Jetpack Compose UI – Stateful, Stateless and State Hoisting

Jetpack Compose is a declarative approach of design and code, where as the typical imperative programming era moving to parallel universe of declarative programming.  Here on this process, it gives more impact on the UX and it rendering process, so it closely works on the single changes that would have to be repaint i.e. re-construct the UI as fully or partially whichever the use case demands.  So, the changes would be caught and works with UI States and these are works on the parameters of events and observers.  Here we going to see where and how the state works in our android app. 

Before we go into these details of Stateful and Stateless, we have to understand fewer things which connects the State and Composables. 

State on Composition & Re-Composition:  Compose is declarative one and through it will also calling the same composable with the new arguments on demand of changes, the arguments are the real representatives of this UI state. It would be anything the updates made on the state, the recomposition of UI will take place. 

For Example, things like TextField, Checkbox, and Option etc... As like as imperative programming (XML layout views) these widgets won’t automatically update while on selecting/toggling in compose. Here in a composable we have to explicitly tell the new state as value changed in order update. 

How the State Drive On: Composable functions can use the remember APIs to store an object in memory. A value computed by remember is stored in the Composition during initial composition, and the stored value is returned during recomposition. remember can be used to store both mutable and immutable objects, these objects are defined with mutableStateOf which is an observable type integrated with the compose runtime.

Now coming to our discussion point, how and what would be the Stateful and Stateless Compose?

Here is the screenshot of the use case and code snippet for both Stateful and Stateless compose will be as follows, 

Simple logic is in the initial screen click me is an action button it will keep increase by 1 on each click, on the regular interval of every 5 count and the ahead a step of 2, that text “I’m Visible between x to y” will be displayed and this keeps on continuing. (Refer this branch for the full Source code)

Stateful: Stateful is when composable uses remember to store an object i.e. create, holds and modifies its own State internally.  Stateful is useful when we don’t need to control the State of a composable.  However, the weakness of Stateful is that it tends to be less reusable and difficult to test.  

Here in the below code the state, ui and logic is completely packed into one single compose function. 

Stateless:  Stateless composable is a composable that doesn't hold any state, rather it only receive & hoist the state and this pattern is called State hoisting.  State hoisting in Compose is a pattern of moving state to a composable's caller to make a composable stateless.  Another thing by using this State Hoisting approach, we can easily mock the parent method by using Mockito and test our parent and child methods

Benefits of State Hoisting:

• By Moving state instead of duplicating it, we’re ensuring there’s only one source of truth. This helps avoid bugs.
• Only stateful composable will be able to modify their state. It’s completely internally encapsulated.
• Hoisted states can be shared with multiple composable.
• Only the callers to the stateless composable can decide to ignore or modify events before changing the state, easy to intercept

Here in the below code snippet, we have 3 compose functions (1 stateful and 2 stateless), maybe we can keep it as 2 compose, but I just wanted to make it intentionally 3 because want to separate the UI into two different stateless and passing state param and action as lambda param to the functions.

Statement from the official docs

“As we develop reusable Composables, we often want to expose both a Stateful and a Stateless version of the same composable. The Stateful version is convenient for callers that don’t care about the state, and the Stateless version is necessary for callers that need to control or hoist the State.”

Above Code Ref Gist: Sateful, StateHoisting

LiveData & Flow

As part of developing apps on android, it is quite important things is how effectively and efficiently the UI can be updated on demand to the changes in data at lower layers, decoupling those layers, handling callbacks. Separation of these layers really helps on the testing part. (i.e. constructing the domain and data layer independently).   Since the legacy android code i.e. code which is written on imperative styles we would have writing lot of boilerplate code to handle the data changes using AsyncTask, callbacks, handlers etc.  But now the development has notably evolved and I would say much easier if one understands the basics.  There are always multiple ways to perform a given task within android and one can only decide the best alternative if the basic difference, pros, and cons of all options are mastered.

Just Glimpses:

LiveData: LiveData is a lifecycle aware observable data holder (means it knows the lifecycle of the activity or a fragment) use it when you play with UI elements (views).

Flow: Flow (cold stream) – In general think of it like a stream of data flowing in a pipe with both ends having a producer and consumer running on a coroutines.

