A presentation I did in IL Java User Group about Java 8, DateTime new features. JSR-310
I wanted it to be informative and a good resource and starting point for this cool feature.
This document summarizes the new Java 8 Date & Time API, which replaces the old date and time classes. The new API includes classes like LocalDate, LocalTime, and ZonedDateTime that are immutable and provide a more fluent interface. It also separates concepts like dates, times, and time zones more precisely. The new API is based on abstract concepts like Temporal and TemporalAdjuster that make it very flexible for manipulating and working with dates and times.
This document summarizes Sualeh Fatehi's presentation on the Java 8 Date and Time API. It discusses problems with previous date and time APIs in Java, introduces concepts like epochs, calendar systems, time zones, and ISO-8601. It provides examples of using the new API classes like LocalDate, LocalTime, Instant, and Duration. Design principles of the new API emphasize clarity, immutability, and rejecting invalid values. The API separates machine and human views of time and supports internationalization.
This presentation provides an overview of using the Java SE 8 Date & Time API. It covers how to:
1. Create and manage date-based and time-based events including a combination of date and time into a single object using LocalDate, LocalTime, LocalDateTime, Instant, Period, and Duration
2. Work with dates and times across timezones and manage changes resulting from daylight savings including format date and times values
3. Define and create and manage date-based and time-based events using Instant, Period, Duration, and TemporalUnit
The document summarizes the new Java 8 Date & Time API. It discusses problems with the old Date and Calendar classes, including inconsistent behavior and lack of thread safety. It then overviews the new API, which was influenced by Joda-Time and aims to distinguish machine and human views of time. Key aspects covered include Instant, LocalDate, Duration, and formatting/parsing utilities. The new API provides a more clear and consistent model for working with dates and times in Java.
The document discusses the new date and time API introduced in Java 8 (JSR-310). It provides examples of using the new classes like LocalDate, LocalTime, LocalDateTime to represent date, time, and date-time. These classes are immutable and thread-safe. The API also supports different calendar systems and time zones. Methods are demonstrated for getting the current date/time, modifying dates, checking for leap years, formatting and parsing dates from strings.
The document discusses Java 8's new date and time API (JSR 310). It provides examples of using the new classes like LocalDate, LocalTime, LocalDateTime and Instant that model dates, times and timestamps without time zones. It also covers formatting and parsing dates and times, working with time zones using ZoneId and ZonedDateTime, durations using Duration, and periods of time using Period. The document compares the new API to the legacy Date and Calendar classes and notes improvements like immutability and removal of bugs in the previous implementation.
A code along session to introduce the java.time library in the upcoming release of Java 8. The materials to code along can be cloned from github here: https://github.com/jpgough/JavaTimeLab
A presentation I did in IL Java User Group about Java 8, DateTime new features. JSR-310
I wanted it to be informative and a good resource and starting point for this cool feature.
This document summarizes the new Java 8 Date & Time API, which replaces the old date and time classes. The new API includes classes like LocalDate, LocalTime, and ZonedDateTime that are immutable and provide a more fluent interface. It also separates concepts like dates, times, and time zones more precisely. The new API is based on abstract concepts like Temporal and TemporalAdjuster that make it very flexible for manipulating and working with dates and times.
This document summarizes Sualeh Fatehi's presentation on the Java 8 Date and Time API. It discusses problems with previous date and time APIs in Java, introduces concepts like epochs, calendar systems, time zones, and ISO-8601. It provides examples of using the new API classes like LocalDate, LocalTime, Instant, and Duration. Design principles of the new API emphasize clarity, immutability, and rejecting invalid values. The API separates machine and human views of time and supports internationalization.
This presentation provides an overview of using the Java SE 8 Date & Time API. It covers how to:
1. Create and manage date-based and time-based events including a combination of date and time into a single object using LocalDate, LocalTime, LocalDateTime, Instant, Period, and Duration
2. Work with dates and times across timezones and manage changes resulting from daylight savings including format date and times values
3. Define and create and manage date-based and time-based events using Instant, Period, Duration, and TemporalUnit
The document summarizes the new Java 8 Date & Time API. It discusses problems with the old Date and Calendar classes, including inconsistent behavior and lack of thread safety. It then overviews the new API, which was influenced by Joda-Time and aims to distinguish machine and human views of time. Key aspects covered include Instant, LocalDate, Duration, and formatting/parsing utilities. The new API provides a more clear and consistent model for working with dates and times in Java.
