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Note: You're currently viewing a new version that I'm enriching with significantly more content. Yet, this version is still under development; please be gentle if you find an issue, and feel free to create a PR.
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Code and Project Organization
Unintended variable shadowing (#1)
@@ -2334,6 +2298,8 @@ What’s the main problem if we overuse interfaces? The answer is that they make
Source code
Being confused about when to use generics (#9)
TL;DR : Relying on generics and type parameters can prevent writing boilerplate code to factor out elements or behaviors. However, do not use type parameters prematurely, but only when you see a concrete need for them. Otherwise, they introduce unnecessary abstractions and complexity.
+
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Source code
Not being aware of the possible problems with type embedding (#10)
TL;DR : Using type embedding can also help avoid boilerplate code; however, ensure that doing so doesn’t lead to visibility issues where some fields should have remained hidden.
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-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"\ud83d\udcd6 Book Presentation","text":"Source code and community space of \ud83d\udcd6 100 Go Mistakes and How to Avoid Them, published by Manning in August 2022.
"},{"location":"#book-description","title":"Book Description","text":"If you're a Go developer looking to improve your skills, this book is for you. With a focus on practical examples, 100 Go Mistakes and How to Avoid Them covers a wide range of topics from concurrency and error handling to testing and code organization. You'll learn to write more idiomatic, efficient, and maintainable code and become a proficient Go developer.
"},{"location":"#quotes","title":"Quotes","text":"This should be the required reading for all Golang developers before they touch code in Production... It's the Golang equivalent of the legendary 'Effective Java' by Joshua Bloch.
\u2013 Neeraj Shah
Not having this will be the 101st mistake a Go programmer could make.
\u2013 Anupam Sengupta
"},{"location":"#where-to-buy","title":"Where to Buy?","text":"100 Go Mistakes and How to Avoid Them (\ud83c\uddec\ud83c\udde7 edition: paper, digital, or audiobook):
Manning O\u2019Reilly Amazon: .com, .co.uk, .de, .fr, .in, .co.jp, .es, .it, .com.br Go\u8a00\u8a9e100Tips \u958b\u767a\u8005\u306b\u3042\u308a\u304c\u3061\u306a\u9593\u9055\u3044\u3078\u306e\u5bfe\u51e6\u6cd5 (\ud83c\uddef\ud83c\uddf5 edition):
"},{"location":"#about-the-author","title":"About the Author","text":"Teiva Harsanyi is a senior software engineer at Google. He has worked in various domains, including insurance, transportation, and safety-critical industries like air traffic management. He is passionate about Go and how to design and implement reliable systems.
"},{"location":"#external-resources","title":"External Resources","text":""},{"location":"#english","title":"English","text":" Book Review: 100 Go Mistakes and How to Avoid Them: Post, YouTube How to make mistakes in Go - Go Time #190: Episode, Spotify 8LU - 100% Test Coverage Some Tips I learned from 100 Mistakes in Go What can be summarized from 100 Go Mistakes? "},{"location":"#chinese","title":"Chinese","text":" \u6df1\u5ea6\u9605\u8bfb\u4e4b\u300a100 Go Mistakes and How to Avoid Them 100 Go Mistakes \u968f\u8bb0 \u6211\u4e3a\u4ec0\u4e48\u653e\u5f03Go\u8bed\u8a00\uff1f "},{"location":"#japanese","title":"Japanese","text":" \u6700\u8fd1\u8aad\u3093\u3060Go\u8a00\u8a9e\u306e\u672c\u306e\u7d39\u4ecb\uff1a100 Go Mistakes and How to Avoid Them \u300e100 Go Mistakes and How to Avoid Them\u300f\u3092\u8aad\u3080 100 Go Mistakes \u968f\u8bb0 - 01 Code and project organization "},{"location":"#portuguese","title":"Portuguese","text":" Um \u00d3TIMO livro para programadores Go "},{"location":"9-generics/","title":"Being confused about when to use generics (#9)","text":"Generics is a fresh addition to the language. In a nutshell, it allows writing code with types that can be specified later and instantiated when needed. However, it can be pretty easy to be confused about when to use generics and when not to. Throughout this post, we will describe the concept of generics in Go and then delve into common use and misuses.
"},{"location":"9-generics/#concepts","title":"Concepts","text":"Consider the following function that extracts all the keys from a map[string]int type:
func getKeys(m map[string]int) []string {\n var keys []string\n for k := range m {\n keys = append(keys, k)\n }\n return keys\n}\nWhat if we would like to use a similar feature for another map type such as a map[int]string? Before generics, Go developers had a couple of options: using code generation, reflection, or duplicating code.
For example, we could write two functions, one for each map type, or even try to extend getKeys to accept different map types:
func getKeys(m any) ([]any, error) {\n switch t := m.(type) {\n default:\n return nil, fmt.Errorf(\"unknown type: %T\", t)\n case map[string]int:\n var keys []any\n for k := range t {\n keys = append(keys, k)\n }\n return keys, nil\n case map[int]string:\n // Copy the extraction logic\n }\n}\nWe can start noticing a couple of issues:
First, it increases boilerplate code. Indeed, whenever we want to add a case, it will require duplicating the range loop. Meanwhile, the function now accepts an empty interface, which means we are losing some of the benefits of Go being a typed language. Indeed, checking whether a type is supported is done at runtime instead of compile-time. Hence, we also need to return an error if the provided type is unknown. Last but not least, as the key type can be either int or string, we are obliged to return a slice of empty interfaces to factor out key types. This approach increases the effort on the caller-side as the client may also have to perform a type check of the keys or extra conversion. Thanks to generics, we can now refactor this code using type parameters.
Type parameters are generic types we can use with functions and types. For example, the following function accepts a type parameter:
func foo[T any](t T) {\n // ...\n}\nWhen calling foo, we will pass a type argument of any type. Passing a type argument is called instantiation because the work is done at compile time which keeps type safety as part of the core language features and avoids runtime overheads.
Let\u2019s get back to the getKeys function and use type parameters to write a generic version that would accept any kind of map:
func getKeys[K comparable, V any](m map[K]V) []K { <1>\n var keys []K <2>\n for k := range m {\n keys = append(keys, k)\n }\n return keys\n}\nTo handle the map, we defined two kinds of type parameters. First, the values can be of any type: V any. However, in Go, the map keys can\u2019t be of any type. For example, we cannot use slices:
var m map[[]byte]int\nThis code leads to a compilation error: invalid map key type []byte. Therefore, instead of accepting any key type, we are obliged to restrict type arguments so that the key type meets specific requirements. Here, being comparable (we can use == or !=). Hence, we defined K as comparable instead of a``ny.
Restricting type arguments to match specific requirements is called a constraint. A constraint is an interface type that can contain:
A set of behaviors (methods) But also arbitrary type Let\u2019s see a concrete example for the latter. Imagine we don\u2019t want to accept any comparable type for map key type. For instance, we would like to restrict it to either int or string types. We can define a custom constraint this way:
type customConstraint interface {\n ~int | ~string // Define a custom type that will restrict types to int and string\n}\n\n// Change the type parameter K to be custom\nfunc getKeys[K customConstraint, V any](m map[K]V) []K {\n // Same implementation\n}\nFirst, we define a customConstraint interface to restrict the types to be either int or string using the union operator | (we will discuss the use of ~ a bit later). Then, K is now a customConstraint instead of a comparable as before.
Now, the signature of getKeys enforces that we can call it with a map of any value type, but the key type has to be an int or a string. For example, on the caller-side:
m = map[string]int{\n \"one\": 1,\n \"two\": 2,\n \"three\": 3,\n}\nkeys := getKeys(m)\nNote that Go can infer that getKeys is called with a string type argument. The previous call was similar to this:
keys := getKeys[string](m)\nNOTE: What\u2019s the difference between a constraint using ~int or int? Using int restricts it to that type, whereas ~int restricts all the types whose underlying type is an int.
