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Mastering Flow Operators in Kotlin: A Comprehensive Guide

Arsham Jafari
22 min readSep 16, 2023

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Kotlin’s Flow is a powerful asynchronous programming construct that allows you to work with sequences of values emitted over time. To harness the full potential of Flow, you need to be well-versed in its operators. In this article, we’ll explore essential Flow operators in Kotlin that are indispensable for reactive and asynchronous programming.

Understanding Flow

Before diving into Flow operators, let’s briefly understand what Flow is. Flow is part of Kotlin’s coroutines library, designed to handle asynchronous or stream-like data. It’s particularly useful when dealing with asynchronous operations, such as network requests, database queries, or UI updates.

In Flow, data is emitted asynchronously and can be processed in a non-blocking, sequential manner. This makes it a robust choice for handling data streams, making your code more responsive and efficient.

1. map - Transforming Values

The map operator is a fundamental operation in Kotlin Flow (and in many other reactive programming libraries) that allows you to transform the elements emitted by a Flow into new elements of a potentially different type. It operates on each item emitted by the source Flow and applies a given transformation function to each item, producing a new Flow with the transformed items. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the map operator
  2. Transformation Function: You provide a lambda or function as an argument to the map operator. This function takes an element from the source Flow as input and returns a new element of a potentially different type.
  3. Resulting Flow: The map operator returns a new Flow that emits the transformed elements produced by applying the transformation function to each element emitted by the source Flow.

Here’s a simple example in code:

import kotlinx.coroutines.flow.*

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)

    val transformedFlow: Flow<String> = sourceFlow.map { number ->
        "Transformed: $number"
    }

    transformedFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the map operator to sourceFlow, and for each integer emitted by sourceFlow, we transform it into a string by adding "Transformed: " before the number. The resulting Flow, transformedFlow, emits these transformed strings.

When we collect and print the values emitted by transformedFlow, we get the following output:

Transformed: 1
Transformed: 2
Transformed: 3
Transformed: 4
Transformed: 5

So, the map operator allows you to apply a transformation to each item in a Flow, making it a powerful tool for data manipulation and processing within Kotlin Flow.

2. filter - Filtering Values

The filter operator allows you to selectively include or exclude elements from a Flow based on a given predicate function. It operates on each item emitted by the source Flow and includes only the items that satisfy the specified condition. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the filter operator.
  2. Predicate Function: You provide a lambda or function as an argument to the filter operator. This function takes an element from the source Flow as input and returns a Boolean value. Elements for which the predicate function returns true are included in the resulting Flow, while elements for which the predicate function returns false are excluded.
  3. Resulting Flow: The filter operator returns a new Flow that emits only those elements from the source Flow that satisfy the condition specified by the predicate function.

Here’s a simple example in code:

import kotlinx.coroutines.flow.*

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)

    val filteredFlow: Flow<Int> = sourceFlow.filter { number ->
        number % 2 == 0 // Include only even numbers
    }

    filteredFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the filter operator to sourceFlow, and for each integer emitted by sourceFlow, we check if it's an even number (i.e., the condition number % 2 == 0). Only the even numbers satisfy this condition, so they are included in the resulting Flow, filteredFlow.

When we collect and print the values emitted by filteredFlow, we get the following output:

2
4

So, the filter operator allows you to selectively include or exclude elements from a Flow based on a condition, which is useful for filtering and processing data streams in Kotlin Flow

3. transform - Custom Flow Transformations

The transform operator is a versatile and powerful operator in Kotlin Flow that allows you to apply a more complex transformation to each element emitted by a source Flow. It differs from the map operator in that it allows you to emit zero or more elements for each input element and even switch to a different Flow for each input element. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the transform operator.
  2. Transformation Function: You provide a lambda or function as an argument to the transform operator. This function takes an element from the source Flow as input and returns a new Flow or a sequence of elements. This new Flow or sequence represents the transformed output for the given input element.
  3. Resulting Flow: The transform operator returns a new Flow that emits the elements produced by applying the transformation function to each element emitted by the source Flow. These elements can be emitted in any order, and you can even merge multiple Flows or sequences into a single Flow.

