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Distributed Systems Concepts

A distributed system is a collection of autonomous computing elements that appears to its users as a single coherent system. These systems are essential for building scalable, fault-tolerant applications that can handle large amounts of data and traffic across multiple machines.

Core Concepts​

1. Scalability​

Definition: The ability of a system to handle increased load by adding resources.

Types of Scalability:

  • Horizontal Scaling (Scale Out): Adding more machines to the pool of resources
  • Vertical Scaling (Scale Up): Adding power (CPU, RAM) to existing machines

Key Considerations:

  • Horizontal scaling is generally preferred for distributed systems
  • Stateless components scale better than stateful ones
  • Load distribution becomes critical

2. Consistency​

Definition: All nodes see the same data at the same time.

Consistency Models:

  • Strong Consistency: All reads receive the most recent write
  • Eventual Consistency: System will become consistent over time
  • Weak Consistency: No guarantees when all nodes will be consistent

Examples in Practice:

  • Banking systems require strong consistency
  • Social media feeds can use eventual consistency
  • DNS systems use weak consistency

3. Availability​

Definition: The system remains operational over time, even during failures.

Measuring Availability:

  • 99.9% (8.77 hours downtime/year)
  • 99.99% (52.6 minutes downtime/year)
  • 99.999% (5.26 minutes downtime/year)

Achieving High Availability:

  • Redundancy and replication
  • Failover mechanisms
  • Health checks and monitoring

4. Partition Tolerance​

Definition: The system continues to operate despite network partitions.

Network Partitions:

  • Communication breakdown between nodes
  • Split-brain scenarios
  • Network delays and packet loss

CAP Theorem​

Statement: In a distributed system, you can only guarantee two out of three properties:

  • Consistency (C)
  • Availability (A)
  • Partition Tolerance (P)

Practical Implications:

  • CP Systems: Sacrifice availability for consistency (e.g., traditional RDBMS)
  • AP Systems: Sacrifice consistency for availability (e.g., DNS, web caches)
  • CA Systems: Only possible without network partitions (single-node systems)

PACELC Theorem​

Extension of CAP: If there is a Partition, choose between Availability and Consistency; Else, when the system is running normally, choose between Latency and Consistency.

Distributed System Patterns​

1. Replication Patterns​

Master-Slave Replication​

  • One master handles writes
  • Multiple slaves handle reads
  • Provides read scalability
  • Examples: MySQL master-slave setup

Master-Master Replication​

  • Multiple masters handle writes
  • Requires conflict resolution
  • Higher availability but more complex
  • Examples: MySQL master-master, CockroachDB

Peer-to-Peer Replication​

  • All nodes are equal
  • No single point of failure
  • Complex consistency management
  • Examples: Cassandra, DynamoDB

2. Sharding (Partitioning)​

Definition: Splitting data across multiple databases/servers.

Sharding Strategies:

  • Horizontal Partitioning: Split by rows (e.g., user ID ranges)
  • Vertical Partitioning: Split by columns/features
  • Directory-Based: Lookup service to find data location
  • Hash-Based: Use hash function to determine shard

Challenges:

  • Rebalancing shards
  • Cross-shard queries
  • Hotspots

3. Load Balancing​

Purpose: Distribute incoming requests across multiple servers.

Load Balancing Algorithms:

  • Round Robin: Requests distributed sequentially
  • Weighted Round Robin: Assign weights based on server capacity
  • Least Connections: Route to server with fewest active connections
  • IP Hash: Use client IP to determine server
  • Geographic: Route based on client location

Types:

  • Layer 4 (Transport): Route based on IP and port
  • Layer 7 (Application): Route based on content (HTTP headers, URLs)

4. Caching Strategies​

Cache Patterns​

  • Cache-Aside (Lazy Loading): Application manages cache
  • Write-Through: Write to cache and database simultaneously
  • Write-Behind (Write-Back): Write to cache first, database later
  • Refresh-Ahead: Proactively refresh cache before expiration

Cache Levels​

  • Browser Cache: Client-side caching
  • CDN: Geographic distribution of static content
  • Reverse Proxy: Server-side caching (e.g., Nginx, Varnish)
  • Application Cache: In-memory caching (e.g., Redis, Memcached)
  • Database Cache: Query result caching

5. Message Queues and Event Streaming​

Purpose: Decouple system components and enable asynchronous processing.

Messaging Patterns:

  • Point-to-Point: One producer, one consumer
  • Publish-Subscribe: One producer, multiple consumers
  • Request-Reply: Synchronous-like communication over async messaging

Message Delivery Guarantees:

  • At-most-once: Messages may be lost but never duplicated
  • At-least-once: Messages may be duplicated but never lost
  • Exactly-once: Messages delivered exactly once (hardest to achieve)

Consensus Algorithms​

1. Raft​

  • Purpose: Achieve consensus in distributed systems
  • Key Concepts: Leader election, log replication, safety
  • Used By: etcd, HashiCorp Consul
  • Advantages: Simpler to understand than Paxos

2. Paxos​

  • Purpose: Solve consensus problem in unreliable networks
  • Variants: Basic Paxos, Multi-Paxos, Fast Paxos
  • Used By: Google Chubby, Apache Cassandra
  • Characteristics: Proven but complex to implement

3. Byzantine Fault Tolerance (BFT)​

  • Purpose: Handle malicious or arbitrary failures
  • Used By: Blockchain systems, critical infrastructure
  • Trade-offs: Higher overhead but handles more failure types

