book

Concurrency in .NET

Name: Concurrency in .NET
Author: Riccardo Terrell
ISBN: 9781617292996

by Riccardo Terrell

July 2018

Intermediate to advanced

568 pages

18h 20m

English

Manning Publications

Read now

Unlock full access

Cover
Titlepage
Dedication
preface
Acknowledgments
About This Book
Who should read this book How this book is organized: a roadmapAbout the code Book forum
About the Author
About the Cover Illustration
Part 1 : Benefits of functional programming applicable to concurrent programs
Chapter 1: Functional concurrency foundations
1.1 What you’ll learn from this book1.2 Let’s start with terminology 1.2.1 Sequential programming performs one task at a time1.2.2 Concurrent programming runs multiple tasks at the same time1.2.3 Parallel programming executes multiples tasks simultaneously 1.2.4 Multitasking performs multiple tasks concurrently over time1.2.5 Multithreading for performance improvement 1.3 Why the need for concurrency?1.3.1 Present and future of concurrent programming1.4 The pitfalls of concurrent programming 1.4.1 Concurrency hazards 1.4.2 The sharing of state evolution 1.4.3 A simple real-world example: parallel quicksort 1.4.4 Benchmarking in F# 1.5 Why choose functional programming for concurrency?1.5.1 Benefits of functional programming1.6 Embracing the functional paradigm 1.7 Why use F# and C# for functional concurrent programming?Summary

