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Home > Programming > Functional Programming > C++17 STL Cookbook
C++17 STL Cookbook
C++17 STL Cookbook

C++17 STL Cookbook: Discover the latest enhancements to functional programming and lambda expressions

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C++17 STL Cookbook

STL Containers

We will cover the following recipes in this chapter:

  • Using the erase-remove idiom on std::vector
  • Deleting items from an unsorted std::vector in O(1) time
  • Accessing std::vector instances the fast or the safe way
  • Keeping std::vector instances sorted
  • Inserting items efficiently and conditionally into std::map
  • Knowing the new insertion hint semantics of std::map::insert
  • Efficiently modifying the keys of std::map items
  • Using std::unordered_map with custom types
  • Filtering duplicates from user input and printing them in alphabetical order with std::set
  • Implementing a simple RPN calculator with std::stack
  • Implementing a word frequency counter with std::map
  • Implementing a writing style helper tool for finding very long sentences in texts with std::set
  • Implementing a personal to-do list using std::priority_queue

 

Introduction

The C++ standard library comes with a wide range of standard containers. A container always contains a collection of data or objects. The cool thing is that containers can be used with practically any kind of object, so we just need to pick the right containers for our specific application. The STL gives us stacks, automatically growing vectors, maps, and so on. This way we can concentrate on our app and don't need to reinvent the wheel. Knowing all containers well is therefore crucial for every C++ programmer.

All containers the STL provides can be categorized as follows, which is explained in detail in the subsequent subsection:

  • Contiguous storage
  • List storage
  • Search trees
  • Hash tables
  • Container adapters

Contiguous storage

...

Using the erase-remove idiom on std::vector

A lot of novice C++ programmers learn about std::vector, that it basically works like an automatically growing array, and stop right there. Later, they only lookup its documentation in order to see how to do very specific things, for example, removing items. Using STL containers like this will only scratch the surface of how much they help writing clean, maintainable, and fast code.

This section is all about removing items from in-between a vector instance. When an item disappears from a vector, and sits somewhere in the middle between other items, then all items right from it must move one slot to the left (which gives this task a runtime cost within O(n)). Many novice programmers will do that using a loop, since it is also not really a hard thing to do. Unfortunately, they will potentially ignore a lot...

Deleting items from an unsorted std::vector in O(1) time

Deleting items from somewhere in the middle of an std::vector takes O(n) time. This is because the resulting gap from removing an item must be filled by moving all the items which come after the gap one slot to the left.

While moving items around like this, which might be expensive if they are complex and/or very large and include many items, we preserve their order. If preserving the order is not important, we can optimize this, as this section shows.

How to do it...

In this section, we will fill an std::vector instance with some example numbers, and implement a quick remove function, which removes any item from a vector in O(1) time.

  1. First, we need to include...

Accessing std::vector instances the fast or the safe way

The std::vector is probably the most widely used container in the STL, because it holds data just like an array, and adds a lot of comfort around that representation. However, wrong access to a vector can still be dangerous. If a vector contains 100 elements, and by accident our code tries to access an element at index 123, this is obviously bad. Such a program could just crash, which might be the best case, because that behavior would make it very obvious that there is a bug! If it does not crash, we might observe that the program just behaves strangely from time to time, which could lead to even more headaches than a crashing program. The experienced programmer might add some checks before any directly indexed vector access. Such checks do not increase the readability of the code, and many people do not know that...

Keeping std::vector instances sorted

Arrays and vectors do not sort their payload objects themselves. But if we need that, this does not mean that we always have to switch to data structures, which were designed to do that automatically. If an std::vector is perfect for our use case, it is still very simple and practical to add items to it in a sorting manner.

How to do it...

In this section, we will fill an std::vector with random words, sort it, and then insert more words while keeping the vector's sorted word order intact.

  1. Let's first include all headers we're going to need.
      #include <iostream>
      #include <vector>
      #include <string>
      #include <algorithm>
      #include...

Inserting items efficiently and conditionally into std::map

Sometimes we want to fill a map with key-value pairs and while filling the map up, we might run into two different cases:

  1. The key does not exist yet. Create a fresh key-value pair.
  2. The key does exist already. Take the existing item and modify it.


