docs/topics/coroutines-and-channels.md

[//]: # (title: Coroutines and channels − tutorial)

In this tutorial, you'll learn how to use coroutines in IntelliJ IDEA to perform network requests without blocking the
underlying thread or callbacks.

> No prior knowledge of coroutines is required, but you're expected to be familiar with basic Kotlin syntax.
>
{type="tip"}

You'll learn:

* Why and how to use suspending functions to perform network requests.
* How to send requests concurrently using coroutines.
* How to share information between different coroutines using channels.

For network requests, you'll need the [Retrofit](https://square.github.io/retrofit/) library, but the approach shown in
this tutorial works similarly for any other libraries that support coroutines.

> You can find solutions for all of the tasks on the `solutions` branch of the [project's repository](http://github.com/kotlin-hands-on/intro-coroutines).
>
{type="tip"}

## Before you start

1. Download and install the latest version of [IntelliJ IDEA](https://www.jetbrains.com/idea/download/index.html).
2. Clone the [project template](http://github.com/kotlin-hands-on/intro-coroutines) by choosing **Get from VCS** on the
   Welcome screen or selecting **File | New | Project from Version Control**.

   You can also clone it from the command line:

   ```Bash
   git clone https://github.com/kotlin-hands-on/intro-coroutines
   ```

### Generate a GitHub developer token

You'll be using the GitHub API in your project. To get access, provide your GitHub account name and either a password or a
token. If you have two-factor authentication enabled, a token will be enough.

Generate a new GitHub token to use the GitHub API with [your account](https://github.com/settings/tokens/new):

1. Specify the name of your token, for example, `coroutines-tutorial`:

   ![Generate a new GitHub token](generating-token.png){width=700}

2. Do not select any scopes. Click **Generate token** at the bottom of the page.
3. Copy the generated token.

### Run the code

The program loads the contributors for all of the repositories under the given organization (named “kotlin” by default).
Later you'll add logic to sort the users by the number of their contributions.

1. Open the `src/contributors/main.kt` file and run the `main()` function. You'll see the following window:

   ![First window](initial-window.png){width=500}

   If the font is too small, adjust it by changing the value of `setDefaultFontSize(18f)` in the `main()` function.

2. Provide your GitHub username and token (or password) in the corresponding fields.
3. Make sure that the _BLOCKING_ option is selected in the _Variant_ dropdown menu.
4. Click _Load contributors_. The UI should freeze for some time and then show the list of contributors.
5. Open the program output to ensure the data has been loaded. The list of contributors is logged after each successful request.

There are different ways of implementing this logic: by using [blocking requests](#blocking-requests)
or [callbacks](#callbacks). You'll compare these solutions with one that uses [coroutines](#coroutines) and see how
[channels](#channels) can be used to share information between different coroutines.

## Blocking requests

You will use the [Retrofit](https://square.github.io/retrofit/) library to perform HTTP requests to GitHub. It allows
requesting the list of repositories under the given organization and the list of contributors for each repository:

```kotlin
interface GitHubService {
    @GET("orgs/{org}/repos?per_page=100")
    fun getOrgReposCall(
        @Path("org") org: String
    ): Call<List<Repo>>

    @GET("repos/{owner}/{repo}/contributors?per_page=100")
    fun getRepoContributorsCall(
        @Path("owner") owner: String,
        @Path("repo") repo: String
    ): Call<List<User>>
}
```

This API is used by the `loadContributorsBlocking()` function to fetch the list of contributors for the given organization.

1. Open `src/tasks/Request1Blocking.kt` to see its implementation:

    ```kotlin
    fun loadContributorsBlocking(
        service: GitHubService,
        req: RequestData
    ): List<User> {
        val repos = service
            .getOrgReposCall(req.org)   // #1
            .execute()                  // #2
            .also { logRepos(req, it) } // #3
            .body() ?: emptyList()      // #4

        return repos.flatMap { repo ->
            service
                .getRepoContributorsCall(req.org, repo.name) // #1
                .execute()                                   // #2
                .also { logUsers(repo, it) }                 // #3
                .bodyList()                                  // #4
        }.aggregate()
    }
    ```

    * At first, you get a list of the repositories under the given organization and store it in the `repos` list. Then for
      each repository, the list of contributors is requested, and all of the lists are merged into one final list of
      contributors.
    * `getOrgReposCall()` and `getRepoContributorsCall()` both return an instance of the `*Call` class (`#1`). At this point,
      no request is sent.
    * `*Call.execute()` is then invoked to perform the request (`#2`). `execute()` is a synchronous call that blocks the
      underlying thread.
    * When you get the response, the result is logged by calling the specific `logRepos()` and `logUsers()` functions (`#3`).
      If the HTTP response contains an error, this error will be logged here.
    * Finally, get the response's body, which contains the data you need. For this tutorial, you'll use an empty list as a
      result in case there is an error, and you'll log the corresponding error (`#4`).

2. To avoid repeating `.body() ?: emptyList()`, an extension function `bodyList()` is declared:

    ```kotlin
    fun <T> Response<List<T>>.bodyList(): List<T> {
        return body() ?: emptyList()
    }
    ```

3. Run the program again and take a look at the system output in IntelliJ IDEA. It should have something like this:

    ```text
    1770 [AWT-EventQueue-0] INFO  Contributors - kotlin: loaded 40 repos
    2025 [AWT-EventQueue-0] INFO  Contributors - kotlin-examples: loaded 23 contributors
    2229 [AWT-EventQueue-0] INFO  Contributors - kotlin-koans: loaded 45 contributors
    ...
    ```

    * The first item on each line is the number of milliseconds that have passed since the program started, then the thread
      name in square brackets. You can see from which thread the loading request is called.
    * The final item on each line is the actual message: how many repositories or contributors were loaded.

    This log output demonstrates that all of the results were logged from the main thread. When you run the code with a _BLOCKING_
    option, the window freezes and doesn't react to input until the loading is finished. All of the requests are executed from
    the same thread as the one called `loadContributorsBlocking()` is from, which is the main UI thread (in Swing, it's an AWT
    event dispatching thread). This main thread becomes blocked, and that's why the UI is frozen:

    ![The blocked main thread](blocking.png){width=700}

    After the list of contributors has loaded, the result is updated.

4. In `src/contributors/Contributors.kt`, find the `loadContributors()` function responsible for choosing how
   the contributors are loaded and look at how `loadContributorsBlocking()` is called:

    ```kotlin
    when (getSelectedVariant()) {
        BLOCKING -> { // Blocking UI thread
            val users = loadContributorsBlocking(service, req)
            updateResults(users, startTime)
        }
    }
    ```

    * The `updateResults()` call goes right after the `loadContributorsBlocking()` call.
    * `updateResults()` updates the UI, so it must always be called from the UI thread.
    * Since `loadContributorsBlocking()` is also called from the UI thread, the UI thread becomes blocked and the UI is
      frozen.

### Task 1

The first task helps you familiarize yourself with the task domain. Currently, each contributor's name is repeated
several times, once for every project they have taken part in. Implement the `aggregate()` function combining the users
so that each contributor is added only once. The `User.contributions` property should contain the total number of
contributions of the given user to _all_ the projects. The resulting list should be sorted in descending order according
to the number of contributions.

