For most of us, "performance" is a word with a single meaning. When it comes to software production, this meaning usually corresponds to something fast. A fast program is a good program. Although this is not is a wrong assertion, the meaning of the word "performance" is wider and deeper.
Writing a responsive UI actually means writing a performing UI. Deploying a worker role across Microsoft Azure, which is able to scale up to 100 cores, and handling millions of messages per hour actually means writing a performing workflow. The two examples show two different kinds of performance, and more exist.
Other than multiple meanings, the word "performance" also refers to multiple implementation levels. For example, a developer has to keep the security aspect of his application in mind right from the outset, because using a simple X509 certificate does not make an insecure web application secure. The same is true when it comes to performance.
If we want to create a high-performance application, we have to design a high-performance architecture right from the start, implement performance-oriented strategies, and bring up a good performance-engineered project that is able to assist the wider development project to create valid performance requisites and tests.
As developers, we cannot avoid mastering all the techniques required to help us face day-to-day challenges. Asynchronous programming and parallel programming are two examples. Mastering such techniques helps us create good software in terms of responsiveness and scalability. A clear understanding of the .NET Framework has to become part of the knowledge arsenal for any .NET developer; understanding memory management, process isolation, and thread life cycle are examples.
Lots of real-world examples will be available in this book. They will be about the most widely used programming techniques and scenarios, together with special-case programming recipes for big data, engineering, and database integration.
All of these topics are covered here with enthusiasm and expertise.
Chapter 1, Performance Thoughts, gives you an overview of the term "performance" and related concepts.
Chapter 2, Architecting High-performance .NET Code, describes the various architectural concerns of software related to performance, with practical real-world examples.
Chapter 3, CLR Internals, gives you in-depth knowledge of the internals of CLR, from memory management to thread life cycle management.
Chapter 4, Asynchronous Programming, equips you with the ability to program methods that will never keep the user waiting.
Chapter 5, Programming for Parallelism, gives you many details of parallelism, and covers scenarios such as this one: a lot of data items are available for our business logic. Mastering parallelism techniques brings any application throughput to extremely high levels, with little effort by the developer.
Chapter 6, Programming for Math and Engineering, shows you real-world examples for specific cases related to scientific elaboration.
Chapter 7, Database Querying, demonstrates working with databases. Any business-related application or enterprise application deals with a lot of data. This chapter also provides good knowledge of all persistence-layer access frameworks and techniques that make such accesses fast and reliable for all kinds of applications.
Chapter 8, Programming for Big Data, covers working with huge and fast-growing datasets that have specific issues and difficulties, with a simplified presentation and a lot of examples.
Chapter 9, Analyzing Code Performance, proves that good programming and design are not enough if we don't always monitor our software performance results in design time and runtime.