When to Use Exactly-Once vs Alternatives

The Core Decision: Choosing exactly-once processing is fundamentally a trade off between correctness and cost. You pay for exactly-once semantics in three ways: increased latency from synchronous coordination, reduced throughput from additional I/O, and higher operational complexity from managing checkpoints and transactions. The question is whether the cost of incorrect results justifies these overheads. Performance Trade-offs: Enabling transactional exactly-once semantics typically increases p99 latency by 5 to 30 percent compared to at least once processing. The overhead comes from synchronous commits that must wait for durable storage writes before proceeding. Maximum throughput often drops by 10 to 20 percent because each checkpoint or transaction introduces coordination points that limit parallelism. For a pipeline processing 100,000 events per second with 50 millisecond p99 latency under at least once semantics, switching to exactly-once might push p99 to 60 to 65 milliseconds and reduce max throughput to 80,000 to 90,000 events per second. Whether this matters depends entirely on your requirements and margin. When to Choose Exactly-Once: Use system level exactly-once semantics when incorrect results create material harm. Clear candidates include money movement (duplicate charges or credits), usage based billing (overcharging customers), inventory reservations (selling stock you do not have), and compliance grade audit logs (regulatory requirements for accuracy). For these domains, the 10 to 30 percent performance cost is trivial compared to the operational and financial damage from duplicates. A single duplicate charge affects customer trust and creates support load. At scale, duplicates in billing can mean millions in revenue recognition errors. When At Least Once Suffices: For rough analytics, click tracking, or A/B test metrics, at least once processing with downstream idempotent aggregation is usually simpler and sufficient. If seeing a click event twice inflates a counter from 1,000,000 to 1,000,001, the 0.0001 percent error is negligible for decision making. Similarly, for metrics pipelines feeding dashboards, slight overcounting from duplicates rarely changes conclusions. The engineering cost of implementing and operating exactly-once semantics exceeds the value of perfect accuracy. Architectural Choices: There are two implementation approaches with different trade-offs. System level exactly-once in frameworks like Flink or Kafka Streams simplifies application code but couples you to that engine and its supported sinks. You must verify that your downstream systems (databases, warehouses) integrate with the framework's transaction protocol. Alternatively, you can use the idempotent consumer pattern, treating the pipeline as at least once and making final side effects idempotent using unique message IDs and conditional writes. This approach reduces coupling to a specific framework but pushes responsibility into each consumer. It also introduces extra writes for deduplication, potentially doubling write load to your transactional store. For companies with many heterogeneous consumers, the idempotent consumer pattern offers flexibility. Each team implements idempotency in their service without depending on a central streaming engine. For organizations standardized on a single streaming platform, system level exactly-once reduces per service complexity.