Open `src/tasks/Aggregation.kt` and implement the `List<User>.aggregate()` function. Users should be sorted by the total
number of their contributions.

The corresponding test file `test/tasks/AggregationKtTest.kt` shows an example of the expected result.

> You can jump between the source code and the test class automatically by using the [IntelliJ IDEA shortcut](https://www.jetbrains.com/help/idea/create-tests.html#test-code-navigation)
> `Ctrl+Shift+T` / `⇧ ⌘ T`.
>
{type="tip"}

After implementing this task, the resulting list for the "kotlin" organization should be similar to the following:

![The list for the "kotlin" organization](aggregate.png){width=500}

#### Solution for task 1 {initial-collapse-state="collapsed"}

1. To group users by login, use [`groupBy()`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/group-by.html),
   which returns a map from a login to all occurrences of the user with this login in different repositories.
2. For each map entry, count the total number of contributions for each user and create a new instance of the `User` class
   by the given name and total of contributions.
3. Sort the resulting list in descending order:

    ```kotlin
    fun List<User>.aggregate(): List<User> =
        groupBy { it.login }
            .map { (login, group) -> User(login, group.sumOf { it.contributions }) }
            .sortedByDescending { it.contributions }
    ```

An alternative solution is to use the [`groupingBy()`](https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/grouping-by.html)
function instead of `groupBy()`.

## Callbacks

The previous solution works, but it blocks the thread and therefore freezes the UI. A traditional approach that avoids this
is to use _callbacks_.

Instead of calling the code that should be invoked right after the operation is completed, you can extract it
into a separate callback, often a lambda, and pass that lambda to the caller in order for it to be called later.

To make the UI responsive, you can either move the whole computation to a separate thread or switch to the Retrofit API
which uses callbacks instead of blocking calls.

### Use a background thread

1. Open `src/tasks/Request2Background.kt` and see its implementation. First, the whole computation is moved to a different
   thread. The `thread()` function starts a new thread:

    ```kotlin
    thread {
        loadContributorsBlocking(service, req)
    }
    ```

    Now that all of the loading has been moved to a separate thread, the main thread is free and can be occupied by other
    tasks:

    ![The freed main thread](background.png){width=700}

2. The signature of the `loadContributorsBackground()` function changes. It takes an `updateResults()`
   callback as the last argument to call it after all the loading completes:

    ```kotlin
    fun loadContributorsBackground(
        service: GitHubService, req: RequestData,
        updateResults: (List<User>) -> Unit
    )
    ```

3. Now when the `loadContributorsBackground()` is called, the `updateResults()` call goes in the callback, not immediately
   afterward as it did before:

    ```kotlin
    loadContributorsBackground(service, req) { users ->
        SwingUtilities.invokeLater {
            updateResults(users, startTime)
        }
    }
    ```

    By calling `SwingUtilities.invokeLater`, you ensure that the `updateResults()` call, which updates the results,
    happens on the main UI thread (AWT event dispatching thread).

However, if you try to load the contributors via the `BACKGROUND` option, you can see that the list is updated but
nothing changes.

### Task 2

Fix the `loadContributorsBackground()` function in `src/tasks/Request2Background.kt` so that the resulting list is shown
in the UI.

#### Solution for task 2 {initial-collapse-state="collapsed"}

If you try to load the contributors, you can see in the log that the contributors are loaded but the result isn't displayed.
To fix this, call `updateResults()` on the resulting list of users:

```kotlin
thread {
    updateResults(loadContributorsBlocking(service, req))
}
```

Make sure to call the logic passed in the callback explicitly. Otherwise, nothing will happen.

### Use the Retrofit callback API

In the previous solution, the whole loading logic is moved to the background thread, but that still isn't the best use of
resources. All of the loading requests go sequentially and the thread is blocked while waiting for the loading result,
while it could have been occupied by other tasks. Specifically, the thread could start loading another request to
receive the entire result earlier.

Handling the data for each repository should then be divided into two parts: loading and processing the
resulting response. The second _processing_ part should be extracted into a callback.

The loading for each repository can then be started before the result for the previous repository is received (and the
corresponding callback is called):

![Using callback API](callbacks.png){width=700}

The Retrofit callback API can help achieve this. The `Call.enqueue()` function starts an HTTP request and takes a
callback as an argument. In this callback, you need to specify what needs to be done after each request.

Open `src/tasks/Request3Callbacks.kt` and see the implementation of `loadContributorsCallbacks()` that uses this API:

```kotlin
fun loadContributorsCallbacks(
    service: GitHubService, req: RequestData,
    updateResults: (List<User>) -> Unit
) {
    service.getOrgReposCall(req.org).onResponse { responseRepos ->  // #1
        logRepos(req, responseRepos)
        val repos = responseRepos.bodyList()

        val allUsers = mutableListOf<User>()
        for (repo in repos) {
            service.getRepoContributorsCall(req.org, repo.name)
                .onResponse { responseUsers ->  // #2
                    logUsers(repo, responseUsers)
                    val users = responseUsers.bodyList()
                    allUsers += users
                }
            }
        }
        // TODO: Why doesn't this code work? How to fix that?
        updateResults(allUsers.aggregate())
    }
```

* For convenience, this code fragment uses the `onResponse()` extension function declared in the same file. It takes a
  lambda as an argument rather than an object expression.
* The logic for handling the responses is extracted into callbacks: the corresponding lambdas start at lines `#1` and `#2`.

However, the provided solution doesn't work. If you run the program and load contributors by choosing the _CALLBACKS_
option, you'll see that nothing is shown. However, the test from `Request3CallbacksKtTest` immediately returns the result
that it successfully passed.

Think about why the given code doesn't work as expected and try to fix it, or see the solutions below.

### Task 3 (optional)

Rewrite the code in the `src/tasks/Request3Callbacks.kt` file so that the loaded list of contributors is shown.

#### The first attempted solution for task 3 {initial-collapse-state="collapsed"}

In the current solution, many requests are started concurrently, which decreases the total loading time. However,
the result isn't loaded. This is because the `updateResults()` callback is called right after all of the loading requests are started,
before the `allUsers` list has been filled with the data.

You could try to fix this with a change like the following:

```kotlin
val allUsers = mutableListOf<User>()
for ((index, repo) in repos.withIndex()) {   // #1
    service.getRepoContributorsCall(req.org, repo.name)
        .onResponse { responseUsers ->
            logUsers(repo, responseUsers)
            val users = responseUsers.bodyList()
            allUsers += users
            if (index == repos.lastIndex) {    // #2
                updateResults(allUsers.aggregate())
            }
        }
}
```

* First, you iterate over the list of repos with an index (`#1`).
* Then, from each callback, you check whether it's the last iteration (`#2`).
* And if that's the case, the result is updated.

However, this code also fails to achieve our objective. Try to find the answer yourself, or see the solution below.

#### The second attempted solution for task 3 {initial-collapse-state="collapsed"}

Since the loading requests are started concurrently, there's no guarantee that the result for the last one comes last. The
results can come in any order.

Thus, if you compare the current index with the `lastIndex` as a condition for completion, you risk losing the results for
some repos.

If the request that processes the last repo returns faster than some prior requests (which is likely to happen), all of the
results for requests that take more time will be lost.