The document discusses the new date and time API introduced in Java 8 (JSR-310). It provides examples of using the new classes like LocalDate, LocalTime, LocalDateTime to represent date, time, and date-time. These classes are immutable and thread-safe. The API also supports different calendar systems and time zones. Methods are demonstrated for getting the current date/time, modifying dates, checking for leap years, formatting and parsing dates from strings.
The document discusses Java 8's new date and time API (JSR 310). It provides examples of using the new classes like LocalDate, LocalTime, LocalDateTime and Instant that model dates, times and timestamps without time zones. It also covers formatting and parsing dates and times, working with time zones using ZoneId and ZonedDateTime, durations using Duration, and periods of time using Period. The document compares the new API to the legacy Date and Calendar classes and notes improvements like immutability and removal of bugs in the previous implementation.
A code along session to introduce the java.time library in the upcoming release of Java 8. The materials to code along can be cloned from github here: https://github.com/jpgough/JavaTimeLab
Reactive programming using rx java & akka actors - pdx-scala - june 2014Thomas Lockney
Reactive programming is an event-driven paradigm focused on data flow and treating code as a set of behaviors in response to events. RxJava is a library that uses observable sequences to compose asynchronous and event-based programs in a reactive style. It provides operators like map, flatMap, and filter to transform streams of events. Akka implements the actor model with actors that react to immutable messages in isolation through message passing. Both RxJava and Akka support reactive principles like scalability, resilience and responsiveness but require additional work.
Real-time driving score service using FlinkDongwon Kim
Dongwon Kim presented on migrating T map's driving score service from a batch processing architecture to a real-time streaming architecture using Apache Flink. The new system calculates driving scores for each session in real-time as GPS data is received, allowing users to see their scores sooner. It utilizes Flink's event time processing, windowing, and a custom trigger to handle out-of-order data. Metrics are collected using Prometheus to monitor performance and latency.
This document discusses various issues related to representing and working with time and dates in programming. It begins with the presenter's background and then covers topics like time zone representations, leap years, daylight saving time, calendar systems, and bugs that can occur due to ambiguous or inconsistent date/time handling. Examples are provided using Java, Scala and other languages to demonstrate different approaches. Testing techniques are discussed as well as libraries like Joda Time that can help address these types of problems.
Performance van Java 8 en verder - Jeroen BorgersNLJUG
We weten allemaal dat de grootste verbetering die Java 8 brengt de ondersteuning voor lambda-expressies is. Dit introduceert functioneel programmeren in Java. Door het toevoegen van de Stream API wordt deze verbetering nog groter: iteratie kan nu intern worden afgehandeld door een bibliotheek, je kunt daarmee nu het beginsel "Tell, don’t ask" toepassen op collecties. Je kunt gewoon vertellen dat er een ??functie uitgevoerd moet worden op je verzameling, of vertellen dat dat parallel, door meerdere cores moet gebeuren. Maar wat betekent dit voor de prestaties van onze Java-toepassingen? Kunnen we nu meteen volledig al onze CPU-cores benutten om betere responstijden te krijgen? Hoe werken filter / map / reduce en parallele streams precies intern? Hoe wordt het Fork-Join framework hierin gebruikt? Zijn lambda's sneller dan inner klassen? - Al deze vragen worden beantwoord in deze sessie. Daarnaast introduceert Java 8 meer performance verbeteringen: tiered compilatie, PermGen verwijdering, java.time, Accumulators, Adders en Map verbeteringen. Ten slotte zullen we ook een kijkje nemen in de keuken van de geplande performance verbeteringen voor Java 9: benutting van GPU's, Value Types en arrays 2.0.
This document discusses RxJava, a library for composing asynchronous and event-based programs in Java. It introduces Observables as the core abstraction in RxJava, which are sequences that emit elements to observers. Operators like filter, map, and flatMap allow modifying and transforming Observables. As an example, it shows how to use RxJava to concurrently search for articles by ID, load each article, fetch like counts, and combine the results into a stream of articles. This is done by chaining flatMap operators to transform the stream of IDs into a stream of articles by asynchronously loading each part in parallel.
Predictive Maintenance with Deep Learning and Apache FlinkDongwon Kim
Flink can be used to build a predictive maintenance system using deep learning models on time-series sensor data. A Flink data stream processing pipeline is designed to handle joining streams, applying convolutional LSTM models through an ensemble, and monitoring outputs. Docker and Prometheus are used to package and monitor the solution.
