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Install ApacheBench from Ubuntu’s apache2-utils package, verify the ab command, then run a controlled test such as ab -n 1000 -c 20 https://example.com/. The -n value is the total number of requests and -c is the number sent concurrently. Increase concurrency gradually while checking failures, non-2xx responses, latency, bandwidth, and server-side resource usage; a high requests-per-second figure is not useful if the test is producing errors.
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What ApacheBench does
ApacheBench is the command-line ab client. Apache HTTP Server documentation describes it as “a tool for benchmarking your Apache Hypertext Transfer Protocol (HTTP) server.” It sends requests to a selected HTTP or HTTPS URL and reports timing, throughput, and error information for that endpoint.
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That makes it useful for a repeatable question such as “How many requests can this URL serve at a concurrency of 20?” It is not a complete simulation of a browser or a real user journey: it does not reproduce browser rendering, a user’s think time, multiple page requests, or every cache and connection pattern. Use it as a controlled endpoint benchmark, and use a workload model or a more sophisticated load-testing system when you need realistic application capacity.
Before you generate traffic
Test only systems you control
Load testing creates intentional traffic. Run it only against a server and endpoint that you own or are explicitly authorized to exercise. Confirm the permitted time window, source IP, request volume, and any provider or hosting limits before starting. A test against an unapproved site can be disruptive and may be treated as an attack.
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Choose an observable endpoint
Start with one stable URL whose behavior you understand. Record whether it is HTTP or HTTPS, the HTTP method, expected response code, typical response size, authentication requirements, and whether the request changes data. A public, read-only endpoint is safer for a first run than a purchase, login, or other state-changing operation.
Watch the server while the client runs
Record CPU, memory, network throughput, connection counts, web-server access and error logs, application metrics, database usage, and any reverse-proxy limits. The client output alone cannot tell you whether the bottleneck is the web server, application code, database, network, or the machine running ab.
Install ApacheBench on Ubuntu
Ubuntu supplies ab in the apache2-utils package. Install it with the package manager, then check that the executable responds:
- Refresh package metadata:
sudo apt update- Install the package:
sudo apt install apache2-utils- Print the installed version:
ab -V- Display the command’s options and syntax:
ab -hIf ab -V and ab -h return command information, the installation is ready. Keep the version output with your test notes because results from different client or server versions are not automatically interchangeable.
Run a safe baseline test
Use explicit request and concurrency values
A basic GET benchmark is:
ab -n 1000 -c 20 https://example.com/-n 1000 asks for 1,000 total requests. -c 20 allows 20 requests to be in flight at once. Specify both options: the documented default is one request, which is normally too small to represent concurrent service behavior.
Replace the example URL with an endpoint you are authorized to test. Keep the trailing slash or path exactly as intended; changing the path, query string, protocol, or redirect behavior changes what you are measuring.
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Ramp instead of jumping to a peak
Begin with a small run, check the result, and raise concurrency in steps. For example:
| Stage | Command | Purpose |
|---|---|---|
| Smoke check | ab -n 20 -c 1 https://example.com/ |
Confirm DNS, routing, TLS, the method, and the expected response. |
| Low concurrency | ab -n 200 -c 5 https://example.com/ |
Establish a quiet baseline before sustained pressure. |
| Moderate concurrency | ab -n 1000 -c 20 https://example.com/ |
Measure a repeatable workload with 20 in-flight requests. |
| Higher concurrency | ab -n 2000 -c 50 https://example.com/ |
Increase pressure only if the previous stage is healthy. |
The numbers in this progression are a test plan, not a capacity claim. Stop or reduce the load if error rates, latency, resource exhaustion, or application behavior becomes unsafe.
Use a duration instead of a fixed request count
For a time-bounded run, add -t with the duration in seconds. A duration test is useful when you want to observe behavior over a fixed interval rather than complete a predetermined number of requests. Still set the concurrency explicitly:
ab -t 60 -c 20 https://example.com/Record the duration, concurrency, and endpoint together. A 60-second run at one concurrency is a different workload from a 60-second run at 20.