One way to fix this is to introduce an index and check whether all of the repositories have already been processed:

```kotlin
val allUsers = Collections.synchronizedList(mutableListOf<User>())
val numberOfProcessed = AtomicInteger()
for (repo in repos) {
    service.getRepoContributorsCall(req.org, repo.name)
        .onResponse { responseUsers ->
            logUsers(repo, responseUsers)
            val users = responseUsers.bodyList()
            allUsers += users
            if (numberOfProcessed.incrementAndGet() == repos.size) {
                updateResults(allUsers.aggregate())
            }
        }
}
```

This code uses a synchronized version of the list and `AtomicInteger()` because, in general, there's no guarantee that
different callbacks that process `getRepoContributors()` requests will always be called from the same thread.

#### The third attempted solution for task 3 {initial-collapse-state="collapsed"}

An even better solution is to use the `CountDownLatch` class. It stores a counter initialized with the number of
repositories. This counter is decremented after processing each repository. It then waits until the latch is counted
down to zero before updating the results:

```kotlin
val countDownLatch = CountDownLatch(repos.size)
for (repo in repos) {
    service.getRepoContributorsCall(req.org, repo.name)
        .onResponse { responseUsers ->
            // processing repository
            countDownLatch.countDown()
        }
}
countDownLatch.await()
updateResults(allUsers.aggregate())
```

The result is then updated from the main thread. This is more direct than delegating the logic to the child threads.

After reviewing these three attempts at a solution, you can see that writing correct code with callbacks is non-trivial
and error-prone, especially when several underlying threads and synchronization occur.

> As an additional exercise, you can implement the same logic using a reactive approach with the RxJava library. All of the
> necessary dependencies and solutions for using RxJava can be found in a separate `rx` branch. It is also possible to
> complete this tutorial and implement or check the proposed Rx versions for a proper comparison.
>
{type="tip"}

## Suspending functions

You can implement the same logic using suspending functions. Instead of returning `Call<List<Repo>>`, define the API
call as a [suspending function](composing-suspending-functions.md) as follows:

```kotlin
interface GitHubService {
    @GET("orgs/{org}/repos?per_page=100")
    suspend fun getOrgRepos(
        @Path("org") org: String
    ): List<Repo>
}
```

* `getOrgRepos()` is defined as a `suspend` function. When you use a suspending function to perform a request, the
  underlying thread isn't blocked. More details about how this works will come in later sections.
* `getOrgRepos()` returns the result directly instead of returning a `Call`. If the result is unsuccessful, an
  exception is thrown.

Alternatively, Retrofit allows returning the result wrapped in `Response`. In this case, the result body is
provided, and it is possible to check for errors manually. This tutorial uses the versions that return `Response`.

In `src/contributors/GitHubService.kt`, add the following declarations to the `GitHubService` interface:

```kotlin
interface GitHubService {
    // getOrgReposCall & getRepoContributorsCall declarations

    @GET("orgs/{org}/repos?per_page=100")
    suspend fun getOrgRepos(
        @Path("org") org: String
    ): Response<List<Repo>>

    @GET("repos/{owner}/{repo}/contributors?per_page=100")
    suspend fun getRepoContributors(
        @Path("owner") owner: String,
        @Path("repo") repo: String
    ): Response<List<User>>
}
```

### Task 4

Your task is to change the code of the function that loads contributors to make use of two new suspending functions,
`getOrgRepos()` and `getRepoContributors()`. The new `loadContributorsSuspend()` function is marked as `suspend` to use the
new API.

> Suspending functions can't be called everywhere. Calling a suspending function from `loadContributorsBlocking()` will
> result in an error with the message "Suspend function 'getOrgRepos' should be called only from a coroutine or another
> suspend function".
>
{type="note"}

1. Copy the implementation of `loadContributorsBlocking()` that is defined in `src/tasks/Request1Blocking.kt`
   into the `loadContributorsSuspend()` that is defined in `src/tasks/Request4Suspend.kt`.
2. Modify the code so that the new suspending functions are used instead of the ones that return `Call`s.
3. Run the program by choosing the _SUSPEND_ option and ensure that the UI is still responsive while the GitHub requests
   are performed.

#### Solution for task 4 {initial-collapse-state="collapsed"}

Replace `.getOrgReposCall(req.org).execute()` with `.getOrgRepos(req.org)` and repeat the same replacement for the
second "contributors" request:

```kotlin
suspend fun loadContributorsSuspend(service: GitHubService, req: RequestData): List<User> {
    val repos = service
        .getOrgRepos(req.org)
        .also { logRepos(req, it) }
        .bodyList()

    return repos.flatMap { repo ->
        service.getRepoContributors(req.org, repo.name)
            .also { logUsers(repo, it) }
            .bodyList()
    }.aggregate()
}
```

* `loadContributorsSuspend()` should be defined as a `suspend` function.
* You no longer need to call `execute`, which returned the `Response` before, because now the API functions return
  the `Response` directly. Note that this detail is specific to the Retrofit library. With other libraries, the API will be different,
  but the concept is the same.

## Coroutines

The code with suspending functions looks similar to the "blocking" version. The major difference from the blocking version
is that instead of blocking the thread, the coroutine is suspended:

```text
block -> suspend
thread -> coroutine
```

> Coroutines are often called lightweight threads because you can run code on coroutines, similar to how you run code on
> threads. The operations that were blocking before (and had to be avoided) can now suspend the coroutine instead.
>
{type="note"}

### Starting a new coroutine

If you look at how `loadContributorsSuspend()` is used in `src/contributors/Contributors.kt`, you can see that it's
called inside `launch`. `launch` is a library function that takes a lambda as an argument:

```kotlin
launch {
    val users = loadContributorsSuspend(req)
    updateResults(users, startTime)
}
```

Here `launch` starts a new computation that is responsible for loading the data and showing the results. The computation
is suspendable – when performing network requests, it is suspended and releases the underlying thread.
When the network request returns the result, the computation is resumed.

Such a suspendable computation is called a _coroutine_. So, in this case, `launch` _starts a new coroutine_ responsible
for loading data and showing the results.

Coroutines run on top of threads and can be suspended. When a coroutine is suspended, the
corresponding computation is paused, removed from the thread, and stored in memory. Meanwhile, the thread is free to be
occupied by other tasks:

![Suspending coroutines](suspension-process.gif){width=700}

When the computation is ready to be continued, it is returned to a thread (not necessarily the same one).

In the `loadContributorsSuspend()` example, each "contributors" request now waits for the result using the suspension
mechanism. First, the new request is sent. Then, while waiting for the response, the whole "load contributors" coroutine
that was started by the `launch` function is suspended.

The coroutine resumes only after the corresponding response is received:

![Suspending request](suspend-requests.png){width=700}

While the response is waiting to be received, the thread is free to be occupied by other tasks. The UI stays responsive,
despite all the requests taking place on the main UI thread:

1. Run the program using the _SUSPEND_ option. The log confirms that all of the requests are sent to the main UI thread:

    ```text
    2538 [AWT-EventQueue-0 @coroutine#1] INFO  Contributors - kotlin: loaded 30 repos
    2729 [AWT-EventQueue-0 @coroutine#1] INFO  Contributors - ts2kt: loaded 11 contributors
    3029 [AWT-EventQueue-0 @coroutine#1] INFO  Contributors - kotlin-koans: loaded 45 contributors
    ...
    11252 [AWT-EventQueue-0 @coroutine#1] INFO  Contributors - kotlin-coroutines-workshop: loaded 1 contributors
    ```

2. The log can show you which coroutine the corresponding code is running on. To enable it, open **Run | Edit configurations**
   and add the `-Dkotlinx.coroutines.debug` VM option:

   ![Edit run configuration](run-configuration.png){width=500}

   The coroutine name will be attached to the thread name while `main()` is run with this option. You can also
   modify the template for running all of the Kotlin files and enable this option by default.