Chapter 2: Functional programming techniques for concurrency
2.1 Using function composition to solve complex problems 2.1.1 Function composition in C# 2.1.2 Function composition in F# 2.2 Closures to simplify functional thinking2.2.1 Captured variables in closures with lambda expressions 2.2.2 Closures in a multithreading environment2.3 Memoization-caching technique for program speedup2.4 Memoize in action for a fast web crawler2.5 Lazy memoization for better performance2.5.1 Gotchas for function memoization2.6 Effective concurrent speculation to amortize the cost of expensive computations2.6.1 Precomputation with natural functional support2.6.2 Let the best computation win 2.7 Being lazy is a good thing2.7.1 Strict languages for understanding concurrent behaviors2.7.2 Lazy caching technique and thread-safe Singleton pattern2.7.3 Lazy support in F# 2.7.4 Lazy and Task, a powerful combination Summary
Chapter 3: Functional data structures and immutability
3.1 Real-world example: hunting the thread-unsafe object3.1.1 .NET immutable collections: a safe solution3.1.2 .NET concurrent collections: a faster solution 3.1.3 The agent message-passing pattern: a faster, better solution 3.2 Safely sharing functional data structures among threads3.3 Immutability for a change3.3.1 Functional data structure for data parallelism 3.3.2 Performance implications of using immutability3.3.3 Immutability in C#3.3.4 Immutability in F#3.3.5 Functional lists: linking cells in a chain3.3.6 Building a persistent data structure: an immutable binary tree 3.4 Recursive functions: a natural way to iterate3.4.1 The tail of a correct recursive function: tail-call optimization3.4.2 Continuation passing style to optimize recursive functionSummary
Part 2: How to approach the different parts of a concurrent program
Chapter 4: The basics of processing big data: data parallelism, part 1
4.1 What is data parallelism?4.1.1 Data and task parallelism4.1.2 The “embarrassingly parallel” concept4.1.3 Data parallelism support in .NET4.2 The Fork/Join pattern: parallel Mandelbrot4.2.1 When the GC is the bottleneck: structs vs. class objects4.2.2 The downside of parallel loops4.3 Measuring performance speed4.3.1 Amdahl’s Law defines the limit of performance improvement4.3.2 Gustafson’s Law: a step further to measure performance improvement4.3.3 The limitations of parallel loops: the sum of prime numbers4.3.4 What can possibly go wrong with a simple loop?4.3.5 The declarative parallel programming modelSummary
Chapter 5: PLINQ and MapReduce: data parallelism, part 2
5.1 A short introduction to PLINQ5.1.1 How is PLINQ more functional?5.1.2 PLINQ and pure functions: the parallel word counter5.1.3 Avoiding side effects with pure functions5.1.4 Isolate and control side effects: refactoring the parallel word counter5.2 Aggregating and reducing data in parallel 5.2.1 Deforesting: one of many advantages to folding5.2.2 Fold in PLINQ: Aggregate functions5.2.3 Implementing a parallel Reduce function for PLINQ5.2.4 Parallel list comprehension in F#: PSeq 5.2.5 Parallel arrays in F# 5.3 Parallel MapReduce pattern5.3.1 The Map and Reduce functions5.3.2 Using MapReduce with the NuGet package gallerySummary
Chapter 6: Real-time event streams: functional reactive programming
6.1 Reactive programming: big event processing6.2. NET tools for reactive programming6.2.1 Event combinators—a better solution 6.2.2 .NET interoperability with F# combinators6.3 Reactive programming in .NET: Reactive Extensions (Rx)6.3.1 From LINQ/PLINQ to Rx6.3.2 IObservable: the dual IEnumerable6.3.3 Reactive Extensions in action6.3.4 Real-time streaming with RX6.3.5 From events to F# observables6.4 Taming the event stream: Twitter emotion analysis using Rx programming 6.4.1 SelectMany: the monadic bind operator6.5 An Rx publisher-subscriber6.5.1 Using the Subject type for a powerful publisher-subscriber hub6.5.2 Rx in relation to concurrency6.5.3 Implementing a reusable Rx publisher-subscriber 6.5.4 Analyzing tweet emotions using an Rx Pub-Sub class6.5.5 Observers in action6.5.6 The convenient F# object expressionSummary
Chapter 7: Task-based functional parallelism
7.1 A short introduction to task parallelism 7.1.1 Why task parallelism and functional programming?7.1.2 Task parallelism support in .NET7.2 The .NET Task Parallel Library 7.2.1 Running operations in parallel with TPL Parallel.Invoke7.3 The problem of void in C#7.3.1 The solution for void in C#: the unit type7.4 Continuation-passing style: a functional control flow7.4.1 Why exploit CPS?7.4.2 Waiting for a task to complete: the continuation model7.5 Strategies for composing task operations 7.5.1 Using mathematical patterns for better composition7.5.2 Guidelines for using tasks 7.6 The parallel functional Pipeline pattern Summary
Chapter 8: Task asynchronicity for the win