To illustrate it, let\u2019s imagine a constraint where we would like to restrict a type to any int type implementing the String() string method:
type customConstraint interface {\n ~int\n String() string\n}\nUsing this constraint will restrict type arguments to custom types like this one:
type customInt int\n\nfunc (i customInt) String() string {\n return strconv.Itoa(int(i))\n}\nAs customInt is an int and implements the String() string method, the customInt type satisfies the constraint defined.
However, if we change the constraint to contain an int instead of an ~int, using customInt would lead to a compilation error because the int type doesn\u2019t implement String() string.
Let\u2019s also note the constraints package contains a set of common constraints such as Signed that includes all the signed integer types. Let\u2019s ensure that a constraint doesn\u2019t already exist in this package before creating a new one.
So far, we have discussed examples using generics for functions. However, we can also use generics with data structures.
For example, we will create a linked list containing values of any type. Meanwhile, we will write an Add method to append a node:
type Node[T any] struct { // Use type parameter\n Val T\n next *Node[T]\n}\n\nfunc (n *Node[T]) Add(next *Node[T]) { // Instantiate type receiver\n n.next = next\n}\nWe use type parameters to define T and use both fields in Node. Regarding the method, the receiver is instantiated. Indeed, because Node is generic, it has to follow also the type parameter defined.
One last thing to note about type parameters: they can\u2019t be used on methods, only on functions. For example, the following method wouldn\u2019t compile:
type Foo struct {}\n\nfunc (Foo) bar[T any](t T) {}\n./main.go:29:15: methods cannot have type parameters\nNow, let\u2019s delve into concrete cases where we should and shouldn\u2019t use generics.
"},{"location":"9-generics/#common-uses-and-misuses","title":"Common uses and misuses","text":"So when are generics useful? Let\u2019s discuss a couple of common uses where generics are recommended:
Data structures. For example, we can use generics to factor out the element type if we implement a binary tree, a linked list, or a heap. Functions working with slices, maps, and channels of any type. For example, a function to merge two channels would work with any channel type. Hence, we could use type parameters to factor out the channel type: cgo func merge[T any](ch1, ch2 <-chan T) <-chan T { // ... }
Meanwhile, instead of factoring out a type, we can factor out behaviors. For example, the sort package contains functions to sort different slice types such as sort.Ints or sort.Float64s. Using type parameters, we can factor out the sorting behaviors that rely on three methods, Len, Less, and Swap: ```go type sliceFn[T any] struct { // Use type parameter s []T compare func(T, T) bool // Compare two T elements }
func (s sliceFn[T]) Len() int { return len(s.s) } func (s sliceFn[T]) Less(i, j int) bool { return s.compare(s.s[i], s.s[j]) } func (s sliceFn[T]) Swap(i, j int) { s.s[i], s.s[j] = s.s[j], s.s[i] } ```
Conversely, when is it recommended not to use generics?
When just calling a method of the type argument. For example, consider a function that receives an io.Writer and call the Write method: cgo func foo[T io.Writer](w T) { b := getBytes() _, _ = w.Write(b) }
When it makes our code more complex. Generics are never mandatory, and as Go developers, we have been able to live without them for more than a decade. If writing generic functions or structures we figure out that it doesn\u2019t make our code clearer, we should probably reconsider our decision for this particular use case. "},{"location":"9-generics/#conclusion","title":"Conclusion","text":"Though generics can be very helpful in particular conditions, we should be cautious about when to use them and not use them.
In general, when we want to answer when not to use generics, we can find similarities with when not to use interfaces. Indeed, generics introduce a form of abstraction, and we have to remember that unnecessary abstractions introduce complexity.
Let\u2019s not pollute our code with needless abstractions, and let\u2019s focus on solving concrete problems for now. It means that we shouldn\u2019t use type parameters prematurely. Let\u2019s wait until we are about to write boilerplate code to consider using generics.
"},{"location":"chapter-1/","title":"Go: Simple to learn but hard to master","text":"This chapter covers * What makes Go an efficient, scalable, and productive language * Exploring why Go is simple to learn but hard to master * Presenting the common types of mistakes made by developers
Making mistakes is part of everyone\u2019s life. As Albert Einstein once said,
A person who never made a mistake never tried anything new.
What matters in the end isn\u2019t the number of mistakes we make, but our capacity to learn from them. This assertion also applies to programming. The seniority we acquire in a language isn\u2019t a magical process; it involves making many mistakes and learning from them. The purpose of this book is centered around this idea. It will help you, the reader, become a more proficient Go developer by looking at and learning from 100 common mistakes people make in many areas of the language.
This chapter presents a quick refresher as to why Go has become mainstream over the years. We\u2019ll discuss why, despite Go being considered simple to learn, mastering its nuances can be challenging. Finally, we\u2019ll introduce the concepts this book covers.
"},{"location":"chapter-1/#go-outline","title":"Go outline","text":"If you are reading this book, it\u2019s likely that you\u2019re already sold on Go. Therefore, this section provides a brief reminder about what makes Go such a powerful language.
Software engineering has evolved considerably during the past decades. Most modern systems are no longer written by a single person but by teams consisting of multiple programmers\u2014sometimes even hundreds, if not thousands. Nowadays, code must be readable, expressive, and maintainable to guarantee a system\u2019s durability over the years. Meanwhile, in our fast-moving world, maximizing agility and reducing the time to market is critical for most organizations. Programming should also follow this trend, and companies strive to ensure that software engineers are as productive as possible when reading, writing, and maintaining code.
In response to these challenges, Google created the Go programming language in 2007. Since then, many organizations have adopted the language to support various use cases: APIs, automation, databases, CLIs (command-line interfaces), and so on. Many today consider Go the language of the cloud.
Feature-wise, Go has no type inheritance, no exceptions, no macros, no partial functions, no support for lazy variable evaluation or immutability, no operator overloading, no pattern matching, and on and on. Why are these features missing from the language? The official Go FAQ gives us some insight:
Why does Go not have feature X? Your favorite feature may be missing because it doesn\u2019t fit, because it affects compilation speed or clarity of design, or because it would make the fundamental system model too difficult.
Judging the quality of a programming language via its number of features is probably not an accurate metric. At least, it\u2019s not an objective of Go. Instead, Go utilizes a few essential characteristics when adopting a language at scale for an organization. These include the following:
Stability\u2014Even though Go receives frequent updates (including improvements and security patches), it remains a stable language. Some may even consider this one of the best features of the language. Expressivity\u2014We can define expressivity in a programming language by how naturally and intuitively we can write and read code. A reduced number of keywords and limited ways to solve common problems make Go an expressive language for large codebases. Compilation\u2014As developers, what can be more exasperating than having to wait for a build to test our application? Targeting fast compilation times has always been a conscious goal for the language designers. This, in turn, enables productivity. Safety\u2014Go is a strong, statically typed language. Hence, it has strict compiletime rules, which ensure the code is type-safe in most cases. Go was built from the ground up with solid features such as outstanding concurrency primitives with goroutines and channels. There\u2019s not a strong need to rely on external libraries to build efficient concurrent applications. Observing how important concurrency is these days also demonstrates why Go is such a suitable language for the present and probably for the foreseeable future.
Some also consider Go a simple language. And, in a sense, this isn\u2019t necessarily wrong. For example, a newcomer can learn the language\u2019s main features in less than a day. So why read a book centered on the concept of mistakes if Go is simple?
"},{"location":"chapter-1/#simple-doesnt-mean-easy","title":"Simple doesn\u2019t mean easy","text":"There is a subtle difference between simple and easy. Simple, applied to a technology, means not complicated to learn or understand. However, easy means that we can achieve anything without much effort. Go is simple to learn but not necessarily easy to master.
Let\u2019s take concurrency, for example. In 2019, a study focusing on concurrency bugs was published: Understanding Real-World Concurrency Bugs in Go. This study was the first systematic analysis of concurrency bugs. It focused on multiple popular Go repositories such as Docker, gRPC, and Kubernetes. One of the most important takeaways from this study is that most of the blocking bugs are caused by inaccurate use of the message-passing paradigm via channels, despite the belief that message passing is easier to handle and less error-prone than sharing memory.