Now all of the code runs on one coroutine, the "load contributors" coroutine mentioned above, denoted as `@coroutine#1`.
While waiting for the result, you shouldn't reuse the thread for sending other requests because the code is
written sequentially. The new request is sent only when the previous result is received.

Suspending functions treat the thread fairly and don't block it for "waiting". However, this doesn't yet bring any concurrency
into the picture.

## Concurrency

Kotlin coroutines are much less resource-intensive than threads.
Each time you want to start a new computation asynchronously, you can create a new coroutine instead.

To start a new coroutine, use one of the main _coroutine builders_: `launch`, `async`, or `runBlocking`. Different
libraries can define additional coroutine builders.

`async` starts a new coroutine and returns a `Deferred` object. `Deferred` represents a concept known by other names
such as `Future` or `Promise`. It stores a computation, but it _defers_ the moment you get the final result;
it _promises_ the result sometime in the _future_.

The main difference between `async` and `launch` is that `launch` is used to start a computation that isn't expected to
return a specific result. `launch` returns a `Job` that represents the coroutine. It is possible to wait until it completes
by calling `Job.join()`.

`Deferred` is a generic type that extends `Job`. An `async` call can return a `Deferred<Int>` or a `Deferred<CustomType>`,
depending on what the lambda returns (the last expression inside the lambda is the result).

To get the result of a coroutine, you can call `await()` on the `Deferred` instance. While waiting for the result,
the coroutine that this `await()` is called from is suspended:

```kotlin
import kotlinx.coroutines.*

fun main() = runBlocking {
    val deferred: Deferred<Int> = async {
        loadData()
    }
    println("waiting...")
    println(deferred.await())
}

suspend fun loadData(): Int {
    println("loading...")
    delay(1000L)
    println("loaded!")
    return 42
}
```

`runBlocking` is used as a bridge between regular and suspending functions, or between the blocking and non-blocking worlds. It works
as an adaptor for starting the top-level main coroutine. It is intended primarily to be used in `main()` functions and
tests.

> Watch [this video](https://www.youtube.com/watch?v=zEZc5AmHQhk) for a better understanding of coroutines.
>
{type="tip"}

If there is a list of deferred objects, you can call `awaitAll()` to await the results of all of them:

```kotlin
import kotlinx.coroutines.*

fun main() = runBlocking {
    val deferreds: List<Deferred<Int>> = (1..3).map {
        async {
            delay(1000L * it)
            println("Loading $it")
            it
        }
    }
    val sum = deferreds.awaitAll().sum()
    println("$sum")
}
```

When each "contributors" request is started in a new coroutine, all of the requests are started asynchronously. A new request
can be sent before the result for the previous one is received:

![Concurrent coroutines](concurrency.png){width=700}

The total loading time is approximately the same as in the _CALLBACKS_ version, but it doesn't need any callbacks.
What's more, `async` explicitly emphasizes which parts run concurrently in the code.

### Task 5

In the `Request5Concurrent.kt` file, implement a `loadContributorsConcurrent()` function by using the
previous `loadContributorsSuspend()` function.

#### Tip for task 5 {initial-collapse-state="collapsed"}

You can only start a new coroutine inside a coroutine scope. Copy the content
from `loadContributorsSuspend()` to the `coroutineScope` call so that you can call `async` functions there:

```kotlin
suspend fun loadContributorsConcurrent(
    service: GitHubService,
    req: RequestData
): List<User> = coroutineScope {
    // ...
}
```

Base your solution on the following scheme:

```kotlin
val deferreds: List<Deferred<List<User>>> = repos.map { repo ->
    async {
        // load contributors for each repo
    }
}
deferreds.awaitAll() // List<List<User>>
```

#### Solution for task 5 {initial-collapse-state="collapsed"}

Wrap each "contributors" request with `async` to create as many coroutines as there are repositories. `async`
returns `Deferred<List<User>>`. This is not an issue because creating new coroutines is not very resource-intensive, so you can
create as many as you need.

1. You can no longer use `flatMap` because the `map` result is now a list of `Deferred` objects, not a list of lists.
   `awaitAll()` returns `List<List<User>>`, so call `flatten().aggregate()` to get the result:

    ```kotlin
    suspend fun loadContributorsConcurrent(
        service: GitHubService,
        req: RequestData
    ): List<User> = coroutineScope {
        val repos = service
            .getOrgRepos(req.org)
            .also { logRepos(req, it) }
            .bodyList()

        val deferreds: List<Deferred<List<User>>> = repos.map { repo ->
            async {
                service.getRepoContributors(req.org, repo.name)
                    .also { logUsers(repo, it) }
                    .bodyList()
            }
        }
        deferreds.awaitAll().flatten().aggregate()
    }
    ```

2. Run the code and check the log. All of the coroutines still run on the main UI thread because
   multithreading hasn't been employed yet, but you can already see the benefits of running coroutines concurrently.
3. To change this code to run "contributors" coroutines on different threads from the common thread pool,
   specify `Dispatchers.Default` as the context argument for the `async` function:

    ```kotlin
    async(Dispatchers.Default) { }
    ```

    * `CoroutineDispatcher` determines what thread or threads the corresponding coroutine should be run on. If you don't
      specify one as an argument, `async` will use the dispatcher from the outer scope.
    * `Dispatchers.Default` represents a shared pool of threads on the JVM. This pool provides a means for parallel execution.
      It consists of as many threads as there are CPU cores available, but it will still have two threads if there's only one core.

4. Modify the code in the `loadContributorsConcurrent()` function to start new coroutines on different threads from the
   common thread pool. Also, add additional logging before sending the request:

    ```kotlin
    async(Dispatchers.Default) {
        log("starting loading for ${repo.name}")
        service.getRepoContributors(req.org, repo.name)
            .also { logUsers(repo, it) }
            .bodyList()
    }
    ```

5. Run the program once again. In the log, you can see that each coroutine can be started on one thread from the
   thread pool and resumed on another:

    ```text
    1946 [DefaultDispatcher-worker-2 @coroutine#4] INFO  Contributors - starting loading for kotlin-koans
    1946 [DefaultDispatcher-worker-3 @coroutine#5] INFO  Contributors - starting loading for dokka
    1946 [DefaultDispatcher-worker-1 @coroutine#3] INFO  Contributors - starting loading for ts2kt
    ...
    2178 [DefaultDispatcher-worker-1 @coroutine#4] INFO  Contributors - kotlin-koans: loaded 45 contributors
    2569 [DefaultDispatcher-worker-1 @coroutine#5] INFO  Contributors - dokka: loaded 36 contributors
    2821 [DefaultDispatcher-worker-2 @coroutine#3] INFO  Contributors - ts2kt: loaded 11 contributors
    ```

   For instance, in this log excerpt, `coroutine#4` is started on the `worker-2` thread and continued on the
   `worker-1` thread.