8.1 The Asynchronous Programming Model (APM)8.1.1 The value of asynchronous programming8.1.2 Scalability and asynchronous programming8.1.3 CPU-bound and I/O-bound operations8.2 Unbounded parallelism with asynchronous programming8.3 Asynchronous support in .NET8.3.1 Asynchronous programming breaks the code structure 8.3.2 Event-based Asynchronous Programming8.4 C# Task-based Asynchronous Programming 8.4.1 Anonymous asynchronous lambdas8.4.2 Task<T> is a monadic container 8.5 Task-based Asynchronous Programming: a case study8.5.1 Asynchronous cancellation 8.5.2 Task-based asynchronous composition with the monadic Bind operator8.5.3 Deferring asynchronous computation enables composition8.5.4 Retry if something goes wrong 8.5.5 Handling errors in asynchronous operations8.5.6 Asynchronous parallel processing of the historical stock market8.5.7 Asynchronous stock market parallel processing as tasks completeSummary
Chapter 9: Asynchronous functional programming in F#
9.1 Asynchronous functional aspects9.2 What’s the F# asynchronous workflow? 9.2.1 The continuation passing style in computation expressions 9.2.2 The asynchronous workflow in action: Azure Blob storage parallel operations 9.3 Asynchronous computation expressions9.3.1 Difference between computation expressions and monads 9.3.2 AsyncRetry: building your own computation expression 9.3.3 Extending the asynchronous workflow 9.3.4 Mapping asynchronous operation: the Async.map functor9.3.5 Parallelize asynchronous workflows: Async.Parallel9.3.6 Asynchronous workflow cancellation support 9.3.7 Taming parallel asynchronous operationsSummary
Chapter 10: Functional combinators for fluent concurrent programming
10.1 The execution flow isn’t always on the happy path: error handling 10.1.1 The problem of error handling in imperative programming 10.2.1 Error handling in FP: exceptions for flow control 10.2.2 Handling errors with Task<Option<T>> in C#10.2.3 The F# AsyncOption type: combining Async and Option 10.2.4 Idiomatic F# functional asynchronous error handling 10.2.5 Preserving the exception semantic with the Result type10.3 Taming exceptions in asynchronous operations 10.3.1 Modeling error handling in F# with Async and Result 10.3.2 Extending the F# AsyncResult type with monadic bind operators 10.4 Abstracting operations with functional combinators10.5 Functional combinators in a nutshell 10.5.1 The TPL built-in asynchronous combinators10.5.2 Exploiting the Task.WhenAny combinator for redundancy and interleaving10.5.3 Exploiting the Task.WhenAll combinator for asynchronous for-each10.5.4 Mathematical pattern review: what you’ve seen so far10.6 The ultimate parallel composition applicative functor10.6.1 Extending the F# async workflow with applicative functor operators10.6.2 Applicative functor semantics in F# with infix operators 10.6.3 Exploiting heterogeneous parallel computation with applicative functors10.6.4 Composing and executing heterogeneous parallel computations10.6.5 Controlling flow with conditional asynchronous combinators 10.6.6 Asynchronous combinators in actionSummary
Chapter 11: Applying reactive programming everywhere with agents
11.1 What’s reactive programming, and how is it useful?11.2 The asynchronous message-passing programming model11.2.1 Relation with message passing and immutability11.2.2 Natural isolation11.3 What is an agent?11.3.1 The components of an agent11.3.2 What an agent can do11.3.3 The share-nothing approach for lock-free concurrent programming11.3.4 How is agent-based programming functional?11.3.5 Agent is object-oriented 11.4 The F# agent: MailboxProcessor11.4.1 The mailbox asynchronous recursive loop11.5 Avoiding database bottlenecks with F# MailboxProcessor11.5.1 The MailboxProcessor message type: discriminated unions11.5.2 MailboxProcessor two-way communication11.5.3 Consuming the AgentSQL from C#11.5.4 Parallelizing the workflow with group coordination of agents11.5.5 How to handle errors with F# MailboxProcessor11.5.6 Stopping MailboxProcessor agents—CancellationToken11.5.7 Distributing the work with MailboxProcessor11.5.8 Caching operations with an agent11.5.9 Reporting results from a MailboxProcessor11.5.10 Using the thread pool to report events from MailboxProcessor11.6 F# MailboxProcessor: 10,000 agents for a game of lifeSummary
Chapter 12: Parallel workflow and agent programming with TPL Dataflow
12.1 The power of TPL Dataflow12.2 Designed to compose: TPL Dataflow blocks 12.2.1 Using BufferBlock<TInput> as a FIFO buffer12.2.2 Transforming data with TransformBlock<TInput, TOutput>12.2.3 Completing the work with ActionBlock<TInput >12.2.4 Linking dataflow blocks12.3 Implementing a sophisticated Producer/Consumer with TDF 12.3.1 A multiple Producer/single Consumer pattern: TPL Dataflow12.3.2 A single Producer/multiple Consumer pattern12.4 Enabling an agent model in C# using TPL Dataflow 12.4.1 Agent fold-over state and messages: Aggregate12.4.2 Agent interaction: a parallel word counter 12.5 A parallel workflow to compress and encrypt a large stream12.5.1 Context: the problem of processing a large stream of data12.5.2 Ensuring the order integrity of a stream of messages12.5.3 Linking, propagating, and completing 12.5.4 Rules for building a TDF workflow12.5.5 Meshing Reactive Extensions (Rx) and TDFSummary
