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Install Ubuntu’s apache2-utils package, run ab with explicit request and concurrency limits, and judge the result using throughput, latency, failed requests, non-2xx responses, and transfer rate together. A safe first test is ab -n 1000 -c 20 https://example.com/ against a server you own or are authorized to exercise. Increase concurrency gradually while watching the server and application, rather than treating one requests-per-second number as a capacity guarantee.
What ApacheBench does
ApacheBench is the ab command-line HTTP benchmarking client. Apache HTTP Server documentation describes it as “a tool for benchmarking your Apache Hypertext Transfer Protocol (HTTP) server.” It sends requests to a URL and reports timing, throughput, and error information. It can target HTTP or HTTPS endpoints.
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Only load-test systems for which you have explicit permission. Even a short run can consume CPU, memory, database connections, worker processes, bandwidth, or rate-limit capacity. Test a staging system where possible, and notify the owner of any shared environment before starting.
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How to install ApacheBench on Ubuntu
Ubuntu supplies ApacheBench in the apache2-utils package. Install it from the Ubuntu package repositories:
- Refresh the local package index:
sudo apt update- Install the package:
sudo apt install apache2-utils- Confirm that the command is available and inspect its help:
ab -V
ab -hab -V prints the installed version. ab -h displays the options supported by that build, which is the best reference when a distribution version differs from another Ubuntu release.
How to run a safe baseline test
Start with a modest, repeatable GET request. The following example sends 1,000 total requests with 20 requests in flight to the site root:
ab -n 1000 -c 20 https://example.com/Replace the URL with an endpoint you control. Use the trailing slash for a root URL so the request target is unambiguous.
What -n and -c mean
-ntotal requests: the number of requests generated by the run. In the example, the client attempts 1,000 requests.-cconcurrency: the number of requests that may be in flight at once. A value of 20 creates up to 20 simultaneous requests as the run proceeds.
The default request count is one. A single request is useful as a connectivity check, but it is not a representative benchmark. Specify both -n and -c for a baseline so another person can reproduce the same workload.
Ramp up instead of starting at maximum pressure
Run a sequence such as 5, 10, 20, 40, and 80 concurrent requests, using the same endpoint and request count at each stage. Stop or reduce the load when error rates, latency, resource saturation, or service health becomes unacceptable. The exact levels depend on the server and are not universal performance targets.
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Time-bounded and connection-behavior tests
Run for a fixed duration with -t
Use -t when the test should run for a specified number of seconds rather than a fixed request count:
ab -t 60 -c 20 https://example.com/This runs with 20 concurrent requests for up to 60 seconds. A duration makes it easier to observe sustained behavior and server-side resource trends. Save the exact duration and concurrency with the output.
Test persistent connections with -k
Add -k to request HTTP keep-alive behavior:
ab -n 1000 -c 20 -k https://example.com/Use the same setting for every run in a comparison. A keep-alive test and a new-connection test measure different costs, particularly TLS handshakes, TCP setup, connection queues, and proxy behavior. Do not compare their requests-per-second figures as if they were the same workload.
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Set a socket timeout in seconds when you need a defined limit for a stalled connection:
ab -n 1000 -c 20 -s 30 https://example.com/A timeout prevents a run from waiting indefinitely on an unresponsive peer, but it also turns slow responses into failures once the limit is reached. Record the timeout because changing it changes the meaning of failed requests.
How to test different request types
The request must match the operation you are trying to measure. A cached public GET, an authenticated API call, and a JSON POST are separate workloads.
Add request headers with -H
Supply a header in the form Header-Name: value:
ab -n 500 -c 10 -H 'Accept: application/json' https://example.com/api/statusInclude headers that select the response format, tenant, or feature flag only when they are part of the authorized test scenario. Keep all other variables unchanged between comparison runs.
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Send a cookie with -C
Pass a cookie as name=value:
ab -n 500 -c 10 -C 'session=test-session' https://example.com/accountUse a test account and a short-lived or disposable session. Do not paste a real user’s cookie into a command or log.
Use Basic Authentication with -A
ApacheBench accepts Basic Authentication credentials in the form username:password:
ab -n 200 -c 5 -A 'loadtest:password' https://example.com/privateCommand-line arguments can be exposed through shell history, process listings, terminal recordings, and CI logs. Use credentials created specifically for testing, restrict their permissions, and remove or rotate them after the run. Avoid placing production secrets in the command line.