In `src/contributors/Contributors.kt`, check the implementation of the _CONCURRENT_ option:

1. To run the coroutine only on the main UI thread, specify `Dispatchers.Main` as an argument:

    ```kotlin
    launch(Dispatchers.Main) {
        updateResults()
    }
    ```

    * If the main thread is busy when you start a new coroutine on it,
      the coroutine becomes suspended and scheduled for execution on this thread. The coroutine will only resume when the
      thread becomes free.
    * It's considered good practice to use the dispatcher from the outer scope rather than explicitly specifying it on each
      end-point. If you define `loadContributorsConcurrent()` without passing `Dispatchers.Default` as an
      argument, you can call this function in any context: with a `Default` dispatcher, with
      the main UI thread, or with a custom dispatcher.
    * As you'll see later, when calling `loadContributorsConcurrent()` from tests, you can call it in the context
      with `TestDispatcher`, which simplifies testing. That makes this solution much more flexible.

2. To specify the dispatcher on the caller side, apply the following change to the project while
   letting `loadContributorsConcurrent` start coroutines in the inherited context:

    ```kotlin
    launch(Dispatchers.Default) {
        val users = loadContributorsConcurrent(service, req)
        withContext(Dispatchers.Main) {
            updateResults(users, startTime)
        }
    }
    ```

    * `updateResults()` should be called on the main UI thread, so you call it with the context of `Dispatchers.Main`.
    * `withContext()` calls the given code with the specified coroutine context, is suspended until it completes, and returns
      the result. An alternative but more verbose way to express this would be to start a new coroutine and explicitly
      wait (by suspending) until it completes: `launch(context) { ... }.join()`.

3. Run the code and ensure that the coroutines are executed on the threads from the thread pool.

## Structured concurrency

* The _coroutine scope_ is responsible for the structure and parent-child relationships between different coroutines. New
  coroutines usually need to be started inside a scope.
* The _coroutine context_ stores additional technical information used to run a given coroutine, like the coroutine custom
  name, or the dispatcher specifying the threads the coroutine should be scheduled on.

When `launch`, `async`, or `runBlocking` are used to start a new coroutine, they automatically create the corresponding
scope. All of these functions take a lambda with a receiver as an argument, and `CoroutineScope` is the implicit receiver type:

```kotlin
launch { /* this: CoroutineScope */ }
```

* New coroutines can only be started inside a scope.
* `launch` and `async` are declared as extensions to `CoroutineScope`, so an implicit or explicit receiver must always
  be passed when you call them.
* The coroutine started by `runBlocking` is the only exception because `runBlocking` is defined as a top-level function.
  But because it blocks the current thread, it's intended primarily to be used in `main()` functions and tests as a bridge
  function.

A new coroutine inside `runBlocking`, `launch`, or `async` is started automatically inside the scope:

```kotlin
import kotlinx.coroutines.*

fun main() = runBlocking { /* this: CoroutineScope */
    launch { /* ... */ }
    // the same as:
    this.launch { /* ... */ }
}
```

When you call `launch` inside `runBlocking`, it's called as an extension to the implicit receiver of
the `CoroutineScope` type. Alternatively, you could explicitly write `this.launch`.

The nested coroutine (started by `launch` in this example) can be considered as a child of the outer coroutine (started
by `runBlocking`). This "parent-child" relationship works through scopes; the child coroutine is started from the scope
corresponding to the parent coroutine.

It's possible to create a new scope without starting a new coroutine, by using the `coroutineScope` function.
To start new coroutines in a structured way inside a `suspend` function without access to the outer scope, you can create
a new coroutine scope that automatically becomes a child of the outer scope that this `suspend` function is called from.
`loadContributorsConcurrent()`is a good example.

You can also start a new coroutine from the global scope using `GlobalScope.async` or `GlobalScope.launch`.
This will create a top-level "independent" coroutine.

The mechanism behind the structure of the coroutines is called _structured concurrency_. It provides the following
benefits over global scopes:

* The scope is generally responsible for child coroutines, whose lifetime is attached to the lifetime of the scope.
* The scope can automatically cancel child coroutines if something goes wrong or a user changes their mind and decides
  to revoke the operation.
* The scope automatically waits for the completion of all child coroutines.
  Therefore, if the scope corresponds to a coroutine, the parent coroutine does not complete until all the coroutines
  launched in its scope have completed.

When using `GlobalScope.async`, there is no structure that binds several coroutines to a smaller scope.
Coroutines started from the global scope are all independent – their lifetime is limited only by the lifetime of the
whole application. It's possible to store a reference to the coroutine started from the global scope and wait for its
completion or cancel it explicitly, but that won't happen automatically as it would with structured concurrency.

### Canceling the loading of contributors

Create two versions of the function that loads the list of contributors. Compare how both versions behave when you try to
cancel the parent coroutine. The first version will use `coroutineScope` to start all of the child coroutines,
whereas the second will use `GlobalScope`.

1. In `Request5Concurrent.kt`, add a 3-second delay to the `loadContributorsConcurrent()` function:

   ```kotlin
   suspend fun loadContributorsConcurrent(
       service: GitHubService,
       req: RequestData
   ): List<User> = coroutineScope {
       // ...
       async {
           log("starting loading for ${repo.name}")
           delay(3000)
           // load repo contributors
       }
       // ...
   }
   ```

   The delay affects all of the coroutines that send requests, so that there's enough time to cancel the loading
   after the coroutines are started but before the requests are sent.

2. Create the second version of the loading function: copy the implementation of `loadContributorsConcurrent()` to
   `loadContributorsNotCancellable()` in `Request5NotCancellable.kt` and then remove the creation of a new `coroutineScope`.
3. The `async` calls now fail to resolve, so start them by using `GlobalScope.async`:

    ```kotlin
    suspend fun loadContributorsNotCancellable(
        service: GitHubService,
        req: RequestData
    ): List<User> {   // #1
        // ...
        GlobalScope.async {   // #2
            log("starting loading for ${repo.name}")
            // load repo contributors
        }
        // ...
        return deferreds.awaitAll().flatten().aggregate()  // #3
    }
    ```

    * The function now returns the result directly, not as the last expression inside the lambda (lines `#1` and `#3`).
    * All of the "contributors" coroutines are started inside the `GlobalScope`, not as children of the coroutine scope
      (line `#2`).