StateFlow: (hot stream) does similar things like LiveData but it is made using flow by kotlin guys and only difference compare to LiveData is it’s not lifecycle aware but this is also been solved using repeatOnLifecycle api’s, so whatever LiveData can do StateFlow can do much better with power of flow’s api. StateFlow won’t emit same value.

SharedFlow: (hot stream) – name itself says it is shared, this flow can be shared by multiple consumers, I mean if multiple collect calls happening on the sharedflow there will be a single flow which will get shared across all the consumers, unlike normal flow.

What are the above highlighted terms, let’s try to address here what are those in details and that helps us to choose among LiveData, Flow, SharedFlow, and StateFlow

LiveData:  Live data is part of Android Architecture Components.  

It is an Observable data class:  it can hold data and that data can be observed from android components. Meaning it can be observed by other components — most profoundly UI controllers (Activities/Fragments).  

It is Lifecycle aware—it sends updates to our components i.e. UI (Activities/Fragments) only when our view is in the active state.

 

Pros	Cons
Would always get the latest data Always check if the subscriber is active before publishing data. No memory leaks No crashes or unnecessary ANR Configuration changes update	Lack of control over the execution context Threading issue especially when used in Repositories Not built on top of Coroutines and Kotlin Lack of seamless data integration across between database and UI especially using Room. Lots of Boiler Plate Codes especially while using Transformations

 

Flow:  Built on top of a coroutines, a flow emits multiple values sequentially.  It’s a stream of data that can be computed sequentially.  Flow can handle streams of values, and transform data in complex multi-threaded ways using an intermediate operators to modify the stream without consuming values. (So basically it’s an alternate to RxJava.)

 

Flows by nature are not lifecycle aware unlike LiveData. Which makes sense as it’s not a part of android component but a type from Kotlin language. However, this can be resolved by responsibly collecting flow values within lifeCycleScopes via coroutines.

 

Flow is declarative/cold: It can only be executed on collection and there are hot flows as well (SharedFlow and StateFlow).

 

COLD: Stops emission when any collector is not active. HOT: It remains in memory as long as the flow is collected or as long as any other references to it exist from a garbage collection root.

 

StateFlow:

 

• State Flow is similar to normal flow but it holds the state. When you initialize the state flow you always need to tell the initial value of state flow. Hence state flow will have always a value.
• State flow is Hot Flow because it starts emitting values even though there is no consumer and those data will be remains in the memory
• State flow always has an initial value, replays one most recent value to new subscribers, does not buffer any more values, but keeps the last emitted one.
• All methods of state flow are thread-safe and can be safely invoked from concurrent coroutines without external synchronization.
• When you collect state flow through collect{} and if the consumer gets recreated due to configuration change it will re-call collect{}
• You can convert Cold flow to state flow using stateIn () operator.

SharedFlow:

• A SharedFlow is a highly-configurable generalization of StateFlow, that emits all value to all consumers in a broadcast fashion.
• A shared flow keeps a specific number of the most recent values in its replay cache. Every new subscriber first gets the values from the replay cache and then gets new emitted values. The maximum size of the replay cache is specified when the shared flow is created by the replay parameter.
• A default implementation of a shared flow that is created with MutableSharedFlow() constructor function without parameters has no replay cache nor additional buffer.
• All methods of shared flow are thread-safe and can be safely invoked from concurrent coroutines without external synchronization.
• When you collect shared flow through collect{} and if the consumer gets recreated due to configuration change it will not re-call collect{}
• You can convert Cold flow to shared flow using shareIn () operator.

Extras….

StateFlow and LiveData have similarities: Both are observable data holder classes, and both follow a similar pattern when used in your app architecture.

The StateFlow and LiveData do behave differently: StateFlow requires an initial state to be passed into the constructor, while LiveData does not.

LiveData.observe() automatically unregisters the consumer when the view goes to the STOPPED state, whereas collecting from a StateFlow or any other flow does not stop collecting automatically. To achieve the same behavior,you need to collect the flow from a Lifecycle.repeatOnLifecycle block.

StateFlow Vs. SharedFlow: The main difference between a SharedFlow and a StateFlow is that a StateFlow takes a default value through the constructor and emits it immediately when someone starts collecting, while a SharedFlow takes no value and emits nothing by default.

Here we see the code snippet in the blog using LiveData, SharedFlow and StateFlow used in ViewModel and that how it handles with the retrofit response and updates the same to the Fragment.

