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What wrk measures
wrk is a multithreaded HTTP benchmarking tool designed to generate substantial load from a single multicore CPU. It uses scalable event-notification systems such as epoll on Linux and supports optional LuaJIT scripts for custom requests, response processing, and reporting.
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For an Ubuntu game server, web API, matchmaking service, leaderboard endpoint, patch manifest, or telemetry gateway, wrk can show how response time changes as concurrent connections increase. It is a load generator, not a browser test: it does not reproduce a complete game client, rendering work, player behavior, or every protocol used by a game. Treat its numbers as measurements of the HTTP path you specify from the machine where wrk runs.
There is no universal “good” latency or throughput number for Ubuntu. The maintainers’ example is a demonstration of the tool, not a performance promise, and no generally applicable Ubuntu benchmark or percentile target is established. Set targets from your own service’s requirements and compare identical runs.
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Install wrk on Ubuntu
Use the Ubuntu package
Ubuntu Noble documentation lists the package as wrk, version 4.1.0-4build2. Install it with APT and verify that the executable is available:
sudo apt update
sudo apt install wrk
wrk --versionThe package command syntax is wrk <options> <url>. Keep the client and server versions, operating-system images, and test configuration recorded when comparing builds; package updates can change the executable or its dependencies.
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Build the upstream source when you need a different version
The upstream wg/wrk project can be built with GNU make on most Unix-like systems. Its installation instructions support bundled dependencies or system installations of LuaJIT and OpenSSL. When using system libraries, the build can be pointed at them with WITH_LUAJIT and WITH_OPENSSL.
git clone https://github.com/wg/wrk.git
cd wrk
makeUse the resulting binary from the build directory, or place it in a directory on your PATH according to your organization’s software-management policy. A source build is useful when the distribution package is unavailable or when you need a particular upstream revision; it is less convenient to maintain than Ubuntu’s packaged version.
Run a controlled baseline benchmark
Start with a locally controlled or staging endpoint. Never generate load against a server, API, or game service unless you own it or have explicit permission. A baseline that is safe to reproduce is more useful than a larger but unauthorized test.
Baseline command
sudo apt update
sudo apt install wrk
wrk --version
wrk -t4 -c100 -d30s --latency http://127.0.0.1:8080/This run uses four wrk worker threads, 100 total open HTTP connections, and a 30-second duration. The --latency switch requests the detailed latency section. Replace the URL with the endpoint you are authorized to test.
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Record the conditions before each run
- Exact URL, HTTP method, path, query string, and protocol (HTTP or HTTPS).
- Headers, cookies, authentication state, and request body.
- wrk version, Ubuntu release, client hostname, server hostname, and relevant application version.
- Thread count, connection count, duration, timeout, script file, and any rate or firewall controls.
- Network path, including whether the client and server are on the same host, LAN, VPN, or public route.
- Server CPU, memory, connection-pool, database, cache, and error metrics during the run.
Warm-up traffic should be treated as a separate phase when caches, JIT compilation, connection pools, or TLS session setup affect the first requests. Do not mix warm-up samples into the measurement interval you use for comparisons.
Choose threads, connections, and duration
| Option | Meaning | How to use it |
|---|---|---|
-t |
Number of wrk worker threads | Start with a modest number that the client CPU can sustain; increase only while the generator has spare capacity. |
-c |
Total open HTTP connections | Represents concurrent connections across all workers, not connections per thread. |
-d |
Test duration | Accepts units such as 2s, 2m, and 2h; longer runs expose sustained behavior. |
--timeout |
Maximum wait for a response | Choose a value appropriate to the endpoint and report timeouts separately from successful latency. |
--latency |
Detailed latency statistics | Include it when you need average, variation, maximum, and distribution information. |
-H |
Additional request header | Use the form -H 'Header-Name: value'; keep headers identical across comparison runs. |
-s |
LuaJIT workload script | Use it for POST bodies, dynamic headers, multiple paths, authentication flows, validation, or custom output. |
Ubuntu documents SI suffixes for numeric arguments, including 1k, 1M, and 1G. Confirm the accepted syntax with the installed binary’s help output if you use suffixes in an automated test.
Allocate connections deliberately
Connections are shared among threads. For example, -t4 -c100 means 100 connections in total, with each worker handling a share; it does not mean 400 connections. Begin with a low connection count, then step upward (for example, 25, 50, 100, 200) while holding every other variable constant. This reveals where latency or errors change instead of hiding the transition inside one very large test.
