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parallel-asynchronous-programming-java.pptx

The document discusses the fundamentals of parallel and asynchronous programming in modern Java, emphasizing the necessity for developers to optimize code performance using features like parallel streams and CompletableFuture APIs. It covers various programming patterns, hardware advancements, and best practices to write efficient, non-blocking code while highlighting the differences between concurrency and parallelism. Additionally, it provides practical examples and guidance for experienced Java developers looking to implement these concepts effectively.

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Dilip Sundarraj Parallel & Asynchronous Programming In Modern Java
About Me • Dilip • Building Software’s since 2008 • Teaching in UDEMY Since 2016
What's Covered ? • Need for Parallel and Asynchronous Programming • Covers the ParallelStreams and CompletableFuture API • Techniques to write Fast Performing Code using Functional Style Concurrency APIs in Java • Covers Best Practices using ParallelStreams/CompletableFuture API in your code • NonBlocking RestFul API Client using CompletableFuture API • Testing ParallelStreams and CompletableFuture API Using JUnit5
Targeted Audience • Experienced Java Developers • Developers who has the need to write code that executes faster • Developers who has the need to write code that executes in Parallel • Developer who has the need to write asynchronous/non-blocking code
Source Code
Thank You!
Prerequisites
Course Prerequisites • Java 11 or Higher is needed • Prior Java knowledge is needed • Experience working with Lambdas, Streams , Method References • Experience writing JUnit tests • Intellij , Eclipse or any other IDE is needed
Why Parallel and Asynchronous Programming ?
Why Parallel and Asynchronous Programming? • We are living in a fast paced environment • In Software Programming: • Code that we write should execute faster • Goal of Asynchronous and Parallel Programming • Provide Techniques to improve the performance of the code
Technology Advancements • Devices or computers comes up with Multiple cores • Developer needs to learn programming patterns to maximize the use of multiple cores • Apply the Parallel Programming concepts • Parallel Streams • MicroServices Architecture style • Blocking I/O calls are common in MicroServices Architecture. This also impacts the latency of the application • Apply the Asynchronous Programming concepts • CompletableFuture Hardware Software Functional Style of Programming Threads
Evolution of Concurrency and Parallelism APIs in Java
Timeline 1996 2004 2011 2014 2017 Threads Java1 Single Core Multi Core Java5 ThreadPool, ExecutorService,Futures, ConcurrentCollections etc., Java7 Fork/Join FrameWork Data Parallelism Parallel Streams CompletableFutures Java8 Lamdas Streams API Java9 Flow API Current Version : Java14 https://en.wikipedia.org/wiki/Java_version_history
Concurrency vs Parallelism
Concurrency • Concurrency is a concept where two or more task can run simultaneously • In Java, Concurrency is achieved using Threads • Are the tasks running in interleaved fashion? • Are the tasks running simultaneously ? Single Core Threa d2 Threa d1 Threa d3 Core1 Core2 Core3 Core4 MultiCore Threa d1 Threa d2 Threa d3 Threa d4
Concurrency Example public class HelloWorldThreadExample { private static String result=""; private static void hello(){ delay(500); result = result.concat("Hello"); } private static void world(){ delay(600); result = result.concat(“ World"); } public static void main(String[] args) throws InterruptedException { Thread helloThread = new Thread(()-> hello()); Thread worldThread = new Thread(()-> world()); //Starting the thread helloThread.start(); worldThread.start(); //Joining the thread (Waiting for the threads to finish) helloThread.join(); worldThread.join(); System.out.println("Result is : " + result); } } Hello World } Threads 1 2 3 • In a real application, Threads normally need to interact with one another • Shared Objects or Messaging Queues • Issues: • Race Condition • DeadLock and more • Tools to handle these issues: • Synchronized Statements/Methods • Reentrant Locks, Semaphores • Concurrent Collections • Conditional Objects and More 0 0
Parallelism • Parallelism is a concept in which tasks are literally going to run in parallel • Parallelism involves these steps: • Decomposing the tasks in to SubTasks(Forking) • Execute the subtasks in sequential • Joining the results of the tasks(Join) • Whole process is also called Fork/Join 1 join Task SubTask1 SubTask2 SubTask1a SubTask1b SubTask2a SubTask2b join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork 3 2
Parallelism Example UseCase: Transform to UpperCase [Bob, Jamie, Jill, Rick] -> [BOB, JAMIE, JILL, RICK] 1 3 2 join Bob, Jamie, Jill, Rick [Bob,Jamie] [Jill,Rick] Bob Jamie Jill Rick BOB JAMI E JILL RICK join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork BOB, JAMIE, JILL, RICK [BOB, JAMIE] [JILL, RICK] Bob Jamie Jill Rick Core1 Core2 Core3 Core4 BOB JAMIE JILL RICK uppercase uppercase uppercase uppercase
Parallelism Example public class ParallelismExample { public static void main(String[] args) { List<String> namesList = List.of("Bob", "Jamie", "Jill", "Rick"); System.out.println("namesList : " + namesList); List<String> namesListUpperCase = namesList .parallelStream() .map(String::toUpperCase) .collect(Collectors.toList()); System.out.println("namesListUpperCase : " + namesListUpperCase); } }
Concurrency vs Parallelism • Concurrency is a concept where two or more tasks can run in simultaneously • Concurrency can be implemented in single or multiple cores • Concurrency is about correctly and efficiently controlling access to shared resources • Parallelism is a concept where two or more tasks are literally running in parallel • Parallelism can only be implemented in a multi-core machine • Parallelism is about using more resources to access the result faster