Send a POST body with -p and -T
Prepare a representative test payload in a file, then identify its media type:
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ab -n 200 -c 5 -p body.json -T application/json https://example.com/api/itemsUse a test endpoint or data set that can tolerate repeated submissions. If the operation writes data, decide in advance how records will be isolated and cleaned up. A POST benchmark can measure database writes, queues, locks, and downstream calls that a read-only GET does not touch.
Use -i for HEAD requests
When you need headers without downloading the response body, use -i:
ab -n 500 -c 10 -i https://example.com/HEAD and GET are not interchangeable tests. Confirm that the application, proxy, and cache handle HEAD correctly before using its result to infer GET capacity.
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How to read ApacheBench output
After a run, ab prints a summary. Interpret the fields as a group rather than selecting the largest requests-per-second value.
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|---|---|---|
| Concurrency Level | The concurrency value used for the run. | Compare only runs made at the same concurrency when making a direct performance comparison. |
| Time taken for tests | Total elapsed time for the test. | Use it with completed requests to understand throughput and test duration. |
| Complete requests | Requests completed by the run. | Check that the count matches the intended workload when no time limit or interruption applies. |
| Failed requests | Requests considered failures because of connection, read, content-length, or exception problems. | Investigate these before accepting a throughput result. |
| Non-2xx responses | Responses outside the successful 200 range. | Inspect application, authentication, routing, throttling, and upstream errors. |
| Keep-Alive requests | Requests handled using persistent connections when keep-alive is enabled. | Confirm that the result represents the connection behavior you intended to test. |
| Total transferred | All bytes transferred during the run. | Use it to spot changes in headers, compression, redirects, or response size. |
| HTML transferred | Response-body bytes counted as HTML transfer. | Compare response sizes when endpoint content changes. |
| Requests per second | Total requests divided by elapsed test time, as reported by ab. |
Read alongside failures, non-2xx responses, latency, and workload details. |
| Time per request | Average timing reported for the run. | Compare with identical concurrency, URL, method, payload, and connection settings. |
| Transfer rate | Received data rate. | Determine whether response size or network bandwidth is influencing the result. |
Requests per second is not an acceptance criterion by itself
A higher throughput number is not an improvement if failed requests or non-2xx responses rise. For example, a configuration that reports more requests per second while returning authentication errors has not demonstrated greater application capacity. Define an acceptance rule before testing, such as zero failed requests, no unexpected non-2xx responses, and a latency or throughput target appropriate to the service. The target must come from your own service requirements; ApacheBench does not provide a universal requests-per-second benchmark.
Use completed and failed counts together
“Complete requests” describes what the client completed, while “failed requests” identifies protocol or transfer problems detected by the client. A run can finish its allotted request count and still contain failures. Review the failure categories, server access and error logs, reverse-proxy logs, and application metrics at the same timestamps.
Distinguish non-2xx responses from client failures
A non-2xx response is an HTTP response outside the successful 200 range. It may indicate a legitimate redirect or a deliberate authorization response, but it may also reveal a broken route, throttling, an application exception, or an upstream failure. A client-side failed request is a different signal: the connection, read, content length, or another exception prevented a valid response from being processed.
A repeatable load-test procedure
- Define the question. Decide whether you are measuring a public GET, a HEAD request, an authenticated read, a JSON POST, or another single endpoint operation. State whether the run represents a cache hit, cache miss, or uncached request if that distinction matters.
- Choose an authorized target. Prefer staging or an isolated production slice. Confirm that test data, queues, email, payments, webhooks, and third-party calls cannot create unintended side effects.
- Verify the client. Run
ab -Vand confirm that the client can reach the URL with a small connectivity check. - Set explicit limits. Choose
-nand-c, or choose-tand-c. Add-k,-s, headers, cookies, authentication, or a payload only when the scenario requires them. - Capture a baseline. Run a low-concurrency test and save the complete terminal output together with the exact command.
- Observe the server. During each run, watch CPU, memory, disk I/O, network throughput, worker or connection limits, access and error logs, application latency, database saturation, and upstream errors.
- Increase pressure in steps. Raise concurrency gradually. Allow the system to return to a steady state between runs when caches, queues, or autoscaling can affect the next result.
- Repeat identical runs. Run each level more than once and investigate large variation instead of averaging away unexplained failures. Keep endpoint, protocol, method, payload, headers, timeout, keep-alive setting, and client location consistent.
- Record the stopping point. Note the first concurrency at which your acceptance criteria fail, which metric failed, and what server resource was saturated.
