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High-Throughput Payment Systems
Section 7 of 9
Benchmarking and capacity planning, database sharding, multi-layer caching, Lambda at scale, CQRS for high-read workloads, and global active-active architecture
4 hours•advanced
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Key Takeaways
- •Payment systems must sustain peak load 10-20x baseline — capacity planning based on average is always wrong
- •Database sharding by payment_id (hash) distributes write load; sharding by merchant_id allows merchant-scoped queries
- •CQRS separates write throughput (event-sourced) from read throughput (denormalised projections) — critical at 10k+ TPS
- •Lambda concurrency limits are a quota, not a guarantee — request reserved concurrency for payment-critical functions
- •Global active-active requires conflict resolution for concurrent writes — DynamoDB Global Tables uses last-writer-wins
📝Personal Notes
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