We could just naively use the insert or emplace methods of map and see if they succeed. If it doesn't, we have case 2 and modify the existing item. In both cases, insert and emplace create the item which we try to insert, and in case 2 the freshly created item is dropped. We get a useless constructor call in both cases.

Since C++17, there is the try_emplace function, which enables us to create items only conditionally upon insertion. Let's implement a program that takes a list of billionaires and constructs a map that tells us the number of billionaires...

Introduction

The C++ standard library comes with a wide range of standard containers. A container always contains a collection of data or objects. The cool thing is that containers can be used with practically any kind of object, so we just need to pick the right containers for our specific application. The STL gives us stacks, automatically growing vectors, maps, and so on. This way we can concentrate on our app and don't need to reinvent the wheel. Knowing all containers well is therefore crucial for every C++ programmer.

All containers the STL provides can be categorized as follows, which is explained in detail in the subsequent subsection:

  • Contiguous storage
  • List storage
  • Search trees
  • Hash tables
  • Container adapters

Contiguous storage

The simplest way to store objects is to put them just next to each other in one large chunk of memory. Such memory can be accessed in a random access manner in O(1) time.

The easiest way to do that is using std::array, which is just a wrapper around normal C-style...

Using the erase-remove idiom on std::vector

A lot of novice C++ programmers learn about std::vector, that it basically works like an automatically growing array, and stop right there. Later, they only lookup its documentation in order to see how to do very specific things, for example, removing items. Using STL containers like this will only scratch the surface of how much they help writing clean, maintainable, and fast code.

 

This section is all about removing items from in-between a vector instance. When an item disappears from a vector, and sits somewhere in the middle between other items, then all items right from it must move one slot to the left (which gives this task a runtime cost within O(n)). Many novice programmers will do that using a loop, since it is also not really a hard thing to do. Unfortunately, they will potentially ignore a lot of optimization potential while doing that. And in the end, a hand crafted loop is neither faster, nor prettier to read than the STL way, which...

Deleting items from an unsorted std::vector in O(1) time

Deleting items from somewhere in the middle of an std::vector takes O(n) time. This is because the resulting gap from removing an item must be filled by moving all the items which come after the gap one slot to the left.

While moving items around like this, which might be expensive if they are complex and/or very large and include many items, we preserve their order. If preserving the order is not important, we can optimize this, as this section shows.

How to do it...

In this section, we will fill an std::vector instance with some example numbers, and implement a quick remove function, which removes any item from a vector in O(1) time.

  1. First, we need to include the required header files.
      #include <iostream>
      #include <vector>
      #include <algorithm>
  1. Then, we define a main function where we instantiate a vector with example numbers.
      int main()
      {
          std::vector<int> v {123, 456, 789,...

Accessing std::vector instances the fast or the safe way

The std::vector is probably the most widely used container in the STL, because it holds data just like an array, and adds a lot of comfort around that representation. However, wrong access to a vector can still be dangerous. If a vector contains 100 elements, and by accident our code tries to access an element at index 123, this is obviously bad. Such a program could just crash, which might be the best case, because that behavior would make it very obvious that there is a bug! If it does not crash, we might observe that the program just behaves strangely from time to time, which could lead to even more headaches than a crashing program. The experienced programmer might add some checks before any directly indexed vector access. Such checks do not increase the readability of the code, and many people do not know that std::vector already has built-in bound checks!

How to do it...

In this section, we will use the two different ways to access...

Keeping std::vector instances sorted

Arrays and vectors do not sort their payload objects themselves. But if we need that, this does not mean that we always have to switch to data structures, which were designed to do that automatically. If an std::vector is perfect for our use case, it is still very simple and practical to add items to it in a sorting manner.

How to do it...

In this section, we will fill an std::vector with random words, sort it, and then insert more words while keeping the vector's sorted word order intact.

  1. Let's first include all headers we're going to need.
      #include <iostream>
      #include <vector>
      #include <string>
      #include <algorithm>
      #include <iterator> 
      #include <cassert>

 

  1. We also declare that we are using namespace std in order to spare us some std:: prefixes:
      using namespace std;
  1. Then we write a little main function, which fills a vector with some random strings.
      int main()
      {
       ...