Course Project Setup
Section Overview
Section Overview • Covers Asynchronous and Parallel Programming prior Java 8 • Threads, Futures and ForkJoin Framework and its limitations • Covers Theory and Hands On
Overview of the Product Service
Product Service Product Service Client ProductInfo Service Review Service ProductId Product Product size, color, price No of reviews, Overall rating
Threads
Threads API • Threads API got introduced in Java1 • Threads are basically used to offload the blocking tasks as background tasks • Threads allowed the developers to write asynchronous style of code
Thread API Limitations • Requires a lot of code to introduce asynchrony • Runnable, Thread • Require additional properties in Runnable • Start and Join the thread • Low level • Easy to introduce complexity in to our code
ThreadPool, ExecutorService & Future
Limitations Of Thread • Limitations of Thread: • Create the thread • Start the thread • Join the thread • Threads are expensive • Threads have their own runtime-stack, memory, registers and more Thread Pool was created specifically to solve this problem
• Thread Pool is a group of threads created and readily available • CPU Intensive Tasks • ThreadPool Size = No of Cores • I/O task • ThreadPool Size > No of Cores • What are the benefits of thread pool? • No need to manually create, start and join the threads • Achieving Concurrency in your application Thread Pool TQ T1 TQ T2 TQ T3 TQ T4 ThreadPool
ExecutorService • Released as part of Java5 • ExecutorService in Java is an Asynchronous Task Execution Engine • It provides a way to asynchronously execute tasks and provides the results in a much simpler way compared to threads • This enabled coarse-grained task based parallelism in Java
ExecutorService ExecutorService WorkQueue CompletionQueue T Q T1 T Q T2 T Q T3 T Q T4 ThreadPool
Working Of ExecutorService T Q T1 T Q T2 T Q T3 T Q T4 ThreadPool WorkQueue CompletionQueue Client Task Resu lt Future ExecutorService
Limitations of ExecutorService • Designed to Block the Thread • No better way to combine futures ProductInfo productInfo = productInfoFuture.get(); Review review = reviewFuture.get(); return new Product(productId, productInfo, review); ProductInfo productInfo = productInfoFuture.get(); Review review = reviewFuture.get();
Fork/Join Framework
Fork/Join Framework • This got introduced as part of Java7 • This is an extension of ExecutorService • Fork/Join framework is designed to achieve Data Parallelism • ExecutorService is designed to achieve Task Based Parallelism Future<ProductInfo> productInfoFuture = executorService.submit(() -> productInfoService.retrieveProductInfo(productId)); Future<Review> reviewFuture = executorService.submit(() -> reviewService.retrieveReviews(productId));
What is Data Parallelism ? • Data Parallelism is a concept where a given Task is recursively split in to SubTasks until it reaches it leaset possible size and execute those tasks in parallel • Basically it uses the divide and conquer approach 1 3 2 join Bob, Jamie, Jill, Rick [Bob,Jamie] [Jill,Rick] Bob Jamie Jill Rick BOB JAMI E JILL RICK join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork BOB, JAMIE, JILL, RICK [BOB, JAMIE] [JILL, RICK] Watch "Concurrency vs Parallelism”
How does Fork/Join Framework Works ? ForkJoin Pool to support Data Parallelism
ForkJoin Pool ForkJoin Pool Shared Work Queue Client Task T1 T2 T3 T4 Double Ended Work Queue(deck) Worker Threads Tas k Tas k ForkJoinTask Tas k WorkStealing Tas k Tas k Tas k Tas k Result
ForkJoin Task • ForkJoin Task represents part of the data and its computation • Type of tasks to submit to ForkJoin Pool • ForkJoinTask • RecursiveTask -> Task that returns a value • RecursiveAction -> Task that does not return a value join Bob, Jamie, Jill, Rick [Bob,Jamie] [Jill,Rick] Bob Jamie Jill Rick BOB JAMI E JILL RICK join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork BOB, JAMIE, JILL, RICK [BOB, JAMIE] [JILL, RICK] Fork/Join Task
Fork/Join Example
ForkJoin - UseCase [Bob, Jamie, Jill, Rick] -> [3 - Bob, 5 - Jamie, 4 - Jill, 4 - Rick] Input Output
Streams API & Parallel Streams
Streams API • Streams API got introduced in Java 8 • Streams API is used to process a collection of Objects • Streams in Java are created by using the stream() method 1 2 3 ]Pipeline Pipeline Intermediate Terminal
ParallelStreams • This allows your code to run in parallel • ParallelStreams are designed to solve Data Parallelism Watch “Concurrency vs Parallelism” & “Fork-Join Framework”
Stream/Parallel Stream Stream Parallel Stream
Parallel Streams - UseCase [Bob, Jamie, Jill, Rick] -> [3 - Bob, 5 - Jamie, 4 - Jill, 4 - Rick] Input Output
Unit Testing Parallel Streams Using JUnit5
Why Unit Tests ? • Unit Testing allows you to programmatically test your code • Manual Testing slows down the development and delivery • Unit Testing allows the developer or the app team to make enhancements to the existing code easily and faster
Sequential/Parallel Functions in Streams API
sequential() and parallel() • Streams API are sequential by default • sequential() -> Executes the stream in sequential • parallel() -> Executes the stream in parallel • Both the functions() changes the behavior of the whole pipeline
sequential() • Changing the parallelStream() behavior to sequential Sequential
parallel() • Changing the stream() behavior to parallel Parallel
When to use sequential() and parallel() ? Used these functions when I would like to evaluate between sequential() and parallel()
Overview of the Retail Checkout Service
Checkout Service(BackEnd) Checkout Service N Price Validator Service CartItem $ UP DOWN
ParallelStreams How it works ?
