| # Distributed Systems: A Practical Overview | |
| ## What Makes a System "Distributed"? | |
| A distributed system is a collection of independent computers that appears to users as a single coherent system. The key challenges arise from: | |
| 1. **Partial failure** - Parts of the system can fail independently | |
| 2. **Unreliable networks** - Messages can be lost, delayed, or duplicated | |
| 3. **No global clock** - Different nodes have different views of time | |
| ## The CAP Theorem | |
| Eric Brewer's CAP theorem states that a distributed system can only provide two of three guarantees: | |
| - **Consistency**: All nodes see the same data at the same time | |
| - **Availability**: Every request receives a response | |
| - **Partition tolerance**: System continues operating despite network partitions | |
| In practice, network partitions happen, so you're really choosing between CP and AP systems. | |
| ### CP Systems (Consistency + Partition Tolerance) | |
| - Examples: ZooKeeper, etcd, Consul | |
| - Sacrifice availability during partitions | |
| - Good for: coordination, leader election, configuration | |
| ### AP Systems (Availability + Partition Tolerance) | |
| - Examples: Cassandra, DynamoDB, CouchDB | |
| - Sacrifice consistency during partitions | |
| - Good for: high-throughput, always-on services | |
| ## Consensus Algorithms | |
| When nodes need to agree on something, they use consensus algorithms. | |
| ### Paxos | |
| - Original consensus algorithm by Leslie Lamport | |
| - Notoriously difficult to understand and implement | |
| - Foundation for many other algorithms | |
| ### Raft | |
| - Designed to be understandable | |
| - Used in etcd, Consul, CockroachDB | |
| - Separates leader election from log replication | |
| ### PBFT (Practical Byzantine Fault Tolerance) | |
| - Handles malicious nodes | |
| - Used in blockchain systems | |
| - Higher overhead than crash-fault-tolerant algorithms | |
| ## Replication Strategies | |
| ### Single-Leader Replication | |
| - One node accepts writes | |
| - Followers replicate from leader | |
| - Simple but leader is bottleneck | |
| ### Multi-Leader Replication | |
| - Multiple nodes accept writes | |
| - Must handle write conflicts | |
| - Good for multi-datacenter deployments | |
| ### Leaderless Replication | |
| - Any node accepts writes | |
| - Uses quorum reads/writes | |
| - Examples: Dynamo-style databases | |
| ## Consistency Models | |
| From strongest to weakest: | |
| 1. **Linearizability** - Operations appear instantaneous | |
| 2. **Sequential consistency** - Operations appear in some sequential order | |
| 3. **Causal consistency** - Causally related operations appear in order | |
| 4. **Eventual consistency** - Given enough time, all replicas converge | |
| ## Partitioning (Sharding) | |
| Distributing data across nodes: | |
| ### Hash Partitioning | |
| - Hash key to determine partition | |
| - Even distribution | |
| - Range queries are inefficient | |
| ### Range Partitioning | |
| - Ranges of keys on different nodes | |
| - Good for range queries | |
| - Risk of hot spots | |
| ## Conclusion | |
| Building distributed systems requires understanding these fundamental concepts. Start simple, add complexity only when needed, and always plan for failure. | |