Distributed systems are inherently complex. Understanding core patterns helps you build systems that handle failures gracefully and scale effectively.
The Challenges#
Distributed systems must handle:
- Network failures (partitions, latency)
- Node failures (crashes, restarts)
- Timing issues (clock skew, ordering)
- Partial failures (some nodes fail, others don't)
The CAP theorem:
You can only guarantee two of three:
- Consistency: All nodes see same data
- Availability: Every request gets a response
- Partition tolerance: System works despite network issues
In practice, you must handle partitions, so choose between CP or AP.
Consensus Patterns#
Leader Election#
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Distributed Locking#
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Consistency Patterns#
Eventual Consistency#
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Read-Your-Writes Consistency#
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Partitioning Patterns#
Consistent Hashing#
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Failure Handling Patterns#
Circuit Breaker#
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Bulkhead#
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Retry with Exponential Backoff#
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Saga Pattern#
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Conclusion#
Distributed systems require thinking about failures at every level. Use consensus for coordination, understand consistency tradeoffs, and implement resilience patterns like circuit breakers and bulkheads.
Test failure scenarios aggressively. The patterns that seem theoretical become critical when systems fail at 3 AM.