What should be an appropriate reaction to such a takeaway? Should we consider that the language designers were wrong about message passing? Should we reconsider how we deal with concurrency in our project? Of course not.
It\u2019s not a question of confronting message passing versus sharing memory and determining the winner. However, it\u2019s up to us as Go developers to thoroughly understand how to use concurrency, its implications on modern processors, when to favor one approach over the other, and how to avoid common traps. This example highlights that although a concept such as channels and goroutines can be simple to learn, it isn\u2019t an easy topic in practice.
This leitmotif\u2014simple doesn\u2019t mean easy\u2014can be generalized to many aspects of Go, not only concurrency. Hence, to be proficient Go developers, we must have a thorough understanding of many aspects of the language, which requires time, effort, and mistakes.
This book aims to help accelerate our journey toward proficiency by delving into 100 Go mistakes.
"},{"location":"chapter-1/#100-go-mistakes","title":"100 Go mistakes","text":"Why should we read a book about common Go mistakes? Why not deepen our knowledge with an ordinary book that would dig into different topics?
In a 2011 article, neuroscientists proved that the best time for brain growth is when we\u2019re facing mistakes. Haven\u2019t we all experienced the process of learning from a mistake and recalling that occasion after months or even years, when some context related to it? As presented in another article, by Janet Metcalfe, this happens because mistakes have a facilitative effect. The main idea is that we can remember not only the error but also the context surrounding the mistake. This is one of the reasons why learning from mistakes is so efficient.
To strengthen this facilitative effect, this book accompanies each mistake as much as possible with real-world examples. This book isn\u2019t only about theory; it also helps us get better at avoiding mistakes and making more well-informed, conscious decisions because we now understand the rationale behind them.
Tell me and I forget. Teach me and I remember. Involve me and I learn.
- Unknown
This book presents seven main categories of mistakes. Overall, the mistakes can be classified as * Bugs * Needless complexity * Weaker readability * Suboptimal or unidiomatic organization * Lack of API convenience * Under-optimized code * Lack of productivity
We introduce each mistake category next.
"},{"location":"chapter-1/#bugs","title":"Bugs","text":"The first type of mistake and probably the most obvious is software bugs. In 2020, a study conducted by Synopsys estimated the cost of software bugs in the U.S. alone to be over $2 trillion.
Furthermore, bugs can also lead to tragic impacts. We can, for example, mention cases such as Therac-25, a radiation therapy machine produced by Atomic Energy of Canada Limited (AECL). Because of a race condition, the machine gave its patients radiation doses that were hundreds of times greater than expected, leading to the death of three patients. Hence, software bugs aren\u2019t only about money. As developers, we should remember how impactful our jobs are.
This book covers plenty of cases that could lead to various software bugs, including data races, leaks, logic errors, and other defects. Although accurate tests should be a way to discover such bugs as early as possible, we may sometimes miss cases because of different factors such as time constraints or complexity. Therefore, as a Go developer, it\u2019s essential to make sure we avoid common bugs.
"},{"location":"chapter-1/#needless-complexity","title":"Needless complexity","text":"The next category of mistakes is related to unnecessary complexity. A significant part of software complexity comes from the fact that, as developers, we strive to think about imaginary futures. Instead of solving concrete problems right now, it can be tempting to build evolutionary software that could tackle whatever future use case arises. However, this leads to more drawbacks than benefits in most cases because it can make a codebase more complex to understand and reason about.
Getting back to Go, we can think of plenty of use cases where developers might be tempted to design abstractions for future needs, such as interfaces or generics. This book discusses topics where we should remain careful not to harm a codebase with needless complexity.
"},{"location":"chapter-1/#weaker-readability","title":"Weaker readability","text":"Another kind of mistake is to weaken readability. As Robert C. Martin wrote in his book Clean Code: A Handbook of Agile Software Craftsmanship, the ratio of time spent reading versus writing is well over 10 to 1. Most of us started to program on solo projects where readability wasn\u2019t that important. However, today\u2019s software engineering is programming with a time dimension: making sure we can still work with and maintain an application months, years, or perhaps even decades later.
When programming in Go, we can make many mistakes that can harm readability. These mistakes may include nested code, data type representations, or not using named result parameters in some cases. Throughout this book, we will learn how to write readable code and care for future readers (including our future selves).
"},{"location":"chapter-1/#suboptimal-or-unidiomatic-organization","title":"Suboptimal or unidiomatic organization","text":"Be it while working on a new project or because we acquire inaccurate reflexes, another type of mistake is organizing our code and a project suboptimally and unidiomatically. Such issues can make a project harder to reason about and maintain. This book covers some of these common mistakes in Go. For example, we\u2019ll look at how to structure a project and deal with utility packages or init functions. All in all, looking at these mistakes should help us organize our code and projects more efficiently and idiomatically.
"},{"location":"chapter-1/#lack-of-api-convenience","title":"Lack of API convenience","text":"Making common mistakes that weaken how convenient an API is for our clients is another type of mistake. If an API isn\u2019t user-friendly, it will be less expressive and, hence, harder to understand and more error-prone.
We can think about many situations such as overusing any types, using the wrong creational pattern to deal with options, or blindly applying standard practices from object-oriented programming that affect the usability of our APIs. This book covers common mistakes that prevent us from exposing convenient APIs for our users.
"},{"location":"chapter-1/#under-optimized-code","title":"Under-optimized code","text":"Under-optimized code is another type of mistake made by developers. It can happen for various reasons, such as not understanding language features or even a lack of fundamental knowledge. Performance is one of the most obvious impacts of this mistake, but not the only one.
We can think about optimizing code for other goals, such as accuracy. For example, this book provides some common techniques to ensure that floating-point operations are accurate. Meanwhile, we will cover plenty of cases that can negatively impact performance code because of poorly parallelized executions, not knowing how to reduce allocations, or the impacts of data alignment, for example. We will tackle optimization via different prisms.
"},{"location":"chapter-1/#lack-of-productivity","title":"Lack of productivity","text":"In most cases, what\u2019s the best language we can choose when working on a new project? The one we\u2019re the most productive with. Being comfortable with how a language works and exploiting it to get the best out of it is crucial to reach proficiency.
In this book, we will cover many cases and concrete examples that will help us to be more productive while working in Go. For instance, we\u2019ll look at writing efficient tests to ensure that our code works, relying on the standard library to be more effective, and getting the best out of the profiling tools and linters. Now, it\u2019s time to delve into those 100 common Go mistakes.
"},{"location":"chapter-1/#summary","title":"Summary","text":" Go is a modern programming language that enables developer productivity, which is crucial for most companies today. Go is simple to learn but not easy to master. This is why we need to deepen our knowledge to make the most effective use of the language. Learning via mistakes and concrete examples is a powerful way to be proficient in a language. This book will accelerate our path to proficiency by exploring 100 common mistakes. "},{"location":"jobs/","title":"\u2764\ufe0f Go Jobs","text":"Is your company hiring? Sponsor this repository and let Go developers know your opportunities in this section (traffic: +400 unique visitor per week).
For example:
"},{"location":"jobs/#company-remote-usa-96000-to-120000-a-year","title":"[Company] Remote (USA) - $96,000 to $120,000 a year","text":""},{"location":"jobs/#job-description","title":"Job Description","text":"...
"},{"location":"jobs/#qualifications","title":"Qualifications","text":"...
"},{"location":"mistakes/","title":"\ud83d\udca1 Go Mistakes","text":"This section contains a summary of the 100 mistakes in the book. Meanwhile, it's also a section open to the community. If you believe that a mistake should be added, please create a community mistake issue.
Note: You're currently viewing a new version that I'm enriching with significantly more content. Yet, this version is still under development; please be gentle if you find an issue, and feel free to create a PR.
"},{"location":"mistakes/#code-and-project-organization","title":"Code and Project Organization","text":""},{"location":"mistakes/#unintended-variable-shadowing-1","title":"Unintended variable shadowing (#1)","text":"TL;DR: Avoiding shadowed variables can help prevent mistakes like referencing the wrong variable or confusing readers.