Keep the client from becoming the bottleneck
wrk can be CPU-bound before the server is. If the generator reaches high CPU utilization, its event loop and request construction limit the offered load, and measured latency may include client-side queuing. Monitor both machines. Add worker capacity, move wrk to a stronger or separate host, or reduce the workload before attributing the result to the server.
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A short run is sensitive to connection establishment, caches, and startup effects. A longer run is better for sustained CPU, memory, database, and connection-pool behavior, but it increases the impact of background traffic and resource exhaustion. Use the same duration for every condition in a comparison and state it with the result.
Benchmark the request your application actually serves
A fast static GET is not a substitute for an authenticated JSON request that performs database work. Compare both concurrency and request shape when alternatives exist: path, method, headers, body, authentication, and duration all matter.
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Headers with the command line
wrk -t4 -c100 -d30s --latency
-H 'Accept: application/json'
-H 'User-Agent: wrk'
https://staging.example.test/api/leaderboardUse a staging hostname or local address that you control. Keep secrets out of shell history where possible; a static bearer token in a command can be exposed to process listings, logs, or shared terminal history.
POST, JSON, and authentication with Lua
Save a script such as post.lua for a fixed JSON request:
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wrk.headers["Content-Type"] = "application/json"
wrk.headers["Accept"] = "application/json"
wrk.headers["Authorization"] = "Bearer REPLACE_WITH_A_TEST_TOKEN"
wrk.body = '{"player_id":12345,"region":"test"}'Run it with:
wrk -t4 -c50 -d30s --latency -s post.lua
https://staging.example.test/api/matchmakingUse a test account, a non-production dataset, and a token with the minimum permissions required. A fixed token measures the endpoint under one authorization state; it does not model token refresh, login, or a population of users.
Multiple paths or generated requests
A Lua script can return a formatted request for each iteration, allowing several endpoints or changing bodies. Keep the script’s own work small. The upstream guidance notes that constructing a new request for every iteration and using a response() callback can reduce the load wrk generates. A script that only changes the method, path, headers, or body is not expected to impose a performance impact by itself; expensive parsing, random-data generation, or response processing is different.
local paths = {
"/api/players/1",
"/api/players/2",
"/api/players/3"
}
request = function()
local path = paths[math.random(#paths)]
return wrk.format("GET", path, {
["Accept"] = "application/json",
["User-Agent"] = "wrk"
})
endValidate that the script sends the intended method, headers, and body before a long run. If response validation is necessary, measure the cost of validation separately or document it as part of the workload; otherwise the client may become the limiting component.
Understand wrk’s output
A normal report identifies the thread and connection configuration, then shows per-thread latency and request rates, totals for requests and bytes, aggregate requests per second, and transfer rate. With --latency, inspect the detailed latency statistics rather than relying on one average.
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Latency fields
- Average: the arithmetic mean of recorded response times for that thread or aggregate report.
- Standard deviation: how widely observations vary around the average; a high value indicates an uneven experience even when the mean looks acceptable.
- Maximum: the slowest recorded response in the run. One maximum is useful for spotting outliers but is not a percentile or service-level target.
- Percentage within one standard deviation: the share of observations in that band, as shown by wrk’s report. It does not replace a tail-percentile analysis.
Requests that time out or fail are not interchangeable with successful latency observations. Record timeout and error counts alongside the latency section. A run that reports high requests per second while dropping requests is not a successful high-throughput configuration.
Throughput fields
Requests/sec is the aggregate completed-request rate for the run. Transfer/sec is the rate of response bytes read by wrk. Total requests and bytes provide the denominators needed to compare runs with different durations or response sizes. A larger response body can lower request rate or raise transfer rate without representing a faster application.
Do not copy the upstream sample as a target
The upstream README illustrates a 30-second run with 12 threads and 400 connections. Its sample reports 635.91 microseconds average latency, 0.89 milliseconds standard deviation, a 12.92 millisecond maximum, 93.69% within one standard deviation, 748,868.53 requests per second, and 606.33 MB/sec transfer. Those values came from that particular hardware, endpoint, response, and network context. They are not expected Ubuntu performance and should not be used as an SLA.
Design a repeatable comparison
Change one workload axis at a time
- Choose a fixed endpoint and request shape, including headers, body, and authentication.
- Run a separate warm-up period if the application needs one.
- Measure a baseline with fixed threads, connections, duration, and timeout.
- Change one factor, such as connection count, while keeping the others unchanged.
