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Start with a question and a pass criterion
Decide what you need to learn before choosing a tool. Capacity planning asks how many requests the system can sustain before a limit is reached. A service-level test asks whether a production-like workload meets a target such as “p95 latency below the application objective with fewer than 1% failed requests.” Regression testing asks whether a code or configuration change altered the same workload.
There is no universal “good” requests-per-second score or latency threshold. A static, cached endpoint and an authenticated checkout request have different costs. Set the pass criterion from your service objectives and representative user journeys, not from another server’s published number.
Define a representative workload
Describe requests, not just URLs
- List the endpoints and method mix, such as 70% GET product pages, 20% search requests and 10% POST orders.
- Record realistic payload sizes, query parameters, headers, authentication state and cookie behavior.
- Specify whether the cache is cold, warm or deliberately bypassed. State whether a CDN is included.
- Include database, queue, object-storage and other downstream dependencies when the goal is user-impact capacity.
Fix the test location and transport
Document the load-generator region, network path, TLS version and connection behavior. A generator in the same rack measures something different from a user across an ocean. Keep HTTP, TLS, proxy and DNS settings consistent between runs.
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Record the environment
Write down server and database versions, hardware or container CPU and memory limits, operating-system settings, autoscaling rules, load-generator specifications and benchmark-tool version. Without this record, results from two environments are not safely comparable.
Use a controlled load profile
- Baseline: send a small, steady load to verify routing, authentication and response correctness.
- Warm up: allow caches, connection pools and runtime compilation to settle. OpenTelemetry’s benchmark guidance specifically recommends a warm-up phase for languages with bootstrap costs such as JIT compilation.
- Ramp: increase concurrency or arrival rate in measured steps.
- Steady state: hold each level long enough to observe typical and tail behavior.
- Stress or breakpoint: continue until the stated error, latency or saturation criterion is crossed, then stop safely.
Keep the generator below its own CPU, network and file-descriptor limits. If it saturates first, you are benchmarking the generator. For large tests, use multiple generators and verify that their clocks, software versions and scenarios match.
Run every condition repeatedly. OpenTelemetry suggests that one iteration run for at least 15 seconds and that measurements be repeated 10 times or more; report average and peak CPU usage when resource cost matters. Longer steady-state windows may be necessary for garbage collection, autoscaling or cache-expiry effects.
Capture the metrics that explain the result
| Metric | Meaning | Why it matters |
|---|---|---|
| Throughput | Completed requests per unit time | Shows delivered capacity at a defined load. |
| p50 latency | Latency below which 50% of requests fall | Approximates the typical request. |
| p90, p95, p99 | Tail latency percentiles | Shows how slower requests affect a meaningful minority of users. |
| Failed-request rate | Share of requests that fail according to the tool or your checks | Prevents a high RPS result built from errors. |
| Status-code distribution | Counts by HTTP status | Separates 2xx success from redirects, 4xx and 5xx responses. |
| Correctness checks | Assertions on body, headers or business outcome | Confirms that fast responses are valid responses. |
| Server saturation | CPU, memory, network, disk, database pools, queues and file descriptors | Identifies the bottleneck and the next scaling limit. |
JMeter defines throughput as requests per unit time and latency as the interval from just before sending a request until the first response is received. In k6, http_req_duration is request latency, http_reqs is request count/rate and http_req_failed is the failed-request rate. Keep the timing definition with your report: “p95” is meaningless if one run includes connection setup and another reuses connections.
How to calculate and report p95
Collect one latency observation per completed request after warm-up. Sort the observations from shortest to longest. The p95 is the value at the rank corresponding to 95% of observations (use your tool’s documented percentile algorithm for interpolation). Report p50, p90, p95 and p99 together with sample count, test window, load level and error rate.
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Example report: “At 120 requests per second for 10 minutes, 72,000 requests completed; p50 was 180 ms, p95 640 ms and p99 1.4 s; 0.2% failed; application CPU averaged 68% and peaked at 84%.” That statement is useful because it binds each number to a workload and environment.
Choose a benchmark tool
| Tool | Best fit | Strengths | Limits |
|---|---|---|---|
| ApacheBench (ab) | Quick single-endpoint baseline | Simple command-line HTTP benchmark distributed with Apache HTTP Server. | Limited scripting and workload realism; unsuitable alone for multi-step user journeys. |
| Apache JMeter | Scripted plans and distributed tests | Thread and throughput controls, distributed execution and HTML dashboards with percentiles, errors, response-time graphs, active threads, throughput and latency-versus-request-rate views. | Incorrect thread sizing can cause coordinated omission and misleading results; large tests need carefully sized distributed generators. |
| Grafana k6 | Version-controlled API and HTTP tests | JavaScript scenarios, thresholds, explicit latency/throughput/error/check metrics and clear automation workflows. | Protocol-level tests do not execute a full browser; add a smaller browser-level test when browser behavior matters. |
Compare tools by workload realism, concurrency versus arrival-rate control, protocol and browser coverage, distributed execution, threshold support, observability and report format. A fast synthetic endpoint test estimates a capacity ceiling; a production-like mix is needed for user-impact estimates.
