Java 9 New Features | Java Tutorial | What’s New in Java 9 | Java 9 Features With Examples | Edureka

Aug 15, 20183 likes361 views

The document discusses several new features in Java 9 including REPL JShell, collection factory methods that provide immutable collections, a new HTTP/2 client API, modularity with the Jigsaw project, and other minor features like Stream API improvements, multi-release JARs, and improved Javadoc. It provides details on each of these features and their benefits.

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Agenda
❑ What Is Artificial Intelligence ?
❑ What Is Machine Learning ?
❑ Limitations Of Machine Learning
❑ Deep Learning To The Rescue
❑ What Is Deep Learning ?
❑ Deep Learning Applications

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Most Important Features of Java 9
1. REPL JShell
2. Collection Factory Methods
3. HTTP/2 client
4. Jigsaw Project - Modularity
5. Other Features

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REPL JShell

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REPL JShell
Read
Print
Eval

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Collection Factory Methods

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Immutable Set using Set.of()

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Immutable Map using Map.of()

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Immutable List using List.of()

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HTTP/2 Client

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▪ The new HTTP client API provides the following:
HTTP/2 Client
A simple and concise API to deal with most HTTP requests

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Why Jigsaw Project?

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Project Jigsaw - Modularity

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Benefits of Modularity
Strong encapsulation
The modules can access only those parts of the module that have been made available for use

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Jigsaw - JDK Enhancement Proposals

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Jigsaw - JDK Enhancement Proposals
JEP 200 - Modular JDK

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Jigsaw - JDK Enhancement Proposals
JEP 201 - Modular Source Code
JEP 200 - Modular JDK

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Jigsaw - JDK Enhancement Proposals
JEP 201 - Modular Source Code
JEP 220 - Modular Runtime Images
JEP 200 - Modular JDK

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Other Java 9 Features

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Stream API Improvements

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Stream API Improvements
▪ There are few new methods added to the Stream Interface, which are:-
dropWhile()

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Multi-release JARs

Copyright © 2017, edureka and/or its affiliates. All rights reserved.
Improved Javadoc

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Presented by: Anish Kumar LinkedIn: https://www.linkedin.com/in/anishkumar/ This lightning talk dives into real-world GenAI projects that scaled from prototype to production using Databricks’ fully managed tools. Facing cost and time constraints, we leveraged four key Databricks features—Workflows, Model Serving, Serverless Compute, and Notebooks—to build an AI inference pipeline processing millions of documents (text and audiobooks). This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights. Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.

Precisely Demo Showcase: Powering ServiceNow Discovery with Precisely Ironstr...Precisely