Book Image

Data Structures and Algorithms with the C++ STL

By : John Farrier
5 (2)
Book Image

Data Structures and Algorithms with the C++ STL

5 (2)
By: John Farrier

Overview of this book

While the Standard Template Library (STL) offers a rich set of tools for data structures and algorithms, navigating its intricacies can be daunting for intermediate C++ developers without expert guidance. This book offers a thorough exploration of the STL’s components, covering fundamental data structures, advanced algorithms, and concurrency features. Starting with an in-depth analysis of the std::vector, this book highlights its pivotal role in the STL, progressing toward building your proficiency in utilizing vectors, managing memory, and leveraging iterators. The book then advances to STL’s data structures, including sequence containers, associative containers, and unordered containers, simplifying the concepts of container adaptors and views to enhance your knowledge of modern STL programming. Shifting the focus to STL algorithms, you’ll get to grips with sorting, searching, and transformations and develop the skills to implement and modify algorithms with best practices. Advanced sections cover extending the STL with custom types and algorithms, as well as concurrency features, exception safety, and parallel algorithms. By the end of this book, you’ll have transformed into a proficient STL practitioner ready to tackle real-world challenges and build efficient and scalable C++ applications.
Table of Contents (30 chapters)
Free Chapter
1
Part 1: Mastering std::vector
7
Part 2: Understanding STL Data Structures
13
Part 3: Mastering STL Algorithms
19
Part 4: Creating STL-Compatible Types and Algorithms
23
Part 5: STL Data Structures and Algorithms: Under the Hood

Summary

This chapter covered the essential techniques for altering and shaping data within STL containers. We began by understanding the nuances of copying and moving semantics in the STL, learning to make deliberate choices between copying versus moving elements depending on the context to optimize performance and resource management. We then explored RVO, a technique for optimizing compilers that removes redundant object copying.

We then examined the methods for filling and generating container contents, which are vital to efficiently initializing and modifying large datasets. We covered the mechanisms for removing and replacing elements within containers, balancing the need for data integrity with performance. The chapter also introduced the operations of swapping and reversing elements, deduplication to eliminate duplicates, and sampling to create representative subsets of data. Throughout, we focused on best practices to ensure that these operations are executed with precision...