Reactive programming is a paradigm oriented around data flows and change propagation. RxJS is a library for reactive programming that provides Observables to compose asynchronous operations through operators like map, filter, combineLatest. It allows building responsive applications by making asynchronous code look like synchronous code. RxJS provides both hot and cold Observables, with hot Observables closing over a producer source and cold Observables creating their own producer.
The document summarizes key aspects of the java.util.Date class including:
1) The java.util.Date class represents a specific point in time with millisecond precision, though most methods are deprecated in favor of Calendar.
2) The Date class has constructors that allow initializing a date from the current time or from a long value of milliseconds since January 1, 1970.
3) Common methods allow getting/setting the date, comparing dates, converting to string, and more.
Stream analysis with kafka native way and considerations about monitoring as ...Andrew Yongjoon Kong
The document discusses stream analysis with Kafka and considerations for monitoring as a service. It provides background on the speaker including their roles in government, academia, and industry related to big data. It then covers some key concepts in stream processing versus batch processing and examples of popular stream processors like Apache Flume, Storm, Spark, and Samza. The document also discusses real-time analytics and defines the term. It introduces the Kafka Streams library and provides sample code for simple pipelines and wordcount analytics. Finally, it briefly mentions the KSQL query language for Kafka.
A More Scaleable Way of Making Recommendations with MLlib-(Xiangrui Meng, Dat...Spark Summit
This document summarizes the implementation of Alternating Least Squares (ALS) in MLlib to make recommendations at scale. It discusses how MLlib reduces communication cost through a block-to-block approach and compressed storage formats. It also describes optimizations like avoiding garbage collection through specialized code. The ALS algorithm is tested on real-world datasets including Amazon reviews and Spotify music data involving billions of ratings.
Akka-demy (a.k.a. How to build stateful distributed systems) I/IIPeter Csala
This is the first part of a mini-series where we discuss how to build distributed stateful real-time applications using actor model and messaging.
The second part: https://www.slideshare.net/PeterCsala/akkademy-aka-how-to-build-stateful-distributed-systems-iiii
The Java Calendar class provides methods for converting between dates and calendar fields like month and year. It inherits from Object and implements Comparable, allowing comparison of Calendar objects. Key methods include get() to retrieve calendar field values, getInstance() to get a calendar using the system timezone and locale, and getMaximum() and getMinimum() to get the valid range of calendar field values. Examples demonstrate using these methods to work with dates in Java.
JEEConf 2016. Effectiveness and code optimization in Java applicationsStrannik_2013
This document discusses code optimization techniques in Java applications. It begins with an overview of code effectiveness and optimization, noting that optimization should not be done prematurely. It then covers various optimization techniques including JVM options, code samples, measurements using JMH of different techniques like method vs field access, strings, arrays, collections, and loops vs streams. It finds that techniques like using ArrayList/HashMap, compiler and JIT optimization, and measurement tools can improve performance. The document emphasizes measuring optimizations to determine real effectiveness.
Scalable Realtime Analytics with declarative SQL like Complex Event Processin...Srinath Perera
This document discusses real-time analytics and introduces WSO2 Complex Event Processing (CEP) as a SQL-like language for real-time analytics. It describes how CEP can be used to define filtering, aggregation, pattern matching, and other common operations on streaming data. It also discusses how CEP queries can be scaled out across multiple nodes by partitioning streams and queries. CEP provides an easy to use yet powerful way to perform real-time analytics on streaming data at scale.
Scala like distributed collections - dumping time-series data with apache sparkDemi Ben-Ari
Spark RDDs are almost identical to Scala collection, just in a distributed manner, all of the transformations and actions are derived from the Scala collections API.
As Martin Odersky mentioned, “Spark - The Ultimate Scala Collections” is the right way to look at RDDs. But with that great distributed power comes a great many data problems: at first you’ll start tackling the concept of partitioning, then the actual data becomes the next thing to worry about.
In the talk we’ll go through an overview on Spark's architecture, and see how similar RDDs are to the Scala collections API. We'll then shift to the world of problems that you’ll be facing when using Spark for processing a vast volume of time-series data with multiple data stores (S3, MongoDB, Apache Cassandra, MySQL).
When you start tackling many scale and performance problems, many questions arise:
> How to handle missing data?
> Should the system handle both serving and backend processes, or should we separate them out?
> Which solution is cheaper?
> How do we get the best performance for money spent?
In the talk we will tell the tale of all of the transformations we’ve made to our data and review the multiple data persistency layers... and I’ll try my best NOT to answer the question “which persistency layer is the best?” but I do promise to share our pains and lessons learned!
