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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsInstall Ubuntu’s wrk package, run a controlled test such as wrk -t4 -c100 -d30s --latency http://127.0.0.1:8080/, and interpret the result together with client CPU, server behavior, errors and timeouts. The command’s four threads and 100 connections are only a starting workload—not a universal performance target.
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What wrk measures
wrk is a multithreaded HTTP load generator designed to create substantial traffic from one multicore CPU. It uses scalable event-notification systems such as Linux epoll (and kqueue on systems that provide it). Optional LuaJIT scripts let you generate request bodies, alter headers and paths, exercise several request types, validate responses or produce custom reports.
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For latency work, wrk measures the time between sending a request and receiving its response from the load generator’s point of view. That figure includes network transit, server queueing, application processing, response transmission and any client-side scheduling delay. It does not, by itself, tell you which component caused a slow response. A saturated wrk host can make a healthy server look slow, while a server that returns errors quickly can produce an attractive average.
Use it only against an endpoint you own or are explicitly authorized to test. Begin with a local or staging service, then move to production-like infrastructure under an agreed load budget.
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Install wrk on Ubuntu
Install the Ubuntu package
Ubuntu’s Noble package documentation lists the package as wrk, version 4.1.0-4build2. The package version on another Ubuntu release can differ, so treat that version as the Noble documentation value rather than a promise for every release.
- Refresh package metadata:
sudo apt update. - Install wrk:
sudo apt install wrk. - Verify that the executable is available:
wrk --version.
The command form is wrk <options> <url>. If wrk --version fails, check that the package installation completed and that the directory containing the executable is on your PATH.
Build the upstream source
Building is useful when the distribution package is unavailable or when you need a source version that is different from Ubuntu’s package. Obtain the wg/wrk source, change into its directory and run make. The upstream installation notes describe builds on most UNIX-like systems with GNU make, LuaJIT and OpenSSL. The build can use bundled dependencies or system installations selected with WITH_LUAJIT and WITH_OPENSSL.
Keep a record of whether each comparison used the Ubuntu package or a source build. Changing the executable, LuaJIT, OpenSSL or compiler between runs can change client-side behavior and make results harder to compare.
Prepare a reproducible baseline
Before changing application code or server settings, fix the variables that affect a latency result:
- Use a URL and HTTP method that represent the operation you want to measure.
- Keep request headers, body, authentication state and path identical between compared runs.
- Record the wrk version, Ubuntu release, client host and server host.
- Record whether the client and server are on the same machine, the same network or a routed path.
- Use the same thread count, connection count, duration and timeout for each condition unless that variable is the subject of the experiment.
- Separate warm-up traffic from measured traffic when caches, JIT compilation, connection pools or TLS session setup need time to settle.
- Repeat each condition and retain the detailed output, including errors and timeouts, rather than keeping only one average.
A simple local baseline is:
sudo apt update
sudo apt install wrk
wrk --version
wrk -t4 -c100 -d30s --latency http://127.0.0.1:8080/This starts four wrk worker threads, maintains 100 total HTTP connections, runs for 30 seconds and requests detailed latency statistics. It is a starting workload, not an expected Ubuntu result. Increase concurrency in controlled steps only after confirming that the client and server can handle the previous step.
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Understand every workload option
| Option | Meaning | How to use it |
|---|---|---|
-t |
Number of wrk worker threads | -t4 creates four workers. More threads can use more client CPU; they do not automatically improve server performance. |
-c |
Total open HTTP connections | -c100 keeps 100 connections open across all workers. Each thread handles a share of them. |
-d |
Test duration | Use units such as 30s, 2m or 2h. A short run is more sensitive to connection setup and warm-up. |
--latency |
Detailed latency report | Add it when averages, spread and maximum latency are needed. |
-H |
Additional request header | For example, -H 'User-Agent: wrk'. Quote the complete header when the value contains spaces. |
--timeout |
Response-wait limit | Use it to classify a response as timed out when it is not received within the specified interval. Choose a value that reflects the operation and document it. |
-s |
LuaJIT workload script | -s script.lua enables POST bodies, dynamic headers, multiple paths, authentication flows, response checks or custom reporting. |
Ubuntu documents SI suffixes for numeric arguments, including 1k, 1M and 1G. Check the resulting command carefully: -c1k means 1,000 connections, which can be a much larger test than an unqualified value.
