Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Learning .NET High-Performance Programming
  • Table Of Contents Toc
Learning .NET High-Performance Programming

Learning .NET High-Performance Programming

By : Antonio Esposito
3 (3)
Buy this Book
close
close
Learning .NET High-Performance Programming

Learning .NET High-Performance Programming

3 (3)
By: Antonio Esposito
Buy this Book

Overview of this book

If you are a .NET developer with an understanding of application development, but want to learn how to optimize the performance of your applications, this is the book for you. Basic knowledge of C# is expected.
Table of Contents (11 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Performance Thoughts
Icon
1. Performance Thoughts
Icon
Understanding performance
Icon
Performance as a requirement
Icon
Performance engineering
Icon
Performance aspects
Icon
Class of applications
Icon
Technical overview
Icon
Summary
2
2. Architecting High-performance .NET Code
Icon
2. Architecting High-performance .NET Code
Icon
Software architecture
Icon
Performance concerns about architecture
Icon
Object-oriented design principles
Icon
Common designs and architectures
Icon
Architecture comparison
Icon
Common platform architectures
Icon
Performance considerations
Icon
Summary
3
3. CLR Internals
Icon
3. CLR Internals
Icon
Introduction to CLR
Icon
Memory management
Icon
Garbage collection
Icon
Working with AppDomains
Icon
Threading
Icon
Multithreading synchronization
Icon
Exception handling
Icon
Summary
4
4. Asynchronous Programming
Icon
4. Asynchronous Programming
Icon
Understanding asynchronous programming
Icon
Asynchronous programming theory
Icon
Asynchronous Programming Model (APM)
Icon
Event-based Asynchronous Pattern (EAP)
Icon
Task-based Asynchronous Pattern (TAP)
Icon
Task UI synchronization
Icon
Async/await
Icon
Summary
5
5. Programming for Parallelism
Icon
5. Programming for Parallelism
Icon
Parallel programming
Icon
Task parallelism with TPL
Icon
Data parallelism with TPL
Icon
Integrated querying with LINQ
Icon
Data parallelism with PLINQ
Icon
Summary
6
6. Programming for Math and Engineering
Icon
6. Programming for Math and Engineering
Icon
Introduction
Icon
Evaluating the performance of data types
Icon
Real-time applications
Icon
Case study: Fourier transform
Icon
Sliding processing
Icon
Summary
7
7. Database Querying
Icon
7. Database Querying
Icon
Introduction
Icon
Overview of ADO.NET
Icon
An overview of Entity Framework
Icon
Entity Framework querying optimization
Icon
Entity Framework persistence optimization
Icon
Performance comparison
Icon
ADO.NET bulk copy
Icon
Summary
8
8. Programming for Big Data
chevron up
Icon
8. Programming for Big Data
Icon
What is big data?
Icon
Architecting big data solutions
Icon
Microsoft Azure for big data
Icon
Simplified grid computing
Icon
Lookup programming
Icon
Summary
9
9. Analyzing Code Performance
Icon
9. Analyzing Code Performance
Icon
Software profiling
Icon
Profiling with Visual Studio
Icon
Testing with Visual Studio 2013
Icon
Static program analysis
Icon
Summary
10
Index
Icon
Index

What is big data?

A big data application deals with large volumes of fast-growing data. This is the most widely accepted definition and the most basic one too. Although a unique academic definition for big data does not exist, a more detailed definition of a big data application states is inclusive of the following criteria:

  • It handles huge volumes of data, to take care of its size on every usage such SQL SELECT queries or similar. As the word Big suggests, to deal with big data, the total data size must be huge. These days, any database that is less than 100 GB in size cannot be considered as a valid big data storage.

  • It handles fast-growing data in the meaning of velocity of growth. Real big data architecture and solutions are applicable only to fast-growing data; otherwise, we are simply dealing with a huge dataset. Any ever-growing large data store can be handled easily by any application in a few hours or days, depending on the scale of the data. The important thing to note here is that...

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Learning .NET High-Performance Programming
End of Section 8
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon