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![]() reduce is called on that Dataset to find the largest word count. This first maps a line to an integer value, creating a new Dataset. reduce (( a, b ) => if ( a > b ) a else b ) res4 : Int = 15 Let’s say we want to find the line with the most words: count () # How many lines contain "Spark"?ĭataset actions and transformations can be used for more complex computations. We can chain together transformations and actions: > textFile. We call filter to return a new DataFrame with a subset of the lines in the file. Now let’s transform this DataFrame to a new one. count () # Number of rows in this DataFrameġ26 > textFile. For more details, please read the API doc. You can get values from DataFrame directly, by calling some actions, or transform the DataFrame to get a new one. Let’s make a new DataFrame from the text of the README file in the Spark source directory: > textFile = spark. As a result, all Datasets in Python are Dataset, and we call it DataFrame to be consistent with the data frame concept in Pandas and R. Due to Python’s dynamic nature, we don’t need the Dataset to be strongly-typed in Python. Datasets can be created from Hadoop InputFormats (such as HDFS files) or by transforming other Datasets. Spark’s primary abstraction is a distributed collection of items called a Dataset. count () // How many lines contain "Spark"? res3 : Long = 15 We can chain together transformations and actions: scala > textFile. contains ( "Spark" )) linesWithSpark : .Dataset = We call filter to return a new Dataset with a subset of the items in the file. Now let’s transform this Dataset into a new one. first () // First item in this Dataset res1 : String = # Apache Spark count () // Number of items in this Dataset res0 : Long = 126 // May be different from yours as README.md will change over time, similar to other outputs scala > textFile. You can get values from Dataset directly, by calling some actions, or transform the Dataset to get a new one. textFile ( "README.md" ) textFile : .Dataset = Let’s make a new Dataset from the text of the README file in the Spark source directory: scala > val textFile = spark. Start it by running the following in the Spark directory: It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) Spark’s shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. Interactive Analysis with the Spark Shell Basics See the SQL programming guide to get more information about Dataset. However, we highly recommend you to switch to use Dataset, which has better performance than RDD. The RDD interface is still supported, and you can get a more detailed reference at the RDD programming guide. After Spark 2.0, RDDs are replaced by Dataset, which is strongly-typed like an RDD, but with richer optimizations under the hood. ![]() Note that, before Spark 2.0, the main programming interface of Spark was the Resilient Distributed Dataset (RDD). ![]() You can download a package for any version of Hadoop. To follow along with this guide, first, download a packaged release of Spark from the Then show how to write applications in Java, Scala, and Python. We will first introduce the API through Spark’s This tutorial provides a quick introduction to using Spark. Interactive Analysis with the Spark Shell. Install the Split APK installer app and then install this hack APK using that. ![]() ![]()
![]() Simply drag & drop your files into the Air Doc App (get it for free in the Mac App Store) and they will open up immediately on your iPad or iPhone.Īccess your files from any operating system (OSX or Windows) using your browser. Works for psd (Photoshop), ai (Illustrator), jpeg, png, pdf and even HTML files. Just drag a graphic file into the Live Preview area on the Mac - every time you save your graphic, the preview on the iPad or iPhone will be updated immediately. New on iOS: share files between iPhones/iPads using an on-the-go bluetooth connection. Reply to emails on accounts configured only on your iOS device Control your Apple TV (2nd or 3rd generation) Use a single keyboard with all your Apple devices Features Connects automatically as you start typing Disconnects when idle or sent to the background (optional) Command-Tab in and out of Type2Phone Customizable keyboard shortcuts for all your devices Copy-paste text from Mac to iPhone, iPad or Apple TV AppleScript to automate text input Support for Mac OS X dictation (10.8 or later) Support for TextExpander on Mac Support for iOS Voice OverĪir Doc is the ultimate solution for transferring documents, movies, songs, etc to your iPad/iPhone. from your Mac to your mobile device Type texts, tweets, status updates, etc. ![]() Use your Mac's full-sized keyboard with your iPhone and iPad Paste passwords, addresses, etc. It works just like a Bluetooth keyboard: no additional app is needed. Type2Phone lets you use your Mac to type on your iPhone, iPad, or Apple TV (2nd or 3rd generation). Select from various keyboard layouts, type straight on your iOS device or paste the text into your portable device, assign keyboard shortcuts to your devices, automatically reconnect upon launch, etc. Type messages for your iPhone, iPad, or Apple TV using your Mac's keyboard. |
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