Latency is the silent killer of real-time systems. It doesn’t announce itself like a network outage. It compounds silently, degrading performance until users notice something feels slow. In real-time processing systems, latency management separates successful implementations from those that disappoint.
The Latency Budget
Every real-time system has an implicit latency budget: the maximum acceptable delay between input and output. For high-frequency trading, this might be milliseconds. For IoT monitoring, it might be seconds. For dashboards, minutes. Understanding your budget is critical because every component in your pipeline consumes some of this budget.
Consider a stream processing pipeline with multiple stages. The event enters at the source. It travels through a queue. A processor consumes it, performs computations, possibly looks up reference data, and emits a result. The result might go through another queue before reaching a sink. Each stage has latency: network delay, queuing delay, processing delay, disk I/O delay.
Measuring Latency
Measuring latency correctly is harder than it seems. Most systems measure end-to-end latency: the time from input to output. But this hides important details. An event might spend 90 milliseconds queued and 10 milliseconds processing. The average latency looks fine, but the queue delay is the real problem.
Percentile-based latency is more informative. P50 (median) latency might be 50 milliseconds. P99 latency might be 500 milliseconds. P99.9 latency might be 2 seconds. These percentiles tell you how your system behaves under load. If you’re building a user-facing system, P99 latency matters more than average latency because one user’s bad experience counts more than average.
Latency attribution is crucial. Where does the latency come from? Network? CPU? Disk? Different optimizations address different bottlenecks. Without attribution, you optimize the wrong thing.
Network Latency
Network latency is often the dominating factor. Every network hop adds delay. Even within a data center, network latency is measured in microseconds. Cross-datacenter communication adds milliseconds.
Minimizing network hops means co-locating components. Process data where it’s generated rather than moving it elsewhere. Use local caches rather than fetching from remote services. But co-location adds operational complexity.
Protocol choice matters. TCP has overhead. UDP is faster but less reliable. HTTP/1.1 has per-request overhead. HTTP/2 and gRPC reduce this. Choose protocols based on your reliability and latency requirements.
Processing Latency
Once data arrives, your code must process it. Processing latency depends on algorithm efficiency and resource contention. Inefficient algorithms cause unnecessary delay. Resource contention (CPU, memory, disk) causes queuing.
Garbage collection is a silent latency killer in many systems. When the garbage collector runs, all processing stops. For latency-sensitive systems, use languages without GC, or tune GC aggressively. Some teams use buffer pools to reduce GC pressure.
I/O latency compounds processing latency. Looking up reference data in a database can add tens of milliseconds. Minimize I/O. Use local caches. Pre-load reference data. Use fast data structures (hash maps, not databases).
Parallelism and Batching
Increasing parallelism seems like the obvious solution to latency: process more events simultaneously, reducing per-event latency. But parallelism increases queuing delay. More parallel processors mean each event waits longer for available capacity.
Batching has similar tradeoffs. Processing events in batches is more efficient, but the last event in a batch waits for the entire batch to complete. Small batches have low latency but high overhead. Large batches have high latency but good throughput.
Finding the right balance is empirical. Measure how parallelism and batch size affect both latency percentiles and throughput. The sweet spot is often counterintuitive.
Tailing Latency
Tailing latency (P99 and above) is particularly important for real-time systems. A few slow requests can degrade the entire system if they block others. Understanding why tail latency happens is key.
Common causes include resource contention, GC pauses, context switches, and cache misses. Some are unavoidable. But you can manage them. Reserve resources for critical paths. Monitor for anomalies. Use deadline propagation so slow operations can bail out early rather than blocking faster ones.
Latency vs Throughput
Latency and throughput often conflict. Higher throughput usually means higher latency. Adding more work per batch improves throughput but increases individual latency. Reducing parallelism improves latency but reduces throughput.
Your latency budget determines the throughput limit. If you need sub-100ms latency and processing takes 10ms per event, you can process at most 10 events per second (assuming single-threaded). Increase parallelism to improve throughput, but you’ll increase latency percentiles.
Optimization Principles
Latency optimization requires discipline. Optimize what matters. Use profiling and measurement, not guesses. Every optimization has tradeoffs. Reducing network hops might increase code complexity. Caching might require consistency management. Make tradeoffs consciously.
End with the simplest solution that meets your latency requirements. Premature optimization creates bugs and maintenance burden. Only optimize when measurements show bottlenecks.
Conclusion
Latency optimization is essential for real-time systems. Understand your latency budget. Measure latency accurately, including percentiles and attribution. Address bottlenecks systematically. Balance latency, throughput, and operational simplicity. Real-time systems reward careful thinking about latency. Invest in understanding and managing it.