Choosing Event Time vs Processing Time: The Trade-offs

The Core Decision: Choosing between event time and processing time is a trade off between correctness and simplicity, and between handling real world delays and operational complexity. There is no universal right answer; it depends on your use case, scale, and tolerance for inaccuracy. When to Use Event Time: Event time based processing gives accurate results in the presence of out of order and late events. You define windows like "00:00:00 to 00:04:59 UTC" and be confident that every event whose event time falls within that range will be considered, regardless of when it arrives, up to your lateness bound. This is crucial for billing, compliance reporting, Machine Learning (ML) feature generation, and any scenario where backfills are common. If you need to recompute metrics for yesterday after fixing a bug, event time gives you reproducible results. The cost is complexity: you need watermarks, state retention policies, explicit handling of late data, and tuning of lateness tolerances. Longer lateness windows increase state size and memory usage, and they delay final results. When to Use Processing Time: Processing time based processing is straightforward. You group events based on the local clock at processing time. This is attractive for low criticality monitoring, A/B test traffic counters, or near real time alerting where you care primarily about "what is happening in the system right now" and not exact alignment with real world timestamps. It has lower memory requirements because you do not wait for late events. However, results can be biased during backpressure, retries, or network partitions. Suddenly reprocessing a backlog can introduce spurious spikes in metrics. If a 5 minute network outage delays 1 million events, processing time windows will show a false dip during the outage and an artificial surge afterward. Hybrid Approaches: Some systems use event time windows with a maximum allowed lateness (for example, 5 minutes), plus a separate mechanism for very late data, such as a correction stream or periodic backfills in batch jobs. This gives you sub 5 second latency for most events while ensuring eventual correctness over hours or days. Others choose processing time for high frequency, high fan out monitoring metrics (like "requests per second per endpoint"), then recompute more accurate event time analytics offline with batch processing. The right choice depends on whether your priority is immediacy or eventual correctness, and how expensive it is to store and manage long lived state at your scale. Decision Framework: Ask these questions. First, what happens if 1 percent of events are delayed by 5 minutes? For billing or compliance, this is unacceptable, use event time. For rough traffic monitoring, you can tolerate it, use processing time. Second, do you need to reprocess historical data? If yes, event time is essential. Third, can you afford hundreds of gigabytes of state and the operational complexity of watermarks? If no, processing time might be more practical for your current scale.