Full Source Code Reference in Branch

updateView(..) in the Fragment (Code Snippet to update the UI View handle)

private fun updateView(res: Resource<Summary>) {
        when (res.status) {
            Status.LOADING -> {
                binding.progressBar.visibility = View.VISIBLE
            }
            Status.SUCCESS -> {
                binding.progressBar.visibility = View.GONE
                res.data?.let {
                    updateCards(it.global)
                    it.countries?.let {
                        statusAdapter.setData(it)
                    } ?: kotlin.run {
                        Toast.makeText(activity, "No Data Available " + res.msg, Toast.LENGTH_LONG)
                            .show()
                    }
                }
            }
            Status.ERROR -> {
                binding.progressBar.visibility = View.GONE
                Toast.makeText(
                    activity,
                    "Something went wrong... Please contact admin " + res.msg,
                    Toast.LENGTH_LONG
                ).show()
            }
        }
    }

LiveData: In ViewModel class:

private val _summaryLiveData = MutableLiveData(Resource.loading(Summary()))
val summaryLiveData = _summaryLiveData as LiveData<Resource<Summary>>

// LiveData Object gets updated

fun getCovidStatusLiveData() {
        viewModelScope.launch(Dispatchers.IO) {
            try {
                _summaryLiveData.postValue(Resource.loading(null))
                val summaryResponse =
                    APIClient.createService(tClass = CovidStatusAPI::class.java)
                        .getCountriesSummary()
                summaryResponse.takeIf { it.isSuccessful }?.let {
                    _summaryLiveData.postValue(Resource.success(it.body() as Summary))
                } ?: kotlin.run {
                    _summaryLiveData.postValue(Resource.error("Error", null))
                }
            } catch (e: Exception) {
                _summaryLiveData.postValue(Resource.error(e.message ?: "Err", null))
            }
        }
    }

// Using LiveData Scope (It uses emits)
fun getCovidStatusLiveDataScope() = liveData {
        try {
            emit(Resource.loading(null))
            val summaryResponse =
                APIClient.createService(tClass = CovidStatusAPI::class.java).getCountriesSummary()
            summaryResponse.takeIf { it.isSuccessful }?.let {
                emit(Resource.success(it.body() as Summary))
            } ?: kotlin.run {
                emit(Resource.error("Error", null))
            }
        } catch (e: Exception) {
            emit(Resource.error(e.message ?: "Err"))
        }
    }

In Fragment (LiveData observer):

// Observing the LiveData object of viewmodel 
viewModel.summaryLiveData.observe(viewLifecycleOwner) {
            updateView(it)
        }

// Observing the direct livedata scope function of viewmodel 
viewModel.getCovidStatusLiveDataScope().observe(viewLifecycleOwner){
            updateView(it)
        }

StateFlow & SharedFlow: In ViewModel class:

private val _summaryStateFlow = MutableStateFlow(Resource.loading(Summary()))
val summaryStateFlow = _summaryStateFlow as StateFlow<Resource<Summary>>

private val _summarySharedFlow = MutableSharedFlow<Resource<Summary>>()
val summarySharedFlow = _summarySharedFlow as SharedFlow<Resource<Summary>>

// Using SharedFlow
fun getCovidStatusSharedFlow() {
        viewModelScope.launch(Dispatchers.IO) {
            try {
                _summarySharedFlow.emit(Resource.loading(null))
                val summaryResponse =
                        APIClient.createService(tClass = CovidStatusAPI::class.java)
                                .getCountriesSummary()
                summaryResponse.takeIf { it.isSuccessful }?.let {
                    _summarySharedFlow.emit(Resource.success(it.body() as Summary))
                } ?: kotlin.run {
                    _summarySharedFlow.emit(Resource.error("Error", null))
                }
            } catch (e: Exception) {
                _summarySharedFlow.emit(Resource.error(e.message ?: "Err", null))
            }
        }
    }

// Using StateFlow
fun getCovidStatusStateFlow() {
        viewModelScope.launch(Dispatchers.IO) {
            try {
                _summaryStateFlow.emit(Resource.loading(null))
                val summaryResponse =
                        APIClient.createService(tClass = CovidStatusAPI::class.java)
                                .getCountriesSummary()
                summaryResponse.takeIf { it.isSuccessful }?.let {
                    _summaryStateFlow.emit(Resource.success(it.body() as Summary))
                } ?: kotlin.run {
                    _summaryStateFlow.emit(Resource.error("Error", null))
                }
            } catch (e: Exception) {
                _summaryStateFlow.value = Resource.error(e.message ?: "Err", null)
            }
        }
    }

In Fragment (Handling state and shared flow emitted values): Since we know Flows by nature are not lifecycle aware, so it need to be handled like below.

