Streams, Collectors, and Optionals for Data Processing in Java 8
Pluralsight
Course Summary
This course shows advanced patterns to process data in Java 8 using lambdas, streams, spliterators, optionals, and collectors. It shows how to build your own spliterators to connect streams to non-standard data sources, and to build your own collectors.
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Course Description
The Stream API and the Collectors are two new additions to the JDK in Java 8 that allow the efficient processing of data using the map / filter / reduce approach. It brings many new patterns on the table, and ways to extend them. We show how to connect streams to non-standard sources of data, how to build streams on other streams. The notion of Optional, introduced to handle the reduction of empty streams is also shown on advanced data processing patterns. And at last, collectors are shown, basic patterns first then advanced ones, where we build our own custom collector.
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Course Syllabus
Connecting Streams to Custom Sources: The Spliterator Pattern- 37m 49s
—Introduction to the Course 2m 1s
—What Are You Going to Learn in This Course? 2m 33s
—What Should You Know to Follow This Course? Agenda of This Module 2m 25s
—What Is a Spliterator? 3m 55s
—The Spliterator Interface, What Do We Need to Implement? 3m 18s
—The ArrayListSpliterator: the tryAdvance() Method 2m 5s
—The ArrayListSpliterator: the trySplit() Method 2m 39s
—Introduction to the Live Coding: How to Write a Spliterator 1m 56s
—Using Our Own Spliterator: Setting up the Application 2m 13s
—Using Our Own Spliterator: Writing the Use Case Pattern 2m 11s
—Implementing Our Own Spliterator: The First Methods 3m 4s
—Implementing Our Own Spliterator: The tryAdvance() Method 3m 37s
—Implementing the tryAdvance() Method: Setting the Returned Object 1m 50s
—Implementing Our Own Spliterator: Running the Example 1m 10s
—Live Coding Session Wrap-up 1m 12s
—Module Wrap-up 1m 32sAdvanced Java 8 Stream Patterns: FlatMap, Streams of Numbers- 55m 53s
—Introduction and Agenda of the module 1m 53s
—Concatenating Streams: The Stream.concat Pattern 3m 5s
—Merging Streams: The Stream.of and flatMap Pattern 2m 50s
—How Does the flatMap Pattern Work: Examples 1m 43s
—Splitting a Text into Words Using the flatMap Pattern 3m 25s
—Splitting a Text into Words: Putting the Result in a Set 1m 33s
—State of a Stream: The Stream.characteristics() Method 2m 51s
—What Are the Eight Characteristics of a Stream? 2m 10s
—How Does the Steam Methods Modify the State of a Stream 2m 30s
—State of a Stream: A First Example 2m 3s
—How to Use the Stream.sorted() Method with a Comparator 0m 54s
—Live Coding Session: Presentation of the Example 1m 48s
—Live Coding: Merging Streams Using Stream.of and flatMap 3m 20s
—Live Coding: Splitting a Large Text in Words Using flatMap 3m 38s
—Live Coding Session Summary 0m 51s
—Streams of Numbers: Converting from a Stream of Objects 1m 50s
—Optimizing the Conversion from a Stream of Objects 1m 27s
—Patterns to Build Streams of Numbers 1m 41s
—Specialized Methods: min(), max() and summaryStatistics() 2m 49s
—Live Coding Session: Introduction of the Scrabble Example 2m 1s
—Live Coding: Computing the Score of a Word in Scrabble 3m 25s
—Live Coding: Getting the Word with the Best Score 3m 18s
—Live Coding: Computing Statistics on the Words of Shakespeare 1m 56s
—Live Coding Session and Module Wrap-up 2m 41sParallel Data Processing Pipelines Using Java 8 Streams- 44m 10sBuilding Errorless Processing Pipelines with Optionals- 41m 45sCollecting Data in Complex Containers Using Collectors- 45m 16sBuilding Custom Collectors for Advanced Data Processing- 44m 40s
