Creating Realistic Load Patterns

Real traffic doesn't look like a flat line. It spikes, dips, surges, and fluctuates. Testing with constant load misses behaviors that only appear when traffic changes.

Realistic load patterns reveal problems that steady-state testing hides.

Why constant load isn't enough

Constant load testing — 100 RPS for 10 minutes — tells you how your system behaves at exactly 100 RPS. Useful, but limited.

It doesn't tell you what happens when traffic suddenly doubles. Or when it drops and then surges back. Or during the morning ramp-up. Or during the evening wind-down. For an in-depth look at the difference, see stress testing vs load testing.

Systems often behave differently during transitions. Caches warm up. Connection pools adjust. Auto-scaling kicks in. These dynamics matter.

Common traffic patterns

Ramp up — traffic increases gradually. Morning as users wake up. Start of business hours. This tests how your system handles growing load.

Spike — sudden surge. Someone posted your link on social media. A marketing email went out. Flash sale started. This tests burst handling.

Step function — discrete jumps in traffic. New deployment, traffic shifts. Feature launch, users flood in. Tests sudden state changes.

Diurnal cycle — daily pattern. Low at night, high during day, peak at specific hours. Tests sustained operation across varying load.

Decline — traffic dropping. End of day. After an event ends. Tests whether resources release properly.

Testing ramp-up

Start at low load. Increase gradually over time. Watch how metrics change.

Good systems show linear degradation. Double the load, response times increase proportionally.

Problematic systems show cliffs. Everything's fine until a threshold, then sudden degradation. The ramp-up reveals where that threshold is.

This is related to finding your API's breaking point, but with gradual discovery instead of binary search.

Testing spikes

Sudden load increases stress different things than gradual ones.

Connection pools might not have time to grow. Caches are cold for new request patterns. Auto-scaling hasn't triggered yet.

Test by jumping from baseline to peak load instantly. Watch how long recovery takes. Some systems handle the spike gracefully. Others fall over and stay down.

Testing with Zoyla

Zoyla's straightforward approach works well for pattern testing. Run a test at one load level, note results. Run at a higher level, compare.

For spike testing, you can run sequential tests — baseline, then immediately high load. Watch how the system transitions.

For sustained patterns, run longer tests and observe stability over time.

Matching production patterns

If you have production analytics, use them. What does your traffic actually look like?

Maybe you have predictable peaks at 9am and 2pm. Test those transitions specifically.

Maybe you get random spikes from social media. Test sudden 3x surges.

Maybe traffic is remarkably steady. Then constant load testing might actually be appropriate.

The think time factor

Real users don't fire requests continuously. They pause, read, think. This affects how load translates to server requests.

If you're simulating user behavior, include realistic pauses. If you're testing raw capacity, continuous requests are fine — just know what you're measuring.

For more on this distinction, see what concurrent users actually means.

Recovery testing

What happens after a spike ends? Does your system return to normal immediately? Or does it stay degraded?

Some systems accumulate state during high load — full queues, exhausted pools, fragmented memory. They need time to recover even after load drops.

Test this by running high load, then dropping to baseline. Measure how long until metrics normalize.

The practical approach

Start with constant load testing to establish baselines. Then add pattern testing for the scenarios that matter for your application.

You don't need to simulate every possible pattern. Focus on the ones that reflect your actual traffic and the failure modes you're worried about. The testing before launch checklist covers what to verify before major releases.

For setting baselines, see setting performance baselines.