Test connection reuse deliberately
Add -k to request persistent HTTP connections:
ab -n 1000 -c 20 -k https://example.com/Run with and without -k only when that comparison answers a real question. Connection setup, TLS handshakes, proxy behavior, and keep-alive limits can materially change the result, so label the setting in your notes.
Set a client-side socket timeout
Use -s to set the socket timeout in seconds:
ab -n 1000 -c 20 -s 30 https://example.com/A timeout limits how long the client waits for a socket operation. It does not make a slow response successful; timed-out or interrupted operations can appear as failed requests.
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Send different kinds of requests
ApacheBench options let you keep the load shape while changing the request details. Each variation should be recorded because method, headers, payload size, authentication, and response size affect server work.
Add a request header
ab -n 500 -c 10 -H 'Accept: application/json' https://example.com/api/status-H adds the specified header. Include only headers that are required to reproduce the authorized request; an Accept value can select a different representation and therefore a different response size or code path.
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ab -n 200 -c 5 -C 'session_id=example-value' https://example.com/account-C sends a cookie. Use a test account and a non-sensitive value whenever possible. A shared session can serialize work or change caching, so do not interpret that run as anonymous traffic.
Supply Basic Authentication carefully
ab -n 100 -c 2 -A 'username:password' https://example.com/protected-A supplies Basic Authentication credentials. The command line can be stored in shell history and may be visible to local process inspection. Use only authorized test credentials, protect the test machine, and remove or secure the history entry afterward.
POST a body
Place the request body in a file and specify its content type:
ab -n 200 -c 10 -p body.json -T application/json https://example.com/api/endpoint-p body.json reads the body from the named file, while -T application/json declares its media type. Use a test endpoint or an idempotent operation. Replaying a state-changing body can create records, send messages, charge an account, or otherwise alter data.
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Use HEAD when you need headers only
ab -n 200 -c 10 -i https://example.com/-i uses HEAD requests, which check response headers without downloading the response body. Confirm that the application handles HEAD correctly; some applications route HEAD differently or return incomplete behavior.
Understand the ApacheBench output
After a run, ab prints a summary containing the workload and the resulting timing, transfer, and error fields. Save the complete output rather than copying only requests per second.
| Field | What it tells you | How to use it |
|---|---|---|
| Concurrency Level | The configured number of simultaneous requests. | Compare runs only when this value and the other workload settings match, or explain the difference. |
| Time taken for tests | Elapsed time for the run. | Use it with the request count to understand the test window. |
| Complete requests | Requests that completed during the run. | Check that the count matches the intended workload and compare it with failed requests. |
| Failed requests | Requests considered failures because of connection, read, content-length, or exception problems. | Any unexpected nonzero value requires investigation before treating throughput as capacity. |
| Non-2xx responses | Responses outside the successful 200 range. | Inspect application and routing behavior; an HTTP error page can still have completed at the transport level. |
| Keep-Alive requests | Requests handled using persistent connections when keep-alive was enabled. | Verify that the result reflects the intended -k setting and server connection policy. |
| Total transferred | All bytes received during the run, including protocol-related data reported by the client. | Use it to spot bandwidth or response-size effects. |
| HTML transferred | Response content bytes transferred by the benchmark. | Compare this when endpoint representations or payload sizes differ. |
| Requests per second | Aggregate requests divided by elapsed test time. | Use it as a throughput measurement only alongside failures, response codes, and latency. |
| Time per request | The average timing reported by ab. |
Compare identical URLs, methods, payloads, protocols, and concurrency values; do not compare unlike runs. |
| Transfer rate | The received data rate. | Determine whether response size or available bandwidth is influencing the run. |
Interpret requests per second with errors
Requests per second is total throughput, not a quality score. A server that returns many errors quickly can show a higher rate than one that returns valid responses more slowly. Treat a run as acceptable only under criteria you define in advance, such as zero unexpected failed requests, no unexpected non-2xx responses, an agreed latency limit, and acceptable CPU, memory, database, and network behavior.
