Thread: 🚨 Imagine a popular social media platform going down during peak hours. Users can't post updates, and engagement plummets. Reliability in distributed systems is crucial for maintaining user trust and service continuity. Let's dive in! #SystemDesign
Reliability hinges on data replication. In a distributed system, data is copied across multiple nodes to ensure availability. If one node fails, others can serve the requests. But how does this work? It involves consensus algorithms like Paxos or Raft to maintain consistency.
At the core, these algorithms ensure that all nodes agree on the state of the system. They use heartbeats to check node health & a quorum mechanism to validate updates.
For example, in a 5-node setup using Raft, at least 3 nodes must agree to commit a change, ensuring fault tolerance.
Consider a 3-replica setup. If one replica goes down, the system can still function with 2 replicas. However, network latency increases as the system waits for consensus, adding to the overall transaction time. This can lead to a worst-case latency of O(N) during recovery.
One common implementation is using a write-ahead log (WAL) for durability. Each change is logged before it's applied, which allows recovery in case of failures. For instance, Kafka uses this method, ensuring messages are not lost, but requires more disk I/O and CPU cycles.
Trade-offs are everywhere! Increasing replication improves reliability but can hurt write performance due to increased coordination. For instance, Google Bigtable uses 3 replicas for data but can incur a 30% latency hit during write operations. Consider your use case carefully!
Use a higher replication factor for critical systems (like payment processing) but lower it for less critical data (like logs). Netflix uses 3 replicas for user data but only 1 for ephemeral logs. This ensures performance while safeguarding against failures where it matters most.
Common pitfalls include over-replicating non-critical data, leading to wasted resources & complexity. For example, a failed deployment at a large tech firm occurred due to unnecessary consensus delays, crippling service availability. Always evaluate the cost of reliability!
Key takeaway: Reliability in distributed systems is a balancing act between availability, consistency, and performance. Understand your trade-offs, and design for failure. Build systems that can gracefully handle issues rather than trying to prevent them entirely. #SystemDesign
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