Variable shadowing occurs when a variable name is redeclared in an inner block, but this practice is prone to mistakes. Imposing a rule to forbid shadowed variables depends on personal taste. For example, sometimes it can be convenient to reuse an existing variable name like err for errors. Yet, in general, we should remain cautious because we now know that we can face a scenario where the code compiles, but the variable that receives the value is not the one expected.
Source code
"},{"location":"mistakes/#unnecessary-nested-code-2","title":"Unnecessary nested code (#2)","text":"TL;DR: Avoiding nested levels and keeping the happy path aligned on the left makes building a mental code model easier.
In general, the more nested levels a function requires, the more complex it is to read and understand. Let\u2019s see some different applications of this rule to optimize our code for readability:
When an if block returns, we should omit the else block in all cases. For example, we shouldn\u2019t write: go if foo() { // ... return true } else { // ... }
Instead, we omit the else block like this:
go if foo() { // ... return true } // ... * We can also follow this logic with a non-happy path:
go if s != \"\" { // ... } else { return errors.New(\"empty string\") }
Here, an empty s represents the non-happy path. Hence, we should flip the condition like so:
go if s == \"\" { return errors.New(\"empty string\") } // ...
Writing readable code is an important challenge for every developer. Striving to reduce the number of nested blocks, aligning the happy path on the left, and returning as early as possible are concrete means to improve our code\u2019s readability.
Source code
"},{"location":"mistakes/#misusing-init-functions-3","title":"Misusing init functions (#3)","text":"TL;DR: When initializing variables, remember that init functions have limited error handling and make state handling and testing more complex. In most cases, initializations should be handled as specific functions.
An init function is a function used to initialize the state of an application. It takes no arguments and returns no result (a func() function). When a package is initialized, all the constant and variable declarations in the package are evaluated. Then, the init functions are executed.
Init functions can lead to some issues: * They can limit error management. * They can complicate how to implement tests (for example, an external dependency must be set up, which may not be necessary for the scope of unit tests). * If the initialization requires us to set a state, that has to be done through global variables.
We should be cautious with init functions. They can be helpful in some situations, however, such as defining static configuration. Otherwise, and in most cases, we should handle initializations through ad hoc functions.
Source code
"},{"location":"mistakes/#overusing-getters-and-setters-4","title":"Overusing getters and setters (#4)","text":"TL;DR: Forcing the use of getters and setters isn\u2019t idiomatic in Go. Being pragmatic and finding the right balance between efficiency and blindly following certain idioms should be the way to go.
Data encapsulation refers to hiding the values or state of an object. Getters and setters are means to enable encapsulation by providing exported methods on top of unexported object fields.
In Go, there is no automatic support for getters and setters as we see in some languages. It is also considered neither mandatory nor idiomatic to use getters and setters to access struct fields. We shouldn\u2019t overwhelm our code with getters and setters on structs if they don\u2019t bring any value. We should be pragmatic and strive to find the right balance between efficiency and following idioms that are sometimes considered indisputable in other programming paradigms.
Remember that Go is a unique language designed for many characteristics, including simplicity. However, if we find a need for getters and setters or, as mentioned, foresee a future need while guaranteeing forward compatibility, there\u2019s nothing wrong with using them.
"},{"location":"mistakes/#interface-pollution-5","title":"Interface pollution (#5)","text":"TL;DR: Abstractions should be discovered, not created. To prevent unnecessary complexity, create an interface when you need it and not when you foresee needing it, or if you can at least prove the abstraction to be a valid one.
An interface provides a way to specify the behavior of an object. We use interfaces to create common abstractions that multiple objects can implement. What makes Go interfaces so different is that they are satisfied implicitly. There is no explicit keyword like implements to mark that an object X implements interface Y.
In general, we can define three main use cases where interfaces are generally considered as bringing value: factoring out a common behavior, creating some decoupling, and restricting a type to a certain behavior. Yet, this list isn't exhaustive and will also depend on the context we face.
In many occasions, interfaces are made to create abstractions. And the main caveat when programming meets abstractions is remembering that abstractions should be discovered, not created. What does this mean? It means we shouldn\u2019t start creating abstractions in our code if there is no immediate reason to do so. We shouldn\u2019t design with interfaces but wait for a concrete need. Said differently, we should create an interface when we need it, not when we foresee that we could need it. What\u2019s the main problem if we overuse interfaces? The answer is that they make the code flow more complex. Adding a useless level of indirection doesn\u2019t bring any value; it creates a worthless abstraction making the code more difficult to read, understand, and reason about. If we don\u2019t have a strong reason for adding an interface and it\u2019s unclear how an interface makes a code better, we should challenge this interface\u2019s purpose. Why not call the implementation directly?
We should be cautious when creating abstractions in our code (abstractions should be discovered, not created). It\u2019s common for us, software developers, to overengineer our code by trying to guess what the perfect level of abstraction is, based on what we think we might need later. This process should be avoided because, in most cases, it pollutes our code with unnecessary abstractions, making it more complex to read.
Don\u2019t design with interfaces, discover them.
\u2013 Rob Pike
Let\u2019s not try to solve a problem abstractly but solve what has to be solved now. Last, but not least, if it\u2019s unclear how an interface makes the code better, we should probably consider removing it to make our code simpler.
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"},{"location":"mistakes/#interface-on-the-producer-side-6","title":"Interface on the producer side (#6)","text":"TL;DR: Keeping interfaces on the client side avoids unnecessary abstractions.
Interfaces are satisfied implicitly in Go, which tends to be a gamechanger compared to languages with an explicit implementation. In most cases, the approach to follow is similar to what we described in the previous section: abstractions should be discovered, not created. This means that it\u2019s not up to the producer to force a given abstraction for all the clients. Instead, it\u2019s up to the client to decide whether it needs some form of abstraction and then determine the best abstraction level for its needs.
An interface should live on the consumer side in most cases. However, in particular contexts (for example, when we know\u2014not foresee\u2014that an abstraction will be helpful for consumers), we may want to have it on the producer side. If we do, we should strive to keep it as minimal as possible, increasing its reusability potential and making it more easily composable.
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"},{"location":"mistakes/#returning-interfaces-7","title":"Returning interfaces (#7)","text":"TL;DR: To prevent being restricted in terms of flexibility, a function shouldn\u2019t return interfaces but concrete implementations in most cases. Conversely, a function should accept interfaces whenever possible.
In most cases, we shouldn\u2019t return interfaces but concrete implementations. Otherwise, it can make our design more complex due to package dependencies and can restrict flexibility because all the clients would have to rely on the same abstraction. Again, the conclusion is similar to the previous sections: if we know (not foresee) that an abstraction will be helpful for clients, we can consider returning an interface. Otherwise, we shouldn\u2019t force abstractions; they should be discovered by clients. If a client needs to abstract an implementation for whatever reason, it can still do that on the client\u2019s side.
"},{"location":"mistakes/#any-says-nothing-8","title":"any says nothing (#8)","text":"TL;DR: Only use any if you need to accept or return any possible type, such as json.Marshal. Otherwise, any doesn\u2019t provide meaningful information and can lead to compile-time issues by allowing a caller to call methods with any data type.
The any type can be helpful if there is a genuine need for accepting or returning any possible type (for instance, when it comes to marshaling or formatting). In general, we should avoid overgeneralizing the code we write at all costs. Perhaps a little bit of duplicated code might occasionally be better if it improves other aspects such as code expressiveness.
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"},{"location":"mistakes/#being-confused-about-when-to-use-generics-9","title":"Being confused about when to use generics (#9)","text":"TL;DR: Relying on generics and type parameters can prevent writing boilerplate code to factor out elements or behaviors. However, do not use type parameters prematurely, but only when you see a concrete need for them. Otherwise, they introduce unnecessary abstractions and complexity.
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"},{"location":"mistakes/#not-being-aware-of-the-possible-problems-with-type-embedding-10","title":"Not being aware of the possible problems with type embedding (#10)","text":"TL;DR: Using type embedding can also help avoid boilerplate code; however, ensure that doing so doesn\u2019t lead to visibility issues where some fields should have remained hidden.