- Repeat each condition enough times to expose run-to-run variation, then report the distribution and errors rather than only the best run.
Useful matrices vary both concurrency and request shape. For example, hold -t4 and 30 seconds constant while testing 25, 50, 100, and 200 connections; then hold the chosen concurrency constant while comparing a cached GET with the authenticated JSON POST. A third axis is duration: short and sustained runs can reveal different bottlenecks.
Separate client, network, and server observations
- Watch wrk CPU utilization and its own error output.
- Watch server CPU, memory, load average, open files, listen queue, application workers, database connections, and cache behavior.
- Capture network loss, retransmissions, and bandwidth when the path is not local.
- Note TLS handshakes and connection reuse assumptions; a test that keeps connections open is not equivalent to one that creates a new connection for every request.
When comparing application versions or configuration changes, preserve the client host, server host, route, test data, and background traffic as closely as possible. Otherwise a change in the environment can look like a code improvement or regression.
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Ephemeral ports and file descriptors
The load-generator machine needs enough ephemeral ports and file descriptors for the chosen connection count and for sockets that are closing. If ports are exhausted, reduce concurrency, allow closed sockets to recycle promptly, or distribute the load across additional authorized generators. Check operating-system limits before interpreting connection errors as application latency.
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Listen backlog and connection bursts
The server’s listen backlog should exceed the number of concurrent connections arriving in the initial burst. A backlog that is too small can cause connection failures before application processing begins. Increase it within the limits of the operating system and server, and verify the effective value rather than assuming a configuration file was accepted.
Protect production systems
Use staging whenever possible. Add a maintenance window, a stop condition, monitoring, and an owner who can terminate the run. Avoid testing third-party endpoints, public game services, or infrastructure you do not control. Load testing can consume bandwidth, database capacity, rate-limit quotas, and player-facing resources even when the request itself is harmless.
Troubleshoot misleading or failed benchmarks
wrk reports timeouts or connection errors
- Confirm the URL resolves from the client and that the port is reachable.
- Check TLS certificates, proxy requirements, firewall rules, and authentication headers.
- Lower connections and increase the timeout only when a longer response is expected; do not hide a real outage with an extreme timeout.
- Inspect server logs and listen-queue statistics to distinguish refusal, backlog overflow, and application errors.
Latency rises as connections increase
That pattern can indicate server CPU saturation, exhausted database or application pools, lock contention, queueing, network capacity, or a client that is approaching its own limit. Correlate the inflection point with host metrics. Do not label it an application regression until the generator has spare CPU and the network path is understood.
Results vary widely between identical runs
Check warm-up state, background jobs, garbage collection, cache contents, autoscaling, noisy neighbors, DNS changes, and route changes. Repeat under a controlled window and report the spread. A single average conceals the variability that players experience during busy periods.
The script produces less load than expected
Profile the Lua workload conceptually: response callbacks, JSON parsing, random-data creation, and constructing complex requests for every iteration all consume client CPU. Simplify the script, precompute static data, or add generator capacity. Verify that the server is actually receiving the intended method, path, and body before changing server tuning.
Use the results for game-service decisions
Translate the measurement into the operation you care about. A matchmaking request may need a small response quickly but perform expensive writes; a leaderboard read may be cacheable; a patch-manifest request may transfer many bytes; telemetry ingestion may be a POST with authentication and batching. Run each materially different shape instead of applying one endpoint’s number to every service.
For a capacity decision, identify the highest tested concurrency at which successful responses, timeout rate, server saturation, and latency variation remain within your service’s chosen limits. State the exact wrk command and environment with that decision. If you need percentile-based service objectives, use a reporting method that explicitly calculates those percentiles; wrk’s default average, standard deviation, maximum, and within-one-standard-deviation figure do not establish a p95 or p99 value.
Quick Recap
Benchmark checklist
- Install and verify wrk with
wrk --version. - Obtain authorization and use a local or staging endpoint first.
- Fix the URL, method, headers, body, authentication, hosts, and network path.
- Warm the application separately when caches, JIT, pools, or TLS setup matter.
- Start with a modest
-tand-c, then increase connections in controlled steps. - Use
--latencyand an explicit--timeout; record errors and timeouts separately. - Use Lua with
-sfor POST, authenticated, multi-path, or dynamic workloads. - Monitor both the wrk host and the server for CPU, sockets, queues, and resource saturation.
- Repeat every condition and report variation, not just a single average.
- Keep the upstream sample figures as tool-output illustrations, never as expected Ubuntu targets.
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