The document discusses the Calendar and GregorianCalendar classes in Java. It provides details on their constructors, methods, and usage. Calendar is an abstract class that provides methods for converting between a date and calendar fields like year, month, etc. GregorianCalendar is a concrete subclass that implements the standard Gregorian calendar system. It supports both the Julian and Gregorian systems and handles leap years according to Gregorian rules. Examples are provided to demonstrate using these classes.
A distributed system is a network that consists of autonomous computers that are connected using a distribution middleware. They help in sharing different resources and capabilities to provide users with a single and integrated coherent network.
Streaming Data Flow with Apache Flink @ Paris Flink Meetup 2015Till Rohrmann
Apache Flink is an open source platform for distributed stream and batch data processing. It provides APIs called DataStream for unbounded streaming data and DataSet for bounded batch data. Flink runs streaming topologies that allow for windowing, aggregation and other stream processing functions. It supports exactly-once processing semantics through distributed snapshots and checkpoints. The system is optimized for low latency and high throughput streaming applications.
This document discusses lambda expressions in Java. It defines lambda as an anonymous function without a name. Lambda expressions allow functional programming by defining classes or functions with a single method using a simple syntax. Functional interfaces are interfaces with only one abstract method, and some examples are Runnable, Callable, Comparator. The document shows examples of using lambda expressions with these built-in interfaces and traversing collections. It also demonstrates defining a new functional interface and using it with lambda expressions.
Lambda expressions are a new and important feature that has been included in Java SE 8. It provides a way to represent one method interface using an expression. A lambda expression is like a method, it provides a list of formal parameters and a body (which can be an expression or a block of code) expressed in terms of those parameters.
Reactive programming using rx java & akka actors - pdx-scala - june 2014Thomas Lockney
Reactive programming is an event-driven paradigm focused on data flow and treating code as a set of behaviors in response to events. RxJava is a library that uses observable sequences to compose asynchronous and event-based programs in a reactive style. It provides operators like map, flatMap, and filter to transform streams of events. Akka implements the actor model with actors that react to immutable messages in isolation through message passing. Both RxJava and Akka support reactive principles like scalability, resilience and responsiveness but require additional work.
Real-time driving score service using FlinkDongwon Kim
Dongwon Kim presented on migrating T map's driving score service from a batch processing architecture to a real-time streaming architecture using Apache Flink. The new system calculates driving scores for each session in real-time as GPS data is received, allowing users to see their scores sooner. It utilizes Flink's event time processing, windowing, and a custom trigger to handle out-of-order data. Metrics are collected using Prometheus to monitor performance and latency.
This document discusses various issues related to representing and working with time and dates in programming. It begins with the presenter's background and then covers topics like time zone representations, leap years, daylight saving time, calendar systems, and bugs that can occur due to ambiguous or inconsistent date/time handling. Examples are provided using Java, Scala and other languages to demonstrate different approaches. Testing techniques are discussed as well as libraries like Joda Time that can help address these types of problems.
Performance van Java 8 en verder - Jeroen BorgersNLJUG
We weten allemaal dat de grootste verbetering die Java 8 brengt de ondersteuning voor lambda-expressies is. Dit introduceert functioneel programmeren in Java. Door het toevoegen van de Stream API wordt deze verbetering nog groter: iteratie kan nu intern worden afgehandeld door een bibliotheek, je kunt daarmee nu het beginsel "Tell, don’t ask" toepassen op collecties. Je kunt gewoon vertellen dat er een ??functie uitgevoerd moet worden op je verzameling, of vertellen dat dat parallel, door meerdere cores moet gebeuren. Maar wat betekent dit voor de prestaties van onze Java-toepassingen? Kunnen we nu meteen volledig al onze CPU-cores benutten om betere responstijden te krijgen? Hoe werken filter / map / reduce en parallele streams precies intern? Hoe wordt het Fork-Join framework hierin gebruikt? Zijn lambda's sneller dan inner klassen? - Al deze vragen worden beantwoord in deze sessie. Daarnaast introduceert Java 8 meer performance verbeteringen: tiered compilatie, PermGen verwijdering, java.time, Accumulators, Adders en Map verbeteringen. Ten slotte zullen we ook een kijkje nemen in de keuken van de geplande performance verbeteringen voor Java 9: benutting van GPU's, Value Types en arrays 2.0.
This document discusses RxJava, a library for composing asynchronous and event-based programs in Java. It introduces Observables as the core abstraction in RxJava, which are sequences that emit elements to observers. Operators like filter, map, and flatMap allow modifying and transforming Observables. As an example, it shows how to use RxJava to concurrently search for articles by ID, load each article, fetch like counts, and combine the results into a stream of articles. This is done by chaining flatMap operators to transform the stream of IDs into a stream of articles by asynchronously loading each part in parallel.