Choose threads, connections and duration deliberately
Threads
Threads are client-side workers, not server worker processes. Start with a modest number and watch the load generator’s CPU. If one client host is CPU-bound, its request-generation ceiling can dominate the measured latency. Add threads only when the client has spare CPU and the server is the component you intend to stress.
Connections
Connections are the total number of open HTTP connections, distributed among wrk threads. A low connection count may describe a single-user or low-concurrency service; a high count tests queueing and simultaneous demand. Compare alternatives on the connection axis instead of presenting one value as “the” benchmark setting.
Duration
A 30-second run is convenient for an initial comparison, but it can hide slow degradation, garbage-collection cycles or cache changes. Use a longer duration when the question concerns sustained behavior. Keep a separate warm-up interval if the application performs one-time initialization.
Request shape
Concurrency alone is not enough. When alternatives exist, compare both connection/thread levels and request shape: method, path, headers, body and authentication. A cached GET and an authenticated database-backed POST are different workloads even when they use the same number of connections.
Run a controlled latency test
- Confirm authorization and health. Verify that the URL is a controlled endpoint and that the service is healthy before introducing load.
- Record the test definition. Write down the exact URL, method, headers, body, credentials or token state, client and server addresses, wrk version, thread count, connection count, duration and timeout.
- Warm the service separately. Send non-measured traffic if caches, JIT compilation, pools or TLS setup would otherwise contaminate the first measured interval.
- Run the baseline. For a simple GET, use
wrk -t4 -c100 -d30s --latency http://127.0.0.1:8080/. - Watch both machines. Observe client CPU, server CPU, memory, network, connection counts and application errors while the test runs.
- Repeat the condition. Keep the same settings for multiple runs and preserve each report. Do not replace a distribution with a single best-looking run.
- Change one planned variable. Increase connections, alter threads, change the request shape or extend duration, then repeat the same observation process.
Do not benchmark an uncontrolled public endpoint. A high-rate test can exhaust another operator’s capacity and can also produce misleading data if a proxy, rate limiter or intermediate network device intervenes.
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Read wrk’s output correctly
A normal report includes the number of threads and connections, per-thread latency average, standard deviation, maximum and the proportion within one standard deviation, per-thread requests per second, total requests and bytes read, aggregate requests per second and transfer per second. The --latency flag adds the detailed latency statistics needed to see spread rather than only a headline average.
- Average latency is the arithmetic mean for the recorded requests. A small mean can coexist with a long tail.
- Standard deviation describes how widely observations vary around the mean. It is not a percentile and does not replace tail measurements.
- Maximum latency is the slowest recorded request in that run. It is useful for spotting outliers, but it is unstable across short runs.
- Requests per second is achieved throughput for the run, not a guaranteed capacity limit.
- Transfer per second depends on response size as well as request rate; it should not be compared across endpoints with different payloads without accounting for bytes.
- Timeouts and errors must be recorded separately from successful latency. A fast error is not a fast successful response.
wrk’s standard report is not a universal service-level objective. If you need a specific percentile or a custom classification, use a Lua script or another reporting method that explicitly calculates it, and document the method.
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Do not copy the upstream sample as an expectation
The upstream README shows an illustrative 30-second run with 12 threads and 400 connections. Its sample reports average latency of 635.91us, standard deviation of 0.89ms, maximum latency of 12.92ms, 93.69% within one standard deviation, 748,868.53 requests per second and 606.33 MB per second of transfer. Those values belong to that particular workload, endpoint, hardware and network path. They are not Ubuntu benchmarks or targets for your service.
No universal expected latency, throughput target or percentile SLA is established for Ubuntu and wrk. Set targets from your own endpoint’s contract and measurements under the traffic conditions you need to support.