4. Run the program and choose the _CONCURRENT_ option to load the contributors.
5. Wait until all of the "contributors" coroutines are started, and then click _Cancel_. The log shows no new results,
   which means that all of the requests were indeed canceled:

    ```text
    2896 [AWT-EventQueue-0 @coroutine#1] INFO  Contributors - kotlin: loaded 40 repos
    2901 [DefaultDispatcher-worker-2 @coroutine#4] INFO  Contributors - starting loading for kotlin-koans
    ...
    2909 [DefaultDispatcher-worker-5 @coroutine#36] INFO  Contributors - starting loading for mpp-example
    /* click on 'cancel' */
    /* no requests are sent */
    ```

6. Repeat step 5, but this time choose the `NOT_CANCELLABLE` option:

    ```text
    2570 [AWT-EventQueue-0 @coroutine#1] INFO  Contributors - kotlin: loaded 30 repos
    2579 [DefaultDispatcher-worker-1 @coroutine#4] INFO  Contributors - starting loading for kotlin-koans
    ...
    2586 [DefaultDispatcher-worker-6 @coroutine#36] INFO  Contributors - starting loading for mpp-example
    /* click on 'cancel' */
    /* but all the requests are still sent: */
    6402 [DefaultDispatcher-worker-5 @coroutine#4] INFO  Contributors - kotlin-koans: loaded 45 contributors
    ...
    9555 [DefaultDispatcher-worker-8 @coroutine#36] INFO  Contributors - mpp-example: loaded 8 contributors
    ```

    In this case, no coroutines are canceled, and all the requests are still sent.

7. Check how the cancellation is triggered in the "contributors" program. When the _Cancel_ button is clicked,
   the main "loading" coroutine is explicitly canceled and the child coroutines are canceled automatically:

    ```kotlin
    interface Contributors {

        fun loadContributors() {
            // ...
            when (getSelectedVariant()) {
                CONCURRENT -> {
                    launch {
                        val users = loadContributorsConcurrent(service, req)
                        updateResults(users, startTime)
                    }.setUpCancellation()      // #1
                }
            }
        }

        private fun Job.setUpCancellation() {
            val loadingJob = this              // #2

            // cancel the loading job if the 'cancel' button was clicked:
            val listener = ActionListener {
                loadingJob.cancel()            // #3
                updateLoadingStatus(CANCELED)
            }
            // add a listener to the 'cancel' button:
            addCancelListener(listener)

            // update the status and remove the listener
            // after the loading job is completed
        }
    }
    ```

The `launch` function returns an instance of `Job`. `Job` stores a reference to the "loading coroutine", which loads
all of the data and updates the results. You can call the `setUpCancellation()` extension function on it (line `#1`),
passing an instance of `Job` as a receiver.

Another way you could express this would be to explicitly write:

```kotlin
val job = launch { }
job.setUpCancellation()
```

* For readability, you could refer to the `setUpCancellation()` function receiver inside the function with the
  new `loadingJob` variable (line `#2`).
* Then you could add a listener to the _Cancel_ button so that when it's clicked, the `loadingJob` is canceled (line `#3`).

With structured concurrency, you only need to cancel the parent coroutine and this automatically propagates cancellation
to all of the child coroutines.

### Using the outer scope's context

When you start new coroutines inside the given scope, it's much easier to ensure that all of them run with the same
context. It is also much easier to replace the context if needed.

Now it's time to learn how using the dispatcher from the outer scope works. The new scope created by
the `coroutineScope` or by the coroutine builders always inherits the context from the outer scope. In this case, the
outer scope is the scope the `suspend loadContributorsConcurrent()` function was called from:

```kotlin
launch(Dispatchers.Default) {  // outer scope
    val users = loadContributorsConcurrent(service, req)
    // ...
}
```

All of the nested coroutines are automatically started with the inherited context. The dispatcher is a part of this
context. That's why all of the coroutines started by `async` are started with the context of the default dispatcher:

```kotlin
suspend fun loadContributorsConcurrent(
    service: GitHubService, req: RequestData
): List<User> = coroutineScope {
    // this scope inherits the context from the outer scope
    // ...
    async {   // nested coroutine started with the inherited context
        // ...
    }
    // ...
}
```

With structured concurrency, you can specify the major context elements (like dispatcher) once, when creating the
top-level coroutine. All the nested coroutines then inherit the context and modify it only if needed.

> When you write code with coroutines for UI applications, for example Android ones, it's a common practice to
> use `CoroutineDispatchers.Main` by default for the top coroutine and then to explicitly put a different dispatcher when
> you need to run the code on a different thread.
>
{type="tip"}

## Showing progress

Despite the information for some repositories being loaded rather quickly, the user only sees the resulting list after all of
the data has been loaded. Until then, the loader icon runs showing the progress, but there's no information about the current
state or what contributors are already loaded.

You can show the intermediate results earlier and display all of the contributors after loading the data for each of the
repositories:

![Loading data](loading.gif){width=500}

To implement this functionality, in the `src/tasks/Request6Progress.kt`, you'll need to pass the logic updating the UI
as a callback, so that it's called on each intermediate state:

```kotlin
suspend fun loadContributorsProgress(
    service: GitHubService,
    req: RequestData,
    updateResults: suspend (List<User>, completed: Boolean) -> Unit
) {
    // loading the data
    // calling `updateResults()` on intermediate states
}
```

On the call site in `Contributors.kt`, the callback is passed to update the results from the `Main` thread for
the _PROGRESS_ option:

```kotlin
launch(Dispatchers.Default) {
    loadContributorsProgress(service, req) { users, completed ->
        withContext(Dispatchers.Main) {
            updateResults(users, startTime, completed)
        }
    }
}
```

* The `updateResults()` parameter is declared as `suspend` in `loadContributorsProgress()`. It's necessary to call
  `withContext`, which is a `suspend` function inside the corresponding lambda argument.
* `updateResults()` callback takes an additional Boolean parameter as an argument specifying whether the loading has
  completed and the results are final.

### Task 6

In the `Request6Progress.kt` file, implement the `loadContributorsProgress()` function that shows the intermediate
progress. Base it on the `loadContributorsSuspend()` function from `Request4Suspend.kt`.

* Use a simple version without concurrency; you'll add it later in the next section.
* The intermediate list of contributors should be shown in an "aggregated" state, not just the list of users loaded for
  each repository.
* The total number of contributions for each user should be increased when the data for each new
  repository is loaded.

#### Solution for task 6 {initial-collapse-state="collapsed"}

To store the intermediate list of loaded contributors in the "aggregated" state, define an `allUsers` variable which
stores the list of users, and then update it after contributors for each new repository are loaded:

```kotlin
suspend fun loadContributorsProgress(
    service: GitHubService,
    req: RequestData,
    updateResults: suspend (List<User>, completed: Boolean) -> Unit
) {
    val repos = service
        .getOrgRepos(req.org)
        .also { logRepos(req, it) }
        .bodyList()

    var allUsers = emptyList<User>()
    for ((index, repo) in repos.withIndex()) {
        val users = service.getRepoContributors(req.org, repo.name)
            .also { logUsers(repo, it) }
            .bodyList()

        allUsers = (allUsers + users).aggregate()
        updateResults(allUsers, index == repos.lastIndex)
    }
}
```

#### Consecutive vs concurrent

An `updateResults()` callback is called after each request is completed:

![Progress on requests](progress.png){width=700}

This code doesn't include concurrency. It's sequential, so you don't need synchronization.

The best option would be to send requests concurrently and update the intermediate results after getting the response
for each repository:

![Concurrent requests](progress-and-concurrency.png){width=700}

To add concurrency, use _channels_.

## Channels

Writing code with a shared mutable state is quite difficult and error-prone (like in the solution using callbacks).
A simpler way is to share information by communication rather than by using a common mutable state.
Coroutines can communicate with each other through _channels_.