ParallelStreams - How it works ? • parallelStream() • Split the data in to chunks • Execute the data chunks • Combine the result
parallelStream() - How it works ? • Split • Data Source is split in to small data chunks • Example - List Collection split into chunks of elements to size 1 • This is done using Spliterators • For ArrayList, the Spliterator is ArrayListSpliterator • Execute • Data chunks are applied to the Stream Pipeline and the Intermediate operations executed in a Common ForkJoin Pool • Watch the Fork/Join FrameWork Lectures • Combine • Combine the executed results into a final result • Combine phase in Streams API maps to terminal operations • Uses collect() and reduce() functions • collect(toList())
parallelStream() - How it works ? combine Exe cute cartItemsList cartItemsSplit cartItemsSplit cartItem cartItem cartItem cartItem cartItem cartItem cartItem cartItem combine combine Process Sequentially Process Sequentially Process Sequentially Process Sequentially parallelStreams() Split Split Split cartItemsList [cartItem, cartitem] [cartItem, cartitem] CheckoutService 1 Split 2 Execute Common ForkJoinPool 3 combine List<CartItem> priceValidationList = cart.getCartItemList() //.stream() .parallelStream() .map(cartItem -> { boolean isPriceValid = priceValidatorService.isCartItemInvalid(cartItem); cartItem.setExpired(isPriceValid); return cartItem; }) .filter(CartItem::isExpired) .collect(toList());
Comparing ArrayList vs LinkedList ParallelStreams Performance
Spliterator in ParallelStreams • Data source is split in to multiple chunks by the Spliterator • Each and every collection has a different Spliterator Implementation • Performance differ based on the implementation
Multiply each value in the collection by a user passed value [ 1, 2, 3, 4 ] -> [2, 4, 6, 8] Value * 2
Summary - Spliterator in ParallelStreams • Invoking parallelStream() does not guarantee faster performance of your code • Need to perform additional steps compared to sequential • Splitting , Executing and Combining Recommendation - Always compare the performance before you use parallelStream()
Parallel Streams Final Computation Result Order
Parallel Streams - Final Computation Result Order [Bob, Jamie, Jill, Rick] [3 - Bob, 5 - Jamie, 4 - Jill, 4 - Rick] 90 90 90 90
Parallel Streams - Final Computation Result Order • The order of the collection depends on: • Type of Collection • Spliterator Implementation of the collection • Example : ArrayList • Type of Collection - Ordered • Spliterator Implementation - Ordered Spliterator Implementation • Example : Set • Type of Collection - UnOrdered • Spliterator Implementation - UnOrdered Spliterator Implementation
Collect & Reduce
Collect( ) vs Reduce( ) • Part of Streams API • Used as a terminal operation in Streams API • Produces a single result • Result is produced in a mutable fashion • Feature rich and used for many different use cases • Example • collect(toList()), collect(toSet()) • collect(summingDouble(Double::doubleV alue)); • Part of Streams API • Used as a terminal operation in Streams API • Produces a single result • Result is produced in a immutable fashion • Reduce the computation into a single value • Sum, Multiplication • Example • Sum -> reduce(0.0, (x , y)->x+y) • Multiply -> reduce(1.0, (x , y)->x * y) Collect Reduce
How reduce() works ? 10 [ 1 ,2 ,3 ,4 ] 0+1 => 1 1+2 => 3 3+3 => 6 6+4 10 => identity The reduce( ) function performs an immutable computation throughout in each and every step.
How reduce() with ParallelStream works ? Sum of N numbers - reduce() and parallelStream() 36 [1,2,3,4,5,6,7,8] [ 1 ,2 ,3 ,4 ] [ 5 ,6 ,7 ,8 ] [ 1 ,2 ] [ 3 ,4 ] [ 5 ,6 ] [ 7 ,8 ] 3 7 11 15 10 26 36 Sum of N numbers Process Sequentially Process Sequentially Process Sequentially Process Sequentially Split Split Split Split Split Split reduce() reduce() reduce() reduce() 1 1 3 2 1 Spliterator 2 ForkJoinPool 3 Reduce 0 0 0 0
Want to learn more ?
Collect() & Reduce() Hands-On
Identity in reduce()
Identity in reduce() • Identity gives you the same value when its used in the computation • Addition: Identity = 0 • 0 + 1 => 1 • 0 + 20 => 20 • Multiplication : Identity = 1 • 1 * 1 => 1 • 1 * 20 => 20 Sum of N numbers - reduce() and parallelStream() reduce() is recommended for computations that are associative
Parallel Stream Operations & Poor Performance
Parallel Stream Operations & Poor Performance • Stream Operations that perform poor • Impact of Boxing and UnBoxing when it comes to parallel Streams • Boxing -> Converting a Primitive Type to Wrapper class equivalent • 1 -> new Integer(1) • UnBoxing -> Converting a Wrapper class to Primitive equivalent • new Integer(1) -> 1
Common ForkJoin Pool
Common ForkJoin Pool Execution Engine for Parallel Streams
parallelStream() - How it works ? combine Exe cute cartItemsList cartItemsSplit cartItemsSplit cartItem cartItem cartItem cartItem cartItem cartItem cartItem cartItem combine combine Process Sequentially Process Sequentially Process Sequentially Process Sequentially parallelStreams() Split Split Split cartItemsList [cartItem, cartitem] [cartItem, cartitem] CheckoutService 1 Split 2 Execute Common ForkJoinPool 3 combine
Common ForkJoin Pool Common ForkJoin Pool Shared Work Queue T1 T2 T3 T4 Double Ended Work Queue(deck) Worker Threads parallelStreams() Task Result 90 90 Watch “Fork-Join Framework” Lecture
Common ForkJoin Pool • Common ForkJoin Pool is used by: • ParallelStreams • CompletableFuture • Completable Future have options to use a User-defined ThreadPools • Common ForkJoin Pool is shared by the whole process
Parallelism & Threads in Common ForkJoinPool
Parallelism & Threads in Common ForkJoinPool • parallelStreams() • Runs your code in parallel • Improves the performance of the code • Is there a way to look in to parallelism and threads involved ? • Yes
Modifying Default parallelism in Parallel Streams
Modifying Default parallelism System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism", "100"); -Djava.util.concurrent.ForkJoinPool.common.parallelism=100 OR
Parallel Streams - Summary
Parallel Streams - When to use them ?
Parallel Streams - When to use them ? • Parallel Streams do a lot compared to sequential(default) Streams • Parallel Streams • Split • Execute • Combine
Parallel Streams - When to use them ? • Computation takes a longer time to complete • Lots of data • More cores in your machine Always compare the performance between sequential and parallel streams
Parallel Streams - When to use them ?
