Task

Mix.install([
  {:jason, "~> 1.4"},
  {:kino, "~> 0.9", override: true},
  {:youtube, github: "brooklinjazz/youtube"},
  {:hidden_cell, github: "brooklinjazz/hidden_cell"}
])

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Review Questions

Upon completing this lesson, a student should be able to answer the following questions.

• How do we use tasks to leverage concurrency?
• How do we use tasks to send one-off fire-and-forget jobs?

Task

We’ve already seen we can use Kernel.spawn/1 or Kernal.spawn_link/1 to spawn a process that performs some work and then dies.

spawn_pid =
  spawn(fn ->
    IO.puts("Job Started")
    # simulating expensive process
    Process.sleep(1000)
    IO.puts("Job Ended")
  end)

When we want to execute some code in a process, we shouldn’t use Kernel.spawn/1 or Kernel.spawn_link/1 directly. Instead, we should rely on the Task module. The Task module allows us to spawn a process, perform some work in that process, then end the process when our work is finished.

Task is also OTP-compliant, meaning it conform to certain OTP conventions that improve error handling, and allow them to start under a supervisor.

Fire-and-Forget

We can use Task.start/1 to create a new short-lived process that dies when it’s function executes. This is a fire-and-forget process which does not block the caller process or return a response.

{:ok, task_pid} = Task.start(fn -> IO.puts("task ran!") end)

IO.puts("Parent keeps running")
Process.sleep(100)
Process.alive?(task_pid) || IO.puts("task is dead")

Awaiting Task Response

With Task, we can use async/1 and await/1 to spawn a process, perform some calculation, and the retrieve the value when it’s finished.

task =
  Task.async(fn ->
    # simulating expensive calculation
    Process.sleep(1000)
    "response!"
  end)

Task.await(task)

We can run two computations concurrently by separating them into two different Task processes. Here, we’re simulating a clock tick-tocking every second in two separate processes to demonstrate the run in parallel.

task1 =
  Task.async(fn ->
    IO.inspect("tick", label: "task 1")
    Process.sleep(2000)
    IO.inspect("tick", label: "task 1")
    Process.sleep(1000)
    "tick"
  end)

task2 =
  Task.async(fn ->
    Process.sleep(1000)
    IO.inspect("tock", label: "task 2")
    Process.sleep(2000)
    IO.inspect("tock", label: "task 2")
    "tock"
  end)

Task.await(task1) |> IO.inspect(label: "Task 1 Response")
Task.await(task2) |> IO.inspect(label: "Task 2 Response")

A computer with a multi-core processor can perform these concurrent computations in parallel, which may make our program faster. In reality, it’s a bit more complicated than this, but this is a reasonable mental model for now to understand why concurrency is useful for improving performance.

Here we use the par boxes to demonstrate operations happening in parallel.

sequenceDiagram
  par 
    ParentProcess ->> Task1: spawns
    ParentProcess ->> Task2: spawns
  end
  par
    Task1 ->> Task1: performs work
    Task2 ->> Task2: performs work
  end
  Task1 ->> ParentProcess: return awaited result
  Task2 ->> ParentProcess: return awaited result

Task.async/1 returns a Task struct, not a pid. The Task struct contains information about who the parent (:owner) process is, the task’s pid (:pid), and a reference (:ref) used to monitor if the task crashes.

Task.async(fn -> nil end)

To demonstrate the performance value of concurrency, let’s say we have two computations which each take 1 second, it would normally take us 2 seconds to run these tasks synchronously.

computation1 = fn -> Process.sleep(1000) end
computation2 = fn -> Process.sleep(1000) end

{microseconds, _result} =
  :timer.tc(fn ->
    computation1.()
    computation2.()
  end)

# Expected To Be ~2 Seconds
microseconds / 1000 / 1000

By running these computations in parallel, we can theoretically reduce this time to 1 second instead of 2.

> Note, if your computer does not have multiple cores, then it will still take 2 seconds rather than the expected 1 second.

computation1 = fn -> Process.sleep(1000) end
computation2 = fn -> Process.sleep(1000) end

{microseconds, _result} =
  :timer.tc(fn ->
    task1 = Task.async(fn -> computation1.() end)
    task2 = Task.async(fn -> computation2.() end)

    Task.await(task1)
    Task.await(task2)
  end)

# Expected To Be ~1 Second
microseconds / 1000 / 1000

Your Turn

Use Task.async/1 and Task.await/1 to demonstrate the performance benefits between synchronous execution and parallel execution.

You may consider using Process.sleep/1 to simulate an expensive computation.

Awaiting Many Tasks

When working with many parallel tasks, we can use enumeration to spawn many tasks.

tasks =
  Enum.map(1..5, fn each ->
    Task.async(fn ->
      Process.sleep(1000)
      each * 2
    end)
  end)

Then we can also use enumeration to await/1 each task.

Enum.map(tasks, fn task -> Task.await(task) end)

Alternatively, you can use the convenient Taskl.await_many/1 function instead.

tasks =
  Enum.map(1..5, fn each ->
    Task.async(fn ->
      Process.sleep(1000)
      each * 2
    end)
  end)

Task.await_many(tasks)

Timeouts

Task.await/1 pauses the current execution to wait until a task has finished. However, it will not wait forever. By default, Task.await/1 and Task.await_many/1 will wait for five seconds for the task to complete. If the task does not finish, it will raise an error.

task = Task.async(fn -> Process.sleep(6000) end)

Task.await(task)

If we want to wait for more or less time, we can override the default value. await/2 and await_many/2 accept a timeout value as the second argument to the function.

task = Task.async(fn -> Process.sleep(6000) end)

Task.await(task, 7000)

task1 = Task.async(fn -> Process.sleep(6000) end)
task2 = Task.async(fn -> Process.sleep(6000) end)

Task.await_many([task1, task2], 7000)

Your Turn

In the Elixir cell below, spawn a task which takes one second to complete. await/2 the task and alter the timeout value to be one second. Awaiting the task should crash.

Further Reading

Consider the following resource(s) to deepen your understanding of the topic.

• HexDocs: Task
• ElixirSchools: Task
• Exercism: Task

Commit Your Progress

DockYard Academy now recommends you use the latest Release rather than forking or cloning our repository.

Run git status to ensure there are no undesirable changes. Then run the following in your command line from the curriculum folder to commit your progress.

$ git add .
$ git commit -m "finish Task reading"
$ git push

We’re proud to offer our open-source curriculum free of charge for anyone to learn from at their own pace.

We also offer a paid course where you can learn from an instructor alongside a cohort of your peers. We will accept applications for the June-August 2023 cohort soon.

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