When creating a struct, Go offers the option to embed types. But this can sometimes lead to unexpected behaviors if we don\u2019t understand all the implications of type embedding. Throughout this section, we look at how to embed types, what these bring, and the possible issues.
In Go, a struct field is called embedded if it\u2019s declared without a name. For example,
type Foo struct {\n Bar // Embedded field\n}\n\ntype Bar struct {\n Baz int\n}\nIn the Foo struct, the Bar type is declared without an associated name; hence, it\u2019s an embedded field.
We use embedding to promote the fields and methods of an embedded type. Because Bar contains a Baz field, this field is promoted to Foo. Therefore, Baz becomes available from Foo.
What can we say about type embedding? First, let\u2019s note that it\u2019s rarely a necessity, and it means that whatever the use case, we can probably solve it as well without type embedding. Type embedding is mainly used for convenience: in most cases, to promote behaviors.
If we decide to use type embedding, we need to keep two main constraints in mind: * It shouldn\u2019t be used solely as some syntactic sugar to simplify accessing a field (such as Foo.Baz() instead of Foo.Bar.Baz()). If this is the only rationale, let\u2019s not embed the inner type and use a field instead. * It shouldn\u2019t promote data (fields) or a behavior (methods) we want to hide from the outside: for example, if it allows clients to access a locking behavior that should remain private to the struct.
Using type embedding consciously by keeping these constraints in mind can help avoid boilerplate code with additional forwarding methods. However, let\u2019s make sure we don\u2019t do it solely for cosmetics and not promote elements that should remain hidden.
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"},{"location":"mistakes/#not-using-the-functional-options-pattern-11","title":"Not using the functional options pattern (#11)","text":"TL;DR: To handle options conveniently and in an API-friendly manner, use the functional options pattern.
Although there are different implementations with minor variations, the main idea is as follows: * An unexported struct holds the configuration: options. * Each option is a function that returns the same type: type Option func(options *options) error. For example, WithPort accepts an int argument that represents the port and returns an Option type that represents how to update the options struct.
type options struct {\n port *int\n}\n\ntype Option func(options *options) error\n\nfunc WithPort(port int) Option {\n return func(options *options) error {\n if port < 0 {\n return errors.New(\"port should be positive\")\n }\n options.port = &port\n return nil\n }\n}\n\nfunc NewServer(addr string, opts ...Option) ( *http.Server, error) { <1>\n var options options <2>\n for _, opt := range opts { <3>\n err := opt(&options) <4>\n if err != nil {\n return nil, err\n }\n }\n\n // At this stage, the options struct is built and contains the config\n // Therefore, we can implement our logic related to port configuration\n var port int\n if options.port == nil {\n port = defaultHTTPPort\n } else {\n if *options.port == 0 {\n port = randomPort()\n } else {\n port = *options.port\n }\n }\n\n // ...\n}\nThe functional options pattern provides a handy and API-friendly way to handle options. Although the builder pattern can be a valid option, it has some minor downsides (having to pass a config struct that can be empty or a less handy way to handle error management) that tend to make the functional options pattern the idiomatic way to deal with these kind of problems in Go.
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"},{"location":"mistakes/#project-misorganization-project-structure-and-package-organization-12","title":"Project misorganization (project structure and package organization) (#12)","text":"TL;DR: Following a layout such as project-layout can be a good way to start structuring Go projects, especially if you are looking for existing conventions to standardize a new project.
Regarding the overall organization, there are different schools of thought. For example, should we organize our application by context or by layer? It depends on our preferences. We may favor grouping code per context (such as the customer context, the contract context, etc.), or we may favor following hexagonal architecture principles and group per technical layer. If the decision we make fits our use case, it cannot be a wrong decision, as long as we remain consistent with it.
Regarding packages, there are multiple best practices that we should follow. First, we should avoid premature packaging because it might cause us to overcomplicate a project. Sometimes, it\u2019s better to use a simple organization and have our project evolve when we understand what it contains rather than forcing ourselves to make the perfect structure up front. Granularity is another essential thing to consider. We should avoid having dozens of nano packages containing only one or two files. If we do, it\u2019s because we have probably missed some logical connections across these packages, making our project harder for readers to understand. Conversely, we should also avoid huge packages that dilute the meaning of a package name.
Package naming should also be considered with care. As we all know (as developers), naming is hard. To help clients understand a Go project, we should name our packages after what they provide, not what they contain. Also, naming should be meaningful. Therefore, a package name should be short, concise, expressive, and, by convention, a single lowercase word.
Regarding what to export, the rule is pretty straightforward. We should minimize what should be exported as much as possible to reduce the coupling between packages and keep unnecessary exported elements hidden. If we are unsure whether to export an element or not, we should default to not exporting it. Later, if we discover that we need to export it, we can adjust our code. Let\u2019s also keep in mind some exceptions, such as making fields exported so that a struct can be unmarshaled with encoding/json.
Organizing a project isn\u2019t straightforward, but following these rules should help make it easier to maintain. However, remember that consistency is also vital to ease maintainability. Therefore, let\u2019s make sure that we keep things as consistent as possible within a codebase.
"},{"location":"mistakes/#creating-utility-packages-13","title":"Creating utility packages (#13)","text":"TL;DR: Naming is a critical piece of application design. Creating packages such as common, util, and shared doesn\u2019t bring much value for the reader. Refactor such packages into meaningful and specific package names.
Also, bear in mind that naming a package after what it provides and not what it contains can be an efficient way to increase its expressiveness.
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"},{"location":"mistakes/#ignoring-package-name-collisions-14","title":"Ignoring package name collisions (#14)","text":"TL;DR: To avoid naming collisions between variables and packages, leading to confusion or perhaps even bugs, use unique names for each one. If this isn\u2019t feasible, use an import alias to change the qualifier to differentiate the package name from the variable name, or think of a better name.
Package collisions occur when a variable name collides with an existing package name, preventing the package from being reused. We should prevent variable name collisions to avoid ambiguity. If we face a collision, we should either find another meaningful name or use an import alias.
"},{"location":"mistakes/#missing-code-documentation-15","title":"Missing code documentation (#15)","text":"TL;DR: To help clients and maintainers understand your code\u2019s purpose, document exported elements.
Documentation is an important aspect of coding. It simplifies how clients can consume an API but can also help in maintaining a project. In Go, we should follow some rules to make our code idiomatic:
First, every exported element must be documented. Whether it is a structure, an interface, a function, or something else, if it\u2019s exported, it must be documented. The convention is to add comments, starting with the name of the exported element.
As a convention, each comment should be a complete sentence that ends with punctuation. Also bear in mind that when we document a function (or a method), we should highlight what the function intends to do, not how it does it; this belongs to the core of a function and comments, not documentation. Furthermore, the documentation should ideally provide enough information that the consumer does not have to look at our code to understand how to use an exported element.
When it comes to documenting a variable or a constant, we might be interested in conveying two aspects: its purpose and its content. The former should live as code documentation to be useful for external clients. The latter, though, shouldn\u2019t necessarily be public.
To help clients and maintainers understand a package\u2019s scope, we should also document each package. The convention is to start the comment with // Package followed by the package name. The first line of a package comment should be concise. That\u2019s because it will appear in the package. Then, we can provide all the information we need in the following lines.
Documenting our code shouldn\u2019t be a constraint. We should take the opportunity to make sure it helps clients and maintainers to understand the purpose of our code.
"},{"location":"mistakes/#not-using-linters-16","title":"Not using linters (#16)","text":"TL;DR: To improve code quality and consistency, use linters and formatters.
A linter is an automatic tool to analyze code and catch errors. The scope of this section isn\u2019t to give an exhaustive list of the existing linters; otherwise, it will become deprecated pretty quickly. But we should understand and remember why linters are essential for most Go projects.