Here’s an example in code:

import kotlinx.coroutines.flow.*
import kotlinx.coroutines.delay
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)

    val transformedFlow: Flow<String> = sourceFlow.transform { number ->
        // Simulate some asynchronous processing with a delay
        delay(100)
        emit("Transformed: $number")
        emit("Additional: $number")
    }

    transformedFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the transform operator to sourceFlow, and for each integer emitted by sourceFlow, we perform some asynchronous processing (simulated with a delay) and emit two transformed strings for each input integer.

When we collect and print the values emitted by transformedFlow, we get the following output:

Transformed: 1
Additional: 1
Transformed: 2
Additional: 2
Transformed: 3
Additional: 3
Transformed: 4
Additional: 4
Transformed: 5
Additional: 5

As you can see, the transform operator is useful when you need to perform asynchronous operations, emit multiple items, or even switch to different Flows based on each input element. It provides great flexibility for transforming and processing data streams in Kotlin Flow.

4. flatMapConcat - Flattening Nested Flows

The flatMapConcat operator is an operator in Kotlin Flow that is used for flattening and merging multiple Flows (or other asynchronous data sources) into a single Flow, while preserving the order of emissions. It applies a transformation function to each item emitted by the source Flow and expects this function to return another Flow. The elements emitted by these nested Flows are then emitted as a single continuous stream in the order in which they were emitted by the nested Flows. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the flatMapConcat operator.
  2. Transformation Function: You provide a lambda or function as an argument to the flatMapConcat operator. This function takes an element from the source Flow as input and returns another Flow or asynchronous data source.
  3. Resulting Flow: The flatMapConcat operator returns a new Flow that emits all the elements from the nested Flows produced by applying the transformation function to each element from the source Flow. The order of these emissions is preserved, meaning elements from the first nested Flow are emitted before elements from the second nested Flow, and so on.

Here’s an example in code:

import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3)

    val transformedFlow: Flow<String> = sourceFlow.flatMapConcat { number ->
        flow {
            emit("Start $number")
            delay(100)
            emit("End $number")
        }
    }

    transformedFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the flatMapConcat operator to sourceFlow, and for each integer emitted by sourceFlow, we generate a new Flow using the flow builder. In this nested Flow, we emit two strings with a delay in between.

When we collect and print the values emitted by transformedFlow, we get the following output:

Start 1
End 1
Start 2
End 2
Start 3
End 3

As you can see, the order of emissions is preserved, and the elements from the nested Flows are concatenated together into a single continuous stream.

The flatMapConcat operator is useful when you need to perform asynchronous operations for each item in a Flow and ensure that the order of emissions is maintained.

5. flatMapMerge - Merging Flattened Flows

The flatMapMerge operator is an operator in Kotlin Flow that is used for flattening and merging multiple Flows (or other asynchronous data sources) into a single Flow, without necessarily preserving the order of emissions. It applies a transformation function to each item emitted by the source Flow and expects this function to return another Flow. The elements emitted by these nested Flows are merged into a single stream, and their order is not guaranteed to be the same as the order in which they were emitted by the nested Flows. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the flatMapMerge operator.
  2. Transformation Function: You provide a lambda or function as an argument to the flatMapMerge operator. This function takes an element from the source Flow as input and returns another Flow or asynchronous data source.
  3. Resulting Flow: The flatMapMerge operator returns a new Flow that emits all the elements from the nested Flows produced by applying the transformation function to each element from the source Flow. The order of these emissions is not guaranteed to be the same as the order of the nested Flows; elements from different nested Flows can interleave.

Here’s an example in code:

import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3)

    val transformedFlow: Flow<String> = sourceFlow.flatMapMerge { number ->
        flow {
            emit("Start $number")
            delay(100)
            emit("End $number")
        }
    }

    transformedFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the flatMapMerge operator to sourceFlow, and for each integer emitted by sourceFlow, we generate a new Flow using the flow builder. In this nested Flow, we emit two strings with a delay in between.