// StateFlow to make it as lifecycle aware.
viewLifecycleOwner.lifecycleScope.launch {
             viewLifecycleOwner.repeatOnLifecycle(Lifecycle.State.STARTED) {
                 launch {
                     viewModel.summaryStateFlow
                         .collectLatest {
                             updateView(it)
                         }
                 }
             }
         }

// SharedFlow to make it as lifecycle aware.
viewLifecycleOwner.lifecycleScope.launch {
             viewLifecycleOwner.repeatOnLifecycle(Lifecycle.State.STARTED) {
                 launch {
                     viewModel.summarySharedFlow.collectLatest {
                         updateView(it)
                     }
                 }
             }
         }

But doing above for all stateflow or sharedflow object is tedious so we can write one common inline function in Utils to make it as easy for the lifetime.

// This Take care of LifeCycle Aware
inline fun <T> Flow<T>.launchAndCollectIn(
    owner: LifecycleOwner,
    minState: Lifecycle.State = Lifecycle.State.STARTED,
    crossinline block: suspend CoroutineScope.(T) -> Unit
) {
    owner.lifecycleScope.launch {
        owner.repeatOnLifecycle(minState) {
            collect {
                block(it)
            }
        }
    }
}

viewModel.summarySharedFlow.launchAndCollectIn(viewLifecycleOwner) {
            updateView(it)
        }

viewModel.summaryStateFlow.launchAndCollectIn(viewLifecycleOwner) {
            updateView(it)
        }

Kotlin Delegation

In object-oriented programming, delegation refers to evaluating a member (property or method) of one object (the receiver) in the context of another original object (the sender). 

Delegation is a design pattern in which an object handles a request by delegating to a helper object, called the delegate. The delegate is responsible for handling the request on behalf of the original object and making the results available to the original object.  

Kotlin supports “delegation” design pattern by introducing a new keyword “by”. Using this keyword or delegation methodology, Kotlin allows the derived class to access all the implemented public methods of an interface through a specific object

Delegation should be used when:

• Any components that behave identically, but we realize were it will have some upgradation on top of it in future point of time where we do our own logical implementation and expose the results.  For E.g. We can take an example of Analytics class, where instead of directly using the analytics method we can have our delegation and use the param to handle the screen views and event logging.
• Place where we feel delegate would be the better than inheritance because, it doesn’t force you to define all the methods of the super class, you can use or override only the methods that really needed.  For e.g. here you can refer the code below that PaymentProcess doesn’t enforced to override the method (Kotlin), but if really needed we can override and do our implementations.
• If we want to extend the behavior of classes that we cannot or don’t want to subclass for the below reasons For E.g. here you can see in the below code snippet Payment classes (CashPayment, UPIPayment, CardPayment) are can’t be inherited.
• Restricted to share or disallowed to create a hierarchy (Class marked as final)
• Planning to expose the limited or different API methods by consuming the existing code functionality and also we may add our feature update. 
• Planned to hide the serious implementation from the calling code.

Example: This will be very simple and self-explanatory code snippet, here we are having an interface IPayment for the payment and it would track the mode of payment happens, and there would be an implementation classes CashPayment, UPIPayment, CardPayment of different mode of payment (Cash, Card and UPI).   There in PaymentProcess class the delegation will be happen through the interface.  Also the method paymentProcess in java and processPayment in Kotlin does the payment process.   

Comparatively with Java and Kotlin, In Kotlin we avoid lot of boilerplate code because of delegation using “by”, else in java we are mandatory to do the implementation of all interface method (see in this example I have used only one interface method, so if it is more than, it would be more override method in PaymentProcess.java class). 

Java Style of Delegate

Kotlin Style of Delegate:

Happy Coding :-)

SAM - Functional Interface

Almost we all of us have seen code like view.setOnClickListener { } or view.setOnLongClickListener { } in Kotlin, and when you click on it to see the source code it will show you the Java OnClickListener interface. Is it a kotlin extension? How can we call our custom listener like this? Let’s see the answer to all of these now.