Predictive Maintenance with Deep Learning and Apache FlinkDongwon Kim
Flink can be used to build a predictive maintenance system using deep learning models on time-series sensor data. A Flink data stream processing pipeline is designed to handle joining streams, applying convolutional LSTM models through an ensemble, and monitoring outputs. Docker and Prometheus are used to package and monitor the solution.
Reactive programming is a paradigm oriented around data flows and change propagation. RxJS is a library for reactive programming that provides Observables to compose asynchronous operations through operators like map, filter, combineLatest. It allows building responsive applications by making asynchronous code look like synchronous code. RxJS provides both hot and cold Observables, with hot Observables closing over a producer source and cold Observables creating their own producer.
The document summarizes key aspects of the java.util.Date class including:
1) The java.util.Date class represents a specific point in time with millisecond precision, though most methods are deprecated in favor of Calendar.
2) The Date class has constructors that allow initializing a date from the current time or from a long value of milliseconds since January 1, 1970.
3) Common methods allow getting/setting the date, comparing dates, converting to string, and more.
Stream analysis with kafka native way and considerations about monitoring as ...Andrew Yongjoon Kong
The document discusses stream analysis with Kafka and considerations for monitoring as a service. It provides background on the speaker including their roles in government, academia, and industry related to big data. It then covers some key concepts in stream processing versus batch processing and examples of popular stream processors like Apache Flume, Storm, Spark, and Samza. The document also discusses real-time analytics and defines the term. It introduces the Kafka Streams library and provides sample code for simple pipelines and wordcount analytics. Finally, it briefly mentions the KSQL query language for Kafka.
A More Scaleable Way of Making Recommendations with MLlib-(Xiangrui Meng, Dat...Spark Summit
This document summarizes the implementation of Alternating Least Squares (ALS) in MLlib to make recommendations at scale. It discusses how MLlib reduces communication cost through a block-to-block approach and compressed storage formats. It also describes optimizations like avoiding garbage collection through specialized code. The ALS algorithm is tested on real-world datasets including Amazon reviews and Spotify music data involving billions of ratings.
Akka-demy (a.k.a. How to build stateful distributed systems) I/IIPeter Csala
This is the first part of a mini-series where we discuss how to build distributed stateful real-time applications using actor model and messaging.
The second part: https://www.slideshare.net/PeterCsala/akkademy-aka-how-to-build-stateful-distributed-systems-iiii
The Java Calendar class provides methods for converting between dates and calendar fields like month and year. It inherits from Object and implements Comparable, allowing comparison of Calendar objects. Key methods include get() to retrieve calendar field values, getInstance() to get a calendar using the system timezone and locale, and getMaximum() and getMinimum() to get the valid range of calendar field values. Examples demonstrate using these methods to work with dates in Java.
JEEConf 2016. Effectiveness and code optimization in Java applicationsStrannik_2013
This document discusses code optimization techniques in Java applications. It begins with an overview of code effectiveness and optimization, noting that optimization should not be done prematurely. It then covers various optimization techniques including JVM options, code samples, measurements using JMH of different techniques like method vs field access, strings, arrays, collections, and loops vs streams. It finds that techniques like using ArrayList/HashMap, compiler and JIT optimization, and measurement tools can improve performance. The document emphasizes measuring optimizations to determine real effectiveness.
Scalable Realtime Analytics with declarative SQL like Complex Event Processin...Srinath Perera
This document discusses real-time analytics and introduces WSO2 Complex Event Processing (CEP) as a SQL-like language for real-time analytics. It describes how CEP can be used to define filtering, aggregation, pattern matching, and other common operations on streaming data. It also discusses how CEP queries can be scaled out across multiple nodes by partitioning streams and queries. CEP provides an easy to use yet powerful way to perform real-time analytics on streaming data at scale.
Scala like distributed collections - dumping time-series data with apache sparkDemi Ben-Ari
Spark RDDs are almost identical to Scala collection, just in a distributed manner, all of the transformations and actions are derived from the Scala collections API.
As Martin Odersky mentioned, “Spark - The Ultimate Scala Collections” is the right way to look at RDDs. But with that great distributed power comes a great many data problems: at first you’ll start tackling the concept of partitioning, then the actual data becomes the next thing to worry about.