- Restore and verify. Remove test records, revoke test credentials, drain test queues where appropriate, and check that the service is healthy after the load ends.
Test designs you can compare
| Design | Request and protocol | Example workload | Best use | Acceptance signals |
|---|---|---|---|---|
| Baseline public read | GET over HTTP or HTTPS | -n 1000 -c 20 |
Establish a controlled endpoint reference. | Zero failed requests, expected status codes, stable latency, and repeatable throughput. |
| Sustained service test | GET with the selected protocol | -t 60 -c 20 |
Observe behavior over a fixed interval. | No growth in errors, resource exhaustion, or queue backlog during the interval. |
| Persistent-connection test | GET with keep-alive | -n 1000 -c 20 -k |
Measure a workload that reuses connections. | Expected keep-alive count, no connection failures, and consistent response behavior. |
| Authenticated read | GET with cookie or Basic Authentication | -C or -A plus explicit limits |
Exercise authorization and session handling. | Expected authorized status, no credential leakage, and no session or rate-limit errors. |
| JSON write | POST with a body and content type | -p body.json -T application/json |
Measure application and database work for a write. | Expected status, safe test data handling, no duplicate side effects, and no failed requests. |
| Header-only check | HEAD | -i with explicit limits |
Inspect header-serving behavior without downloading bodies. | Correct headers and status codes; do not treat it as a GET benchmark. |
These designs are not interchangeable. Protocol choice, concurrency, request count or duration, keep-alive behavior, payload size, authentication, and headers all change the work performed by the server. Compare results only when those axes match or when the difference is the subject of the experiment.
Common problems and how to fix them
The command is not found
Install apache2-utils, then run ab -V. If the package manager reports repository or permission problems, resolve those Ubuntu package issues before interpreting benchmark results.
The run shows failed requests
Separate connection, read, content-length, and exception failures from HTTP status failures. Check the client timeout, server logs, proxy logs, firewall rules, listener limits, worker pools, and upstream dependencies. Repeat with lower concurrency to determine whether the problem starts at a particular pressure level.
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The run shows unexpected non-2xx responses
Inspect the response class and correlate timestamps with application logs. Redirects, authentication failures, missing routes, throttling, and server errors have different remedies. Confirm that the URL, Host routing, cookies, headers, and credentials match the intended scenario.
Throughput is unexpectedly low
First verify the client’s network path and server-side saturation. Compare a small concurrency run with a larger one, inspect transfer rate and response size, and check whether HTTPS handshakes or new connections dominate the test. A large response, a slow database query, or a constrained worker pool can each limit requests per second for different reasons.
Results vary widely between runs
Keep the command and environment fixed, allow caches and queues to settle, and repeat the run. Record concurrent background traffic, autoscaling events, garbage collection, database activity, and network changes. Do not combine results from different URLs, payloads, client locations, or keep-alive settings into one average.
The test appears to overload the service
Stop the run, reduce concurrency, and verify recovery. Use a staging target or a narrow test window, establish a rollback and cleanup plan, and coordinate with the service owner. A load test is not successful if it causes an avoidable outage.
What ApacheBench cannot tell you
ApacheBench is a focused HTTP benchmark, not a browser automation system or a full workload model. It does not reproduce every browser behavior, user think time, asset waterfall, cache pattern, multi-step journey, or mix of endpoints. A single URL also cannot reveal how capacity changes when several routes compete for the same database, connection pool, queue, or upstream service.
Use ab for controlled endpoint experiments and regression checks. When the capacity question concerns realistic user behavior, model the journeys and traffic mix with a workload tool that can represent those interactions. Keep the ApacheBench command as a lower-level diagnostic so you can isolate one endpoint when a broader test reports a problem.
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Quick Recap
Checklist before accepting a result
- The target is owned by you or explicitly authorized for load testing.
- The Ubuntu package and
abversion are recorded. - The URL, protocol, method, headers, cookies, authentication, payload, and content type are documented.
-nand-c, or-tand-c, are explicit.- Keep-alive and timeout settings are recorded.
- Server CPU, memory, connection limits, logs, application metrics, database behavior, and upstream errors were observed.
- Failed requests and non-2xx responses were investigated rather than ignored.
- At least one repeat run used exactly the same workload.
- Test data, credentials, queues, and other side effects were cleaned up.
- The conclusion states the tested conditions and does not present the result as a universal requests-per-second capacity.
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