When developing web services and tuning the infrastructure that runs them, you’ll want to make sure that they handle requests quickly enough, and at a high enough volume, to meet your requirements. ApacheBench (
ab) is a benchmarking tool that measures the performance of a web server by inundating it with HTTP requests and recording metrics for latency and success. ApacheBench can help you determine how much traffic your HTTP server can sustain before performance degrades, and set baselines for typical response times.
While ApacheBench was designed to benchmark the Apache web server, it is a fully fledged HTTP client that benchmarks actual connections, and you can use it to test the performance of any backend that processes HTTP requests. In this post, we will explain how ApacheBench works, and walk you through the ApacheBench metrics that can help you tune your web servers and optimize your applications, including:
- Metrics for request latency, which range from percentile breakdowns of all requests to durations of specific phases within TCP connections.
- Metrics for request and connection status, including HTTP status codes and request failures.
ApacheBench is a command line tool that is bundled in the
apache2-utils package. To install
ab, run the following commands on Debian/Ubuntu platforms:
apt-get update apt-get install -y apache2-utils
ApacheBench allows you to configure the number of requests to send, a timeout limit, and request headers.
ab will send the requests, wait for a response (up to a user-specified timeout), and output statistics as a report.
You can run an ApacheBench command with the following format:
ab <OPTIONS> <WEB_SERVER_ADDRESS>/<PATH>
The only required argument is the address of the web server, followed by a
/ (URLs without the trailing
/ will cause
ab to return an error) and an optional URL path. If you don’t specify any options, ApacheBench will send a single request.
ab’s options allow you to adjust the volume of requests, as well as (for specialized cases) their headers and request bodies. Some commonly used options include:
-n: The number of requests to send
-t: A duration in seconds after which
abwill stop sending requests
-c: The number of concurrent requests to make
If you’re using both the
-t and the
-n flags, note that
-t should always go first—otherwise ApacheBench will override the value of
-n and assign the default
-n value of
50000, or 50,000 requests. Additionally,
ab runs on a single thread—the
-c value tells
ab how many file descriptors to allocate at a time for TCP connections, not how many HTTP requests to send simultaneously. The
-c flag does allow
ab to complete its tests in less time, and simulates a higher number of concurrent connections. The two timeseries graphs below, for example, show the number of concurrent connections as well as requests per second for the command:
ab -n 100000 -c 1000 <SERVER_ADDRESS>
When using ApacheBench, you’ll want to understand its constraints, which stem from the fact that
ab measures actual HTTP requests between two servers that may or may not run on the same host. First, you’ll want to make sure that the host conducting an
ab test has enough CPU resources to make all TCP connections you’ve specified, as well as enough memory available to store the state of each connection (
ab allocates memory based on the number of requests).
Other constraints include the fact that ApacheBench’s send-everything-at-once approach may not reflect the way your application handles requests over time; and the distance between the server and
ab client (e.g., the same host or different availability zones) will affect latency scores. As a result,
ab requests may not reflect the sorts of latencies you can expect to see in your usual production load. You’ll get the most out of ApacheBench when using the relative results of your tests—not “How fast are responses?” but “Are responses getting faster?"—to see how effective your efforts are to optimize your application code, server configuration, load balancing architecture, and so on.
After finishing its tests, ApacheBench produces a report that resembles the code snippet below. Later in this section, we will explain the different metrics provided in this report, such as:
- How long requests took to complete, including latency for different parts of the request-response cycle
- How successful the requests were, including how many requests received non-2xx responses, and how many requests failed
Server Software: AmazonS3 Server Hostname: <SOME_HOST> Server Port: 443 SSL/TLS Protocol: TLSv1.2,ECDHE-RSA-AES128-GCM-SHA256,2048,128 TLS Server Name: <SOME_HOST> Document Path: / Document Length: 45563 bytes Concurrency Level: 2 Time taken for tests: 3.955 seconds Complete requests: 100 Failed requests: 0 Total transferred: 4625489 bytes HTML transferred: 4556300 bytes Requests per second: 25.29 [#/sec] (mean) Time per request: 79.094 [ms] (mean) Time per request: 39.547 [ms] (mean, across all concurrent requests) Transfer rate: 1142.21 [Kbytes/sec] received Connection Times (ms) min mean[+/-sd] median max Connect: 40 53 8.1 51 99 Processing: 12 24 9.4 23 98 Waiting: 5 14 10.6 12 95 Total: 57 77 15.0 75 197 Percentage of the requests served within a certain time (ms) 50% 75 66% 77 75% 80 80% 81 90% 85 95% 92 98% 116 99% 197 100% 197 (longest request)
Time taken for tests measures the duration between when ApacheBench first connects to the server and when it receives the final response or is interrupted with Ctrl-C. This metric is useful if you’re conducting repeated
ab tests while optimizing your application or tuning your web server, and want a single score to consult for signs of improvement.
