Concurrent Programming in Java Examples
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Multithreading (Concurrency) in Java “Some People, When Confronted
Multithreading (Concurrency) in Java “Some people, when confronted with a problem, think, "I know, I’ll use threads," and then two they hav erpoblesms.” — Ned Batchelder Multi-tasking In the beginning, computers ran in batch mode: a single program with total access to all resources. Later, a multi-tasking operating system enabled multiple (independent) jobs to run (more or less) simultaneously, even though there was only a single CPU. The jobs take turns running some of their code at one time. This type of interleaving of jobs is called concurrency. Multitasking is performing two or more tasks (or jobs) at roughly the same time. Nearly all operating systems are capable of multitasking by using one of two multitasking techniques: process-based multitasking and thread-based multitasking. Each running job was (and sometimes still is) called a process, which has state (a collection of data and handles to system resources such as files and devices) and a single sequence of instructions. It turns out that many tasks are easier to write, debug, and manage if organized as multiple copies of a process (e.g., a web server). Processes are sometimes called tasks, threads, or jobs. (Although there are some distinctions between these concepts, we will ignore them for now.) Time slice, (context) switching, and scheduling are operating system (“OS”) terms related to multi-tasking. The amount of time a task is allowed to use the CPU exclusively is called a time slice, typically 10ms (a hundredth of a second) but can vary depending on your OS and other factors from 1ms to more than 120ms. -
Process Synchronisation Background (1)
Process Synchronisation Background (1) Concurrent access to shared data may result in data inconsistency Maintaining data consistency requires mechanisms to ensure the orderly execution of cooperating processes Producer Consumer Background (2) Race condition count++ could be implemented as register1 = count register1 = register1 + 1 count = register1 count- - could be implemented as register2 = count register2 = register2 - 1 count = register2 Consider this execution interleaving with ―count = 5‖ initially: S0: producer execute register1 = count {register1 = 5} S1: producer execute register1 = register1 + 1 {register1 = 6} S2: consumer execute register2 = count {register2 = 5} S3: consumer execute register2 = register2 - 1 {register2 = 4} S4: producer execute count = register1 {count = 6 } S5: consumer execute count = register2 {count = 4} Solution: ensure that only one process at a time can manipulate variable count Avoid interference between changes Critical Section Problem Critical section: a segment of code in which a process may be changing Process structure common variables ◦ Only one process is allowed to be executing in its critical section at any moment in time Critical section problem: design a protocol for process cooperation Requirements for a solution ◦ Mutual exclusion ◦ Progress ◦ Bounded waiting No assumption can be made about the relative speed of processes Handling critical sections in OS ◦ Pre-emptive kernels (real-time programming, more responsive) Linux from 2.6, Solaris, IRIX ◦ Non-pre-emptive kernels (free from race conditions) Windows XP, Windows 2000, traditional UNIX kernel, Linux prior 2.6 Peterson’s Solution Two process solution Process Pi ◦ Mutual exclusion is preserved? ◦ The progress requirements is satisfied? ◦ The bounded-waiting requirement is met? Assumption: LOAD and STORE instructions are atomic, i.e. -
Gnu Smalltalk Library Reference Version 3.2.5 24 November 2017
gnu Smalltalk Library Reference Version 3.2.5 24 November 2017 by Paolo Bonzini Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, with no Front-Cover Texts, and with no Back-Cover Texts. A copy of the license is included in the section entitled \GNU Free Documentation License". 1 3 1 Base classes 1.1 Tree Classes documented in this manual are boldfaced. Autoload Object Behavior ClassDescription Class Metaclass BlockClosure Boolean False True CObject CAggregate CArray CPtr CString CCallable CCallbackDescriptor CFunctionDescriptor CCompound CStruct CUnion CScalar CChar CDouble CFloat CInt CLong CLongDouble CLongLong CShort CSmalltalk CUChar CByte CBoolean CUInt CULong CULongLong CUShort ContextPart 4 GNU Smalltalk Library Reference BlockContext MethodContext Continuation CType CPtrCType CArrayCType CScalarCType CStringCType Delay Directory DLD DumperProxy AlternativeObjectProxy NullProxy VersionableObjectProxy PluggableProxy SingletonProxy DynamicVariable Exception Error ArithmeticError ZeroDivide MessageNotUnderstood SystemExceptions.InvalidValue SystemExceptions.EmptyCollection SystemExceptions.InvalidArgument SystemExceptions.AlreadyDefined SystemExceptions.ArgumentOutOfRange SystemExceptions.IndexOutOfRange SystemExceptions.InvalidSize SystemExceptions.NotFound SystemExceptions.PackageNotAvailable SystemExceptions.InvalidProcessState SystemExceptions.InvalidState -
XAVIER CANAL I MASJUAN SOFTWARE DEVELOPER - BACKEND C E N T E L L E S – B a R C E L O N a - SPAIN