Parallel Streams - When not to use them ? • Parallel Streams • Split • Execute • Combine • Data set is small • Auto Boxing and Unboxing doesn’t perform better • Stream API operators -> iterate(), limit()
CompletableFuture
CompletableFuture • Introduced in Java 8 • CompletableFuture is an Asynchronous Reactive Functional Programming API • Asynchronous Computations in a functional Style • CompletableFutures API is created to solve the limitations of Future API
CompletableFuture and Reactive Programming • Responsive: • Fundamentally Asynchronous • Call returns immediately and the response will be sent when its available • Resilient: • Exception or error won’t crash the app or code • Elastic: • Asynchronous Computations normally run in a pool of threads • No of threads can go up or down based on the need • Message Driven: • Asynchronous computations interact with each through messages in a event-driven style 1 2 3 4
CompletableFuture API • Factory Methods • Initiate asynchronous computation • Completion Stage Methods • Chain asynchronous computation • Exception Methods • Handle Exceptions in an Asynchronous Computation E
Lets Write our First CompletableFuture
CompletableFuture supplyAsync() • FactoryMethod • Initiate Asynchronous computation • Input is Supplier Functional Interface • Returns CompletableFuture<T>() thenAccept() • CompletionStage Method • Chain Asynchronous Computation • Input is Consumer Functional Interface • Consumes the result of the previous • Returns CompletableFuture<Void> • Use it at the end of the Asynchronous computation
thenApply()
thenApply() • Completion Stage method • Transform the data from one form to another • Input is Function Functional Interface • Returns CompletableFuture<T>
CompletableFuture ]Pipeline Pipeline 1 2 3
Unit Testing CompletableFuture
Combing independent Async Tasks using “thenCombine"
thenCombine() • This is a Completion Stage Method • Used to Combine Independent Completable Futures • Takes two arguments • CompletionStage , BiFunction • Returns a CompletableFuture Client Service Service1 Service2 Latency = maxOf(Service1, Service2)
thenCompose
thenCompose() • Completion Stage method • Transform the data from one form to another • Input is Function Functional Interface • Deals with functions that return CompletableFuture • thenApply deals with Function that returns a value • Returns CompletableFuture<T> public CompletableFuture<String> worldFuture(String input) { return CompletableFuture.supplyAsync(()->{ delay(1000); return input+" world!"; }); }
Product Service Product Service Client ProductInfo Service Review Service ProductId Product Product
Combining Streams & CompletableFuture
Product Service with Inventory Product Service Client ProductInfo Service Review Service ProductId Product Product Inventory Service
Exception Handling In CompletableFuture
Exception Handling in Java • Exception Handling in Java is available since the inception of Java ]
Exception Handling in CompletableFuture • CompletableFuture is a functional style API
Exception Handling in CompletableFuture try/catch
Exception Handling in CompletableFuture • CompletableFuture API has functional style of handling exceptions • Three options available: • handle() • exceptionally() • whenComplete() Catch Exception and Recover Catch Exception and Does not Recover
Exception Handling using handle() Success Path hello thenCombine thenCombine thenApply handle handle handle handle Failure Path
Exception Handling using exceptionally() Success Path hello thenCombine thenCombine thenApply exceptionally Failure Path exceptionally
whenHandle()
whenHandle() • Exception handler in CompletableFuture API • Catches the Exception but does not recover from the exception
Exception Handling using whenComplete() Success Path hello thenCombine thenCombine thenApply whenComplete whenComplete whenComplete whenComplete Failure Path
Product Service with Inventory Product Service Client ProductInfo Service Review Service ProductId Product Product Inventory Service !
CompletableFuture Default ThreadPool
CompletableFuture - ThreadPool • By default, CompletableFuture uses the Common ForkJoinPool • The no of threads in the pool == number of cores
Common ForkJoin Pool Common ForkJoin Pool Shared Work Queue T1 T2 T3 T4 Double Ended Work Queue(deck) Worker Threads completableFuture() Task Result Watch “Internals of Common ForkJoin Pool” Lecture
CompletableFuture & User Defined ThreadPool using ExecutorService
Why use a different ThreadPool ? • Common ForkJoinPool is shared by • ParallelStreams • CompletableFuture • Its common for applications to use ParallelStreams and CompletableFuture together • The following issues may occur: • Thread being blocked by a time consuming task • Thread not available
Creating a User-Defined ThreadPool Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors());
Threads In CompletableFuture
Async() Overloaded Functions In CompletableFuture
Async Overloaded Functions • thenAccept()
Async() Overloaded Functions • thenCombine() • thenApply() • thenCompose() • thenAccept() • thenCombineAsync() • thenApplyAsync() • thenComposeAsync() • thenAcceptAsync() Regular Functions Async() overloaded Functions
Async() Overloaded Functions • Using async() functions allows you to change the thread of execution • Use this when you have blocking operations in your Completablefuture pipeline
Introduction to Spring WebClient and Overview of the GitHub Jobs API
About this section Rest Client Github Jobs API
Build Restful API Using Spring WebClient
Why Spring WebClient? • Spring is one of the popular framework in the Java Community • Spring WebClient is a rest client library that’s got released as part of Spring 5 • Spring WebClient is a functional style RestClient • Spring WebClient can be used as a blocking or non blocking Rest Client
Github Jobs API
allOf()
allOf() - Dealing with Multiple CompletableFutures • static method that’s part of CompletableFuture API • Use allOf() when you are dealing with Multiple CompletableFuture Client Service 1 Service 2 Service 2
anyOf()
anyOf() - Dealing with Multiple CompletableFutures • static method that’s part of CompletableFuture API • Use anyOf() when you are dealing with retrieving data from multiple Data Sources Client REST API SOAP Service DB
TimeOuts In CompletableFuture
Timeouts in CompletableFuture • Asynchronous tasks may run indefinitely • Used to timeout a task in CompletableFuture • orTimeout() in CompletableFutureAPI

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parallel-asynchronous-programming-java.pptx

  • 1.