When we collect and print the values emitted by transformedFlow, we get output like the following:

Start 1
Start 2
Start 3
End 1
End 2
End 3

As you can see, the order of emissions is not guaranteed to match the order of the nested Flows. Elements from different nested Flows can interleave, depending on their completion times.

The flatMapMerge operator is useful when you want to concurrently process items in a Flow and merge the results into a single stream without worrying about the order of emissions. This can be beneficial for scenarios where you want to maximize concurrency and don't rely on the specific order of results.

6. flatMapLatest - Handling the Latest Values

The flatMapLatest operator is an operator in Kotlin Flow that is used for flattening and merging multiple Flows (or other asynchronous data sources) into a single Flow, while ensuring that only the latest emitted item from the source Flow is considered for the transformation. It applies a transformation function to each item emitted by the source Flow, and it expects this function to return another Flow. When a new item is emitted by the source Flow, any previously started transformation is canceled, and only the transformation for the latest item is considered. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the flatMapLatest operator.
  2. Transformation Function: You provide a lambda or function as an argument to the flatMapLatest operator. This function takes an element from the source Flow as input and returns another Flow or asynchronous data source.
  3. Cancellation of Previous Transformations: When a new item is emitted by the source Flow, any ongoing transformations for previous items are canceled. Only the transformation for the latest item is considered.
  4. Resulting Flow: The flatMapLatest operator returns a new Flow that emits all the elements from the nested Flows produced by applying the transformation function to each element from the source Flow. Only the elements from the latest transformation are emitted.

Here’s an example in code:

import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3)

    val transformedFlow: Flow<String> = sourceFlow.flatMapLatest { number ->
        flow {
            emit("Start $number")
            delay(100)
            emit("End $number")
        }
    }

    transformedFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the flatMapLatest operator to sourceFlow, and for each integer emitted by sourceFlow, we generate a new Flow using the flow builder. In this nested Flow, we emit two strings with a delay in between.

However, the key difference here is that when a new item is emitted by sourceFlow, it cancels the ongoing transformation for the previous item. As a result, only the transformation for the latest item is considered.

When we collect and print the values emitted by transformedFlow, we get output like the following:

Start 1
Start 2
Start 3
End 3

As you can see, the transformations for items 1 and 2 are canceled when item 3 is emitted by the source Flow. Therefore, only the transformation for item 3 is considered and emitted.

The flatMapLatest operator is useful when you want to work with the latest data and cancel any ongoing work related to previous data when new data arrives. This can be especially helpful in scenarios where you want to keep your processing in sync with the most recent updates.

7. take - Limiting Flow Emissions

The take operator is to limit the number of elements emitted by a Flow. It takes an integer parameter specifying the maximum number of items you want to receive from the source Flow. Once the specified number of items is emitted, the Flow is completed, and no more items are emitted. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the take operator.
  2. Parameter: You provide an integer as an argument to the take operator, specifying the maximum number of items you want to receive from the source Flow.
  3. Resulting Flow: The take operator returns a new Flow that emits only the first n items (where n is the integer parameter) from the source Flow. Once n items have been emitted, the Flow completes, and no more items are emitted.

Here’s an example in code:

import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)

    val takenFlow: Flow<Int> = sourceFlow.take(3)

    takenFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the take operator to sourceFlow with a parameter of 3, indicating that we want to receive only the first three items.

When we collect and print the values emitted by takenFlow, we get the following output:

1
2
3

The take operator is useful when you want to limit the amount of data you receive from a Flow, especially in scenarios where you are only interested in the initial items or need to prevent excessive data processing or resource consumption.

8. drop - Skipping Initial Flow Items

The drop operator is an operator in Kotlin Flow that allows you to skip a specified number of elements at the beginning of a Flow and emit the remaining elements. It is useful when you want to ignore or "drop" a certain number of items from the start of a Flow before processing the remaining elements. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the drop operator.
  2. Parameter: You provide an integer as an argument to the drop operator, specifying the number of items you want to skip from the beginning of the source Flow.
  3. Resulting Flow: The drop operator returns a new Flow that emits all elements from the source Flow except for the first n items (where n is the integer parameter).