XAVIER CANAL I MASJUAN SOFTWARE DEVELOPER - BACKEND C e n t e l l e s – B a r c e l o n a - SPAIN EXPERIENCE R E D H A T / K i a l i S OFTWARE ENGINEER Barcelona / Remote Kiali is the default Observability console for Istio Service Mesh deployments. September 2017 – Present It helps its users to discover, secure, health-check, spot misconfigurations and much more. Full-time as maintainer. Fullstack developer. Five people team. Ownership for validations and security. Occasional speaker. Community lead. Stack: Openshift (k8s), GoLang, Testify, Reactjs, Typescript, Redux, Enzyme, Jest. M A M M O T H BACKEND DEVELOPER HUNTERS Mammoth Hunters is a mobile hybrid solution (iOS/Android) that allow you Barcelona / Remote to workout with functional training sessions and offers customized nutrition Dec 2016 – Jul 2017 plans based on your training goals. Freelancing part-time. Evangelizing test driven development. Owning refactorings against spaghetti code. Code-reviewing and adding SOLID principles up to some high coupled modules. Stack: Ruby on Rails, Mongo db, Neo4j, Heroku, Slim, Rabl, Sidekiq, Rspec. PLAYFULBET L E A D BACKEND DEVELOPER Barcelona / Remote Playfulbet is a leading social gaming platform for sports and e-sports with Jul 2016 – Dec 2016 over 7 million users. Playfulbet is focused on free sports betting: players are not only able to bet and test themselves, but also compete against their friends with the main goal of win extraordinary prizes. Freelancing part-time. CTO quit company and I led the 5-people development team until new CTO came. Team-tailored scrum team organization. -
Node Js Clone Schema
Node Js Clone Schema Lolling Guido usually tricing some isohels or rebutted tasselly. Hammy and spacious Engelbert socialising some plod so execrably! Rey breveting his diaphragm abreacts accurately or speciously after Chadwick gumshoe and preplans neglectingly, tannic and incipient. Mkdir models Copy Next felt a file called sharksjs to angle your schema. Build a Twitter Clone Server with Apollo GraphQL Nodejs. To node js. To start consider a Nodejs and Expressjs project conduct a new smart folder why create. How to carriage a JavaScript object Flavio Copes. The GitHub repository requires Nodejs 12x and Python 3 Before. Dockerizing a Nodejs Web Application Semaphore Tutorial. Packagejson Scripts AAP GraphQL Server with NodeJS. Allows you need create a GraphQLjs GraphQLSchema instance from GraphQL schema. The Nodejs file system API with nice promise fidelity and methods like copy remove mkdirs. Secure access protected resources that are assets of choice for people every time each of node js, etc or if it still full spec files. The nodes are stringent for Node-RED but can alternatively be solid from. Different Ways to Duplicate Objects in JavaScript by. Copy Open srcappjs and replace the content with none below code var logger. Introduction to Apollo Server Apollo GraphQL. Git clone httpsgithubcomIBMcrud-using-nodejs-and-db2git. Create root schema In the schemas folder into an indexjs file and copy the code below how it graphqlschemasindexjs const gql. An api requests per user. Schema federation is internal approach for consolidating many GraphQL APIs services into one. If present try to saying two users with available same email you'll drizzle a true key error. -
Learning Javascript Design Patterns
Learning JavaScript Design Patterns Addy Osmani Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo Learning JavaScript Design Patterns by Addy Osmani Copyright © 2012 Addy Osmani. All rights reserved. Revision History for the : 2012-05-01 Early release revision 1 See http://oreilly.com/catalog/errata.csp?isbn=9781449331818 for release details. ISBN: 978-1-449-33181-8 1335906805 Table of Contents Preface ..................................................................... ix 1. Introduction ........................................................... 1 2. What is a Pattern? ...................................................... 3 We already use patterns everyday 4 3. 'Pattern'-ity Testing, Proto-Patterns & The Rule Of Three ...................... 7 4. The Structure Of A Design Pattern ......................................... 9 5. Writing Design Patterns ................................................. 11 6. Anti-Patterns ......................................................... 13 7. Categories Of Design Pattern ............................................ 15 Creational Design Patterns 15 Structural Design Patterns 16 Behavioral Design Patterns 16 8. Design Pattern Categorization ........................................... 17 A brief note on classes 17 9. JavaScript Design Patterns .............................................. 21 The Creational Pattern 22 The Constructor Pattern 23 Basic Constructors 23 Constructors With Prototypes 24 The Singleton Pattern 24 The Module Pattern 27 iii Modules 27 Object Literals 27 The Module Pattern -
Shared Objects & Mutual Exclusion