In the talk we’ll go through an overview on Spark's architecture, and see how similar RDDs are to the Scala collections API. We'll then shift to the world of problems that you’ll be facing when using Spark for processing a vast volume of time-series data with multiple data stores (S3, MongoDB, Apache Cassandra, MySQL).
When you start tackling many scale and performance problems, many questions arise:
> How to handle missing data?
> Should the system handle both serving and backend processes, or should we separate them out?
> Which solution is cheaper?
> How do we get the best performance for money spent?
In the talk we will tell the tale of all of the transformations we’ve made to our data and review the multiple data persistency layers... and I’ll try my best NOT to answer the question “which persistency layer is the best?” but I do promise to share our pains and lessons learned!
The document discusses the Calendar and GregorianCalendar classes in Java. It provides details on their constructors, methods, and usage. Calendar is an abstract class that provides methods for converting between a date and calendar fields like year, month, etc. GregorianCalendar is a concrete subclass that implements the standard Gregorian calendar system. It supports both the Julian and Gregorian systems and handles leap years according to Gregorian rules. Examples are provided to demonstrate using these classes.
A distributed system is a network that consists of autonomous computers that are connected using a distribution middleware. They help in sharing different resources and capabilities to provide users with a single and integrated coherent network.
Streaming Data Flow with Apache Flink @ Paris Flink Meetup 2015Till Rohrmann
Apache Flink is an open source platform for distributed stream and batch data processing. It provides APIs called DataStream for unbounded streaming data and DataSet for bounded batch data. Flink runs streaming topologies that allow for windowing, aggregation and other stream processing functions. It supports exactly-once processing semantics through distributed snapshots and checkpoints. The system is optimized for low latency and high throughput streaming applications.
This document discusses lambda expressions in Java. It defines lambda as an anonymous function without a name. Lambda expressions allow functional programming by defining classes or functions with a single method using a simple syntax. Functional interfaces are interfaces with only one abstract method, and some examples are Runnable, Callable, Comparator. The document shows examples of using lambda expressions with these built-in interfaces and traversing collections. It also demonstrates defining a new functional interface and using it with lambda expressions.
Lambda expressions are a new and important feature that has been included in Java SE 8. It provides a way to represent one method interface using an expression. A lambda expression is like a method, it provides a list of formal parameters and a body (which can be an expression or a block of code) expressed in terms of those parameters.
The document provides an overview of streams and lambdas in Java 8. It discusses key topics such as:
- Functional interfaces like Predicate, Function, and Consumer that work with lambda expressions.
- Stream operations like filter, map, reduce, collect that allow processing collections in a declarative way. Streams are lazy and support both intermediate and terminal operations.
- Common examples of using streams, lambdas and functional interfaces to perform tasks like filtering collections, mapping values, finding max/min, and reducing to a single value.
A short introduction to, and practical experiment of, the new features available to the Java craftsman.
Java 8 is mostly about new functional programming paradigms.
Lambdas, method references and the stream API are powerful and yet sometimes hard to understand concepts. These slides are mainly about describing those features, in a way, that is easy to understand, through, analogies and thought experiements discussions.
This document provides an overview of new features in Java 8 including lambda expressions, interface improvements, and streams. Lambda expressions allow for anonymous functions and method references in Java. Functional interfaces define a single abstract method that lambda expressions can implement. Interface changes allow interfaces to define static and default methods. Streams provide a functional-style way to process collections of objects through intermediate and terminal operations like filter, map, reduce, and forEach.
Java8 Neue Sprachfeatures - Lambda/Streams/default Methods/FunctionalInterfacesDirk Detering
The document discusses new features introduced in Java versions 1.4 through 1.8, including lambda expressions, method references, default methods, and streams. Key features include lambda expressions and functional interfaces in Java 8, which allow implementing functional-style code in a more concise way than anonymous classes. Various Java.util.function interfaces like Consumer and Function are also introduced to work with lambda expressions.
Why is Java relevant? New features of Java8 xshyamx
Presentation slides from ISDC2013 held on 16-mar-2013
http://www.siliconindia.com/events-overview/India--Software-Developer-Conference-Bangalore-ISDC_2013Blr.html
Java 8 was released in 2014 and introduced several new features including lambda expressions, functional interfaces, method references, and default methods in interfaces. It also included a new Stream API for functional-style data processing, a date/time API, and Project Nashorn for embedding JavaScript in Java applications. Future versions like Java 9 will focus on modularity, new APIs, and further improvements.