Inserting items efficiently and conditionally into std::map

Sometimes we want to fill a map with key-value pairs and while filling the map up, we might run into two different cases:

  1. The key does not exist yet. Create a fresh key-value pair.
  2. The key does exist already. Take the existing item and modify it.

We could just naively use the insert or emplace methods of map and see if they succeed. If it doesn't, we have case 2 and modify the existing item. In both cases, insert and emplace create the item which we try to insert, and in case 2 the freshly created item is dropped. We get a useless constructor call in both cases.

Since C++17, there is the try_emplace function, which enables us to create items only conditionally upon insertion. Let's implement a program that takes a list of billionaires and constructs a map that tells us the number of billionaires per country. In addition to that, it stores the wealthiest person in every country. Our example will not contain expensive to create items...

Knowing the new insertion hint semantics of std::map::insert

Looking up items in an std::map takes O(log(n)) time. This is the same for inserting new items, because the position where to insert them must be looked up. Naive insertion of M new items would thus take O(M * log(n)) time.

In order to make this more efficient, std::map insertion functions accept an optional insertion hint parameter. The insertion hint is basically an iterator, which points near the future position of the item that is to be inserted. If the hint is correct, then we get amortizedO(1) insertion time.

How to do it...

In this section, we will insert multiple items into an std::map, and use insertion hints for that, in order to reduce the number of lookups.

  1. We are mapping strings to numbers, so we need the header files included for std::map and std::string.
      #include <iostream>
      #include <map>
      #include <string>
  1. The next step is to instantiate a map, which already contains some example characters...

Efficiently modifying the keys of std::map items

Since the std::map data structure maps from keys to values in a way that the keys are always unique and sorted, it is of crucial value that users cannot modify the keys of map nodes that are already inserted. In order to prevent the user from modifying the key items of perfectly sorted map nodes, the const qualifier is added to the key type.

This kind of restriction is perfectly sane because it makes it harder for the user to use std::map the wrong way. But what shall we do if we really need to change the keys of some map items?

Prior to C++17, we had to remove the items of which we need to change the key value from the tree, in order to reinsert them. The downside of this approach is that this always needlessly reallocates some memory, which sounds bad in terms of performance.

Since C++17, we can remove and reinsert map nodes without any reallocation of memory. We will see how that works in this recipe. 

 

How to do it...

We implement a little...

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Key benefits

  • Learn the latest features of C++ and how to write better code by using the Standard Library (STL). Reduce the development time for your applications.
  • Understand the scope and power of STL features to deal with real-world problems.
  • Compose your own algorithms without forfeiting the simplicity and elegance of the STL way.

Description

C++ has come a long way and is in use in every area of the industry. Fast, efficient, and flexible, it is used to solve many problems. The upcoming version of C++ will see programmers change the way they code. If you want to grasp the practical usefulness of the C++17 STL in order to write smarter, fully portable code, then this book is for you. Beginning with new language features, this book will help you understand the language’s mechanics and library features, and offers insight into how they work. Unlike other books, ours takes an implementation-specific, problem-solution approach that will help you quickly overcome hurdles. You will learn the core STL concepts, such as containers, algorithms, utility classes, lambda expressions, iterators, and more, while working on practical real-world recipes. These recipes will help you get the most from the STL and show you how to program in a better way. By the end of the book, you will be up to date with the latest C++17 features and save time and effort while solving tasks elegantly using the STL.

Who is this book for?

This book is for intermediate-to-advanced C++ programmers who want to get the most out of the Standard Template Library of the newest version of C++: C++ 17.

