Puma performance without all the (T)pain
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Fix link to puma_auto_tune.rb in README
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README.md

Puma Auto Tune

Build Status

Stability

Please do not use this library in production until this schneems/get_process_mem#7 has been fixed.

What

Performance without the (T)pain: puma_auto_tune will automatically adjust the number of puma workers to optimize the performance of your Ruby web application.

How

Puma is a web server that allows you to adjust the amount of processes and threads it uses to process requests. At a very simple level the more processes and threads you have the more requests you can process concurrently. However this comes at a cost, more processes means more RAM and more threads means more CPU usage. You want to get as close to maxing out your resources without going over.

The amount of memory and CPU your program consumes is also a factor of your code, as well as the amount of load it is under. Larger applications require more RAM. More requests mean more Ruby objects are created and garbage collected as your application generates web pages. Because of these factors, there is no one size fits all number for workers and threads, that's where Puma Auto Tune comes in.

Run Puma Auto Tune in production under load, or in staging while simulating load with tools like siege, blitz.io, or flood.io for a long enough time and we will compute and set your application numbers to maximize concurrent requests without going over your system limits.

Currently Puma Auto Tune will optimize the number of workers (processes) based on RAM.

Install

In your Gemfile add:

gem 'puma_auto_tune'

Then run $ bundle install.

Use

In your application call:

PumaAutoTune.start

In Rails you could place this in an initializer such as config/initializers/puma_auto_tune.rb.

Puma Auto Tune will attempt to find an ideal number of workers for your application.

Config

You will need to configure your Puma Auto Tune to be aware of the maximum amount of RAM it can use.

PumaAutoTune.config do |config|
  config.ram = 512 # mb: available on system
end

We will attempt to detect your RAM size if you are running on Heroku. If we cannot, the default is 512 mb. There are a few other advanced config options:

PumaAutoTune.config do |config|
  config.ram           = 1024 # mb: available on system
  config.frequency     = 20   # seconds: the duration to check memory usage
  config.reap_duration = 30   # seconds: how long `reap_cycle` will be run for
end

To see defaults check out puma_auto_tune.rb

Hitting the Sweet Spot

Puma Auto Tune is designed to tune the number of workers for a given application while it is running. Once you restart the program the tuning must start over. Once the algorithm has found the "sweet spot" you can maximize your application throughput by manually setting the number of workers that puma starts with. To help you do this Puma Auto Tune outputs semi-regular logs with formatted values.

measure#puma.resource_ram_mb=476.6328125 measure#puma.current_cluster_size=5

You can use a service such as librato to pull values out of your logs and graph them. When you see over time that your server settles on a given cluster_size you should set this as your default puma -w $PUMA_WORKERS if you're using the CLI to start your app or if you're using a config/puma.rb file:

workers Integer(ENV['PUMA_WORKERS'] || 3)

Puma Worker Killer

Do not use with puma_worker_killer gem. Puma Auto Tune takes care of memory leaks in addition to tuning your puma workers.

How it Works: Tuning Algorithm (RAM)

Simple by default, custom for true Puma hackers. The best way to think of the tuner is to start with the different states of memory consumption Puma can be under:

  • Unused RAM: we can add a worker
  • Memory leak (too much RAM usage): we should restart a worker
  • Too much RAM usage: we can remove a worker
  • Just right: No need to scale up or down.

The algorithm will periodically get the total memory used by Puma and take action appropriately.

Memory States: Unused RAM

The memory of the smallest worker is recorded. If adding another worker does not put the total memory over the threshold then one will be added.

Memory States: Memory Leak (too much RAM usage)

When the amount of memory is more than that on the system, we assume a memory leak and restart the largest worker. This will trigger a check to determine if the result was due to a memory leak or because we have too many workers.

Memory States: Too much RAM Usage

After a worker has been restarted we will aggressively check for memory usage for a fixed period of time, default is 90 seconds(PumaAutoTune.reap_reap_duration). If memory goes over the limit, it is assumed that the cause is due to excess workers. The number of workers will be decreased by one. Puma Auto Tune will record the number of total workers that were present when we went over and set this as a new maximum worker number. After removing a process, Puma Auto Tune again checks for memory overages for the same duration and continues to decrement the number of workers until the total memory consumed is under the maximum.

Memory States: Just Right

Periodically the tuner will wake up and take note of memory usage. If it cannot scale up, and doesn't need to scale down it goes back to sleep.

Customizing the Algorithm

Here's the fun part. You can write your own algorithm using the included hook system. The default algorithm is implemented as a series of pre-defined hooks.

You can over-write one or more of the hooks to add custom behavior. To define hooks call:

PumaAutoTune.hooks(:ram) do |auto|

end

Each hook has a name and can be over-written by calling set and passing in the symbol of the hook you wish to over-write. These are the default RAM hooks:

  • :cycle
  • :reap_cycle
  • :out_of_memory
  • :under_memory
  • :add_worker
  • :remove_worker

Once you have the hook object you can use the call method to jump to other hooks.

Cycle

This is the main event loop of your program. This code will be called every PumaAutoTune.frequency seconds. To over-write you can do this:

PumaAutoTune.hooks(:ram) do |auto|
  auto.set(:cycle) do |memory, master, workers|
    if memory > PumaAutoTune.ram # mb
      auto.call(:out_of_memory)
    else
      auto.call(:under_memory) if memory + workers.last.memory
    end
  end
end

Reap Cycle

When you think you might run out of memory call the reap_cycle. The code in this hook will be called in a loop for PumaAutoTune.reap_duration seconds.

PumaAutoTune.hooks do |auto|
  auto.set(:reap_cycle) do |memory, master, workers|
    if memory > PumaAutoTune.ram
      auto.call(:remove_worker)
    end
  end
end

Add Worker

Bumps up the worker size by one.

PumaAutoTune.hooks do |auto|
  auto.set(:add_worker) do |memory, master, workers|
    auto.log "Cluster too small. Resizing to add one more worker"
    master.add_worker
    auto.call(:reap_cycle)
  end
end

Here we're calling :reap_cycle just in case we accidentally went over our memory limit after the increase.

Remove Worker

Removes a worker. When remove_worker is called it will automatically set PumaAutoTune.max_workers to be one less than the current number of workers.

PumaAutoTune.hooks do |hook|
  auto.set(:remove_worker) do |memory, master, workers|
    auto.log "Cluster too large. Resizing to remove one worker"
    master.remove_worker
    auto.call(:reap_cycle)
  end
end

In case removing one worker wasn't enough we call reap_cycle again. Once a worker has been flagged with restart it will report zero RAM usage even if it has not completely terminated.

License

MIT