  • 2.
    About Me • Dilip •Building Software’s since 2008 • Teaching in UDEMY Since 2016
  • 3.
    What's Covered ? •Need for Parallel and Asynchronous Programming • Covers the ParallelStreams and CompletableFuture API • Techniques to write Fast Performing Code using Functional Style Concurrency APIs in Java • Covers Best Practices using ParallelStreams/CompletableFuture API in your code • NonBlocking RestFul API Client using CompletableFuture API • Testing ParallelStreams and CompletableFuture API Using JUnit5
  • 4.
    Targeted Audience • ExperiencedJava Developers • Developers who has the need to write code that executes faster • Developers who has the need to write code that executes in Parallel • Developer who has the need to write asynchronous/non-blocking code
  • 5.
  • 6.
  • 7.
  • 8.
    Course Prerequisites • Java11 or Higher is needed • Prior Java knowledge is needed • Experience working with Lambdas, Streams , Method References • Experience writing JUnit tests • Intellij , Eclipse or any other IDE is needed
  • 9.
    Why Parallel andAsynchronous Programming ?
  • 10.
    Why Parallel andAsynchronous Programming? • We are living in a fast paced environment • In Software Programming: • Code that we write should execute faster • Goal of Asynchronous and Parallel Programming • Provide Techniques to improve the performance of the code
  • 11.
    Technology Advancements • Devicesor computers comes up with Multiple cores • Developer needs to learn programming patterns to maximize the use of multiple cores • Apply the Parallel Programming concepts • Parallel Streams • MicroServices Architecture style • Blocking I/O calls are common in MicroServices Architecture. This also impacts the latency of the application • Apply the Asynchronous Programming concepts • CompletableFuture Hardware Software Functional Style of Programming Threads
  • 12.
  • 13.
    Timeline 1996 2004 20112014 2017 Threads Java1 Single Core Multi Core Java5 ThreadPool, ExecutorService,Futures, ConcurrentCollections etc., Java7 Fork/Join FrameWork Data Parallelism Parallel Streams CompletableFutures Java8 Lamdas Streams API Java9 Flow API Current Version : Java14 https://en.wikipedia.org/wiki/Java_version_history
  • 14.
  • 15.
    Concurrency • Concurrency isa concept where two or more task can run simultaneously • In Java, Concurrency is achieved using Threads • Are the tasks running in interleaved fashion? • Are the tasks running simultaneously ? Single Core Threa d2 Threa d1 Threa d3 Core1 Core2 Core3 Core4 MultiCore Threa d1 Threa d2 Threa d3 Threa d4
  • 16.
    Concurrency Example public classHelloWorldThreadExample { private static String result=""; private static void hello(){ delay(500); result = result.concat("Hello"); } private static void world(){ delay(600); result = result.concat(“ World"); } public static void main(String[] args) throws InterruptedException { Thread helloThread = new Thread(()-> hello()); Thread worldThread = new Thread(()-> world()); //Starting the thread helloThread.start(); worldThread.start(); //Joining the thread (Waiting for the threads to finish) helloThread.join(); worldThread.join(); System.out.println("Result is : " + result); } } Hello World } Threads 1 2 3 • In a real application, Threads normally need to interact with one another • Shared Objects or Messaging Queues • Issues: • Race Condition • DeadLock and more • Tools to handle these issues: • Synchronized Statements/Methods • Reentrant Locks, Semaphores • Concurrent Collections • Conditional Objects and More 0 0
  • 17.
    Parallelism • Parallelism isa concept in which tasks are literally going to run in parallel • Parallelism involves these steps: • Decomposing the tasks in to SubTasks(Forking) • Execute the subtasks in sequential • Joining the results of the tasks(Join) • Whole process is also called Fork/Join 1 join Task SubTask1 SubTask2 SubTask1a SubTask1b SubTask2a SubTask2b join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork 3 2
  • 18.
    Parallelism Example UseCase: Transformto UpperCase [Bob, Jamie, Jill, Rick] -> [BOB, JAMIE, JILL, RICK] 1 3 2 join Bob, Jamie, Jill, Rick [Bob,Jamie] [Jill,Rick] Bob Jamie Jill Rick BOB JAMI E JILL RICK join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork BOB, JAMIE, JILL, RICK [BOB, JAMIE] [JILL, RICK] Bob Jamie Jill Rick Core1 Core2 Core3 Core4 BOB JAMIE JILL RICK uppercase uppercase uppercase uppercase
  • 19.
    Parallelism Example public classParallelismExample { public static void main(String[] args) { List<String> namesList = List.of("Bob", "Jamie", "Jill", "Rick"); System.out.println("namesList : " + namesList); List<String> namesListUpperCase = namesList .parallelStream() .map(String::toUpperCase) .collect(Collectors.toList()); System.out.println("namesListUpperCase : " + namesListUpperCase); } }
  • 20.
    Concurrency vs Parallelism •Concurrency is a concept where two or more tasks can run in simultaneously • Concurrency can be implemented in single or multiple cores • Concurrency is about correctly and efficiently controlling access to shared resources • Parallelism is a concept where two or more tasks are literally running in parallel • Parallelism can only be implemented in a multi-core machine • Parallelism is about using more resources to access the result faster
  • 21.
  • 22.
  • 23.
    Section Overview • CoversAsynchronous and Parallel Programming prior Java 8 • Threads, Futures and ForkJoin Framework and its limitations • Covers Theory and Hands On
  • 24.
  • 25.
  • 26.
  • 27.
    Threads API • ThreadsAPI got introduced in Java1 • Threads are basically used to offload the blocking tasks as background tasks • Threads allowed the developers to write asynchronous style of code
  • 28.
    Thread API Limitations •Requires a lot of code to introduce asynchrony • Runnable, Thread • Require additional properties in Runnable • Start and Join the thread • Low level • Easy to introduce complexity in to our code
  • 29.
