Map and flatmap

Swift gives us map(), compactMap() and flatMap() methods, but although they might sound similar they do very different things. So, in this article we’ll look at map() vs compactMap() vs flatMap() to help you understand what each one does and when it’s useful. The word all three methods share is “map”, which in this context means “transform from one thing to another.” So, the map() method lets us write code to double all the numbers in an array: let numbers = [1, 2, 3, 4, 5] let doubled = numbers.map SAVE 50% To celebrate WWDC23, all our books and bundles are half price, so you can take your Swift knowledge further without spending big! Get the Swift Power Pack to build your iOS career faster, get the Swift Platform Pack to builds apps for macOS, watchOS, and beyond, or get the Swift Plus Pack to learn advanced design patterns, testing skills, and more. compactMap(): transform then unwrap Working with optionals can be annoying, but compactMap() can make life much easier: it performs a transformation (the “map” part of its name), but then unwraps all the optionals and discards any that are nil. So, this line of code does the same string to integer conversion, but results in an array of integers rather than an array of optional integers: let definitelyNumbers = strings.compactMap Mapping and flat mapping: universal friends It’s possible to use map() and flatMap() with many other things, not just arrays and optionals, which is why we have a general name for types that allow ...

• Spark • Spark RDD • Spark DataFrame • Spark SQL Functions • What’s New in Spark 3.0? • Spark Streaming • Apache Spark Interview Questions • PySpark • Pandas • R • R Programming • R Data Frame • R dplyr Tutorial • R Data Frame • R Vector • R dplyr Tutorial • Snowflake • Hive • Int Q • Spark Interview Questions • MongoDB Interview Questions • Machine Learning Interview Questions • More • Python • MongoDB • Apache Kafka • H2O.ai • Apache Hadoop • NumPy • Apache HBase • Apache Cassandra • H2O Sparkling Water • Scala Language What is the difference between Spark map() vs flatMap() is a most asked interview question, if you are taking an interview on Spark (Java/Scala/PySpark), so let’s understand the differences with examples? Regardless of an interview, you have to know the differences as this is also one of the most used Spark transformations. • map() transformation applies a function to each row in a DataFrame/Dataset and returns the new transformed Dataset. • flatMap() transformation flattens the DataFrame/Dataset after applying the function on every element and returns a new transformed Dataset. The returned Dataset will return more rows than the current DataFrame. It is also referred to as a one-to-many transformation function. This is one of the major differences between flatMap() and map() Key points • Both map() & flatMap() returns Dataset (DataFrame=Dataset[Row]). • Both these transformations are narrow meaning they do not result in • flatMap() results in redundant ...

Featured Solutions API Management Manage and secure any API, built and deployed anywhere Integration Connect any system, data, or API to integrate at scale Automation Automate processes and tasks for every team Featured Integration Salesforce Power connected experiences with Salesforce integration SAP Unlock SAP and connect your IT landscape AWS Get the most out of AWS with integration and APIs Instead of returning an array of arrays (as map does when you iterate over the values within an input like [ [1,2], [3,4] ]), flatMap returns a flattened array that looks like this: [1, 2, 3, 4]. flatMap is similar to flatten, but flatten only acts on the values of the arrays, while flatMap can act on values and indices of items in the array. This example returns an array containing each value in order. Though it names the optional index parameter in its anonymous function (value, index) → value, it does not use index as a selector for the output, so it is possible to write the anonymous function using (value) → value. You can also use an anonymous parameter for the value to write the example like this: [ [3,5], [0.9,5.5] ] flatMap $. Note that this example produces the same result as flatten([ [3,5], [0.9,5.5] ]), which uses flatten.

It might not seem like it at first, but a lot of the code that we write as app developers is about transforming data and values into different shapes and forms. For example, we might transform a URL into a piece of data by performing a network request, and then transform that data into an array of models, which we then finally transform into a list-based UI. One way of performing such value transformations is by mapping a collection of values into an array of new values, using a transform. The Swift standard library offers three main APIs for that kind of mapping — map, flatMap and compactMap. Let’s take a look at how they work. Let’s say that we’ve written a function that extracts any #hashtags that appear within a string, by splitting that string up into words, and then filtering those words to only include strings that start with the # character — like this: func hashtags(in string: String) -> [ String] Swift’s various map functions are great to keep in mind when we need to transform sequences, or optionals, into a new form. They’re also general patterns that are not unique to Swift, and learning how each of them works can let us unlock a whole suite of powerful functional programming features that can also result in simpler, more elegant code. Thanks for reading! 🚀 • •

Stream map(Function mapper) The Syntax of the flatMap() is represented as:- Stream flatMap(Function> mapper) Where R is the element type of the new stream. The stream is an interface and T is the type of stream elements and mapper is a stateless function that is applied to each element and the function returns the new stream. map() can be used where we have to map the elements of a particular collection to a certain function, and then we need to return the stream which contains the updated results. Example: Multiplying All the elements of the list by 3 and returning the updated list. flatMap() can be used where we have to flatten or transform out the string, as we cannot flatten our string using map(). Example: Getting the 1st Character of all the String present in a List of Strings and returning the result in form of a stream. Difference Between map() and flatmap() map() flatMap() The function passed to map() operation returns a single value for a single input. The function you pass to flatmap() operation returns an arbitrary number of values as the output. One-to-one mapping occurs in map(). One-to-many mapping occurs in flatMap(). Only perform the mapping. Perform mapping as well as flattening. Produce a stream of value. Produce a stream of stream value. map() is used only for transformation. flatMap() is used both for transformation and mapping. Below are the Java Programs using map() function:

As a seasoned developer, you’re likely already familiar with Spring. But Kotlin can take your developer experience with Spring to the next level! Join the • Add new functionality to existing classes with Kotlin extension functions. • Use Kotlin bean definition DSL. • Better configure your application using lateinit. • Use sequences and default argument values to write more expressive code. By the end of this talk, you’ll have a deeper understanding of the advanced Kotlin techniques that are available to you as a Spring developer, and be able to use them effectively in your projects. map() is an extension function in Kotlin that is defined as: fun Iterable.map(transform: (T) -> R): List As shown above, this function iterates over all elements of an Iterable one by one. During this iteration, it transforms every single element of type T to another element of type R. At the end, it converts all elements of the receiving collection, and we’ll end up with a List. This function is usually useful in one-to-one mapping situations. For example, let’s suppose each order consists of many order lines as its detailed purchase items: class Order(val lines: List) class OrderLine(val name: String, val price: Int) Now, if we have an Order, we can use map() to find the name of each item : val order = Order( listOf(OrderLine("Tomato", 2), OrderLine("Garlic", 3), OrderLine("Chives", 2)) ) val names = order.lines.map When we’re using map(), we just have to write the transform part. Defining ...