ApacheBench provides two variations on this metric, and both depend on the number of responses that
ab has finished processing (
done), as well as the value of the metric
Time taken for tests (
timetaken). Both multiply their results by 1,000 to get a number in milliseconds.
Time per request metric doesn’t take the concurrency value into account:
timetaken * 1000 / done
The second version of
Time per request accounts for the number of concurrent connections the user has configured
ab to make, using the
-c option (
concurrency * timetaken * 1000 / done
If you set a
-c value greater than 1, the second
Time per request metric should (in theory) provide a more accurate assessment of how long each request takes.
You should treat both
Time taken for tests and
Time per request as rough indicators of web server performance under specific levels of load. Tracking these values over time can help you assess your optimization efforts, and unusually high or low values can point to CPU saturation on the web server, code changes, or other events that you’ll want to investigate in more detail.
For each connection it makes,
ab stores five timestamps:
abbegan making a connection with the web server
abfinished making the connection and began writing the request
abfinished writing the request
abbegan reading from the response
abclosed the connection
ab uses these timestamps to create a
data object for each connection, containing the following properties:
waittime: How long
abspent waiting before it began reading from the response, after it finished writing the request (i.e.,
beginread - endwrite)
ctime: How long
abspent making a connection with the web server (i.e.,
connect - start)
time: How long
abspent handling the connection, from the beginning of its attempt to contact the server to when the connection closed (i.e.,
done - start)
ab uses the
data object to calculate the remaining latency-related metrics: aggregated connection times, percentiles, and, if you use the
-g flag, data for individual requests.
After inspecting ApacheBench’s top-level latency metrics, you can track the duration of different stages of the request-response cycle.
ab aggregates properties from its per-request
data objects to calculate the
sd (standard deviation),
max of each stage, all in milliseconds:
- Connect: How long it takes
abto establish a TCP connection with the target server before writing the request to the connection (
- Processing: How long the connection was open after being created (
time - ctime)
- Waiting: How long
abwaits after sending the request before beginning to read a response from the connection (
- Total: The time elapsed from the moment
abattempts to make the connection to the time the connection closes (
The output will resemble the following:
Connection Times (ms) min mean[+/-sd] median max Connect: 42 71 24.3 63 198 Processing: 13 32 11.5 33 80 Waiting: 5 19 10.7 15 40 Total: 65 103 30.9 95 233
These metrics give you a more nuanced understanding than
Time taken for tests and
Time per request, allowing you to see which part of the request-response cycle was responsible for the overall latency. Further, these metrics are based on the
data object for each request, rather than the start and end times of
In the above example, we can see that
Connect was on average (i.e., in both mean and median) the longest part of the cycle, though also the one with the highest standard deviation. Since the
Connect metric depends on client latency as well as server latency, we could investigate each of these, determining which side of the connection is responsible for the variation. Then we’d consider focusing our optimization efforts on how our backend handles new TCP connections.
The final ApacheBench report also includes a breakdown of request latency percentiles, giving you a more detailed view of request latency distribution than the standard deviations within the
Connection Times table. The percentile breakdown resembles the following. In the report, “certain time” refers to the value of the
time property (defined above).
Percentage of the requests served within a certain time (ms) 50% 95 66% 102 75% 107 80% 115 90% 138 95% 163 98% 229 99% 233 100% 233 (longest request)
Connection Times table, these metrics are not broken down by stage of the request-response cycle. As a result,
ab’s latency metrics are best viewed together to get a comprehensive view of web server performance:
- Use the top-level latency metrics to get a quick read of the server’s overall performance, especially in comparison to other
abtests applied in similar conditions.
- From there, use the higher-granularity aggregated connection times to find out which part(s) of the request-response cycle to investigate further.