This syntax is not an extension, it is SAM conversion, SAM stands for Single Abstract Method interface and it’s called also Functional interface which is an interface with only one non-default method (abstract method) and any number of default methods (non-abstract methods), for examples In the JDK we have Runnable class which has only one method called run and in Android SDK we have OnClickListener, OnLongClickListener etc..

How can we create a custom functional interface?

In Kotlin starting from Version 1.4, we can declare a functional interface in Kotlin, we need to use the fun modifier before to the interface keyword.

fun interface MyInterface {
    fun aMethod()
}

Creating a function with the functional interface as param

fun runMyInterface(fi : MyInterface) { ... }

We can pass MyInterface as an anonymous object 

runMyInterface(object : MyInterface {

    override fun aMethod() {

        println("Welcome to www.rajendhiraneasu.in")

    }

})

For functional interfaces, SAM conversion can be achieved through lambda expressions, which makes the code more concise and more readable.  Using lambda expressions can replace manually creating classes that implement functional interfaces. Through SAM conversion, Kotlin can convert any lambda expression whose signature matches the signature of a single abstract method of an interface into an instance of a class that implements the interface.  Please see the above anonymous reference with lambda expressions as below

runMyInterface ({ println("Welcome to www.rajendhiraneasu.in") })

Also, in Kotlin if your last parameter is functional interface you can move your lambda out the brackets ()

runMyInterface {

    println("Welcome to www.rajendhiraneasu.in")

}

Now you can realize that setOnClickListener { } is just because this method takes functional interface which is OnClickListener as a parameter.

Eg: Let’s see with the use case to get the Students Grade of different class levels.

 

// Class Levels
const val PRIMARY_SCHOOL = 1
const val HIGH_SCHOOL = 2

// Data Class holds the student details
data class StudentMarks(val sName: String, val classLevel: Int, val sAvg: Double) {
    fun getStudentGrade(predicate: GradePredicate) = predicate.getGrade((sAvg))
}

// Functional interface (SAM)
fun interface GradePredicate {
    fun getGrade(avg: Double): String
} 

// Grade Predicate definition as lambda 
val primarySchoolGrade = GradePredicate {
    when {
        it > 90 -> "A+"
        it > 80 -> "A"
        it > 65 -> "B"
        it > 45 -> "C"
        it > 34 -> "D"
        else -> "F"
    }
}

val highSchoolGrade = GradePredicate {
    when {
        it > 80 -> "Very Good"
        it > 60 -> "Good"
        it > 34 -> "Fair"
        else -> "Fail"
    }
}

// Lambda expression to select the grade predicate based on the class level 
val gradeSel by lazy { { classLevel: Int -> if (classLevel == PRIMARY_SCHOOL) primarySchoolGrade else highSchoolGrade } }

fun main() {
 val studentList = listOf(
        StudentMarks("Ragavan", PRIMARY_SCHOOL, 92.79),
        StudentMarks("Rajeevan", PRIMARY_SCHOOL, 65.15),
        StudentMarks("Rajeevan", PRIMARY_SCHOOL, 52.23),
        StudentMarks("Arun", HIGH_SCHOOL, 83.21),
        StudentMarks("Harish", HIGH_SCHOOL, 63.56)
    )

    println("Name || Grade")
    studentList.forEach {
        println(
            "${it.sName} ||  ${
                it.getStudentGrade(gradeSel(it.classLevel))
            }"
        )
    }
}

 

Output:

Kotlin Enum Class versus Sealed Class

 Enum Class: 

• It helps to represent a constant set of possible options and values i.e. group of constants, 
• This can be used when a variable can only accept value out of a small set of known values 
• It will increases the compile-time checking and avoid errors from passing in invalid constants. 
• Easy to document which values are valid to use and what it implies.

For example, enumeration defines as like as below 
Flags accounting ledger (DEBIT, CREDIT),
Factory outlet process, (CRUSHING, WINDING, DRYING)
A concrete set of payment mode (UPI, NEFT, IMPS, AEPS) 
Handling API request status (LOADING, SUCCESS, RETRY, FAILURE), etc.