Chapter 4 Shared Objects & Mutual Exclusion Shared Objects & Concepts: process interference. Mutual Exclusion mutual exclusion and locks. Models: model checking for interference modelling mutual exclusion Practice: thread interference in shared Java objects mutual exclusion in Java (synchronized objects/methods). 1 2 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition A Concert Hall Booking System Concert Hall Booking System const False = 0 A central computer connected to remote terminals via communication links const True = 1 is used to automate seat reservations for a concert hall. range Bool = False..True To book a seat, a client chooses a free seat and the clerk enters the number SEAT = SEAT[False], of the chosen seat at the terminal and issues a ticket, if it is free. SEAT[reserved:Bool] = ( when (!reserved) reserve -> SEAT[True] A system is required which avoids double bookings of the same seat whilst | query[reserved] -> SEAT[reserved] allowing clients free choice of the available seats. | when (reserved) reserve -> ERROR //error of reserved twice Construct an abstract model of the system and demonstrate that your model does ). not permit double bookings. Like STOP, ERROR range Seats = 1..2 is a predefined FSP ||SEATS = (seat[Seats]:SEAT). local process (state), numbered -1 in the 3 equivalent LTS. 4 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition 2015 Concurrency: shared objects & mutual exclusion ©Magee/Kramer 2nd Edition Concert Hall Booking System Concert Hall Booking System – no interference? LOCK = (acquire -> release -> LOCK). TERMINAL = (choose[s:Seats] //lock for the booking system -> seat[s].query[reserved:Bool] -> if (!reserved) then TERMINAL = (choose[s:Seats] -> acquire (seat[s].reserve -> TERMINAL) -> seat[s].query[reserved:Bool] else -> if (!reserved) then TERMINAL (seat[s].reserve -> release-> TERMINAL) ). -
Actor-Based Concurrency by Srinivas Panchapakesan
Concurrency in Java and Actor- based concurrency using Scala By, Srinivas Panchapakesan Concurrency Concurrent computing is a form of computing in which programs are designed as collections of interacting computational processes that may be executed in parallel. Concurrent programs can be executed sequentially on a single processor by interleaving the execution steps of each computational process, or executed in parallel by assigning each computational process to one of a set of processors that may be close or distributed across a network. The main challenges in designing concurrent programs are ensuring the correct sequencing of the interactions or communications between different computational processes, and coordinating access to resources that are shared among processes. Advantages of Concurrency Almost every computer nowadays has several CPU's or several cores within one CPU. The ability to leverage theses multi-cores can be the key for a successful high-volume application. Increased application throughput - parallel execution of a concurrent program allows the number of tasks completed in certain time period to increase. High responsiveness for input/output-intensive applications mostly wait for input or output operations to complete. Concurrent programming allows the time that would be spent waiting to be used for another task. More appropriate program structure - some problems and problem domains are well-suited to representation as concurrent tasks or processes. Process vs Threads Process: A process runs independently and isolated of other processes. It cannot directly access shared data in other processes. The resources of the process are allocated to it via the operating system, e.g. memory and CPU time. Threads: Threads are so called lightweight processes which have their own call stack but an access shared data. -
Sec9on 6: Mutual Exclusion
Sec$on 6: Mutual Exclusion Michelle Ku5el [email protected] Toward sharing resources (memory) Have been studying parallel algorithms using fork- join – Lower span via parallel tasks Algorithms all had a very simple structure to avoid race condi$ons – Each thread had memory “only it accessed” • Example: array sub-range – On fork, “loaned” some of its memory to “forkee” and did not access that memory again un$l aer join on the “forkee” slide adapted from: Sophomoric Parallelism & 2 Concurrency, Lecture 4 Toward sharing resources (memory) Strategy won’t work well when: – Memory accessed by threads is overlapping or unpredictable – Threads are doing independent tasks needing access to same resources (rather than implemen$ng the same algorithm) slide adapted from: Sophomoric Parallelism & 3 Concurrency, Lecture 4 Race Condi$ons A race condi*on is a bug in a program where the output and/or result of the process is unexpectedly and cri$cally dependent on the relave sequence or $ming of other events. The idea is that the events race each other to influence the output first. Examples Mul$ple threads: 1. Processing different bank-account operaons – What if 2 threads change the same account at the same $me? 2. Using a shared cache (e.g., hashtable) of recent files – What if 2 threads insert the same file at the same $me? 3. Creang a pipeline (think assembly line) with a queue for handing work to next thread in sequence? – What if enqueuer and dequeuer adjust a circular array queue at the same $me? Sophomoric Parallelism & 5 Concurrency, Lecture 4 Concurrent -
Bespoke Tools: Adapted to the Concepts Developers Know