Here’s an example in code:

import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)

    val droppedFlow: Flow<Int> = sourceFlow.drop(2)

    droppedFlow.collect { value ->
        println(value)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the drop operator to sourceFlow with a parameter of 2, indicating that we want to skip the first two items.

When we collect and print the values emitted by droppedFlow, we get the following output:

3
4
5

As you can see, the drop operator skips the first two items from the source Flow and emits the remaining items.

The drop operator is useful in situations where you have data at the beginning of a Flow that you want to ignore or don't need for your current processing logic. It allows you to efficiently skip over those items and work with the elements that follow.

9. collect - Consuming Flow Items

The collect operator is not an operator; rather, it's a terminal operator or a terminal function used to collect and consume elements emitted by a Flow. When you apply the collect function to a Flow, you can specify a lambda function to process each emitted element. Here's a detailed explanation:

  1. Source Flow: The Flow from which you want to collect and process elements.
  2. Lambda Function: You provide a lambda function as an argument to the collect function. This function takes each emitted element from the source Flow as its parameter and specifies what to do with that element. This is where you define your custom logic to handle each element.
  3. Consuming Elements: When the collect function is called on the source Flow, it initiates the collection process. For each element emitted by the source Flow, the lambda function specified in collect is invoked. This allows you to perform actions like printing elements, accumulating values, or processing data in a customized way.

Here’s an example in code:

import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)

    sourceFlow.collect { value ->
        // Process each element (in this case, print the element)
        println("Received: $value")
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the collect function to sourceFlow, and for each integer emitted by sourceFlow, the lambda function inside collect is executed, printing the element to the console.

When we run this code, we get the following output:

Received: 1
Received: 2
Received: 3
Received: 4
Received: 5

the collect function is a terminal operation that allows you to consume and process elements emitted by a Flow. It's a key component for working with Flow data because it enables you to interact with and handle the values emitted by the Flow in a structured way.

10. onEach - Applying Actions to Flow Items

The onEach operator in Kotlin Flow is used to perform a side effect for each element emitted by the Flow while allowing the elements to continue flowing through the Flow unchanged. It doesn't modify the elements but enables you to perform some action, such as logging, debugging, or monitoring, as the elements pass through the Flow. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the onEach operator.
  2. Lambda Function: You provide a lambda function as an argument to the onEach operator. This function takes each element emitted by the source Flow as its parameter and specifies what side effect to perform. The lambda function returns Unit, indicating that it doesn't modify the elements.
  3. Resulting Flow: The onEach operator returns a new Flow that is identical to the source Flow in terms of the elements it emits. However, for each element that passes through, the lambda function specified in onEach is executed.

Here’s an example in code:

import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)

    val modifiedFlow: Flow<Int> = sourceFlow
        .onEach { value ->
            // Perform a side effect, such as printing the element
            println("Element: $value")
        }

    modifiedFlow.collect { value ->
        // Process the elements (in this case, just collecting them)
    }
}

In this example, we have a source Flow of integers (sourceFlow). We apply the onEach operator to sourceFlow and provide a lambda function that prints each element as it passes through the Flow.

When we run this code, we get the following output:

Element: 1
Element: 2
Element: 3
Element: 4
Element: 5

The onEach operator allows you to observe or log the elements without modifying them. It is useful for debugging, logging, monitoring, or any other scenarios where you need to perform side effects while processing a Flow. The elements continue to flow through the Flow unchanged, making it a non-intrusive way to inspect the data within a Flow.

