A flexible tool for stress testing servers with easily configurable requests.
Bombard allows you to simulate high-load scenarios by sending customizable requests to your server, helping you assess its performance and stability under stress.
It's especially good at simulating heavy loads and initial bursts of simultaneous HTTP requests with complex logic.
Bombard is designed to be an extremely simple yet powerful tool for load testing functional behavior.
Thanks to optional Python inlines, you can quickly and easily describe complex logic for your tests.
The test report shows you how many requests per second your server is capable of serving and with what latency.
pip install bombard --upgradeAfter that, use the bombard (bombard.exe on Windows) executable:
bombard --helpRequests can be a simple URL or contain JSON configuration like this:
supply: # you can redefine variables from command line (--supply host=http://localhost/)
host: https://jsonplaceholder.typicode.com/
getToken:
url: "{host}auth" # use custom {host} variable to stay DRY
method: POST
body: # below is JSON object for request body
email: name@example.com
password: admin
extract: # get token for next requests
token:In the first request, you can get a security token as in the example above.
Then use it in subsequent requests:
postsList:
url: "{host}posts"
headers:
Authorization: "Bearer {token}" # we get {token} in 1st request
script: |
for post in resp[:3]: # for 1st three posts from response
# schedule getPost request (from ammo section)
# and provide it with id we got from the response
reload(ammo.getPost, id=post['id'])Bombard includes examples. To list available examples:
bombard --examplesFrom the command line, you can change the number of threads, loop count, supply variables, customize the report, and more.
You can also bootstrap your own bombard.yaml file from any example you
like:
bombard --init --example simple
Example of report for the command:
bombard --example simple --repeat 2 --threshold 100
Automatically published to PyPI when creating a release on GitHub.
If you want to publish from a local machine: 1) Place PyPI password in ~/.pypirc 2) Run make upload
make help