What you will learn

  • Learn about the new core language features and the problems they were intended to solve
  • Understand the inner workings and requirements of iterators by implementing them
  • Explore algorithms, functional programming style, and lambda expressions
  • Leverage the rich, portable, fast, and well-tested set of well-designed algorithms provided in the STL
  • Work with strings the STL way instead of handcrafting C-style code
  • Understand standard support classes for concurrency and synchronization, and how to put them to work
  • Use the filesystem library addition available with the C++17 STL
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ISBN-13 : 9781787120495
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Concepts :
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Edition : 1st
Language : English
ISBN-13 : 9781787120495
Category :
Programming
Languages :
C++
Concepts :
Functional Programming
Tools :
STL

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Table of Contents

10 Chapters
The New C++17 Features Chevron down icon Chevron up icon
The New C++17 Features
Introduction
Using structured bindings to unpack bundled return values
Limiting variable scopes to if and switch statements
Profiting from the new bracket initializer rules
Letting the constructor automatically deduce the resulting template class type
Simplifying compile time decisions with constexpr-if
Enabling header-only libraries with inline variables
Implementing handy helper functions with fold expressions
STL Containers Chevron down icon Chevron up icon
STL Containers
Introduction
Using the erase-remove idiom on std::vector
Deleting items from an unsorted std::vector in O(1) time
Accessing std::vector instances the fast or the safe way
Keeping std::vector instances sorted
Inserting items efficiently and conditionally into std::map
Knowing the new insertion hint semantics of std::map::insert
Efficiently modifying the keys of std::map items
Using std::unordered_map with custom types
Filtering duplicates from user input and printing them in alphabetical order with std::set
Implementing a simple RPN calculator with std::stack
Implementing a word frequency counter with std::map
Implement a writing style helper tool for finding very long sentences in text with std::multimap
Implementing a personal to-do list using std::priority_queue
Iterators Chevron down icon Chevron up icon
Iterators
Introduction
Building your own iterable range
Making your own iterators compatible with STL iterator categories
Using iterator adapters to fill generic data structures
Implementing algorithms in terms of iterators
Iterating the other way around using reverse iterator adapters
Terminating iterations over ranges with iterator sentinels
Automatically checking iterator code with checked iterators
Building your own zip iterator adapter
Lambda Expressions Chevron down icon Chevron up icon
Lambda Expressions
Introduction
Defining functions on the run using lambda expressions
Adding polymorphy by wrapping lambdas into std::function
Composing functions by concatenation
Creating complex predicates with logical conjunction
Calling multiple functions with the same input
Implementing transform_if using std::accumulate and lambdas
Generating cartesian product pairs of any input at compile time
STL Algorithm Basics Chevron down icon Chevron up icon
STL Algorithm Basics
Introduction
Copying items from containers to other containers
Sorting containers
Removing specific items from containers
Transforming the contents of containers
Finding items in ordered and unordered vectors
Limiting the values of a vector to a specific numeric range with std::clamp
Locating patterns in strings with std::search and choosing the optimal implementation
Sampling large vectors
Generating permutations of input sequences
Implementing a dictionary merging tool
Advanced Use of STL Algorithms Chevron down icon Chevron up icon
Advanced Use of STL Algorithms
Introduction
Implementing a trie class using STL algorithms
Implementing a search input suggestion generator with tries
Implementing the Fourier transform formula with STL numeric algorithms
Calculating the error sum of two vectors
Implementing an ASCII Mandelbrot renderer
Building our own algorithm - split
Composing useful algorithms from standard algorithms - gather
Removing consecutive whitespace between words
Compressing and decompressing strings
Strings, Stream Classes, and Regular Expressions Chevron down icon Chevron up icon
Strings, Stream Classes, and Regular Expressions
Introduction
Creating, concatenating, and transforming strings
Trimming whitespace from the beginning and end of strings
Getting the comfort of std::string without the cost of constructing std::string objects
Reading values from user input
Counting all words in a file
Formatting your output with I/O stream manipulators
Initializing complex objects from file input
Filling containers from std::istream iterators
Generic printing with std::ostream iterators
Redirecting output to files for specific code sections
Creating custom string classes by inheriting from std::char_traits
Tokenizing input with the regular expression library
Comfortably pretty printing numbers differently per context on the fly
Catching readable exceptions from std::iostream errors
Utility Classes Chevron down icon Chevron up icon
Utility Classes
Introduction
Converting between different time units using std::ratio
Converting between absolute and relative times with std::chrono
Safely signalizing failure with std::optional
Applying functions on tuples
Quickly composing data structures with std::tuple
Replacing void* with std::any for more type safety
Storing different types with std::variant
Automatically handling resources with std::unique_ptr
Automatically handling shared heap memory with std::shared_ptr
Dealing with weak pointers to shared objects
Simplifying resource handling of legacy APIs with smart pointers
Sharing different member values of the same object
Generating random numbers and choosing the right random number engine
Generating random numbers and letting the STL shape specific distributions
Parallelism and Concurrency Chevron down icon Chevron up icon
Parallelism and Concurrency
Introduction
Automatically parallelizing code that uses standard algorithms
Putting a program to sleep for specific amounts of time
Starting and stopping threads
Performing exception safe shared locking with std::unique_lock and std::shared_lock
Avoiding deadlocks with std::scoped_lock
Synchronizing concurrent std::cout use
Safely postponing initialization with std::call_once
Pushing the execution of tasks into the background using std::async
Implementing the producer/consumer idiom with std::condition_variable
Implementing the multiple producers/consumers idiom with std::condition_variable
Parallelizing the ASCII Mandelbrot renderer using std::async
Implementing a tiny automatic parallelization library with std::future
Filesystem Chevron down icon Chevron up icon
Filesystem
Introduction
Implementing a path normalizer
Getting canonical file paths from relative paths
Listing all files in directories
Implementing a grep-like text search tool
Implementing an automatic file renamer
Implementing a disk usage counter
Calculating statistics about file types
Implementing a tool that reduces folder size by substituting duplicates with symlinks