Channels are communication primitives that allow data to be passed between coroutines. One coroutine can _send_
some information to a channel, while another can _receive_ that information from it:

![Using channels](using-channel.png)

A coroutine that sends (produces) information is often called a producer, and a coroutine that receives (consumes)
information is called a consumer. One or multiple coroutines can send information to the same channel, and one or multiple
coroutines can receive data from it:

![Using channels with many coroutines](using-channel-many-coroutines.png)

When many coroutines receive information from the same channel, each element is handled only once by one of the
consumers. Once an element is handled, it is immediately removed from the channel.

You can think of a channel as similar to a collection of elements, or more precisely, a queue, in which elements are added
to one end and received from the other. However, there's an important difference: unlike collections, even in their
synchronized versions, a channel can _suspend_ `send()`and `receive()` operations. This happens when the channel is empty
or full. The channel can be full if the channel size has an upper bound.

`Channel` is represented by three different interfaces: `SendChannel`, `ReceiveChannel`, and `Channel`, with the latter
extending the first two. You usually create a channel and give it to producers as a `SendChannel` instance so that only
they can send information to the channel.
You give a channel to consumers as a `ReceiveChannel` instance so that only they can receive from it. Both `send`
and `receive` methods are declared as `suspend`:

```kotlin
interface SendChannel<in E> {
    suspend fun send(element: E)
    fun close(): Boolean
}

interface ReceiveChannel<out E> {
    suspend fun receive(): E
}

interface Channel<E> : SendChannel<E>, ReceiveChannel<E>
```

The producer can close a channel to indicate that no more elements are coming.

Several types of channels are defined in the library. They differ in how many elements they can internally store and
whether the `send()` call can be suspended or not.
For all of the channel types, the `receive()` call behaves similarly: it receives an element if the channel is not empty;
otherwise, it is suspended.

<deflist collapsible="true">
   <def title="Unlimited channel">
       <p>An unlimited channel is the closest analog to a queue: producers can send elements to this channel and it will
keep growing indefinitely. The <code>send()</code> call will never be suspended.
If the program runs out of memory, you'll get an <code>OutOfMemoryException</code>.
The difference between an unlimited channel and a queue is that when a consumer tries to receive from an empty channel,
it becomes suspended until some new elements are sent.</p>
       <img src="unlimited-channel.png" alt="Unlimited channel" width="500"/>
   </def>
   <def title="Buffered channel">
       <p>The size of a buffered channel is constrained by the specified number.
Producers can send elements to this channel until the size limit is reached. All of the elements are internally stored.
When the channel is full, the next `send` call on it is suspended until more free space becomes available.</p>
       <img src="buffered-channel.png" alt="Buffered channel" width="500"/>
   </def>
   <def title="Rendezvous channel">
       <p>The "Rendezvous" channel is a channel without a buffer, the same as a buffered channel with zero size.
One of the functions (<code>send()</code> or <code>receive()</code>) is always suspended until the other is called. </p>
       <p>If the <code>send()</code> function is called and there's no suspended <code>receive()</code> call ready to process the element, then <code>send()</code>
is suspended. Similarly, if the <code>receive()</code> function is called and the channel is empty or, in other words, there's no
suspended <code>send()</code> call ready to send the element, the <code>receive()</code> call is suspended. </p>
       <p>The "rendezvous" name ("a meeting at an agreed time and place") refers to the fact that <code>send()</code> and <code>receive()</code>
should "meet on time".</p>
       <img src="rendezvous-channel.png" alt="Rendezvous channel" width="500"/>
   </def>
   <def title="Conflated channel">
       <p>A new element sent to the conflated channel will overwrite the previously sent element, so the receiver will always
get only the latest element. The <code>send()</code> call is never suspended.</p>
       <img src="conflated-channel.gif" alt="Conflated channel" width="500"/>
   </def>
</deflist>

When you create a channel, specify its type or the buffer size (if you need a buffered one):

```kotlin
val rendezvousChannel = Channel<String>()
val bufferedChannel = Channel<String>(10)
val conflatedChannel = Channel<String>(CONFLATED)
val unlimitedChannel = Channel<String>(UNLIMITED)
```

By default, a "Rendezvous" channel is created.

In the following task, you'll create a "Rendezvous" channel, two producer coroutines, and a consumer coroutine:

```kotlin
import kotlinx.coroutines.channels.Channel
import kotlinx.coroutines.*

fun main() = runBlocking<Unit> {
    val channel = Channel<String>()
    launch {
        channel.send("A1")
        channel.send("A2")
        log("A done")
    }
    launch {
        channel.send("B1")
        log("B done")
    }
    launch {
        repeat(3) {
            val x = channel.receive()
            log(x)
        }
    }
}

fun log(message: Any?) {
    println("[${Thread.currentThread().name}] $message")
}
```

> Watch [this video](https://www.youtube.com/watch?v=HpWQUoVURWQ) for a better understanding of channels.
>
{type="tip"}

### Task 7

In `src/tasks/Request7Channels.kt`, implement the function `loadContributorsChannels()` that requests all of the GitHub
contributors concurrently and shows intermediate progress at the same time.

Use the previous functions, `loadContributorsConcurrent()` from `Request5Concurrent.kt`
and `loadContributorsProgress()` from `Request6Progress.kt`.

#### Tip for task 7 {initial-collapse-state="collapsed"}

Different coroutines that concurrently receive contributor lists for different repositories can send all of the received
results to the same channel:

```kotlin
val channel = Channel<List<User>>()
for (repo in repos) {
    launch {
        val users = TODO()
        // ...
        channel.send(users)
    }
}
```

Then the elements from this channel can be received one by one and processed:

```kotlin
repeat(repos.size) {
    val users = channel.receive()
    // ...
}
```

Since the `receive()` calls are sequential, no additional synchronization is needed.

#### Solution for task 7 {initial-collapse-state="collapsed"}

As with the `loadContributorsProgress()` function, you can create an `allUsers` variable to store the intermediate
states of the "all contributors" list.
Each new list received from the channel is added to the list of all users. You aggregate the result and update the state
using the `updateResults` callback:

```kotlin
suspend fun loadContributorsChannels(
    service: GitHubService,
    req: RequestData,
    updateResults: suspend (List<User>, completed: Boolean) -> Unit
) = coroutineScope {

    val repos = service
        .getOrgRepos(req.org)
        .also { logRepos(req, it) }
        .bodyList()

    val channel = Channel<List<User>>()
    for (repo in repos) {
        launch {
            val users = service.getRepoContributors(req.org, repo.name)
                .also { logUsers(repo, it) }
                .bodyList()
            channel.send(users)
        }
    }
    var allUsers = emptyList<User>()
    repeat(repos.size) {
        val users = channel.receive()
        allUsers = (allUsers + users).aggregate()
        updateResults(allUsers, it == repos.lastIndex)
    }
}
```

* Results for different repositories are added to the channel as soon as they are ready. At first, when all of the requests
  are sent, and no data is received, the `receive()` call is suspended. In this case, the whole "load contributors" coroutine
  is suspended.
* Then, when the list of users is sent to the channel, the "load contributors" coroutine resumes, the `receive()` call
  returns this list, and the results are immediately updated.

You can now run the program and choose the _CHANNELS_ option to load the contributors and see the result.

Although neither coroutines nor channels completely remove the complexity that comes with concurrency,
they make life easier when you need to understand what's going on.

