kotlin flow version

Using Flow, it's natural to collect data in the ViewModel, Repository, or other data layers when needed. Some further changes might still be possible before it is released as a stable API, but the overall API shape looks quite solid now. To get used to how Flow produces values on demand (or lazily), take a look at the following flow that emits the values (1, 2, 3) and prints before, during, and after each item is produced. This defines a new ConflatedBroadcastChannel. Similarly, Flow works on the same condition where the code inside a flow builder does not run until the flow is collected. Also in the ViewModel, add a cache update to the init block. Kotlin Flow is an addition to Kotlin Coroutines. Select Expression (experimental) Multiplatform Programming. If nothing happens, download Xcode and try again. Now we’ll switch to the Kotlin Flow version: From the very beginning we fix the main thread as being our end thread. Add dependencies. Invoking synchronous and asynchronous APIs. A switchMap applies a given function to the input LiveData (growZone in this case) and returns the transformed result as a LiveData. When the app first runs, a list of cards appears, each displaying the name and image of a specific plant: Each Plant has a growZoneNumber, an attribute that represents the region where the plant is most likely to thrive. In many cases, it's fine to use suspending transformations like we're doing here, which makes all async operations sequential. Featured in Kotlin Weekly Issue #194. If you do need to pass all events between coroutines and don't want conflation, consider using a Channel which offers the semantics of a BlockingQueue using suspend functions. Kotlin Flow is an experimental type in kotlinx.coroutines library as of the second milestone (preview) version 1.3.0-M2 to the upcoming 1.3.0 release. Note that each call to emitSource() removes the previously-added source. In the next step,, we'll explore a variation of getPlantsWithGrowZone using a Transformation. There are other suspending terminal operators such as toList, first and single shipped with kotlinx-coroutines, and you can build your own. This step showed you how you can control concurrency using Flow, as well as consume Flows inside a ViewModel without depending on a UI observer. As one final (optional) step, let's move the network requests into a flow-based coroutine. The coroutine launched by flowOn is allowed to produce results faster than the caller consumes them, and it will buffer a large number of them by default. This code is now entirely main-safe by deferring the main safety concerns to regular suspend functions. Kotlin is influenced by other programming languages such as Java, Scala, Groovy, Gosu, etc. This pattern shows how to integrate events (grow zone changing) into a flow. Just like the LiveData builder, this adds a configurable timeout to the LiveData generated. If you run this, it produces this output: You can see how execution bounces between the collect lambda and the flow builder. It's important to be aware of the buffer when using flowOn with large objects such as Room results since it is easy to use a large amount of memory buffering results. In addition, in the .combine version, the network request and the database query run concurrently, while in this version they run in sequence. Let us create an android project and then let's start integrating the Kotlin Flow APIs. Here is the existing code to query the Room database: The LiveData will be updated using the LiveData builder and coroutines with additional sorting logic: You'll also implement the same logic with Flow: If you run into any issues (code bugs, grammatical errors, unclear wording, etc.) Now that the sorting logic is in place, replace the code for plants and getPlantsWithGrowZone with the LiveData builder below: Now if you run the app, the custom sorted plant list should appear: The LiveData builder allows us to calculate values asynchronously, as liveData is backed by coroutines. Just like the liveData builder, the timeout will keep the flow active through rotations so your collection doesn't restart. If you run the app again, you should see that you're now loading the data using Flow! Open up PlantRepository.kt and add a map transform to getPlantsWithGrowZoneNumber. Why not use Coroutines channels you say? Then, if a new value is emitted by the growZoneChannel before the previous coroutine completes, it'll cancel it before starting a new one. This time, however, we use the launchIn operator to collect the flow inside our ViewModel. However, unlike LiveData, you have to use coroutines to read values on multiple threads. It runs on JVM. As you get started with Flow, carefully consider how you can use suspending transforms to simplify your code. Similar to plants LiveData above, the coroutine starts execution when it is observed and is terminated either on completion or if either the database or network call fails. Usage. Publish a … The operator flowOn launches a new coroutine to collect the flow above it and introduces a buffer to write the results. Similarly, Flow works on the same condition where the code inside a flow builder does not run until the flow is collected. We'll begin by writing a suspending function to fetch the custom sort order from the network and then cache it in memory. Since this flow only emits a single value, you can also build it directly from the getOrAwait function using asFlow. To continue wiring up the new return values to the UI, open up PlantRepository.kt, and add the following code: For now, we're just passing the Flow values through to the caller. So, why did Kotlin introduce a new Flow type, and how ; Incremental releases (1.x.y) that are shipped between feature releases and include updates in the tooling, performance improvements, and bug fixes. As usual, flow collection can be cancelled when the flow is suspended in a cancellable suspending function (like delay). ; Bug fix releases (1.x.yz) that include bug fixes for incremental releases. A flow is conceptually a stream of data that can be computed asynchronously. Flow takes this a step further by adding streams to the mix. The key difference is that it provides a suspending lambda for you in a new coroutine, so you can call regular suspend functions directly from mapLatest. By using the suspend and resume mechanism of coroutines, you can often orchestrate sequential async calls easily without using declarative transforms. It is an error to emit a value from a different coroutine than the one that called the suspending transformation. As a challenge step, try to define a function to encapsulate the data loading of this flow with the following signature: Sign up for the Google Developers newsletter, Using Kotlin Coroutines in your Android App. If you run the app again, you should see that you're now loading the data and applying the custom sort order using Flow! So, even though we've written an infinite loop in our flow builder, we can safely consume it without leaks due to structured concurrency. Once that value is processed, the flow resumes and calls suspendUntilChanged, which will do as it says–suspend the flow until one of the tables changes. In the next step we'll take a look at another way to provide main safety using flow. The combine operator combines two flows together. Start with the regular SharedPreferences: Create an instance of FlowSharedPreferences from that: Preferences expose the regular get() and put() (named as set()) functions from SharedPreferences. StateFlow only returns if the value has updated and doesn't return the same value. Run tests. Introduces a buffer to send results from the new coroutine to later calls. should feel familiar to anyone with experience there. You can emit multiple values from a LiveData by calling the emitSource() function whenever you want to emit a new value. And here is the result: 1: pool-1-thread-1 @coroutine#6 1: pool-1-thread-1 @coroutine#6 1: pool …

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