nodes can’t communicate with each other
- Solutions: Implement partition detection, graceful degradation
Cascade Failures
- Symptoms: Failure in one service causes failures in others
- Solutions: Circuit breakers, bulkhead pattern, proper timeouts
Data Inconsistency
- Symptoms: Different nodes have different data
- Solutions: Repair processes, read repair, anti-entropy protocols
Debugging Techniques
Distributed Tracing
- Use tools like Jaeger or Zipkin
- Trace requests across service boundaries
- Identify bottlenecks and failures
Log Aggregation
- Centralize logs from all services
- Use correlation IDs for request tracking
- Implement structured logging
Chaos Engineering
- Introduce controlled failures
- Test system resilience
- Validate failure recovery procedures
Resources for Further Learning
Essential Books
- “Designing Data-Intensive Applications” by Martin Kleppmann
- “Distributed Systems: Concepts and Design” by George Coulouris
- “Building Reliable Distributed Systems” by Patrick Hunt
- “Microservices Patterns” by Chris Richardson
Online Resources
- MIT 6.824: Distributed Systems course materials
- Raft Consensus Algorithm: Interactive visualization
- AWS Architecture Center: Real-world patterns and practices
- Google Cloud Architecture Framework: Best practices guide
Tools for Practice
- Docker & Kubernetes: Container orchestration
- Apache Kafka: Event streaming platform
- Consul: Service discovery and configuration
- Prometheus & Grafana: Monitoring and visualization
Research Papers
- “The Part-Time Parliament” (Paxos) – Leslie Lamport
- “In Search of an Understandable Consensus Algorithm” (Raft)
- “Amazon’s Dynamo” – DeCandia et al.
- “Google’s MapReduce” – Dean and Ghemawat
This cheatsheet serves as a comprehensive reference for distributed systems concepts, patterns, and best practices. Bookmark it for quick reference during system design and troubleshooting.