Course Introduction

What You'll Learn in This Course

Golang Useful Resources and Tools

The authors

The history of Go

Installing Go

Golang: An Overview of Fundamental Concepts

Introduction

A Quick Primer on Key-Value Servers and Hash Tables

How to Create a Basic hello world Webserver with Go

Build a Basic hello world JSON API Server with Go + Chi

How to Create an In-Memory Hash Table API with Go + Chi

Conclusion

A key-value server

Testing a key-value server

What is End-to-End Testing, Integration Testing, and Unit Testing?

Writing Unit Tests for JSON Functions in Go

Parallel Testing in Golang: How to Use t.Parallel()

A Brief Overview of Golang Benchmarks

Integration testing

Further testing types

Distributed key-value server

What is Distributed Consensus and How Does it Work?

An Introduction to Using Raft for Distributed Consensus

Create a Distributed Webserver using Golang and Raft

Connecting Golang Servers With a Handler and Middleware

Building a Store Package to Sync Data in Go with Raft's FSM

Testing Distributed Golang Webservers in Docker Containers

Distributed Key Value Server

Monitoring

White Box Monitoring vs Black Box Monitoring

A Introduction to Golang Logging

Defining and Implementing Distributed System Tracing with Go

Alerting

Understanding the Basics of Canary Deployments

Wait, what's a load balancer?

What aren't we doing?

So what will we be doing?

How to Create a Basic Random-per-request Load Balancer

What is Weighted Round Robin Load Balancing?

Streamlining Configuration Updates for Canary Deployments

Other considerations

Three Open-Source Load Balancers Written in Go

A canary deployment server

Chat server

Chatting about chat servers

What features don't we want (yet)?

What features do we want?

Using a SQLite database in Golang

How to Create an API for a Golang Chat Server

Creating a Frontend Interface in Go for a Chat Server

How to Deploy a Golang Webserver With Docker

Future improvements

Future thinking

Project ideas

Areas of more research

In this book we will show you the some of the concerns of backend web development through the lens of Go.

It is taught by Nat Welch (formerly an SRE at Google) and Steve McCarthy (Etsy), two Go developers who have been writing software for more than 25 years combined.

In the course, we'll be covering the creation and concerns of a number of fundamental backend building blocks or larger applications:

- A key value server, including the use of distributed consensus algorithms
- A canary deployment server
- A real time chat service
- Testing and monitoring

Regardless of your background, Go can make you a _better backend developer_, but learning Go is challenging because the language looks simple on the outside, but you won't get its full benefit unless you know what it can _really_ do. 

**By the end of the first chapter, you'll already _have built a key-value server reminiscent of similar systems like Redis and memcached._** The subsequent chapters will dive into more hands-on projects and Go concepts including:

- How to test your Go systems with unit tests and integration tests
- **Project**: create a **distributed version of the key-value server** in order to solve the typical problems of server systems such as _node failure_
- How to solve the **problem of "consensus"** in distributed Go systems
- How to write code to **keep data in sync** when running a system for servers located around the globe
- Implementing **black box and white box monitoring** systems for Go servers
- **Project**: building a **load balancer server** from scratch
- Why and how to implement a **canary deployment server** for bug-free gradual deployments
- How to build a **configuration server** to allow zero-downtime deploys 
- Understanding the various tradeoffs in the **design decisions** for Go server systems
- **Project**: build a Go chat server system from the ground up 
- **Database modeling** for backend development as part of your chat server project
- **and a lot more**...

## A Key-Value Server Like Redis And Memcached

n the early chapters of the book we learn how to build a Golang version of a key-value server that works in a manner similar to Redis and Memcached. You'll implement each of these features in Go:

- set and retrieve values by key
- adding persistent storage
- using data encodings such as `JSON` and `base64`
- testing the server with unit tests and integration tests

In these chapters, we also learn how to build a more advanced solution for the problem: **a distributed version of the key-value server**. You will learn how to use **consensus algorithms** in Go to solve the problem of keeping distributed servers in sync. The solid grounding you get in programming Go in these first chapters readies you for more complex Go projects.

## A Real-Time Chat Service

A real-time chat server implemented in Go gives you the opportunity to bring together systems thinking as well as concerns common for web apps:

- relational data modeling with SQL
- authentication
- implementing RESTful resources

Working through this comprehensive project will give you **a high degree of confidence about using Go to implement a huge project from scratch**. You will feel ready to use Go with relational databases such as PostgreSQL or MySQL, and databases in general.