  • 30.
    Limitations Of Thread •Limitations of Thread: • Create the thread • Start the thread • Join the thread • Threads are expensive • Threads have their own runtime-stack, memory, registers and more Thread Pool was created specifically to solve this problem
  • 31.
    • Thread Poolis a group of threads created and readily available • CPU Intensive Tasks • ThreadPool Size = No of Cores • I/O task • ThreadPool Size > No of Cores • What are the benefits of thread pool? • No need to manually create, start and join the threads • Achieving Concurrency in your application Thread Pool TQ T1 TQ T2 TQ T3 TQ T4 ThreadPool
  • 32.
    ExecutorService • Released aspart of Java5 • ExecutorService in Java is an Asynchronous Task Execution Engine • It provides a way to asynchronously execute tasks and provides the results in a much simpler way compared to threads • This enabled coarse-grained task based parallelism in Java
  • 33.
  • 34.
  • 35.
    Limitations of ExecutorService •Designed to Block the Thread • No better way to combine futures ProductInfo productInfo = productInfoFuture.get(); Review review = reviewFuture.get(); return new Product(productId, productInfo, review); ProductInfo productInfo = productInfoFuture.get(); Review review = reviewFuture.get();
  • 36.
  • 37.
    Fork/Join Framework • Thisgot introduced as part of Java7 • This is an extension of ExecutorService • Fork/Join framework is designed to achieve Data Parallelism • ExecutorService is designed to achieve Task Based Parallelism Future<ProductInfo> productInfoFuture = executorService.submit(() -> productInfoService.retrieveProductInfo(productId)); Future<Review> reviewFuture = executorService.submit(() -> reviewService.retrieveReviews(productId));
  • 38.
    What is DataParallelism ? • Data Parallelism is a concept where a given Task is recursively split in to SubTasks until it reaches it leaset possible size and execute those tasks in parallel • Basically it uses the divide and conquer approach 1 3 2 join Bob, Jamie, Jill, Rick [Bob,Jamie] [Jill,Rick] Bob Jamie Jill Rick BOB JAMI E JILL RICK join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork BOB, JAMIE, JILL, RICK [BOB, JAMIE] [JILL, RICK] Watch "Concurrency vs Parallelism”
  • 39.
    How does Fork/JoinFramework Works ? ForkJoin Pool to support Data Parallelism
  • 40.
    ForkJoin Pool ForkJoin Pool SharedWork Queue Client Task T1 T2 T3 T4 Double Ended Work Queue(deck) Worker Threads Tas k Tas k ForkJoinTask Tas k WorkStealing Tas k Tas k Tas k Tas k Result
  • 41.
    ForkJoin Task • ForkJoinTask represents part of the data and its computation • Type of tasks to submit to ForkJoin Pool • ForkJoinTask • RecursiveTask -> Task that returns a value • RecursiveAction -> Task that does not return a value join Bob, Jamie, Jill, Rick [Bob,Jamie] [Jill,Rick] Bob Jamie Jill Rick BOB JAMI E JILL RICK join join Process Sequentially Process Sequentially Process Sequentially Process Sequentially Fork/Join (Parallelism) fork fork fork BOB, JAMIE, JILL, RICK [BOB, JAMIE] [JILL, RICK] Fork/Join Task
  • 42.
  • 43.
    ForkJoin - UseCase [Bob,Jamie, Jill, Rick] -> [3 - Bob, 5 - Jamie, 4 - Jill, 4 - Rick] Input Output
  • 44.
  • 45.
    Streams API • StreamsAPI got introduced in Java 8 • Streams API is used to process a collection of Objects • Streams in Java are created by using the stream() method 1 2 3 ]Pipeline Pipeline Intermediate Terminal
  • 47.
    ParallelStreams • This allowsyour code to run in parallel • ParallelStreams are designed to solve Data Parallelism Watch “Concurrency vs Parallelism” & “Fork-Join Framework”
  • 48.
  • 49.
    Parallel Streams -UseCase [Bob, Jamie, Jill, Rick] -> [3 - Bob, 5 - Jamie, 4 - Jill, 4 - Rick] Input Output
  • 50.
  • 51.
    Why Unit Tests? • Unit Testing allows you to programmatically test your code • Manual Testing slows down the development and delivery • Unit Testing allows the developer or the app team to make enhancements to the existing code easily and faster
  • 52.
  • 53.
    sequential() and parallel() •Streams API are sequential by default • sequential() -> Executes the stream in sequential • parallel() -> Executes the stream in parallel • Both the functions() changes the behavior of the whole pipeline
  • 54.
    sequential() • Changing theparallelStream() behavior to sequential Sequential
  • 55.
    parallel() • Changing thestream() behavior to parallel Parallel
  • 56.
    When to usesequential() and parallel() ? Used these functions when I would like to evaluate between sequential() and parallel()
  • 57.
  • 58.
    Checkout Service(BackEnd) Checkout Service N PriceValidator Service CartItem $ UP DOWN
  • 59.
  • 60.
    ParallelStreams - Howit works ? • parallelStream() • Split the data in to chunks • Execute the data chunks • Combine the result
  • 61.
    parallelStream() - Howit works ? • Split • Data Source is split in to small data chunks • Example - List Collection split into chunks of elements to size 1 • This is done using Spliterators • For ArrayList, the Spliterator is ArrayListSpliterator • Execute • Data chunks are applied to the Stream Pipeline and the Intermediate operations executed in a Common ForkJoin Pool • Watch the Fork/Join FrameWork Lectures • Combine • Combine the executed results into a final result • Combine phase in Streams API maps to terminal operations • Uses collect() and reduce() functions • collect(toList())
  • 62.
    parallelStream() - Howit works ? combine Exe cute cartItemsList cartItemsSplit cartItemsSplit cartItem cartItem cartItem cartItem cartItem cartItem cartItem cartItem combine combine Process Sequentially Process Sequentially Process Sequentially Process Sequentially parallelStreams() Split Split Split cartItemsList [cartItem, cartitem] [cartItem, cartitem] CheckoutService 1 Split 2 Execute Common ForkJoinPool 3 combine List<CartItem> priceValidationList = cart.getCartItemList() //.stream() .parallelStream() .map(cartItem -> { boolean isPriceValid = priceValidatorService.isCartItemInvalid(cartItem); cartItem.setExpired(isPriceValid); return cartItem; }) .filter(CartItem::isExpired) .collect(toList());
  • 63.