The document discusses Java 8 lambda expressions and how they improved Java by allowing for anonymous functions. It provides examples of code before and after Java 8 that demonstrate lambda expressions providing a clearer syntax compared to anonymous inner classes. Specifically, it shows how lambda expressions allowed sorting a list of strings in a more readable way. It also discusses how functions can be treated as data by being passed as parameters or returned from other functions.
This document provides an overview of functional programming features in Java 8 such as lambdas, streams, and method references. Lambdas allow implementing functional interfaces with small anonymous functions. Streams make it easier to perform filtered, mapped and reduced operations on collections in a declarative way. While Java 8 adds some FP capabilities, it is not considered a purely functional language since it lacks features like recursion and immutable data structures.
O documento discute as principais novidades da versão Java 8, incluindo: 1) expressões lambda; 2) interfaces funcionais; 3) Stream API para processamento de dados.
Introduction to functional programming with java 8JavaBrahman
Recently gave a presentation in a Java Meetup in Bangalore on basics of functional programming, new functional features in Java 8, and introduction to the basic concepts of functional interfaces and lambda expressions...
This document discusses new features in Java 8 including lambda expressions, default methods, streams, optionals, and date/time API improvements. It provides examples of using lambda expressions and streams to more concisely represent and process collections of data in a functional way. The document also discusses optionals as a way to safely handle potential null values without exceptions.
Java 8 introduced several new features including default and static methods in interfaces, lambda expressions, the Optional class, streams, method references, a new date/time API, the Nashorn JavaScript engine, and parallel arrays. Some of the key features are default methods that allow interfaces to provide default implementations, lambda expressions that allow passing code as data, and streams that enable functional-style programming for collections.
The document discusses Java 8 features like lambda expressions, streams, and method references. It provides examples of filtering a list of books by pages or genre using lambda expressions and streams. Lambda expressions allow implementing functional interfaces concisely without anonymous classes. Streams provide a way to process data elements sequentially and support operations like filtering, mapping, matching, reducing, and collecting results.
This is a 3 part series on Java8 Features. Drop me an email for a discussion - singh.marut@gmail.com
Code is available at https://github.com/singhmarut/java8training
Videos available at my youtube channel https://www.youtube.com/channel/UCBM4yHwfjQ_syW6Lz8kYpmA
This is a 3 part series on Java8 Features. Drop me an email for a discussion - singh.marut@gmail.com
Code is available at https://github.com/singhmarut/java8training
Videos available at my youtube channel https://www.youtube.com/channel/UCBM4yHwfjQ_syW6Lz8kYpmA
This is a 3 part series on Java8 Features. Drop me an email for a discussion - singh.marut@gmail.com
https://github.com/singhmarut/java8training
Videos available at my youtube channel https://www.youtube.com/channel/UCBM4yHwfjQ_syW6Lz8kYpmA
This document provides an overview of new features in Java 8, including lambda expressions, modularity with the Java Platform Module System, and performance enhancements with parallel array sorting and concurrency updates. It discusses the motivation and design of modularity in Java 8, how to compile and install modules, and differences from OSGi. Lambda expressions add support for operating on blocks of Java code. The presentation encourages attending to documentation, downloading SDKs, testing code, and providing feedback on the Java Technology Community site.
The document discusses the new features introduced in Java 8 including lambda expressions, method references, streams, default methods, the new date and time API, and the Nashorn JavaScript engine. Lambda expressions allow eliminating anonymous classes and nested functions. Method references provide a simpler way to refer to existing methods. Streams enable parallel processing of data. Default methods allow adding new methods to interfaces without breaking existing code. The date and time API improves on the previous APIs by making it thread-safe and more intuitive. The Nashorn engine allows embedding JavaScript in Java applications.
The document discusses different ways to represent date and time in Java. It introduces the new Date Time API introduced in Java 8, which includes classes like LocalDate, LocalTime, LocalDateTime, ZonedDateTime, Clock, Instant, Duration, Period, ZoneId, ZoneOffset, and DateTimeFormatter. The document then provides examples of common date and time tasks like getting the current date and time, parsing and formatting dates, measuring elapsed time between two points, and calculating days between two dates.