- Finally, percentiles tell you how much variation there is between the fastest and slowest requests, so you can address any long-tail latencies.
ApacheBench can also display data about each connection in tab-separated values (TSV) format, allowing you to calculate values that are not available within the standard
ab report, such as
wait time percentiles. This data comes from the same
data objects that
ab uses to calculate
Connection Times and percentiles. These per-request values are:
starttime: The time the connection began, printed in the
ctime()format (from the
starttimeproperty of the connection)
seconds: The same value as
starttime, but given as the number of seconds since the Unix Epoch
ctime: The time it takes to connect to the server before making this request, in milliseconds
dtime: How long an established connection was open, in milliseconds (i.e,
time - ctime)
ttime: The total time spent on this connection (i.e., the
timeproperty) in milliseconds
wait: The time spent waiting between sending the request and reading from the response (i.e., the
waittimeproperty), in milliseconds)
To access per-request data in TSV format, use the
-g flag in your
ab command, specifying the path to the output file. For example, let’s say you run this command:
ab -n 100 -g /path/to/plot.tsv <WEB_SERVER_ADDRESS>
The first five rows of the data in the plot.tsv file will resemble the following.
starttime seconds ctime dtime ttime wait Tue Jun 18 15:56:10 2019 1560887770 46 14 60 6 Tue Jun 18 15:56:07 2019 1560887767 48 15 63 7 Tue Jun 18 15:56:11 2019 1560887771 51 13 63 6 Tue Jun 18 15:56:12 2019 1560887772 47 17 64 12 Tue Jun 18 15:56:06 2019 1560887766 46 20 66 9
-g flag gives you flexibility in how you analyze request data. You could, for example, plot the
dtime of each request in a timeseries graph (i.e., by using gnuplot, spreadsheet software, or a graphics library), indicating whether increased load on the server affected the performance of requests after a certain point.
Since ApacheBench makes real requests to an HTTP server, it’s important to understand the sorts of responses your web servers are returning.
ab allows you to track both non-2xx response codes and errors that arise when making requests.
One way to understand the responses from your web servers is to count those that return an error or a failure. This is helpful if your goal is to benchmark deliberately unsuccessful requests (e.g., the time it takes to load a 404 page after a redesign), or requests that are not guaranteed to return a successful response (e.g., in production environments).
It’s important to know that
ab will classify 3xx, 4xx, and 5xx response codes as
Non-2xx responses, and report this metric in a format similar to this:
Non-2xx responses: 10
You can see which status codes make up the “non-2xx” responses by adding the
-v <INTEGER> option when running
ab, where a value of
2 will show the headers and body of each response in STDOUT. You can also send HTTP logs from your server to a dedicated monitoring platform like Datadog to aggregate responses by status code.
Connections between ApacheBench and your web server can fail just like any TCP connection, and
ab counts failures within four different categories:
- Length: After the first response, a subsequent response displays varying content length (in bytes)
ab’s attempt to connect to a server results in an error
abencounters an error when reading data from the connection
abencounters an error while setting up file descriptors for making TCP connections
The following comes from running an
ab command with the
-n option set to
1000 and the URL set to
Complete requests: 1000 Failed requests: 992 (Connect: 0, Receive: 0, Length: 992, Exceptions: 0)
The high number of
Length-related errors is likely due to Google’s dynamic content. In a well-known bug (since marked as “a bug which will never be fixed”),
ab will treat requests as failures if the content length of their responses is variable. This is because
ab stores the length of the first response it receives, and compares subsequent lengths to that value. This means that you should disregard length-related failures when you’re using
ab to test dynamic sites.
In general, the
Failed request metrics monitor activity at the transport layer, i.e., with your TCP connections, rather than at the application layer. If you see nonzero results for
Failed requests, you’ll want to investigate your web server for possible issues with handling TCP connections.
In this post, we’ve shown you how to use ApacheBench to measure the performance of your HTTP servers, explored some limitations of ApacheBench, and explained the metrics that ApacheBench can provide. You can also use Datadog to get full visibility into your HTTP servers, including Apache, NGINX, and Gunicorn, along with 600 other technologies. Datadog can provide complete visibility into HTTP response latency by analyzing server logs, tracing requests as they travel across distributed services, collecting network metrics from your hosts, and simulating HTTP requests from clients around the world—check out our free trial.