designing-distributed-systems

# Designing Distributed Systems

Design scalable, reliable, and fault-tolerant distributed systems using proven patterns and consistency models.

## Purpose

Distributed systems are the foundation of modern cloud-native applications. Understanding fundamental trade-offs (CAP theorem, PACELC), consistency models, replication patterns, and resilience strategies is essential for building systems that scale globally while maintaining correctness and availability.

## When to Use This Skill

Apply when:
- Designing microservices architectures with multiple services
- Building systems that must scale across multiple datacenters or regions
- Choosing between consistency vs availability during network partitions
- Selecting replication strategies (single-leader, multi-leader, leaderless)
- Implementing distributed transactions (saga pattern, event sourcing, CQRS)
- Designing partition-tolerant systems with proper consistency guarantees
- Building resilient services with circuit breakers, bulkheads, retries
- Implementing service discovery and inter-service communication

## Core Concepts

### CAP Theorem Fundamentals

**CAP Theorem:** In a distributed system experiencing a network partition, choose between Consistency (C) or Availability (A). Partition tolerance (P) is mandatory.

```
Network partitions WILL occur → Always design for P

During partition:
├─ CP (Consistency + Partition Tolerance)
│  Use when: Financial transactions, inventory, seat booking
│  Trade-off: System unavailable during partition
│  Examples: HBase, MongoDB (default), etcd
│
└─ AP (Availability + Partition Tolerance)
   Use when: Social media, caching, analytics, shopping carts
   Trade-off: Stale reads possible, conflicts need resolution
   Examples: Cassandra, DynamoDB, Riak
```

**PACELC:** Extends CAP to consider normal operations (no partition).
- **If Partition:** Choose Availability (A) or Consistency (C)
- **Else (normal):** Choose Latency (L) or Consistency (C)

### Consistency Models Spectrum

```
Strong Consistency ◄─────────────────────► Eventual Consistency
      │                    │                      │
  Linearizable      Causal Consistency     Convergent
  (Slowest,         (Middle Ground,        (Fastest,
   Most Consistent)  Causally Ordered)     Eventually Consistent)
```

**Strong Consistency (Linearizability):**
- All operations appear atomically in sequential order
- Reads always return most recent write
- Use for: Bank balances, inventory stock, seat booking
- Trade-off: Higher latency, reduced availability

**Eventual Consistency:**
- If no new updates, all replicas eventually converge
- Use for: Social feeds, product catalogs, user profiles, DNS
- Trade-off: Stale reads possible, conflict resolution needed

**Causal Consistency:**
- Causally related operations seen in same order by all nodes
- Use for: Chat apps, collaborative editing, comment threads
- Trade-off: More complex than eventual, requires causality tracking

**Bounded Staleness:**
- Staleness bounded by time or version count
- Use for: Real-time dashboards, leaderboards, monitoring
- Trade-off: Must monitor lag, more complex than eventual

### Replication Patterns

**1. Leader-Follower (Single-Leader):**
- All writes to leader, replicated to followers
- Followers handle reads (load distribution)
- **Synchronous:** Wait for follower ACK (strong consistency, higher latency)
- **Asynchronous:** Don't wait (eventual consistency, possible data loss)
- Use for: Most common pattern, strong consistency with sync replication

**2. Multi-Leader:**
- Multiple leaders accept writes in different datacenters
- Leaders replicate to each other
- **Conflict resolution required:** Last-Write-Wins, application merge, vector clocks
- Use for: Multi-datacenter, low write latency, geo-distributed users
- Trade-off: Conflict resolution complexity

**3. Leaderless (Dynamo-style):**
- No single leader, quorum-based reads/writes
- **Quorum rule:** W + R > N (W=write quorum, R=read quorum, N=replicas)
- Example: N=5, W=3, R=2 → Strong consistency (overlap guaranteed)
- Use for: Maximum availability, partition tolerance
- Trade-off: Complexity, read repair needed

### Partitioning Strategies

**Hash Partitioning (Consistent Hashing):**
- Key → Hash(Key) → Partition assignment
- Even distribution, minimal rebalancing when nodes added/removed
- Use for: Point queries by ID, even distribution critical
- Examples: Cassandra, DynamoDB, Redis Cluster

**Range Partitioning:**
- Key ranges assigned to partitions (A-F, G-M, N-S, T-Z)
- Enables range queries, ordered data
- Risk: Hot spots if data skewed
- Use for: Time-series data, leaderboards, range scans
- Examples: HBase, Bigtable

**Geographic Partitioning:**
- Partition by location (US-East, EU-West, APAC)
- Use for: Data locality, GDPR compliance, low latency
- Examples: Spanner, Cosmos DB

### Resilience Patterns

**Circuit Breaker:**
```
[Closed] → Normal operation
   │ (failures exceed threshold)
   ▼
[Open] → Fail fast (don't call failing service)
   │ (timeout expires)
   ▼
[Half-Open] → Try single request
   │ success → [Closed]
   │ failure → [Open]
```
- Prevents cascading failures
- Fast-fail instead of waiting for timeout
- See references/resilience-patterns.md

**Bulkhead Isolation:**
- Isolate resources (thread pools, connection pools)
- Failure in one partition doesn't affect others
- Like ship compartments preventing total flooding

**Timeout and Retry:**
- **Timeout:** Set deadlines, fail fast if exceeded
- **Retry:** Exponential backoff with jitter
- **Idempotency:** Ensure safe retry (critical)

**Rate Limiting and Backpressure:**
- Protect services from overload
- Token bucket, leaky bucket algorithms
- Backpressure: Signal upstream to slow down

### Transaction Patterns

**Saga Pattern:**
- Coordinate distributed transactions across services
- No distributed 2PC (two-phase commit)

**Choreography:** Services react to events
```
Order Service → OrderCreated event
Payment Service → listens →