Run a safe baseline with ApacheBench
Use ab only against a system you own or are authorized to test. This example sends 1,000 requests with a concurrency of 20:
ab -n 1000 -c 20 -k https://example.com/healthInspect “Requests per second,” connection and processing-time summaries, non-2xx responses and failed requests. Repeat after warm-up and save the exact command, output, host and date. For authenticated or stateful traffic, use a more capable script rather than treating a health endpoint as a capacity result.
Build a repeatable k6 test
k6 lets you express a ramp and enforce thresholds in source control:
import http from 'k6/http';
import { check, sleep } from 'k6';
export const options = {
stages: [
{ duration: '1m', target: 20 },
{ duration: '5m', target: 20 },
{ duration: '1m', target: 60 },
{ duration: '5m', target: 60 },
],
thresholds: {
http_req_failed: ['rate<0.01'],
http_req_duration: ['p(95)<800'],
},
};
export default function () {
const res = http.get('https://example.com/');
check(res, {
'status is 200': (r) => r.status === 200,
'body is non-empty': (r) => r.body && r.body.length > 0,
});
sleep(1);
}Run it with k6 run benchmark.js. Replace the URL, stages and thresholds with your service objective. Use an arrival-rate executor when you need a fixed request arrival rate rather than a fixed number of virtual users. For websites, Grafana recommends mostly protocol-level load plus a smaller browser-level test when browser behavior itself is part of the question.
Model complex journeys in JMeter
- Create a Test Plan and add a Thread Group.
- Add HTTP Request samplers for the actual journey, HTTP Cookie Manager for session behavior and assertions for status and business content.
- Choose either a concurrency-oriented thread schedule or a throughput-oriented controller; do not infer arrival rate from an arbitrary thread count.
- Add listeners or run in non-GUI mode, then generate the HTML dashboard after the test.
- Review percentiles, errors, active threads, throughput and latency versus request rate together with server telemetry.
JMeter warns that incorrectly sizing threads can produce the “Coordinated Omission” problem. Select a load model that represents how requests arrive, and use distributed generators when one machine cannot create the required load.
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Interpret results without fooling yourself
- Throughput rises while p95 is stable: the system has headroom under that workload.
- Throughput plateaus while latency climbs: a saturated resource or queue is likely limiting capacity.
- Errors appear before CPU is high: inspect database pools, connection limits, rate limits, upstream dependencies and file descriptors.
- Only p99 degrades: investigate tail causes such as garbage collection, lock contention, cold cache paths or slow downstream calls.
- High RPS with failed or incorrect responses: reject the result; correctness and error rate are part of performance.
Keep cache state, cookies, TLS, CDN inclusion and downstream services intentional. Never compare two results while changing hardware, software versions, network path or test configuration without recording the change.
Troubleshoot common benchmark failures
The generator reaches 100% CPU or drops connections
Reduce load, increase generator capacity or distribute the test. Check network throughput and file-descriptor limits. A client-side bottleneck invalidates the server conclusion.
Results vary dramatically between identical runs
Extend warm-up and steady-state windows, repeat at least 10 times where practical, and check autoscaling, cache expiry, background jobs, garbage collection and noisy neighbors. Record peak as well as average resource use.
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Latency is low but users report slow pages
You may be measuring one API call rather than browser work. Add a smaller browser-level scenario, include realistic assets and dependencies, and distinguish first load from cached navigation.
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Validate credentials, cookies, CSRF tokens, allowed source IPs and rate-limit policy with a low-rate baseline. Do not classify authorization or throttling responses as successful capacity.
Responses are fast but incorrect
Add body and business assertions, not only status-code checks. Confirm that test data is valid and that writes are isolated or safely cleaned up.
One run is much slower during startup
Separate warm-up from measurement. Runtime JIT compilation, connection establishment and cache population can dominate an un-warmed sample.
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What a publishable benchmark report contains
- Question, pass criterion and workload mix.
- Exact commands or scripts, tool version and scenario data.
- Server, generator, network, TLS, cache and dependency configuration.
- Warm-up, ramp, steady-state and breakpoint durations.
- Number of repetitions and the rule used to summarize them.
- Throughput, p50/p90/p95/p99, failures, status codes, correctness results and resource saturation.
- Known exclusions, anomalies and the next bottleneck to investigate.
Frequently Asked Questions
How long should a web-server benchmark run?
OpenTelemetry guidance suggests at least 15 seconds per iteration, but use longer steady-state windows when caches, garbage collection or autoscaling need time to appear, and repeat conditions rather than relying on one short run.
Is concurrency the same as requests per second?
No. Concurrency is the number of in-flight users or requests; requests per second is completed throughput. Their relationship changes with response time, think time and the load model.
Should I benchmark through a CDN?
Include the CDN when measuring user-facing behavior and document it. Exclude it for an origin-capacity test; do not compare the two as if they were the same system.
Can ApacheBench test an authenticated workflow?
It can send simple headers or cookies, but multi-step tokens, assertions and realistic mixes are better handled by k6 or JMeter.
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