Benchmark POST and authenticated endpoints with Lua
Use -s when a command-line GET cannot express the operation. A static POST body can be defined in a script:
wrk.method = "POST"
wrk.body = '{"name":"example"}'
wrk.headers["Content-Type"] = "application/json"
wrk.headers["Authorization"] = "Bearer REPLACE_WITH_TEST_TOKEN"
Save the file as post.lua and run it against the intended path:
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wrk -t4 -c100 -d30s --latency -s post.lua http://127.0.0.1:8080/api/itemsUse a test-only token or account with the minimum permissions required. Never put a production secret in a script that will be shared or committed. If tokens expire, generate or rotate them outside the measured interval and keep the authentication state consistent between runs.
Scripts can also choose among multiple paths, create dynamic headers, generate request data, inspect responses and implement custom reporting. The upstream guidance warns that work performed for every request—especially constructing a new request and using a response handler—reduces the load wrk can generate. A script that only changes the method, path, headers or body does not have a performance impact according to that guidance. Keep per-request Lua logic minimal when measuring the server rather than the client script.
Validate the endpoint independently before a high-rate run. A malformed JSON body, missing header or expired token can turn the benchmark into an error-rate test without making that obvious from a throughput number alone.
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Instead of one arbitrary command, define a small matrix that answers a specific capacity question. For example:
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| Sustained run | Chosen from prior runs | Chosen from prior runs | Longer interval | Same as the target workload | Look for degradation that a short test misses. |
The values in this matrix are experimental starting points. Replace them with levels justified by your traffic model, and change one dimension at a time when you need a causal comparison.
Troubleshoot misleading or failed runs
The wrk host reaches 100% CPU
Client-side CPU saturation means wrk may be unable to generate the requested load. Measured latency then includes load-generator limits and scheduling delay. Reduce concurrency, use fewer or better-targeted request-script operations, add another load generator or move the client to a more capable host. Monitor the client and server together before drawing a server conclusion.
Connection attempts fail at the start
The load generator needs enough ephemeral ports, and closed sockets must recycle quickly. Inspect the client’s available port range and socket state using the operating-system tools appropriate to your Ubuntu release. The server’s listen backlog should exceed the number of concurrent connections expected during the initial burst; otherwise, connection establishment rather than request processing becomes the measured bottleneck.
Timeouts appear
Set an explicit --timeout that reflects the operation and report the count with successful latency. Investigate server queues, upstream dependencies, network loss and client saturation. Do not silently discard timeout-heavy runs or compare their request rate with an error-free run.
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Check warm-up, cache state, TLS setup, connection reuse, background jobs, CPU frequency behavior, noisy neighbors and network path changes. Keep the endpoint, request shape and wrk settings fixed, repeat each condition and report the observed distribution. A single average cannot explain run-to-run variability.
Throughput rises while latency becomes unacceptable
That pattern usually indicates that additional concurrency is filling queues. Treat throughput and latency as paired outcomes: identify the point at which the latency limit or error budget is exceeded rather than selecting the highest requests-per-second figure.
A script reports errors unexpectedly
Check the method, URL path, body encoding, content type, authorization state and token lifetime. Test one request manually with the same values before rerunning wrk. Keep response validation in the script when correctness matters, because a fast status-code error should not be counted as a successful transaction.
Report results so another engineer can reproduce them
For every run, save the complete command, wrk version, Ubuntu release, script text or checksum, endpoint and request shape, test start time, client and server hardware, network placement, warm-up procedure, thread and connection counts, duration, timeout, latency statistics, requests and transfer rates, errors, timeouts and relevant system metrics. State whether the result came from a package installation or an upstream build.
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Quick Recap
A practical checklist
- Install and verify wrk with
wrk --version. - Use an authorized local or staging endpoint first.
- Warm the service outside the measured interval when appropriate.
- Start with a documented command such as
-t4 -c100 -d30s --latency. - Keep URL, method, headers, body, authentication, client, server and network path fixed for comparisons.
- Change connections, threads, request shape or duration deliberately—not all at once.
- Watch client and server resources during every run.
- Record errors and timeouts separately from successful latency.
- Repeat conditions and retain the full output.
- Set performance targets from your service’s own requirements; do not borrow the upstream sample numbers.
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