Distinguish failed requests from non-2xx responses
A failed request indicates a client-observed connection, read, content-length, or exception problem. A non-2xx response reached the client but had an HTTP status outside the successful 200 range. They can occur separately: an application may return an error page cleanly, or a connection may fail before any HTTP status is received. Examine server logs and the endpoint’s expected status to determine which case you have.
Design comparable runs
Repeatability matters more than a single impressive number. Keep these axes constant when comparing two runs, unless the axis is the variable you intentionally changed:
- Request method: GET, HEAD, or POST.
- Protocol and URL: HTTP versus HTTPS, host, path, and query string.
- Concurrency and total request count, or the exact duration.
- Keep-alive behavior.
- Request headers, cookies, authentication, and body content.
- Response and payload size.
- Client machine, network location, and test time.
- Server software configuration and application version.
Run the same command multiple times after the server reaches a comparable state. Record warm-up effects, cache state, deployment changes, background jobs, and any rate limiting. Compare the complete output and server-side observations, not only the headline throughput.
Define an acceptance rule before testing
An acceptance rule turns output into a decision. For example, you might require the intended number of complete requests, zero unexpected failed requests, no unexpected non-2xx responses, a maximum average time per request, and no exhaustion of a monitored resource. The thresholds must come from your service objective; ApacheBench does not provide a universal requests-per-second target.
Troubleshoot unexpected results
The command is missing
Install apache2-utils with sudo apt update followed by sudo apt install apache2-utils, then rerun ab -V. If a managed Ubuntu image uses restricted package repositories, resolve package availability before interpreting any benchmark result.
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- Run the one-request smoke test with
-n 1 -c 1and verify DNS, routing, TLS, and the URL. - Check web-server and application error logs at the exact test time.
- Confirm that firewalls, reverse proxies, connection limits, and rate limits are not rejecting the client.
- Increase the client-side timeout with
-sonly after confirming that the endpoint is expected to take that long; do not hide a genuine server problem by choosing an arbitrary large timeout. - Verify that the request method, headers, cookie, credentials, and body match the authorized test case.
Non-2xx responses increase with concurrency
Inspect the actual status and server logs. The application may be overloaded, a dependency may be timing out, a protection layer may be rate limiting the source, or the test may be sending an invalid or state-conflicting request. Reduce concurrency, restore a safe request body, and repeat only after identifying the cause.
Requests per second changes between identical runs
- Check whether the client or server is CPU- or network-bound.
- Look for cache warming, garbage collection, database pools, background jobs, or autoscaling changes.
- Confirm that DNS, TLS, redirects, keep-alive, and response size are the same.
- Ensure that another workload is not sharing the test machine or server.
- Repeat the run and retain every command and output so an outlier is visible.
The client becomes the bottleneck
ab is itself a client. At high concurrency or with large HTTPS responses, its host, network path, ephemeral ports, or CPU can limit the observed rate before the server is saturated. Monitor the load generator and, if necessary, distribute or redesign the test rather than claiming the server reached its limit.
When ApacheBench is the right tool
Use ApacheBench when you need a compact, scriptable benchmark of one HTTP or HTTPS endpoint with a clearly defined method, concurrency, request count or duration, and request data. It is especially useful for checking a configuration change, comparing two controlled deployments, or finding the point at which a single endpoint begins returning errors.
Choose a different workload approach when the question involves several dependent requests, user think time, browser assets and JavaScript, many user identities, complex authentication flows, WebSockets, long-lived sessions, or a realistic mix of endpoints. ApacheBench’s summary is intentionally narrow; it cannot by itself establish whole-site capacity or user-perceived performance.
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For each run, save the exact command, ab -V output, date and time, client location, target URL, method, payload and headers, concurrency, request count or duration, keep-alive and timeout settings, complete client output, and server-side metrics. Note deployments, cache state, rate limits, and any interruption. This record lets you reproduce a result and prevents an isolated requests-per-second number from being mistaken for a durable capacity limit.
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