Distributed System Challenges​

1. Network Failures​

  • Partial Failures: Some components fail while others continue
  • Network Partitions: Communication breakdown between nodes
  • Timeouts: Distinguishing between slow responses and failures

2. Clock Synchronization​

  • Problem: Different clocks on different machines
  • Solutions:
    • NTP (Network Time Protocol)
    • Logical clocks (Lamport timestamps)
    • Vector clocks

3. Distributed Transactions​

  • Two-Phase Commit (2PC): Coordinator ensures all participants commit or abort
  • Three-Phase Commit (3PC): Adds prepared-to-commit phase
  • Saga Pattern: Long-running transactions with compensation

4. Data Consistency​

  • Read-Your-Writes: Users see their own writes immediately
  • Monotonic Reads: Users don't see data going backwards in time
  • Causal Consistency: Causally related operations are seen in order

Monitoring and Observability​

1. The Three Pillars​

  • Metrics: Numerical measurements over time
  • Logs: Discrete events with timestamps
  • Traces: Request flows through distributed systems

2. Key Metrics​

  • Latency: Time to process a request
  • Throughput: Requests processed per unit time
  • Error Rate: Percentage of failed requests
  • Saturation: How "full" your service is

3. Distributed Tracing​

  • Purpose: Track requests across multiple services
  • Tools: Jaeger, Zipkin, AWS X-Ray
  • Concepts: Spans, traces, sampling

Real-World Examples from Our Wiki​

Message Queues​

Our wiki covers several distributed message queue systems:

  • Apache Kafka: High-throughput, fault-tolerant event streaming
  • Apache RocketMQ: Alibaba's distributed messaging platform
  • Apache Pulsar: Multi-tenant, geo-replicated messaging
  • RabbitMQ: Robust messaging for complex routing

Distributed Databases​

  • Elasticsearch: Distributed search and analytics
  • ScyllaDB: High-performance NoSQL database
  • CockroachDB: Distributed SQL database

Container Orchestration​

  • Kubernetes: Distributed container orchestration platform

Caching Systems​

  • Redis: In-memory data structure store
  • Memcached: High-performance distributed memory caching

Service Coordination​

  • ZooKeeper: Centralized service for configuration and synchronization

Design Patterns for Distributed Systems​

1. Circuit Breaker​

Purpose: Prevent cascading failures by stopping calls to failing services.

States:

  • Closed: Normal operation
  • Open: Calls fail immediately
  • Half-Open: Test if service recovered

2. Bulkhead​

Purpose: Isolate resources to prevent total system failure. Example: Separate thread pools for different operations.

3. Retry with Exponential Backoff​

Purpose: Handle transient failures gracefully. Implementation: Increase delay between retries exponentially.

4. Timeout​

Purpose: Avoid waiting indefinitely for responses. Considerations: Set appropriate timeout values based on SLA.

5. Idempotency​

Purpose: Ensure operations can be safely retried. Implementation: Design operations to have same effect regardless of repetition.

Building Distributed Systems: Best Practices​

1. Design Principles​

  • Assume failures will happen
  • Design for eventual consistency
  • Make components stateless when possible
  • Use asynchronous communication
  • Implement proper monitoring and alerting

2. Testing Strategies​

  • Unit Testing: Test individual components
  • Integration Testing: Test component interactions
  • Chaos Engineering: Deliberately introduce failures
  • Load Testing: Test system under expected load
  • Disaster Recovery Testing: Test failure scenarios

3. Deployment Strategies​

  • Blue-Green Deployment: Maintain two identical environments
  • Canary Deployment: Gradual rollout to subset of users
  • Rolling Deployment: Replace instances one by one
  • Feature Flags: Control feature rollout dynamically

Common Distributed System Architectures​

1. Microservices Architecture​

Characteristics:

  • Small, independent services
  • Business capability focused
  • Decentralized governance
  • Technology diversity

Benefits:

  • Independent scaling
  • Technology flexibility
  • Team autonomy
  • Fault isolation

Challenges:

  • Network complexity
  • Data consistency
  • Testing complexity
  • Operational overhead

2. Service-Oriented Architecture (SOA)​

Characteristics:

  • Services communicate through well-defined interfaces
  • Platform and language independent
  • Reusable business functionality

3. Event-Driven Architecture​

Characteristics:

  • Components communicate through events
  • Loose coupling between producers and consumers
  • Asynchronous processing

Benefits:

  • High scalability
  • Flexibility
  • Real-time processing

Conclusion​

Distributed systems are complex but essential for modern applications. Understanding these concepts helps in:

  • Making informed architectural decisions
  • Designing for scalability and reliability
  • Troubleshooting distributed system issues
  • Choosing appropriate technologies

The key is to understand the trade-offs and choose the right approach based on your specific requirements, whether that's consistency, availability, partition tolerance, or performance.

Further Reading​

  • Books:

    • "Designing Data-Intensive Applications" by Martin Kleppmann
    • "Distributed Systems: Concepts and Design" by George Coulouris
    • "Building Microservices" by Sam Newman

  • Papers:

    • "Time, Clocks, and the Ordering of Events in a Distributed System" by Leslie Lamport
    • "The Byzantine Generals Problem" by Lamport, Shostak, and Pease
    • "Harvest, Yield, and Scalable Tolerant Systems" by Fox & Brewer

  • Related Wiki Pages:

    • System Performance Troubleshooting Guide
    • Kubernetes Architecture
    • Message Queue Comparison