11. catch - Handling Errors in Flows

The catch operator is for handling exceptions that might occur during the execution of a Flow and allow you to gracefully recover from those exceptions. It is a mechanism for error handling within Flow streams. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the catch operator.
  2. Lambda Function: You provide a lambda function as an argument to the catch operator. This function takes an exception as its parameter and specifies what action to take when an exception is thrown during the execution of the source Flow.
  3. Resulting Flow: The catch operator returns a new Flow that behaves similarly to the source Flow. However, when an exception occurs within the source Flow, it's caught by the catch operator, and the specified lambda function is invoked to handle the exception. You can decide what to emit in case of an exception or how to recover from it.

Here’s an example in code:

import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flow {
        emit(1)
        emit(2)
        throw RuntimeException("Error occurred!")
        emit(3)
        emit(4)
    }

    val recoveredFlow: Flow<Int> = sourceFlow.catch { exception ->
        // Handle the exception gracefully
        println("Caught exception: ${exception.message}")
        emit(5) // Emit a recovery value
    }

    recoveredFlow.collect { value ->
        println("Received: $value")
    }
}

In this example, we have a source Flow (sourceFlow) that emits integers but throws an exception in the middle of the emission sequence.

We apply the catch operator to sourceFlow and provide a lambda function to handle exceptions. When the exception occurs in sourceFlow, the lambda function is called, and we print a message indicating that an exception was caught. Additionally, we emit a recovery value (5) to continue the Flow.

When we run this code, we get the following output:

Received: 1
Received: 2
Caught exception: Error occurred!
Received: 5

In this example, it emits a recovery value (5) after catching the exception, but you can customize the behavior according to your specific error handling requirements.

12. flowOn - Managing Flow Execution Context

The flowOn operator is used to change the context or thread in which the emissions of a Flow are performed. It's often used to switch the execution context of a Flow, especially when you want to offload heavy or blocking work to a different thread or dispatcher. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the flowOn operator.
  2. Coroutine Dispatcher: You provide a coroutine dispatcher as an argument to the flowOn operator. The dispatcher represents the context in which the downstream operations, such as collect, will execute.
  3. Resulting Flow: The flowOn operator returns a new Flow with a different execution context. When you apply flowOn, it only affects the context in which the upstream operations, such as map, filter, or other Flow operators, are executed. It does not affect the context of downstream collectors, like collect.

Here’s an example in code:

import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking
import kotlinx.coroutines.withContext

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flow {
        for (i in 1..5) {
            println("Emitting $i in thread ${Thread.currentThread().name}")
            emit(i)
            delay(100)
        }
    }

    val transformedFlow: Flow<String> = sourceFlow
        .map { value ->
            "Transformed $value in thread ${Thread.currentThread().name}"
        }
        .flowOn(Dispatchers.IO) // Switch execution context to IO dispatcher

    withContext(Dispatchers.Default) {
        // Collect in a different context (Default dispatcher)
        transformedFlow.collect { value ->
            println("Received: $value in thread ${Thread.currentThread().name}")
        }
    }
}

In this example, we have a source Flow that emits integers with some delays between emissions. We apply the map operator to transform each emitted integer into a string. However, we use flowOn to switch the execution context to the IO dispatcher just before the map operator. This means that the map operation will run in the IO dispatcher, potentially offloading any blocking or time-consuming work.

The collect operation, which is a downstream operation, runs in a different context (the Default dispatcher), as indicated by withContext. This demonstrates that the flowOn operator affects only the upstream operators and not the downstream collectors.

When we run this code, we can see that the map operation runs in the IO dispatcher while the collect operation runs in the Default dispatcher:

Emitting 1 in thread DefaultDispatcher-worker-2
Received: Transformed 1 in thread DefaultDispatcher-worker-2 in thread DefaultDispatcher-worker-1
Emitting 2 in thread DefaultDispatcher-worker-2
Received: Transformed 2 in thread DefaultDispatcher-worker-2 in thread DefaultDispatcher-worker-2
Emitting 3 in thread DefaultDispatcher-worker-2
Received: Transformed 3 in thread DefaultDispatcher-worker-2 in thread DefaultDispatcher-worker-2
Emitting 4 in thread DefaultDispatcher-worker-2
Received: Transformed 4 in thread DefaultDispatcher-worker-2 in thread DefaultDispatcher-worker-1
Emitting 5 in thread DefaultDispatcher-worker-1
Received: Transformed 5 in thread DefaultDispatcher-worker-1 in thread DefaultDispatcher-worker-1

As you can see, the map operation runs in a different thread (IO dispatcher), as specified by flowOn, while the collect operation runs in the Default dispatcher. This separation of contexts can help improve concurrency and resource utilization in your Flow-based code.

