Parallel

Run any code in parallel Processes(> use all CPUs), Threads(> speedup blocking operations), or Ractors(> use all CPUs).
Best suited for map-reduce or e.g. parallel downloads/uploads.

Install

gem install parallel

Usage

# 2 CPUs -> work in 2 processes (a,b + c)
results = Parallel.map(['a','b','c']) do |one_letter|
  SomeClass.expensive_calculation(one_letter)
end

# 3 Processes -> finished after 1 run
results = Parallel.map(['a','b','c'], in_processes: 3) { |one_letter| SomeClass.expensive_calculation(one_letter) }

# 3 Threads -> finished after 1 run
results = Parallel.map(['a','b','c'], in_threads: 3) { |one_letter| SomeClass.expensive_calculation(one_letter) }

# 3 Ractors -> finished after 1 run
results = Parallel.map(['a','b','c'], in_ractors: 3, ractor: [SomeClass, :expensive_calculation])

Same can be done with each

Parallel.each(['a','b','c']) { |one_letter| ... }

or each_with_index, map_with_index, flat_map

Produce one item at a time with lambda (anything that responds to .call) or Queue.

items = [1,2,3]
Parallel.each( -> { items.pop || Parallel::Stop }) { |number| ... }

Also supports any? or all?

Parallel.any?([1,2,3,4,5,6,7]) { |number| number == 4 }
# => true

Parallel.all?([1,2,nil,4,5]) { |number| number != nil }
# => false

Processes/Threads are workers, they grab the next piece of work when they finish.

Processes

Speedup through multiple CPUs
Speedup for blocking operations
Variables are protected from change
Extra memory used
Child processes are killed when your main process is killed through Ctrl+c or kill -2

Threads

Speedup for blocking operations
Variables can be shared/modified
No extra memory used

Ractors

Ruby 3.0+ only
Speedup for blocking operations
No extra memory used
Very fast to spawn
Experimental and unstable
start and finish hooks are called on main thread
Variables must be passed in Parallel.map([1,2,3].map { |i| [i, ARGV, local_var] }, ...
use Ractor.make_shareable to pass in global objects

ActiveRecord

Connection Lost

Multithreading needs connection pooling, forks need reconnects
Adjust connection pool size in config/database.yml when multithreading

# reproducibly fixes things (spec/cases/map_with_ar.rb)
Parallel.each(User.all, in_processes: 8) do |user|
  user.update_attribute(:some_attribute, some_value)
end
User.connection.reconnect!

# maybe helps: explicitly use connection pool
Parallel.each(User.all, in_threads: 8) do |user|
  ActiveRecord::Base.connection_pool.with_connection do
    user.update_attribute(:some_attribute, some_value)
  end
end

# maybe helps: reconnect once inside every fork
Parallel.each(User.all, in_processes: 8) do |user|
  @reconnected ||= User.connection.reconnect! || true
  user.update_attribute(:some_attribute, some_value)
end

NameError: uninitialized constant

A race happens when ActiveRecord models are autoloaded inside parallel threads in environments that lazy-load, like development, test, or migrations.

To fix, autoloaded classes before the parallel block with either require '<modelname>' or ModelName.class.

Break

Parallel.map([1, 2, 3]) do |i|
  raise Parallel::Break # -> stops after all current items are finished
end

Parallel.map([1, 2, 3]) { |i| raise Parallel::Break, i if i == 2 } == 2

Kill

Only use if whatever is executing in the sub-command is safe to kill at any point

Parallel.map([1,2,3]) do |x|
  raise Parallel::Kill if x == 1# -> stop all sub-processes, killing them instantly
  sleep 100 # Do stuff
end

Progress / ETA

# gem install ruby-progressbar

Parallel.map(1..50, progress: "Doing stuff") { sleep 1 }

# Doing stuff | ETA: 00:00:02 | ====================               | Time: 00:00:10

Use :finish or :start hook to get progress information.

:start has item and index
:finish has item, index, and result

They are called on the main process and protected with a mutex. (To just get the index, use the more performant Parallel.each_with_index)

Parallel.map(1..100, finish: -> (item, i, result) { ... do something ... }) { sleep 1 }

Set finish_in_order: true to call the :finish hook in the order of the input (will take longer to see initial output).

Parallel.map(1..9, finish: -> (item, i, result) { puts "#{item} ok" }, finish_in_order: true) { sleep rand }

Worker number

Use Parallel.worker_number to determine the worker slot in which your task is running.

Parallel.each(1..5, in_processes: 2) { |i| puts "Item: #{i}, Worker: #{Parallel.worker_number}" }
Item: 1, Worker: 1
Item: 2, Worker: 0
Item: 3, Worker: 1
Item: 4, Worker: 0
Item: 5, Worker: 1

Dynamically generating jobs

Example: wait for work to arrive or sleep

queue = []
Thread.new { loop { queue << rand(100); sleep 2 } } # job producer
Parallel.map(Proc.new { queue.pop }, in_processes: 3) { |f| f ? puts("#{f} received") : sleep(1) }

Tips

[Benchmark/Test] Disable threading/forking with in_threads: 0 or in_processes: 0, to run the same code with different setups
[Isolation] Do not reuse previous worker processes: isolation: true
[Stop all processes with an alternate interrupt signal] 'INT' (from ctrl+c) is caught by default. Catch 'TERM' (from kill) with interrupt_signal: 'TERM'
[Process count via ENV] PARALLEL_PROCESSOR_COUNT=16 will use 16 instead of the number of processors detected. This is used to reconfigure a tool using parallel without inserting custom logic.

grosser/parallel

grosser

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