However, if you\u2019re not a regular user of linters, here is a list that you may want to use daily: * https://golang.org/cmd/vet/\u2014A standard Go analyzer * https://github.com/kisielk/errcheck\u2014An error checker * https://github.com/fzipp/gocyclo\u2014A cyclomatic complexity analyzer * https://github.com/jgautheron/goconst\u2014A repeated string constants analyzer * Besides linters, we should also use code formatters to fix code style. Here is a list of some code formatters for you to try: * https://golang.org/cmd/gofmt/\u2014A standard Go code formatter * https://godoc.org/golang.org/x/tools/cmd/goimports\u2014A standard Go imports formatter * Meanwhile, we should also look at golangci-lint (https://github.com/golangci/ golangci-lint). It\u2019s a linting tool that provides a facade on top of many useful linters and formatters. Also, it allows running the linters in parallel to improve analysis speed, which is quite handy.
Linters and formatters are a powerful way to improve the quality and consistency of our codebase. Let\u2019s take the time to understand which one we should use and make sure we automate their execution (such as a CI or Git precommit hook).
"},{"location":"mistakes/#data-types","title":"Data Types","text":""},{"location":"mistakes/#creating-confusion-with-octal-literals-17","title":"Creating confusion with octal literals (#17)","text":"TL;DR: When reading existing code, bear in mind that integer literals starting with 0 are octal numbers. Also, to improve readability, make octal integers explicit by prefixing them with 0o.
Octal numbers start with a 0 (e.g., 010 is equal to 8 in base 10). To improve readability and avoid potential mistakes for future code readers, we should make octal numbers explicit using the 0o prefix (e.g., 0o10).
We should also note the other integer literal representations: * Binary\u2014Uses a 0b or 0B prefix (for example, 0b100 is equal to 4 in base 10) * Hexadecimal\u2014Uses an 0x or 0X prefix (for example, 0xF is equal to 15 in base 10) * Imaginary\u2014Uses an i suffix (for example, 3i)
We can also use an underscore character (_) as a separator for readability. For example, we can write 1 billion this way: 1_000_000_000. We can also use the under- score character with other representations (for example, 0b00_00_01).
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"},{"location":"mistakes/#neglecting-integer-overflows-18","title":"Neglecting integer overflows (#18)","text":"TL;DR: Because integer overflows and underflows are handled silently in Go, you can implement your own functions to catch them.
In Go, an integer overflow that can be detected at compile time generates a compila- tion error. For example,
var counter int32 = math.MaxInt32 + 1\nconstant 2147483648 overflows int32\nHowever, at run time, an integer overflow or underflow is silent; this does not lead to an application panic. It is essential to keep this behavior in mind, because it can lead to sneaky bugs (for example, an integer increment or addition of positive integers that leads to a negative result).
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"},{"location":"mistakes/#not-understanding-floating-points-19","title":"Not understanding floating-points (#19)","text":"TL;DR: Making floating-point comparisons within a given delta can ensure that your code is portable. When performing addition or subtraction, group the operations with a similar order of magnitude to favor accuracy. Also, perform multiplication and division before addition and subtraction.
In Go, there are two floating-point types (if we omit imaginary numbers): float32 and float64. The concept of a floating point was invented to solve the major problem with integers: their inability to represent fractional values. To avoid bad surprises, we need to know that floating-point arithmetic is an approximation of real arithmetic.
For that, we\u2019ll look at a multiplication example:
var n float32 = 1.0001\nfmt.Println(n * n)\nWe may expect this code to print the result of 1.0001 * 1.0001 = 1.00020001, right? However, running it on most x86 processors prints 1.0002, instead.
Because Go\u2019s float32 and float64 types are approximations, we have to bear a few rules in mind: * When comparing two floating-point numbers, check that their difference is within an acceptable range. * When performing additions or subtractions, group operations with a similar order of magnitude for better accuracy. * To favor accuracy, if a sequence of operations requires addition, subtraction, multiplication, or division, perform the multiplication and division operations first.
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"},{"location":"mistakes/#not-understanding-slice-length-and-capacity-20","title":"Not understanding slice length and capacity (#20)","text":"TL;DR: Understanding the difference between slice length and capacity should be part of a Go developer\u2019s core knowledge. The slice length is the number of available elements in the slice, whereas the slice capacity is the number of elements in the backing array.
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"},{"location":"mistakes/#inefficient-slice-initialization-21","title":"Inefficient slice initialization (#21)","text":"TL;DR: When creating a slice, initialize it with a given length or capacity if its length is already known. This reduces the number of allocations and improves performance.
While initializing a slice using make, we can provide a length and an optional capacity. Forgetting to pass an appropriate value for both of these parameters when it makes sense is a widespread mistake. Indeed, it can lead to multiple copies and additional effort for the GC to clean the temporary backing arrays. Performance-wise, there\u2019s no good reason not to give the Go runtime a helping hand.
Our options are to allocate a slice with either a given capacity or a given length. Of these two solutions, we have seen that the second tends to be slightly faster. But using a given capacity and append can be easier to implement and read in some contexts.
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"},{"location":"mistakes/#being-confused-about-nil-vs-empty-slice-22","title":"Being confused about nil vs. empty slice (#22)","text":"TL;DR: To prevent common confusions such as when using the encoding/json or the reflect package, you need to understand the difference between nil and empty slices. Both are zero-length, zero-capacity slices, but only a nil slice doesn\u2019t require allocation.
In Go, there is a distinction between nil and empty slices. A nil slice is equals to nil, whereas an empty slice has a length of zero. A nil slice is empty, but an empty slice isn\u2019t necessarily nil. Meanwhile, a nil slice doesn\u2019t require any allocation. We have seen throughout this section how to initialize a slice depending on the con- text by using
var s []string if we aren\u2019t sure about the final length and the slice can be empty[]string(nil) as syntactic sugar to create a nil and empty slicemake([]string, length) if the future length is known The last option, []string{}, should be avoided if we initialize the slice without ele- ments. Finally, let\u2019s check whether the libraries we use make the distinctions between nil and empty slices to prevent unexpected behaviors.
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"},{"location":"mistakes/#not-properly-checking-if-a-slice-is-empty-23","title":"Not properly checking if a slice is empty (#23)","text":"TL;DR: To check if a slice doesn\u2019t contain any element, check its length. This check works regardless of whether the slice is nil or empty. The same goes for maps. To design unambiguous APIs, you shouldn\u2019t distinguish between nil and empty slices.
To determine whether a slice has elements, we can either do it by checking if the slice is nil or if its length is equal to 0. Checking the length is the best option to follow as it will cover both if the slice is empty or is the slice is nil.
Meanwhile, when designing interfaces, we should avoid distinguishing nil and empty slices, which leads to subtle programming errors. When returning slices, it should make neither a seman- tic nor a technical difference if we return a nil or empty slice. Both should mean the same thing for the callers. This principle is the same with maps. To check if a map is empty, check its length, not whether it\u2019s nil.
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"},{"location":"mistakes/#not-making-slice-copies-correctly-24","title":"Not making slice copies correctly (#24)","text":"TL;DR: To copy one slice to another using the copy built-in function, remember that the number of copied elements corresponds to the minimum between the two slice\u2019s lengths.
Copying elements from one slice to another is a reasonably frequent operation. When using copy, we must recall that the number of elements copied to the destina- tion corresponds to the minimum between the two slices\u2019 lengths. Also bear in mind that other alternatives exist to copy a slice, so we shouldn\u2019t be surprised if we find them in a codebase.
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"},{"location":"mistakes/#unexpected-side-effects-using-slice-append-25","title":"Unexpected side effects using slice append (#25)","text":"TL;DR: Using copy or the full slice expression is a way to prevent append from creating conflicts if two different functions use slices backed by the same array. However, only a slice copy prevents memory leaks if you want to shrink a large slice.
When using slicing, we must remember that we can face a situation leading to unintended side effects. If the resulting slice has a length smaller than its capacity, append can mutate the original slice. If we want to restrict the range of possible side effects, we can use either a slice copy or the full slice expression, which prevents us from doing a copy.
[!NOTE] s[low:high:max] (full slice expression): This statement creates a slice similar to the one created with s[low:high], except that the resulting slice\u2019s capacity is equal to max - low.
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"},{"location":"mistakes/#slice-and-memory-leaks-26","title":"Slice and memory leaks (#26)","text":"TL;DR: Working with a slice of pointers or structs with pointer fields, you can avoid memory leaks by marking as nil the elements excluded by a slicing operation.
