Phoenix Connection Benchmark
This is a reproduction of the 2 million connections in Phoenix benchmark with the focus on making it repeatable by utilizing Docker Machine, Swarm and Compose.
DISCLAIMER:Like the original, this does not benchmark real use cases! It only opens and maintains a lot of connections to one Phoenix chat application.
Jump to theresults
- Docker and Docker Compose
- DigitalOcean account
Run the benchmark
Export your DigitalOcean API key (can also be set in in the configuration)
export DO_TOKEN=<your token>
Copy and edit the example configuration:
cp config.yml.example config.yml
Setup the droplets (this will take a few minutes depending on the amount of workers configured):
When the setup is finished it will output the IPs of the benchmark target and the Tsung master Droplet. Open the URLs in a browser, the sites will be available after starting the application.
Start the Phoenix Chat application on the Benchmark target, either by running
docker-machine ssh bench-target and running it from the shell, or by using this command:
docker-machine ssh bench-target "cd chat; MIX_ENV=prod PORT=4000 iex --name firstname.lastname@example.org --cookie 123 --erl '+P 5000000 -kernel inet_dist_listen_min 9001 inet_dist_listen_max 9001' -S mix phoenix.server"
Reload the browser tab and test if the chat application works.
docker-compose up to start the Tsung cluster. It will first wait for all slaves to be available and then run the benchmark.
Reload the Tsung master browser tab and watch the graphs 🙂
After you are done stop and remove the droplets:
The benchmark was run with this example configuration , the provisioning script of the server , and apatch to thephoenix_chat_example of @chrismccord. On the largest available Droplet with 64GB of RAM and 20 CPU cores Phoenix was able to accept and maintain 2.3 million websocket connections. The limit was the amount of available memory, the arrival rate dropped after the server started swapping:
Tsung graphs are available here
- Phoenix, Elixir and/or Erlang got HUGE memory efficiency improvements over the last months, the previous benchmark needed around 84GB RAM for 2 million connection, which was reduced to 64GB!
- The Tsung slave nodes need about 3.8GB RAM to maintain 60k connections, a larger arrival rate does not work because the chosen droplet only had 1 CPU.
- Docker Overlay networking is a bigger overhead then expected, running the Tsung slaves without docker would probably allow a higher arrival rate.
- This kind of benchmark is cheaper then one would expect. A full run (including setup and tear down) took less then 2 hours and thus costs less then 7$