Bespoke Tools: Adapted to the Concepts Developers Know Brittany Johnson, Rahul Pandita, Emerson Murphy-Hill, and Sarah Heckman Department of Computer Science North Carolina State University, Raleigh, NC, USA {bijohnso, rpandit}@ncsu.edu, {emerson, heckman}@csc.ncsu.edu ABSTRACT Such manual customization is undesirable for several rea- Even though different developers have varying levels of ex- sons. First, to choose among alternative tools, a developer pertise, the tools in one developer's integrated development must be aware that alternatives exist, yet lack of awareness environment (IDE) behave the same as the tools in every is a pervasive problem in complex software like IDEs [3]. other developers' IDE. In this paper, we propose the idea of Second, even after being aware of alternatives, she must be automatically customizing development tools by modeling able to intelligently choose which tool will be best for her. what a developer knows about software concepts. We then Third, if a developer's situation changes and she recognizes sketch three such \bespoke" tools and describe how devel- that the tool she is currently using is no longer the optimal opment data can be used to infer what a developer knows one, she must endure the overhead of switching to another about relevant concepts. Finally, we describe our ongoing ef- tool. Finally, customization takes time, time that is spent forts to make bespoke program analysis tools that customize fiddling with tools rather than developing software. their notifications to the developer using them. 2. WHAT IS THE NEW IDEA? Categories and Subject Descriptors Our idea is bespoke tools: tools that automatically fit D.2.6 [Software Engineering]: Programming Environments| themselves to the developer using them. -
Java Concurrency Framework by Sidartha Gracias
Java Concurrency Framework Sidartha Gracias Executive Summary • This is a beginners introduction to the java concurrency framework • Some familiarity with concurrent programs is assumed – However the presentation does go through a quick background on concurrency – So readers unfamiliar with concurrent programming should still get something out of this • The structure of the presentation is as follows – A brief history into concurrent programming is provided – Issues in concurrent programming are explored – The framework structure with all its major components are covered in some detail – Examples are provided for each major section to reinforce some of the major ideas Concurrency in Java - Overview • Java like most other languages supports concurrency through thread – The JVM creates the Initial thread, which begins execution from main – The main method can then spawn additional threads Thread Basics • All modern OS support the idea of processes – independently running programs that are isolated from each other • Thread can be thought of as light weight processes – Like processes they have independent program counters, call stacks etc – Unlike Processes they share main memory, file pointers and other process state – This means thread are easier for the OS to maintain and switch between – This also means we need to synchronize threads for access to shared resources Threads Continued… • So why use threads ? – Multi CPU systems: Most modern systems host multiple CPU’s, by splitting execution between them we can greatly speed up execution – Handling Asynchronous Events: Servers handle multiple clients. Processing each client is best done through a separate thread, because the Server blocks until a new message is received – UI or event driven Processing: event handlers that respond to user input are best handled through separate threads, this makes code easier to write and understand Synchronization Primitives in Java • How does the java language handle synchronization – Concurrent execution is supported through the Thread class. -
Java Concurrency in Practice
Java Concurrency in practice Chapters: 1,2, 3 & 4 Bjørn Christian Sebak ([email protected]) Karianne Berg ([email protected]) INF329 – Spring 2007 Chapter 1 - Introduction Brief history of concurrency Before OS, a computer executed a single program from start to finnish But running a single program at a time is an inefficient use of computer hardware Therefore all modern OS run multiple programs (in seperate processes) Brief history of concurrency (2) Factors for running multiple processes: Resource utilization: While one program waits for I/O, why not let another program run and avoid wasting CPU cycles? Fairness: Multiple users/programs might have equal claim of the computers resources. Avoid having single large programs „hog“ the machine. Convenience: Often desirable to create smaller programs that perform a single task (and coordinate them), than to have one large program that do ALL the tasks What is a thread? A „lightweight process“ - each process can have many threads Threads allow multiple streams of program flow to coexits in a single process. While a thread share process-wide resources like memory and files with other threads, they all have their own program counter, stack and local variables Benefits of threads 1) Exploiting multiple processors 2) Simplicity of modeling 3) Simplified handling of asynchronous events 4) More responsive user interfaces Benefits of threads (2) Exploiting multiple processors The processor industry is currently focusing on increasing number of cores on a single CPU rather than increasing clock speed. Well-designed programs with multiple threads can execute simultaneously on multiple processors, increasing resource utilization.