It holds the value which are always specific to the item, this value is mutable and static to the each items, this functionality is often used to attach some constant values on each item through the constructor.

Kotlin enums have methods and their implementations are also specific to the items, when we define them, enum class must have the abstract method, and each item mandatory to override it.

enum class Flavours(val preparationTime: Long) {
    VANILLA(3000L) {
	  // Even I don’t have the discount, I need to be overrided
        override fun discount(amt: Double): String {
            return ""
        }
    },
    CHOCOLATE(5000L) {
        override fun discount(amt: Double): String {
            return "Discount is $amt"
        }
    },
    COOKIES_CREAM(1000L) {
        override fun discount(amt: Double): String {
            return "Discount is $amt"
        }
    };

    var brand: String = "Amul"
    abstract fun discount(amt: Double): String
}

val flav1 = Flavours.COOKIES_CREAM
flav1.brand = "Arun"
val flav2 = Flavours.COOKIES_CREAM

// changing the brand of flav2 but it affects flav1 too, because It is item specific
flav2.brand = "Ponlait"   

val flav3 = Flavours.CHOCOLATE
flav3.brand = "Vadilal"
    
println("${flav1.name} | ${flav1.brand} | ${flav1.discount(7.5)}")
println("${flav2.name} | ${flav2.brand} | ${flav2.discount(2.5)}")
println("${flav3.name} | ${flav3.brand} | ${flav3.discount(3.5)}")

Output:
COOKIES_CREAM | Ponlait | Discount is 7.5
COOKIES_CREAM | Ponlait | Discount is 2.5
CHOCOLATE | Vadilal | Discount is 3.5

Here in the above code snippet we are able to see the brand will be the value which is specific to item (refer the comment and output value highlighted) and discount function is mandatory to override on all the enum values.  

Therefore iterating over enum values is easy, and their serialization/deserialization is simple and efficient (as they are generally represented just by name) and automatically supported by most libraries for serialization (like Gson, Jackson, Kotlin Serialization, etc.). They also have ordinal, and automatically implemented toString, hashCode and equals. 

Sealed Class: 

• It helps to represent constrained hierarchies in which an Object can only be of one of the given types.
• This class can have a specific number of subclasses i.e. restricted subclass hierarchy and each can be handled through multiple instances, 
• It is an abstract classes (no possibility to create an instance)

In case, if we are sharing our code as a compiled jar/aar file to our client with the sealed class in it.  Our client can’t inherit or subclass our sealed classes.

Code Snippet for Reference:

sealed class Flavours(val preparationTime: Long)

class Vanilla(preparationTime: Long) : Flavours(preparationTime) {
    fun getCreamType() {
        println("Inside Vanilla")
    }
}

class Chocolate(preparationTime: Long, val isDiscount: Double) : Flavours(preparationTime) {
    fun knowChocolateType() {
        println("Inside Chocolate")
    }
}

class CookieCream(preparationTime: Long, val isDiscount: Double, val isSmokey: Boolean) : Flavours(preparationTime) {
    fun getCookieType() {
        println("Inside Cookie")
    }
}

fun getMyFlavor(flavorType: Flavours) = when (flavorType) {
    is Chocolate -> {
        flavorType.knowChocolateType()
    }

    is CookieCream -> {
        flavorType.getCookieType()
    }

    is Vanilla -> {
        flavorType.getCreamType()
    }
} 

//While calling this function
getMyFlavor(Chocolate(3000L, 10.50))
getMyFlavor(CookieCream(3000L, 10.50, true))
getMyFlavor(Vanilla(3000L))

Output:
Inside Chocolate
Inside Cookie
Inside Vanilla

Other Uses Case – Code Snippet:

// Handling Ledger balance & API Response along with generics (Covariance)
sealed class LedgerExpenses<out D, out C>
class Expense<D>: LedgerExpenses <D, Nothing>()
class Incomes<C>: LedgerExpenses <Nothing, C>()

// Handling multiple Eye lense
sealed class EyeLense
object IndianLense(val manuf:String, val degree:Double): EyeLense ()
object CollaborationLense: EyeLense (val degree:Double): EyeLense ()
class CustomizedLense(val src: String, val owner:Owner): EyeLense ()

// Handling API Response along with generics (Covariance)
sealed class Response<out R>
class Success<R>(val value: R): Response<R>()
class Failure(val error: Throwable): Response<Nothing>()

Conclusion:

Similarities:

• The set of values for an enum type is also restricted like the sealed class restricting the subclasses.
• Enum and sealed class increase the compile-time checking by restricting the constants or types to be matched at compile-time instead of runtime check.