"},{"location":"mistakes/#leaking-capacity","title":"Leaking capacity","text":"Remember that slicing a large slice or array can lead to potential high memory consumption. The remaining space won\u2019t be reclaimed by the GC, and we can keep a large backing array despite using only a few elements. Using a slice copy is the solution to prevent such a case.
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"},{"location":"mistakes/#slice-and-pointers","title":"Slice and pointers","text":"When we use the slicing operation with pointers or structs with pointer fields, we need to know that the GC won\u2019t reclaim these elements. In that case, the two options are to either perform a copy or explicitly mark the remaining elements or their fields to nil.
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"},{"location":"mistakes/#inefficient-map-initialization-27","title":"Inefficient map initialization (#27)","text":"TL;DR: When creating a map, initialize it with a given length if its length is already known. This reduces the number of allocations and improves performance.
A map provides an unordered collection of key-value pairs in which all the keys are dis- tinct. In Go, a map is based on the hash table data structure. Internally, a hash table is an array of buckets, and each bucket is a pointer to an array of key-value pairs.
If we know up front the number of elements a map will contain, we should create it by providing an initial size. Doing this avoids potential map growth, which is quite heavy computation-wise because it requires reallocating enough space and rebalancing all the elements.
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"},{"location":"mistakes/#map-and-memory-leaks-28","title":"Map and memory leaks (#28)","text":"TL;DR: A map can always grow in memory, but it never shrinks. Hence, if it leads to some memory issues, you can try different options, such as forcing Go to recreate the map or using pointers.
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"},{"location":"mistakes/#comparing-values-incorrectly-29","title":"Comparing values incorrectly (#29)","text":"TL;DR: To compare types in Go, you can use the == and != operators if two types are comparable: Booleans, numerals, strings, pointers, channels, and structs are composed entirely of comparable types. Otherwise, you can either use reflect.DeepEqual and pay the price of reflection or use custom implementations and libraries.
It\u2019s essential to understand how to use == and != to make comparisons effectively. We can use these operators on operands that are comparable: * Booleans\u2014Compare whether two Booleans are equal. * Numerics (int, float, and complex types)\u2014Compare whether two numerics are equal. * Strings\u2014Compare whether two strings are equal. * Channels\u2014Compare whether two channels were created by the same call to make or if both are nil. * Interfaces\u2014Compare whether two interfaces have identical dynamic types and equal dynamic values or if both are nil. * Pointers\u2014Compare whether two pointers point to the same value in memory or if both are nil. * _Structs and arrays)\u2014Compare whether they are composed of similar types.
[!NOTE] We can also use the ?, >=, <, and > operators with numeric types to compare values and with strings to compare their lexical order.
If operands are not comparable (e.g., slices and maps), we have to use other options such as reflection. Reflection is a form of metaprogramming, and it refers to the ability of an applica- tion to introspect and modify its structure and behavior. For example, in Go, we can use reflect.DeepEqual. This function reports whether two elements are deeply equal by recursively traversing two values. The elements it accepts are basic types plus arrays, structs, slices, maps, pointers, interfaces, and functions. Yet, the main catch is the performance penalty.
If performance is crucial at run time, implementing our custom method might be the best solution. One additional note: we must remember that the standard library has some exist- ing comparison methods. For example, we can use the optimized bytes.Compare function to compare two slices of bytes. Before implementing a custom method, we need to make sure we don\u2019t reinvent the wheel.
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"},{"location":"mistakes/#control-structures","title":"Control Structures","text":""},{"location":"mistakes/#ignoring-that-elements-are-copied-in-range-loops-30","title":"Ignoring that elements are copied in range loops (#30)","text":"TL;DR: The value element in a range loop is a copy. Therefore, to mutate a struct, for example, access it via its index or via a classic for loop (unless the element or the field you want to modify is a pointer).
A range loop allows iterating over different data structures: * String * Array * Pointer to an array * Slice * Map * Receiving channel
Compared to a classic for loop, a range loop is a convenient way to iterate over all the elements of one of these data structures, thanks to its concise syntax.
Yet, we should remember that the value element in a range loop is a copy. Therefore, if the value is a struct we need to mutate, we will only update the copy, not the element itself, unless the value or field we modify is a pointer. The favored options are to access the element via the index using a range loop or a classic for loop.
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"},{"location":"mistakes/#ignoring-how-arguments-are-evaluated-in-range-loops-channels-and-arrays-31","title":"Ignoring how arguments are evaluated in range loops (channels and arrays) (#31)","text":"TL;DR: Understanding that the expression passed to the range operator is evaluated only once before the beginning of the loop can help you avoid common mistakes such as inefficient assignment in channel or slice iteration.
The range loop evaluates the provided expression only once, before the beginning of the loop, by doing a copy (regardless of the type). We should remember this behavior to avoid common mistakes that might, for example, lead us to access the wrong element. For example:
a := [3]int{0, 1, 2}\nfor i, v := range a {\n a[2] = 10\n if i == 2 {\n fmt.Println(v)\n }\n}\nThis code updates the last index to 10. However, if we run this code, it does not print 10; it prints 2.
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"},{"location":"mistakes/#ignoring-the-impacts-of-using-pointer-elements-in-range-loops-32","title":"Ignoring the impacts of using pointer elements in range loops (#32)","text":"TL;DR: Using a local variable or accessing an element using an index, you can prevent mistakes while copying pointers inside a loop.
When iterating over a data structure using a range loop, we must recall that all the values are assigned to a unique variable with a single unique address. Therefore, if we store a pointer referencing this variable during each iteration, we will end up in a situ- ation where we store the same pointer referencing the same element: the latest one. We can overcome this issue by forcing the creation of a local variable in the loop\u2019s scope or creating a pointer referencing a slice element via its index. Both solutions are fine.
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"},{"location":"mistakes/#making-wrong-assumptions-during-map-iterations-ordering-and-map-insert-during-iteration-33","title":"Making wrong assumptions during map iterations (ordering and map insert during iteration) (#33)","text":"TL;DR: To ensure predictable outputs when using maps, remember that a map data structure: * Doesn\u2019t order the data by keys * Doesn\u2019t preserve the insertion order * Doesn\u2019t have a deterministic iteration order * Doesn\u2019t guarantee that an element added during an iteration will be produced during this iteration
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"},{"location":"mistakes/#ignoring-how-the-break-statement-works-34","title":"Ignoring how the break statement works (#34)","text":"TL;DR: Using break or continue with a label enforces breaking a specific statement. This can be helpful with switch or select statements inside loops.
A break statement is commonly used to terminate the execution of a loop. When loops are used in conjunction with switch or select, developers frequently make the mistake of breaking the wrong statement. For example:
for i := 0; i < 5; i++ {\n fmt.Printf(\"%d \", i)\n\n switch i {\n default:\n case 2:\n break\n }\n}\nThe break statement doesn\u2019t terminate the for loop: it terminates the switch statement, instead. Hence, instead of iterating from 0 to 2, this code iterates from 0 to 4: 0 1 2 3 4.
One essential rule to keep in mind is that a break statement terminates the execu- tion of the innermost for, switch, or select statement. In the previous example, it terminates the switch statement.
To break the loop instead of the switch statement, the most idiomatic way is to use a label:
loop:\n for i := 0; i < 5; i++ {\n fmt.Printf(\"%d \", i)\n\n switch i {\n default:\n case 2:\n break loop\n }\n }\nHere, we associate the loop label with the for loop. Then, because we provide the loop label to the break statement, it breaks the loop, not the switch. Therefore, this new version will print 0 1 2, as we expected.
Source code
"},{"location":"mistakes/#using-defer-inside-a-loop-35","title":"Using defer inside a loop (#35)","text":"TL;DR: Extracting loop logic inside a function leads to executing a defer statement at the end of each iteration.
The defer statement delays a call\u2019s execution until the surrounding function returns. It\u2019s mainly used to reduce boilerplate code. For example, if a resource has to be closed eventually, we can use defer to avoid repeating the closure calls before every single return.