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Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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1. New Date & Time API of Java8
- Buddha Jyothiprasad
2. Agenda
A Quick look at Old API
for Date and Time
Discuss the issues with
it
Introduce the new API
from Java 8
See some examples of
various concepts in new
API
Buddha Java User Group - Hyderabad
3. A Quick Look at old API
java.util.Date
java.sql.Date
Calendar
GregorianCalendar
TimeZone
DateFormat
SimpleDateFormat
Some bugs that doesn’t look like Bugs with old
API
3 Java User Group - Hyderabad
4. Issues with the old approach
Ø Date is actually DateTime
Ø Different class for SQL
Ø Date doesn’t have timezone
Ø getMonth() is zero-based, getYear() is 1900-based
(i.e., the year 2009 is represented as 109)
Ø Mutable hence not thread-safe by default
Ø java.util.Date represents an instant on the timeline
but invoking toString() prints time stamp along with
time zone, causing confusion among developers
Ø Calendar can’t format date directly
Ø Arithmetic operations are still tricky
Buddha Java User Group - Hyderabad
5. Fixed Code that doesn’t look right
Buddha Java User Group - Hyderabad
6. New API
New package java.time
New classes LocalDate,
LocalTime
A composite class
LocalDateTime
Supports numerous
ways of creating Date
and Time objects
Supports Truncation,
Timezones, Periods,
Durations, Chronologies
ANSI SQL Java SE 8
DATE LocalDate
TIME LocalTime
TIMESTAMP LocalDateTime
TIME WITH TIMEZONE OffsetTime
TIMESTAMP WITH
TIMEZONE
OffsetDateTime
And many more….
6 Java User Group - Hyderabad
7. Java 8 packages for Date & Time
• Consists of major base classes
• LocalDate, LocalTime, Instant, Duration
java.time
• Consists of generic API for non ISO Calendar
Systems java.time.chrono
• Contains temporal objects to find out specific
date/time related like firstDay of month java.time.temporal
• Classes for formatting and parsing date time
objects java.time.format
java.time.zone • Classes for supporting different timezones
Buddha Java User Group - Hyderabad
8. Creating Objects
All the core classes are
constructed by fluent
factory methods
When constructing a
value by its constituent
fields, the factory is
called of
when converting from
another type, the factory
is called from
There are also parse
methods that take strings
as parameters
Standard Java getter
conventions are used in
order to obtain values
8 Java User Group - Hyderabad
10. Date & Time manipulation
We can alter the object
values in order to perform
calculations
All objects are immutable
Setters are no more
New API also has a
concept of Adjuster
methods
These methods are called
with and return new
objects
There are also plus
adjusters
We can write our own with
adjusters and plus
adjusters
10 Java User Group - Hyderabad
12. Time Zones
The local classes that we
looked at previously
abstract away the
complexity introduced by
time zones
ZonedDateTime is a date
and time with a fully
qualified time zone
This can resolve an offset
at any point in time
Other classes are ZoneId,
ZoneOffset,
OffsetDateTime,
12OffsetTime Java User Group - Hyderabad
14. Instant, Periods & Duration
Instant is the closest sibling of java.util.Date. Instant
class is used to work with machine readable time
format, it stores date time in unix timestamp.
A Period represents a value such as “3 months and
1 day,”
A Duration is a distance on the timeline measured in
terms of time, and it fulfills a similar purpose
to Period, but with different precision
Buddha Java User Group - Hyderabad
16. Parsing & Formatting
Every class in date time
formatter has methods for
parsing and formatting
format() and parse()
directly on the date
objects
DateTimeFormatter
Loads of Predefined
formatters like ISO_DATE
Custom formats can also
be provided via
DateTimeFormatter.ofPa
ttern() method
16 Java User Group - Hyderabad
18. Query
A TemporalQuery can be used to retrieve
information from a temporal-based object
The TemporalQueries class(note the plural)
provides several predefined queries
The precision query, for example, returns the
smallest ChronoUnit that can be returned by a
particular temporal-based object
We can also create custom queries by implementing
the interface TemporaryQuery
18 Java User Group - Hyderabad
20. Legacy Support
Legacy Date/Time
classes are used in
almost all the
applications
Backward compatibility
is a must
Following Utility
methods are provided
for that purpose
toInstant()
toZoneId()
from()
20 Java User Group - Hyderabad
21. Summary
Immutability: All the classes in the new API are immutable hence
they are thread-safe.
Separation of Concerns: The new API separates clearly between
human readable date time and machine time. It defines separate
classes for Date, Time, DateTime, Timestamp etc.
Consistency: The methods are clearly defined and perform the
same action in all the classes. There are format() and parse()
methods defined in all these classes rather than having a separate
class for them.
Utility operations: All the new Date Time API classes comes with
methods to perform common tasks, such as plus, minus, format,
parsing, getting separate part in date/time etc.
Extendable: The new Date Time API works on ISO-8601 calendar
system but we can use it with other non ISO calendars as well.
Buddha Java User Group - Hyderabad