Differences:

• Each enum constant exists only as a single instance, whereas a subclass of a sealed class can have multiple instances, each with its own state. The state of an object is stored in fields (Variables).

Final Touch…

• Enum handles with concrete set of values, whereas sealed classes uses concrete set of classes. 
• Enum have the methods values() and valueOf, so we can serialize and de-serialize the values. 
• Enums have ordinal and we can hold constant data. 
• Sealed classes can hold instance-specific hierarchy. 
• Sealed class helps to define on our own custom set of objects and we can use them with the multiple instance.
• Sealed class is packed and secured way of creating a hierarchy of defined instance and those definitions are restricted to inherit.  (It violates the Open Close Principle, but this required for such an use cases)

Android registerForActivityResult

As a very fundamentals that we Android developer has learned, and it shows the way for communicating between two components. The provided startActivityForResult(..), onActivityForResut(..) was simple enough to implement for many years, but now we are hard to accept the change, it would be the evolution of language and its features which really helps to do the code in concise way.  So, as per this we see some pitfalls on the earlier approach like

• Difficult to search the caller function in our project, 
• Tedious in getting and handles the results on the fragment, 
• Chance of missing out the results when the component is recreated, 
• Conflicts with the same request code, 
• Handling self-permissions request etc.

Earlier Approach:

Now, with updates to androidx.activity:activity-ktx to 1.2.0. It has deprecated startActivityForResult in favor of registerForActivityResult, notable thing is this implementation avoids and taken care of the above mentioned issues.  Notable thing is here we no need of permission request code, it will be taken care automatically. 

Let’s see the new implementation on implicit and explicit calls with registerForActivityResult, ActivityResultLauncher, ActivityResultContracts, ActivityResultCallback.

The class registerForActivityResult helps to register with ActivityResultContracts (which handles explicit or implicit calls) and other param as the callback action as lambda blocks with (ActivityResultCallback) and returns the launcher (ActivityResultLauncher) object, where we use the launch (...) method with params.

New Approach:

Explicit Calls: We all know calling another activity or component and getting result from there on finish on explicit intent calls.

In the above approach we are creating our explicit launcher which gets data on the resultant callbacks, so this way it would be easy to define our multiple launcher and those are handled independently.  Here the same approach can be used on the Fragment as well.

Implicit Calls: The implicit calls which invoke the system contracts such as take a Picture from camera gallery, accessing contacts, etc.  Please refer the below link for all available ActivityResultContracts

Conclusion:  The approach towards registerForActivityResult is really clean & concise, IMO below are the pros 

• Improved code readability, here we no need to jump between onActivityResult() & startActivityForResult.
• ActivityResultLauncher is returned by registerForActivityResult and this used to launch the components, the input parameter is clearly defined to get the desired results is the callback.
• There is no Boilerplate code for requesting permission from the user.

Code Reference: Gist

Get context in Jetpack Compose

In Android Compose, you can get the context by using the LocalContext, but it should be call'd from the composable function / scope.

val context = LocalContext.current

In the below code snippet we are retrieving the context and show a toast message inside the composable.

@Composable
fun MyToastDisplay(name: String) {
    val ctx = LocalContext.current
    Column(
        Modifier
            .fillMaxHeight()
            .fillMaxWidth(), verticalArrangement = Arrangement.Center,
        horizontalAlignment = Alignment.CenterHorizontally
    ) {
        Text(text = "Hello $name", color = Color.Red,
            modifier = Modifier
                .background(Color.Green)
                .clickable {
                    Toast
                        .makeText(ctx, "Welcome to the Compose World", Toast.LENGTH_SHORT)
                        .show()
                })
    }
}

If you use the LocalContext.current directly inside the clickable function results in the compilation error “@composable invocations can only happen from the context of an @composable function”.

Since the LocalContext.current is composable, you can’t invoke it within the non-composable function.  i.e. clickable function is not a composable function and so can’t accept other composable functions. 

Alternatively, you can get the context outside the clickable function scope and use, as shown in the above code snippet.

Happy Coding :-)