Part 3: Modern patterns of concurrent programming applied
Chapter 13: Recipes and design patterns for successful concurrent programming
13.1 Recycling objects to reduce memory consumption 13.1.1 Solution: asynchronously recycling a pool of objects 13.2 Custom parallel Fork/Join operator 13.2.1 Solution: composing a pipeline of steps forming the Fork/Join pattern13.3 Parallelizing tasks with dependencies: designing code to optimize performance13.3.1 Solution: implementing a dependencies graph of tasks13.4 Gate for coordinating concurrent I/O operations sharing resources: one write, multiple reads 13.4.1 Solution: applying multiple read/write operations to shared thread-safe resources13.5 Thread-safe random number generator13.5.1 Solution: using the ThreadLocal object13.6 Polymorphic event aggregator13.6.1 Solution: implementing a polymorphic publisher-subscriber pattern13.7 Custom Rx scheduler to control the degree of parallelism 13.7.1 Solution: implementing a scheduler with multiple concurrent agents 13.8 Concurrent reactive scalable client/server 13.8.1 Solution: combining Rx and asynchronous programming 13.9 Reusable custom high-performing parallel filter‑map operator 13.9.1 Solution: combining filter and map parallel operations13.10 Non-blocking synchronous message-passing model 13.10.1 Solution: coordinating the payload between operations using the agent programming model13.11 Coordinating concurrent jobs using the agent programming model 13.11.1 Solution: implementing an agent that runs jobs with a configured degree of parallelism 13.12 Composing monadic functions 13.12.1 Solution: combining asynchronous operations using the Kleisli composition operatorSummary
Chapter 14: Building a scalable mobile app with concurrent functional programming
14.1 Functional programming on the server in the real world 14.2 How to design a successful performant application 14.2.1 The secret sauce: ACD14.2.2 A different asynchronous pattern: queuing work for later execution14.3 Choosing the right concurrent programming model14.3.1 Real-time communication with SignalR14.4 Real-time trading: stock market high-level architecture 14.5 Essential elements for the stock market application14.6 Let’s code the stock market trading application14.6.1 Benchmark to measure the scalability of the stock ticker applicationSummary
Appendix A: Functional programming
What is functional programming? The benefits of functional programming The tenets of functional programmingThe clash of program paradigms: from imperative to object-oriented to functional programmingHigher-order functions for increasing abstractionHOFs and lambda expressions for code reusabilityLambda expressions and anonymous functions CurryingPartially applied functionsPower of partial function application and currying in C#
Appendix B: F# overview
The let binding Understanding function signatures in F#Creating mutable types: mutable and refFunctions as first-class typesComposition: pipe and composition operatorsDelegatesComments Open statements Basic data types Special string definitionTupleRecord typesDiscriminated unions Pattern matchingActive patterns Collections Arrays Sequences (seq) Lists SetsMapsLoopsClasses and inheritanceAbstract classes and inheritanceInterfacesObject expressionsCastingUnits of measureEvent module API reference
Appendix C: Interoperability between an F# asynchronous workflow and .NET Task
Index

Content preview from Concurrency in .NET

1 Functional concurrency foundations

This chapter covers

Why you need concurrency
Differences between concurrency, parallelism, and multithreading
Avoiding common pitfalls when writing concurrent applications
Sharing variables between threads
Using the functional paradigm to develop concurrent programs

In the past, software developers were confident that, over time, their programs would run faster than ever. This proved true over the years due to improved hardware that enabled programs to increase speed with each new generation.

For the past 50 years, the hardware industry has experienced uninterrupted improvements. Prior to 2005, the processor evolution continuously delivered faster single-core CPUs, until finally reaching the limit ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781617292996Publisher Support Publisher Website Errata Page

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Concurrency in .NET

by Riccardo Terrell

1 Functional concurrency foundations

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.