autocannon-ci
v0.1.0
Published
run your benchmarks as part of your dev flow, for Node.js
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autocannon-ci
autocannon-ci can store the results and generate the flamegraphs of your HTTP/1.1 benchmarks of Node.js server. Run your autocannon benchmarks as part of your CI/dev flow, for Node.js.
It can also generate a little website containing all the result of your benchmarking, including flamegraphs with 0x:
Install
npm i autocannon-ci -g
Usage
autocannon-ci -c autocannon.yml
Configuration Example
server: ./server.js
benchmarks:
root:
connections: 100
duration: 5
url: localhost:3000
b:
connections: 100
duration: 5
url: localhost:3000/b
storage:
type: fs
path: perf-results
Available commands and full options
autocannon-ci is a tool to run multiple HTTP/1.1 benchmarks, and generate the relative flamegraphs, with the help of 0x.
Available commands:
- run (default)
- compare
- help
Run
Usage: autocannon-ci run [OPTS]
Runs the benchmarks configured in the autocannon-ci configuration file, and
save them according to the storage configured in the config file. The job id
is used to identify the single run.
Options:
--config/-c CONFIG Use the given config file; default: `autocannon.yml`.
--job/-j ID Use the specific job id.
--flamegrah/-F Generate and store flamegraphs.
Compare
Usage: autocannon-ci compare [OPTS] [A] [B]
Compare the job with id A against the job id B. A and B are defaulted to the
latest two jobs.
Options:
--config/-c CONFIG Use the given config file; default: `autocannon.yml`.
Launch 'autocannon-ci help [command]' to know more about the commands.
Storage
autocannon-ci can store the results and flamegraphs within a storage, which is configured in the config file.
type: fs
storage:
type: fs
path: perf-results
type: s3
storage:
type: s3
bucket: autocannon-ci-test
region: us-west-2
This will also require the environment variables S3_ACCESS_KEY
and S3_SECRET_KEY
containing the proper credentials to access S3. It uses the
aws-sdk, so any other way of configuring the
credential for that will work for autocannon-ci as well.
Acknowledgements
This project was kindly sponsored by nearForm.
License
MIT