  • 64.
    Spliterator in ParallelStreams •Data source is split in to multiple chunks by the Spliterator • Each and every collection has a different Spliterator Implementation • Performance differ based on the implementation
  • 65.
    Multiply each valuein the collection by a user passed value [ 1, 2, 3, 4 ] -> [2, 4, 6, 8] Value * 2
  • 66.
    Summary - Spliteratorin ParallelStreams • Invoking parallelStream() does not guarantee faster performance of your code • Need to perform additional steps compared to sequential • Splitting , Executing and Combining Recommendation - Always compare the performance before you use parallelStream()
  • 67.
  • 68.
    Parallel Streams -Final Computation Result Order [Bob, Jamie, Jill, Rick] [3 - Bob, 5 - Jamie, 4 - Jill, 4 - Rick] 90 90 90 90
  • 69.
    Parallel Streams -Final Computation Result Order • The order of the collection depends on: • Type of Collection • Spliterator Implementation of the collection • Example : ArrayList • Type of Collection - Ordered • Spliterator Implementation - Ordered Spliterator Implementation • Example : Set • Type of Collection - UnOrdered • Spliterator Implementation - UnOrdered Spliterator Implementation
  • 70.
  • 71.
    Collect( ) vsReduce( ) • Part of Streams API • Used as a terminal operation in Streams API • Produces a single result • Result is produced in a mutable fashion • Feature rich and used for many different use cases • Example • collect(toList()), collect(toSet()) • collect(summingDouble(Double::doubleV alue)); • Part of Streams API • Used as a terminal operation in Streams API • Produces a single result • Result is produced in a immutable fashion • Reduce the computation into a single value • Sum, Multiplication • Example • Sum -> reduce(0.0, (x , y)->x+y) • Multiply -> reduce(1.0, (x , y)->x * y) Collect Reduce
  • 72.
    How reduce() works? 10 [ 1 ,2 ,3 ,4 ] 0+1 => 1 1+2 => 3 3+3 => 6 6+4 10 => identity The reduce( ) function performs an immutable computation throughout in each and every step.
  • 73.
    How reduce() withParallelStream works ? Sum of N numbers - reduce() and parallelStream() 36 [1,2,3,4,5,6,7,8] [ 1 ,2 ,3 ,4 ] [ 5 ,6 ,7 ,8 ] [ 1 ,2 ] [ 3 ,4 ] [ 5 ,6 ] [ 7 ,8 ] 3 7 11 15 10 26 36 Sum of N numbers Process Sequentially Process Sequentially Process Sequentially Process Sequentially Split Split Split Split Split Split reduce() reduce() reduce() reduce() 1 1 3 2 1 Spliterator 2 ForkJoinPool 3 Reduce 0 0 0 0
  • 74.
  • 75.
  • 76.
  • 77.
    Identity in reduce() •Identity gives you the same value when its used in the computation • Addition: Identity = 0 • 0 + 1 => 1 • 0 + 20 => 20 • Multiplication : Identity = 1 • 1 * 1 => 1 • 1 * 20 => 20 Sum of N numbers - reduce() and parallelStream() reduce() is recommended for computations that are associative
  • 78.
  • 79.
    Parallel Stream Operations& Poor Performance • Stream Operations that perform poor • Impact of Boxing and UnBoxing when it comes to parallel Streams • Boxing -> Converting a Primitive Type to Wrapper class equivalent • 1 -> new Integer(1) • UnBoxing -> Converting a Wrapper class to Primitive equivalent • new Integer(1) -> 1
  • 80.
  • 81.
    Common ForkJoin Pool ExecutionEngine for Parallel Streams
  • 82.
    parallelStream() - Howit works ? combine Exe cute cartItemsList cartItemsSplit cartItemsSplit cartItem cartItem cartItem cartItem cartItem cartItem cartItem cartItem combine combine Process Sequentially Process Sequentially Process Sequentially Process Sequentially parallelStreams() Split Split Split cartItemsList [cartItem, cartitem] [cartItem, cartitem] CheckoutService 1 Split 2 Execute Common ForkJoinPool 3 combine
  • 83.
    Common ForkJoin Pool CommonForkJoin Pool Shared Work Queue T1 T2 T3 T4 Double Ended Work Queue(deck) Worker Threads parallelStreams() Task Result 90 90 Watch “Fork-Join Framework” Lecture
  • 84.
    Common ForkJoin Pool •Common ForkJoin Pool is used by: • ParallelStreams • CompletableFuture • Completable Future have options to use a User-defined ThreadPools • Common ForkJoin Pool is shared by the whole process
  • 85.
  • 86.
    Parallelism & Threadsin Common ForkJoinPool • parallelStreams() • Runs your code in parallel • Improves the performance of the code • Is there a way to look in to parallelism and threads involved ? • Yes
  • 87.
  • 88.
    Modifying Default parallelism System.setProperty("java.util.concurrent.ForkJoinPool.common.parallelism","100"); -Djava.util.concurrent.ForkJoinPool.common.parallelism=100 OR
  • 89.
  • 90.
    Parallel Streams -When to use them ?
  • 91.
    Parallel Streams -When to use them ? • Parallel Streams do a lot compared to sequential(default) Streams • Parallel Streams • Split • Execute • Combine
  • 92.
    Parallel Streams -When to use them ? • Computation takes a longer time to complete • Lots of data • More cores in your machine Always compare the performance between sequential and parallel streams
  • 93.
    Parallel Streams -When to use them ?
