In this module, you'll learn concurrency in Go and master Go parallelism for improved performance. You will also explore Go Fan-In Fan-out patterns to optimize task and data processing.

Go.tar.gz

DataRaceBuilder

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Golang for Programmers

Mastering concurrency in Go can make you a highly sought-after developer in today's job market, where multi-core devices are becoming common. The good news is that concurrency in Go is straightforward and efficient, thanks to Go’s robust support for concurrency out of the box. In fact, it's easier and faster to implement than in other major programming languages.
In this module, we'll begin with a general introduction to concurrency and build up to more advanced concepts like Go parallelism. We'll also dive into common patterns, including Go Fan-In Fan-out, which will make our code more efficient. By the end of this module, you'll be equipped to write high-performance, concurrent applications using Go.

Go Concurrency

## What is concurrency?

Concurrency, by definition, is the ability to break down a computer program or algorithm into individual parts, which can be executed independently. The final outcome of a concurrent program is the same as that of a program which has been executed sequentially. Using concurrency, we are able to achieve the same results in lesser time, thus increasing the overall performance and efficiency of our programs. 

The trick with writing concurrent programs is to ensure the *correctness* of your program yourself. Therefore, keep in mind that all the individual chunks of your concurrent program should be executed independently and have access to the shared memory i.e. they can read from or write to the same memory location. Also, you need to take care of the allocation of resources so that no process **starves** due to a lack of resources. In addition, you will have to synchronize and coordinate between processes so that you can prevent a **deadlock** if they are dependent on each other.

In short, concurrency is all about designing and structuring your algorithm. It is widely used in computer science today to solve problems efficiently. One such example is that of bank operations in which someone deposits and withdraws cash from their bank account at the same time. The bank account is the shared resource and both the operations have to be executed concurrently in order to get the correct bank balance at the end. Hence, you have to write your code concurrently, where `deposit()` and `withdraw()` are independent operations which will read/write to the same bank account at the same time and obtain the exact balance after the execution. 

The bottom line is that the concept of concurrency is tricky to understand and implement. In general, concurrency implies that a lot of things are happening at the same time so people tend to confuse it with parallelism. Please remember that concurrency and parallelism are two separate concepts in computer science, so make sure that you don't mix them up! 


## What to expect from this course?


# What is concurrency?

Concurrency, by definition, is the ability to break down a computer program or algorithm into individual parts, which can be executed independently. The final outcome of a concurrent program is the same as that of a program which has been executed sequentially. Using concurrency, we are able to achieve the same results in lesser time, thus increasing the overall performance and efficiency of our programs. 

The trick with writing concurrent programs is to ensure the *correctness* of your program yourself. Therefore, keep in mind that all the individual chunks of your concurrent program should be executed independently and have access to the shared memory i.e. they can read from or write to the same memory location. Also, you need to take care of the allocation of resources so that no process **starves** due to a lack of resources. In addition, you will have to synchronize and coordinate between processes so that you can prevent a **deadlock** if they are dependent on each other.

In short, concurrency is all about designing and structuring your algorithm. It is widely used in computer science today to solve problems efficiently. One such example is that of bank operations in which someone deposits and withdraws cash from their bank account at the same time. The bank account is the shared resource and both the operations have to be executed concurrently in order to get the correct bank balance at the end. Hence, you have to write your code concurrently, where `deposit()` and `withdraw()` are independent operations which will read/write to the same bank account at the same time and obtain the exact balance after the execution. 

The bottom line is that the concept of concurrency is tricky to understand and implement. In general, concurrency implies that a lot of things are happening at the same time so people tend to confuse it with parallelism. Please remember that concurrency and parallelism are two separate concepts in computer science, so make sure that you don't mix them up! 


# What to expect from this course?


Let's begin our learning by getting familiar with the basics of concurrency. 

Introduction to Concurrency

Building Blocks of Concurrency in Go

Exam I on Mastering Concurrency in Go

Basic Concurrency Patterns in Go

Exam II on Mastering Concurrency in Go

Conclusion

An Introduction to Concurrency

What is concurrency?