13. zip - Pairing and Combining Flow Items

The zip operator in Kotlin Flow is used to combine elements from multiple Flows into pairs or tuples. It takes two or more Flow instances as arguments and emits elements as pairs, where each pair contains one element from each of the input Flows. The zip operator aligns elements from the input Flows based on their emission order, and it emits a new element only when all input Flows have emitted an element. Here's a more detailed explanation:

  1. Input Flows: You provide two or more Flow instances as arguments to the zip operator. These are the Flows whose elements you want to combine into pairs.
  2. Resulting Flow: The zip operator returns a new Flow that emits elements as pairs (or tuples) where each element in the pair corresponds to the elements emitted by the input Flows. The resulting Flow emits pairs only when all input Flows have emitted elements.

Here’s an example in code:

import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val numbersFlow: Flow<Int> = flowOf(1, 2, 3, 4, 5)
    val lettersFlow: Flow<String> = flowOf("A", "B", "C", "D", "E")

    val zippedFlow: Flow<Pair<Int, String>> = numbersFlow.zip(lettersFlow) { number, letter ->
        Pair(number, letter)
    }

    zippedFlow.collect { pair ->
        println("Received: ${pair.first} - ${pair.second}")
    }
}

In this example, we have two input Flows, numbersFlow and lettersFlow, which emit integers and strings, respectively. We apply the zip operator to numbersFlow and pass lettersFlow as the second argument. The lambda function inside zip combines elements from both Flows into pairs of integers and strings.

When we collect and print the values emitted by zippedFlow, we get the following output:

Received: 1 - A
Received: 2 - B
Received: 3 - C
Received: 4 - D
Received: 5 - E

As you can see, the zip operator aligns elements from both input Flows based on their emission order and emits them as pairs. It ensures that a new pair is emitted only when both input Flows have emitted an element.

The zip operator is useful when you want to combine and process elements from multiple Flows in a coordinated manner, such as when you need to correlate data from different sources or streams.

14. conflate - Efficiently Handling Flow Data

The conflate operator is used to control the emission of items when the downstream (the consumer of the flow) is slower than the upstream (the producer of the flow). It is a flow operator that helps in managing backpressure by dropping intermediate emitted values when the downstream cannot keep up with the rate of emissions from the upstream.

Here’s how the conflate operator works:

  1. When you apply conflate to a Flow, it allows the downstream to consume items at its own pace.
  2. When the upstream emits a new item while the previous item is still being processed or hasn’t been collected by the downstream, conflate will drop the intermediate item and only keep the latest emitted value.

Here’s an example:

import kotlinx.coroutines.delay
import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val flow = flow {
        emit(1)
        delay(100) // Simulate some processing time
        emit(2)
        delay(100) // Simulate some processing time
        emit(3)
    }

    flow
        .conflate() // Use the conflate operator
        .collect { value ->
            delay(200) // Simulate slow processing
            println(value)
        }
}

In this example, the upstream flow emits values 1, 2, and 3 with delays in between. However, the downstream (consumer) is slow in processing the items due to the delay(200) in the collect block. Without conflate, the intermediate values would queue up, potentially causing a memory and performance issue.

With conflate, only the latest emitted value is preserved, and intermediate values are dropped. So, the output of this code will be:

1
3

The value 2 is dropped because it was emitted before the downstream could process it.

In summary, the conflate operator is useful when you want to handle backpressure by ensuring that the downstream always processes the latest emitted value and drops intermediate values when it cannot keep up..

