When evaluating the performance of a library, most developers rely on published benchmarks.
These benchmarks often show impressive performance gains compared to competitors — sometimes even 5x or 10x faster.
But there’s a catch: these numbers typically come from static, highly optimized flows, where the same operators and middleware are applied repeatedly.
This allows modern JIT engines to fully optimize the process, leading to artificially high performance numbers.
What Happens in Real-World Systems
In real-world systems, especially large-scale streaming or event-driven systems, the actual data flow is rarely static.
Operators or middleware may change depending on request type, data structure, or user input.
Dynamic branching and real-time decisions prevent JIT engines from fully optimizing the flow.
Buffers, pausing, resuming, and handling errors further complicate optimization.
This dynamic nature significantly reduces the effectiveness of JIT-based performance gains.
My Experiment: Static vs Dynamic Performance
I conducted a performance test on a streaming library I built.
Here’s how it worked:
I processed 1 billion events in batches of 1 million.
In the first test, the same operators/middleware were applied consistently across all events — maximizing JIT optimization.
In the second test, operators were selected dynamically using Math.random, simulating real-world conditions where different transformations may apply to different events.
| Test Scenario | Performance Difference vs RxJS |
|---|---|
| Static (same operators) | 8x faster |
| Dynamic (random operators) | ~80% faster |
What This Means
The difference is striking:
- In a controlled, static flow, my library was over 8x faster than RxJS.
- But in a realistic, dynamic flow, that advantage shrank to just 80% faster.
This doesn’t mean my library is slow — even at 80% faster, it still outperforms.
But it highlights the huge gap between artificial benchmarks and real-world performance.
Why This Matters for Enterprise Systems
For enterprise-grade systems, realistic performance matters more than synthetic numbers.
Real-world systems almost never have perfectly static data flows.
They deal with dynamic configurations, feature toggles, request-specific processing, and more.
In such environments, JIT optimization is far less effective, making raw architecture efficiency far more important.
If you’re building or evaluating a performance-critical library,
make sure your benchmarks reflect reality — not just ideal conditions.
Closing Thought
The next time you see a benchmark claiming "10x faster",
ask how the test was conducted.
- Was it a perfectly static pipeline?
- Were real-world conditions like dynamic operators, error handling, and state transitions included?
- Did they intentionally disrupt JIT optimizations to simulate real production?
Performance claims without context can be misleading.
Always dig deeper.
Optional CTA (Call to Action)
If you’re interested in how I built a streaming library optimized for real-world conditions,
check out Asyncrush and the full benchmark results.
Top comments (1)
Adding randomness to performance testing is essential