  • 94.
    Parallel Streams -When not to use them ? • Parallel Streams • Split • Execute • Combine • Data set is small • Auto Boxing and Unboxing doesn’t perform better • Stream API operators -> iterate(), limit()
  • 95.
  • 96.
    CompletableFuture • Introduced inJava 8 • CompletableFuture is an Asynchronous Reactive Functional Programming API • Asynchronous Computations in a functional Style • CompletableFutures API is created to solve the limitations of Future API
  • 97.
    CompletableFuture and ReactiveProgramming • Responsive: • Fundamentally Asynchronous • Call returns immediately and the response will be sent when its available • Resilient: • Exception or error won’t crash the app or code • Elastic: • Asynchronous Computations normally run in a pool of threads • No of threads can go up or down based on the need • Message Driven: • Asynchronous computations interact with each through messages in a event-driven style 1 2 3 4
  • 98.
    CompletableFuture API • FactoryMethods • Initiate asynchronous computation • Completion Stage Methods • Chain asynchronous computation • Exception Methods • Handle Exceptions in an Asynchronous Computation E
  • 99.
  • 100.
    CompletableFuture supplyAsync() • FactoryMethod • InitiateAsynchronous computation • Input is Supplier Functional Interface • Returns CompletableFuture<T>() thenAccept() • CompletionStage Method • Chain Asynchronous Computation • Input is Consumer Functional Interface • Consumes the result of the previous • Returns CompletableFuture<Void> • Use it at the end of the Asynchronous computation
  • 101.
  • 102.
    thenApply() • Completion Stagemethod • Transform the data from one form to another • Input is Function Functional Interface • Returns CompletableFuture<T>
  • 103.
  • 104.
  • 105.
  • 106.
    thenCombine() • This isa Completion Stage Method • Used to Combine Independent Completable Futures • Takes two arguments • CompletionStage , BiFunction • Returns a CompletableFuture Client Service Service1 Service2 Latency = maxOf(Service1, Service2)
  • 107.
  • 108.
    thenCompose() • Completion Stagemethod • Transform the data from one form to another • Input is Function Functional Interface • Deals with functions that return CompletableFuture • thenApply deals with Function that returns a value • Returns CompletableFuture<T> public CompletableFuture<String> worldFuture(String input) { return CompletableFuture.supplyAsync(()->{ delay(1000); return input+" world!"; }); }
  • 109.
  • 110.
  • 111.
    Product Service withInventory Product Service Client ProductInfo Service Review Service ProductId Product Product Inventory Service
  • 112.
  • 113.
    Exception Handling inJava • Exception Handling in Java is available since the inception of Java ]
  • 114.
    Exception Handling inCompletableFuture • CompletableFuture is a functional style API
  • 115.
    Exception Handling inCompletableFuture try/catch
  • 116.
    Exception Handling inCompletableFuture • CompletableFuture API has functional style of handling exceptions • Three options available: • handle() • exceptionally() • whenComplete() Catch Exception and Recover Catch Exception and Does not Recover
  • 117.
    Exception Handling usinghandle() Success Path hello thenCombine thenCombine thenApply handle handle handle handle Failure Path
  • 118.
    Exception Handling usingexceptionally() Success Path hello thenCombine thenCombine thenApply exceptionally Failure Path exceptionally
  • 119.
  • 120.
    whenHandle() • Exception handlerin CompletableFuture API • Catches the Exception but does not recover from the exception
  • 121.
    Exception Handling usingwhenComplete() Success Path hello thenCombine thenCombine thenApply whenComplete whenComplete whenComplete whenComplete Failure Path
  • 122.
    Product Service withInventory Product Service Client ProductInfo Service Review Service ProductId Product Product Inventory Service !
  • 123.
  • 124.
    CompletableFuture - ThreadPool •By default, CompletableFuture uses the Common ForkJoinPool • The no of threads in the pool == number of cores
  • 125.
    Common ForkJoin Pool CommonForkJoin Pool Shared Work Queue T1 T2 T3 T4 Double Ended Work Queue(deck) Worker Threads completableFuture() Task Result Watch “Internals of Common ForkJoin Pool” Lecture
  • 126.
  • 127.
    Why use adifferent ThreadPool ? • Common ForkJoinPool is shared by • ParallelStreams • CompletableFuture • Its common for applications to use ParallelStreams and CompletableFuture together • The following issues may occur: • Thread being blocked by a time consuming task • Thread not available
  • 128.
    Creating a User-DefinedThreadPool Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors());
  • 129.
  • 130.
  • 131.
  • 132.
    Async() Overloaded Functions •thenCombine() • thenApply() • thenCompose() • thenAccept() • thenCombineAsync() • thenApplyAsync() • thenComposeAsync() • thenAcceptAsync() Regular Functions Async() overloaded Functions
  • 133.
    Async() Overloaded Functions •Using async() functions allows you to change the thread of execution • Use this when you have blocking operations in your Completablefuture pipeline
  • 134.
  • 135.
    About this section RestClient Github Jobs API
  • 136.
  • 137.
    Why Spring WebClient? •Spring is one of the popular framework in the Java Community • Spring WebClient is a rest client library that’s got released as part of Spring 5 • Spring WebClient is a functional style RestClient • Spring WebClient can be used as a blocking or non blocking Rest Client
  • 138.
  • 139.
  • 140.
    allOf() - Dealingwith Multiple CompletableFutures • static method that’s part of CompletableFuture API • Use allOf() when you are dealing with Multiple CompletableFuture Client Service 1 Service 2 Service 2
  • 141.
  • 142.
    anyOf() - Dealingwith Multiple CompletableFutures • static method that’s part of CompletableFuture API • Use anyOf() when you are dealing with retrieving data from multiple Data Sources Client REST API SOAP Service DB
  • 143.
  • 144.
    Timeouts in CompletableFuture •Asynchronous tasks may run indefinitely • Used to timeout a task in CompletableFuture • orTimeout() in CompletableFutureAPI