## Testing coroutines

Let's now test all solutions to check that the solution with concurrent coroutines is faster than the solution with
the `suspend` functions, and check that the solution with channels is faster than the simple "progress" one.

In the following task, you'll compare the total running time of the solutions. You'll mock a GitHub service and make
this service return results after the given timeouts:

```text
repos request - returns an answer within 1000 ms delay
repo-1 - 1000 ms delay
repo-2 - 1200 ms delay
repo-3 - 800 ms delay
```

The sequential solution with the `suspend` functions should take around 4000 ms (4000 = 1000 + (1000 + 1200 + 800)).
The concurrent solution should take around 2200 ms (2200 = 1000 + max(1000, 1200, 800)).

For the solutions that show progress, you can also check the intermediate results with timestamps.

The corresponding test data is defined in `test/contributors/testData.kt`, and the files `Request4SuspendKtTest`,
`Request7ChannelsKtTest`, and so on contain the straightforward tests that use mock service calls.

However, there are two problems here:

* These tests take too long to run. Each test takes around 2 to 4 seconds, and you need to wait for the results each
  time. It's not very efficient.
* You can't rely on the exact time the solution runs because it still takes additional time to prepare and run the code.
  You could add a constant, but then the time would differ from machine to machine. The mock service delays
  should be higher than this constant so you can see a difference. If the constant is 0.5 sec, making the delays
  0.1 sec won't be enough.

A better way would be to use special frameworks to test the timing while running the same code several times (which increases
the total time even more), but that is complicated to learn and set up.

To solve these problems and make sure that solutions with provided test delays behave as expected, one faster than the other,
use _virtual_ time with a special test dispatcher. This dispatcher keeps track of the virtual time passed from
the start and runs everything immediately in real time. When you run coroutines on this dispatcher,
the `delay` will return immediately and advance the virtual time.

Tests that use this mechanism run fast, but you can still check what happens at different moments in virtual time. The
total running time drastically decreases:

![Comparison for total running time](time-comparison.png){width=700}

To use virtual time, replace the `runBlocking` invocation with a `runTest`. `runTest` takes an
extension lambda to `TestScope` as an argument.
When you call `delay` in a `suspend` function inside this special scope, `delay` will increase the virtual time instead
of delaying in real time:

```kotlin
@Test
fun testDelayInSuspend() = runTest {
    val realStartTime = System.currentTimeMillis()
    val virtualStartTime = currentTime

    foo()
    println("${System.currentTimeMillis() - realStartTime} ms") // ~ 6 ms
    println("${currentTime - virtualStartTime} ms")             // 1000 ms
}

suspend fun foo() {
    delay(1000)    // auto-advances without delay
    println("foo") // executes eagerly when foo() is called
}
```

You can check the current virtual time using the `currentTime` property of `TestScope`.

The actual running time in this example is several milliseconds, whereas virtual time equals the delay argument, which
is 1000 milliseconds.

To get the full effect of "virtual" `delay` in child coroutines,
start all of the child coroutines with `TestDispatcher`. Otherwise, it won't work. This dispatcher is
automatically inherited from the other `TestScope`, unless you provide a different dispatcher:

```kotlin
@Test
fun testDelayInLaunch() = runTest {
    val realStartTime = System.currentTimeMillis()
    val virtualStartTime = currentTime

    bar()

    println("${System.currentTimeMillis() - realStartTime} ms") // ~ 11 ms
    println("${currentTime - virtualStartTime} ms")             // 1000 ms
}

suspend fun bar() = coroutineScope {
    launch {
        delay(1000)    // auto-advances without delay
        println("bar") // executes eagerly when bar() is called
    }
}
```

If `launch` is called with the context of `Dispatchers.Default` in the example above, the test will fail. You'll get an
exception saying that the job has not been completed yet.

You can test the `loadContributorsConcurrent()` function this way only if it starts the child coroutines with the
inherited context, without modifying it using the `Dispatchers.Default` dispatcher.

You can specify the context elements like the dispatcher when _calling_ a function rather than when _defining_ it,
which allows for more flexibility and easier testing.

> The testing API that supports virtual time is [Experimental](components-stability.md) and may change in the future.
>
{type="warning"}

By default, the compiler shows warnings if you use the experimental testing API. To suppress these warnings, annotate
the test function or the whole class containing the tests with `@OptIn(ExperimentalCoroutinesApi::class)`.
Add the compiler argument instructing the compiler that you're using the experimental API:

```kotlin
compileTestKotlin {
    kotlinOptions {
        freeCompilerArgs += "-Xuse-experimental=kotlin.Experimental"
    }
}
```

In the project corresponding to this tutorial, the compiler argument has already been added to the Gradle script.

### Task 8

Refactor the following tests in `tests/tasks/` to use virtual time instead of real time:

* Request4SuspendKtTest.kt
* Request5ConcurrentKtTest.kt
* Request6ProgressKtTest.kt
* Request7ChannelsKtTest.kt

Compare the total running times before and after applying your refactoring.

#### Tip for task 8 {initial-collapse-state="collapsed"}

1. Replace the `runBlocking` invocation with `runTest`, and replace `System.currentTimeMillis()` with `currentTime`:

    ```kotlin
    @Test
    fun test() = runTest {
        val startTime = currentTime
        // action
        val totalTime = currentTime - startTime
        // testing result
    }
    ```

2. Uncomment the assertions that check the exact virtual time.
3. Don't forget to add `@UseExperimental(ExperimentalCoroutinesApi::class)`.

#### Solution for task 8 {initial-collapse-state="collapsed"}

Here are the solutions for the concurrent and channels cases:

```kotlin
fun testConcurrent() = runTest {
    val startTime = currentTime
    val result = loadContributorsConcurrent(MockGithubService, testRequestData)
    Assert.assertEquals("Wrong result for 'loadContributorsConcurrent'", expectedConcurrentResults.users, result)
    val totalTime = currentTime - startTime

    Assert.assertEquals(
        "The calls run concurrently, so the total virtual time should be 2200 ms: " +
                "1000 for repos request plus max(1000, 1200, 800) = 1200 for concurrent contributors requests)",
        expectedConcurrentResults.timeFromStart, totalTime
    )
}
```

First, check that the results are available exactly at the expected virtual time, and then check the results
themselves:

```kotlin
fun testChannels() = runTest {
    val startTime = currentTime
    var index = 0
    loadContributorsChannels(MockGithubService, testRequestData) { users, _ ->
        val expected = concurrentProgressResults[index++]
        val time = currentTime - startTime
        Assert.assertEquals(
            "Expected intermediate results after ${expected.timeFromStart} ms:",
            expected.timeFromStart, time
        )
        Assert.assertEquals("Wrong intermediate results after $time:", expected.users, users)
    }
}
```

The first intermediate result for the last version with channels becomes available sooner than the progress version, and you
can see the difference in tests that use virtual time.

> The tests for the remaining "suspend" and "progress" tasks are very similar – you can find them in the project's
> `solutions` branch.
>
{type="tip"}

## What's next

* Check out the [Asynchronous Programming with Kotlin](https://kotlinconf.com/workshops/) workshop at KotlinConf.
* Find out more about using [virtual time and the experimental testing package](https://kotlinlang.org/api/kotlinx.coroutines/kotlinx-coroutines-test/).