## A Canary Deployment Server And Load Balancer

The work you do in the chapters covering building a canary deployment server and load balancer will open your eyes to the full power of Go. Gradual deployments are a commonly used strategy for code deployments, and the projects you build in these chapters get you familiar with building this kind of **infrastructure software**. 

Your load balancer will implement _random-per-request_ as well as _round-robin_ load balancing. These projects will give you a thorough immersion in systems problem solving with Go. You'll be able to show off your skills on bold projects of your own, in job interviews, and on the job when collaborating with colleagues.

## This book is the _fastest_ way to learn modern backend Go development

With _The newline Guide to Reliable Webservers with Go_, you won't waste time reading shallow blog posts that can't go beyond showing you the simplest Go examples or Todo apps. Instead, **within the first chapter** you'll be well on your way to creating a **Redis-like key-value server**, and the complexity of the systems you're able to build only accelerates from there.

This book opens the door to **more effective backend development** and utilizing the full power of Golang for **microservices**. As Go transforms the systems development landscape, you can use this book and the skills it teaches you to contribute to a faster, more effective systems development experience for your team.

With _The newline Guide to Reliable Webservers with Go_ you can **go from novice to proficiency in Go in a predictable way, without wasting time, and without picking up bad Go coding habits**.

So grab a copy now to advance your journey with Go.

In this book we show important concepts in backend web development through the lens of Go.

Reliable Webservers with Go

The importance of testing your web servers

How to apply distributed consensus algorithms to your key value server

Benchmarking is another useful feature of the golang 

 standard library. It allows us to conduct performance tests for the parts of our code where being fast matters most. That will help us ensure that we don't cause performance regressions when we are making changes.

Benchmarks are very similar to tests. If you replace 

The other 10% is that you'll need to write a for loop around whatever you're trying to benchmark. 

, which determines the number of loops to do over the benchmarked function. A very short benchmarking function might look like this:

func BenchmarkEmptyFunc(b *testing.B) {
	for i := 0; i < b.N; i++ {
		func() {}()
	}
}

 function is a very critical path in a key-value store. We want it to be fast and remain fast in the future.

In order to make our benchmark we're going to copy the entirety of our 

 rather than coming up with a common set of abstractions and using them in both functions, is because we actually expect these functions to differ greatly over time. 

 will likely receive a large number of varried test cases, some added as bugs are discovered and some added to help ensure the function will work as intended with new keys and values. 

, however, is likely to remain very stable, and will only need to be adjusted for significant changes made to the 

 function. So copying everything allows us to more easily write 

 while preventing a tight coupling of these two functions that are currently similar but will likely be very different in the future.

---
title: "A Brief Overview of Golang Benchmarks"
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code: https://gitlab.com/fullstackio/books/newline-course-apps/nat-welch-and-steve-mcarthy-reliable-webservers-in-go-app/-/tree/main/code/key-value-store/lesson_03.04
---

# Benchmarks

Benchmarking is another useful feature of the golang `testing` standard library. It allows us to conduct performance tests for the parts of our code where being fast matters most. That will help us ensure that we don't cause performance regressions when we are making changes.

Benchmarks are very similar to tests. If you replace `Test` with `Benchmark` and `testing.T` with `testing.B`, you're 90% of the way there.

The other 10% is that you'll need to write a for loop around whatever you're trying to benchmark. `testing.B` provides a parameter, `N`, which determines the number of loops to do over the benchmarked function. A very short benchmarking function might look like this:

```go
func BenchmarkEmptyFunc(b *testing.B) {
	for i := 0; i < b.N; i++ {
		func() {}()
	}
}
```

Let's set up a `Get` benchmark since the `Get` function is a very critical path in a key-value store. We want it to be fast and remain fast in the future.

In order to make our benchmark we're going to copy the entirety of our `TestGet` method and make some small changes. `BenchmarkGet` will look exactly like `TestGet`, except we remove the `t.Parallel()` call, rename the function to `BenchmarkGet`, and change `t *testing.T` to `b *testing.B`.

The reason we are copying `TestGet` rather than coming up with a common set of abstractions and using them in both functions, is because we actually expect these functions to differ greatly over time. `TestGet` will likely receive a large number of varried test cases, some added as bugs are discovered and some added to help ensure the function will work as intended with new keys and values. `BenchmarkGet`, however, is likely to remain very stable, and will only need to be adjusted for significant changes made to the `Get` function. So copying everything allows us to more easily write `BenchmarkGet` while preventing a tight coupling of these two functions that are currently similar but will likely be very different in the future.