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Learn to Code with Rust
Learn to Code with Rust
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Nov 2024 37hrs 6mins
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Modern Python Cookbook
Modern Python Cookbook
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Jul 2024 818 pages
Full star icon 4.9 (18)
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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(15 Ratings)
5 star 53.3%
4 star 33.3%
3 star 6.7%
2 star 6.7%
1 star 0%
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thierry Mar 31, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Ce livre et le code source l'accompagnant est à ma connaissance celui qui décrit le mieux les différences de syntaxe entre les différentes versions du C++ et ce jusqu'à la version C++17 sur le STL bien entendu.Il est essentiellement tourné vers la pratique --> how to ..A vous développeur de faire le reste !
Amazon Verified review Amazon
Edgar Aroutiounian Jun 10, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
In terms of being a cookbook, then it serves its purpose well but it completely breaks down mid way through on the kindle edition on web. About midway through the pages become one column chars, which is ridiculously broken. Author should have tested this, so its kinda useless to me now. Please fix.EDIT: Kindle App on mac works fine and this is a great cookbook.
Amazon Verified review Amazon
Bartek F. Aug 30, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Another solid book about C++17 just before the C++17 Standard is announced!Pros:- Practical, full of examples, demos, little utilities- Details of the approach, even with pictures and diagrams! :)- Cookbook style, read what you want- Shows how to make code compact- Shows how to use STL as building block for an app/problem- Focuses on modern C++, avoids using raw code style: pointers, C-style arrays, even raw loops- Introduces functional conceptsCons: Nitpicking: some samples might not work yet if your compiler is not in the C++ latest mode, or even experimental (will change soon).Not aimed at beginners, but if you have some experience with C++, you'll find something for yourself. The chapters are structured to be as clear as possible, with introductions and even pictures and diagrams.
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Tadas Nov 06, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very detailed book. I'm not saying it has all what you need in your life, but it is full of information.
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pat26 Feb 08, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The authors provides easy to understand examples. If you have been programming in c++ for awhile, his comparison of C++17 features with older C++ versions, will be very helpful.
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About the author

Profile icon Galowicz
Galowicz
Jacek Galowicz obtained his master of science in electrical engineering/computer engineering at RWTH Aachen University, Germany. While at university, he enjoyed working as a student assistant in teaching and research, and he participated in several scientific publications. During and after his studies, he worked as a freelancer and implemented applications as well as kernel drivers in C and C++, touching various areas, including 3D graphics programming, databases, network communication, and physics simulation. In recent years, he has been programming performance- and security-sensitive microkernel operating systems for Intel x86 virtualization at Intel and FireEye in Braunschweig, Germany. He has a strong passion for modern C++ implementations of low-level software, and he tries hard to combine high performance with an elegant coding style. Learning purely functional programming and Haskell in recent years triggered his drive to implement generic code with the aid of meta programming.
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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


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