15. distinctUntilChanged - Filtering Consecutive Duplicate Values in Flow

The distinctUntilChanged operator in Kotlin Flow is used to filter out consecutive duplicate elements emitted by the source Flow. It ensures that only distinct, non-consecutive elements are emitted downstream. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the distinctUntilChanged operator.
  2. Resulting Flow: The distinctUntilChanged operator returns a new Flow that emits elements from the source Flow only if they are different from the previously emitted element. If the source Flow emits consecutive elements with the same value, the operator filters out the duplicates, emitting only the first occurrence of each distinct value.

Here’s an example in code:

import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flow {
        emit(1)
        emit(2)
        emit(2) // Duplicate
        emit(3)
        emit(3) // Duplicate
        emit(3) // Duplicate
        emit(4)
    }

    val distinctFlow: Flow<Int> = sourceFlow.distinctUntilChanged()

    distinctFlow.collect { value ->
        println("Received: $value")
    }
}

In this example, we have a source Flow (sourceFlow) that emits integers. We apply the distinctUntilChanged operator to sourceFlow.

When we run this code, we get the following output:

Received: 1
Received: 2
Received: 3
Received: 4

As you can see, the distinctUntilChanged operator filters out consecutive duplicate values (2 and 3) and only emits the first occurrence of each distinct value. This ensures that only distinct, non-consecutive elements are emitted downstream.

The distinctUntilChanged operator is useful when you want to ensure that consecutive duplicate values are not processed or acted upon in your Flow-based code, especially in scenarios where you want to process or display only changes in data.

16. retry - Handling Failures and Retrying in Flows

The retry operator in Kotlin Flow is used to handle errors by allowing the Flow to be resubscribed and retried when an exception occurs during its execution. It provides a way to recover from errors and continue processing the Flow. Here's a more detailed explanation:

  1. Source Flow: The Flow on which you apply the retry operator.
  2. Parameter: You provide an integer or a lambda function as an argument to the retry operator, specifying how many times the Flow should be retried or under what conditions the retry should occur.
  3. Resulting Flow: The retry operator returns a new Flow with the retry behavior applied. When an exception occurs during the execution of the source Flow, the retry operator can re-subscribe to the source Flow and retry the operation, either a fixed number of times or based on custom logic.

Here’s an example in code:

import kotlinx.coroutines.flow.*
import kotlinx.coroutines.runBlocking

fun main() = runBlocking {
    val sourceFlow: Flow<Int> = flow {
        emit(1)
        emit(2)
        throw RuntimeException("Error occurred!")
        emit(3)
    }

    val retriedFlow: Flow<Int> = sourceFlow.retry(2) // Retry up to 2 times

    retriedFlow.collect { value ->
        println("Received: $value")
    }
}

In this example, we have a source Flow (sourceFlow) that emits integers but intentionally throws an exception in the middle of the emission sequence. We apply the retry operator to sourceFlow with a parameter of 2, indicating that we want to retry the Flow up to two times in case of an exception.

When we run this code, we get the following output:

Received: 1
Received: 2
Received: 1
Received: 2
Received: 1
Received: 2

As you can see, when the exception occurs in sourceFlow, the retry operator re-subscribes to the source Flow and retries the operation up to two times, as specified. In this case, the first retry attempts to restart the Flow, and the second retry does the same.

You can also use a lambda function as an argument to the retry operator to implement custom retry logic based on the exception type or other conditions. For example:

val retriedFlow: Flow<Int> = sourceFlow.retry { cause, _ ->
    // Retry only if the cause of the exception is of a specific type
    cause is MyCustomException
}

This allows you to implement more fine-grained control over when to retry the Flow based on specific error conditions.

The retry operator is useful for handling transient errors in your Flow-based code and providing a way to recover and continue processing when exceptions occur.

Wrapping Up

Flow operators in Kotlin are essential for working with asynchronous data streams efficiently. By mastering these operators, you can handle complex data flows with ease, making your code more responsive and maintainable. Whether you’re filtering, transforming, or combining data, Flow operators empower you to tackle asynchronous programming challenges effectively.

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Arsham Jafari

Written by Arsham Jafari

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