One common mistake with defer is to forget that it schedules a function call when the surrounding function returns. For example:
func readFiles(ch <-chan string) error {\n for path := range ch {\n file, err := os.Open(path)\n if err != nil {\n return err\n }\n\n defer file.Close()\n\n // Do something with file\n }\n return nil\n}\nThe defer calls are executed not during each loop iteration but when the readFiles function returns. If readFiles doesn\u2019t return, the file descriptors will be kept open forever, causing leaks.
One common option to fix this problem is to create a surrounding function after defer, called during each iteration:
func readFiles(ch <-chan string) error {\n for path := range ch {\n if err := readFile(path); err != nil {\n return err\n }\n }\n return nil\n}\n\nfunc readFile(path string) error {\n file, err := os.Open(path)\n if err != nil {\n return err\n }\n\n defer file.Close()\n\n // Do something with file\n return nil\n}\nAnother solution is to make the readFile function a closure but intrinsically, this remains the same solution: adding another surrounding function to execute the defer calls during each iteration.
Source code
"},{"location":"mistakes/#strings","title":"Strings","text":""},{"location":"mistakes/#not-understanding-the-concept-of-rune-36","title":"Not understanding the concept of rune (#36)","text":"TL;DR: Understanding that a rune corresponds to the concept of a Unicode code point and that it can be composed of multiple bytes should be part of the Go developer\u2019s core knowledge to work accurately with strings.
As runes are everywhere in Go, it's important to understand the following:
A charset is a set of characters, whereas an encoding describes how to translate a charset into binary. In Go, a string references an immutable slice of arbitrary bytes. Go source code is encoded using UTF-8. Hence, all string literals are UTF-8 strings. But because a string can contain arbitrary bytes, if it\u2019s obtained from somewhere else (not the source code), it isn\u2019t guaranteed to be based on the UTF-8 encoding. A rune corresponds to the concept of a Unicode code point, meaning an item represented by a single value. Using UTF-8, a Unicode code point can be encoded into 1 to 4 bytes. Using len() on a string in Go returns the number of bytes, not the number of runes. Source code
"},{"location":"mistakes/#inaccurate-string-iteration-37","title":"Inaccurate string iteration (#37)","text":"TL;DR: Iterating on a string with the range operator iterates on the runes with the index corresponding to the starting index of the rune\u2019s byte sequence. To access a specific rune index (such as the third rune), convert the string into a []rune.
Iterating on a string is a common operation for developers. Perhaps we want to per- form an operation for each rune in the string or implement a custom function to search for a specific substring. In both cases, we have to iterate on the different runes of a string. But it\u2019s easy to get confused about how iteration works.
For example, consider the following example:
s := \"h\u00eallo\"\nfor i := range s {\n fmt.Printf(\"position %d: %c\\n\", i, s[i])\n}\nfmt.Printf(\"len=%d\\n\", len(s))\nposition 0: h\nposition 1: \u00c3\nposition 3: l\nposition 4: l\nposition 5: o\nlen=6\nLet's highlight three points that might be confusing:
The second rune is \u00c3 in the output instead of \u00ea. We jumped from position 1 to position 3: what is at position 2? len returns a count of 6, whereas s contains only 5 runes. Let\u2019s start with the last observation. We already mentioned that len returns the num- ber of bytes in a string, not the number of runes. Because we assigned a string literal to s, s is a UTF-8 string. Meanwhile, the special character \"\u00ea\" isn\u2019t encoded in a single byte; it requires 2 bytes. Therefore, calling len(s) returns 6.
Meanwhile, in the previous example, we have to understand that we don't iterate over each rune; instead, we iterate over each starting index of a rune:
Printing s[i] doesn\u2019t print the ith rune; it prints the UTF-8 representation of the byte at index i. Hence, we printed \"h\u00c3llo\" instead of \"h\u00eallo\".
If we want to print all the different runes, we can either use the value element of the range operator:
s := \"h\u00eallo\"\nfor i, r := range s {\n fmt.Printf(\"position %d: %c\\n\", i, r)\n}\nOr, we can convert the string into a slice of runes and iterate over it:
s := \"h\u00eallo\"\nrunes := []rune(s)\nfor i, r := range runes {\n fmt.Printf(\"position %d: %c\\n\", i, r)\n}\nNote that this solution introduces a run-time overhead compared to the previous one. Indeed, converting a string into a slice of runes requires allocating an additional slice and converting the bytes into runes: an O(n) time complexity with n the number of bytes in the string. Therefore, if we want to iterate over all the runes, we should use the first solution.
However, if we want to access the ith rune of a string with the first option, we don\u2019t have access to the rune index; rather, we know the starting index of a rune in the byte sequence.
s := \"h\u00eallo\"\nr := []rune(s)[4]\nfmt.Printf(\"%c\\n\", r) // o\nSource code
"},{"location":"mistakes/#misusing-trim-functions-38","title":"Misusing trim functions (#38)","text":"TL;DR: strings.TrimRight/strings.TrimLeft removes all the trailing/leading runes contained in a given set, whereas strings.TrimSuffix/strings.TrimPrefix returns a string without a provided suffix/prefix.
For example:
fmt.Println(strings.TrimRight(\"123oxo\", \"xo\"))\nThe example prints 123:
Conversely, strings.TrimLeft removes all the leading runes contained in a set.
On the other side, strings.TrimSuffix / strings.TrimPrefix returns a string without the provided trailing suffix / prefix.
Source code
"},{"location":"mistakes/#under-optimized-strings-concatenation-39","title":"Under-optimized strings concatenation (#39)","text":"TL;DR: Concatenating a list of strings should be done with strings.Builder to prevent allocating a new string during each iteration.
Let\u2019s consider a concat function that concatenates all the string elements of a slice using the += operator:
func concat(values []string) string {\n s := \"\"\n for _, value := range values {\n s += value\n }\n return s\n}\nDuring each iteration, the += operator concatenates s with the value string. At first sight, this function may not look wrong. But with this implementation, we forget one of the core characteristics of a string: its immutability. Therefore, each iteration doesn\u2019t update s; it reallocates a new string in memory, which significantly impacts the performance of this function.
Fortunately, there is a solution to deal with this problem, using strings.Builder:
func concat(values []string) string {\n sb := strings.Builder{}\n for _, value := range values {\n _, _ = sb.WriteString(value)\n }\n return sb.String()\n}\nDuring each iteration, we constructed the resulting string by calling the WriteString method that appends the content of value to its internal buffer, hence minimizing memory copying.
[!NOTE] WriteString returns an error as the second output, but we purposely ignore it. Indeed, this method will never return a non-nil error. So what\u2019s the purpose of this method returning an error as part of its signature? strings.Builder imple- ments the io.StringWriter interface, which contains a single method: WriteString(s string) (n int, err error). Hence, to comply with this interface, WriteString must return an error.
Internally, strings.Builder holds a byte slice. Each call to WriteString results in a call to append on this slice. There are two impacts. First, this struct shouldn\u2019t be used concurrently, as the calls to append would lead to race conditions. The second impact is something that we saw in mistake #21, \"Inefficient slice initialization\": if the future length of a slice is already known, we should preallocate it. For that purpose, strings.Builder exposes a method Grow(n int) to guarantee space for another n bytes:
func concat(values []string) string {\n total := 0\n for i := 0; i < len(values); i++ {\n total += len(values[i])\n }\n\n sb := strings.Builder{}\n sb.Grow(total) (2)\n for _, value := range values {\n _, _ = sb.WriteString(value)\n }\n return sb.String()\n}\nLet\u2019s run a benchmark to compare the three versions (v1 using +=; v2 using strings.Builder{} without preallocation; and v3 using strings.Builder{} with pre- allocation). The input slice contains 1,000 strings, and each string contains 1,000 bytes:
BenchmarkConcatV1-4 16 72291485 ns/op\nBenchmarkConcatV2-4 1188 878962 ns/op\nBenchmarkConcatV3-4 5922 190340 ns/op\nAs we can see, the latest version is by far the most efficient: 99% faster than v1 and 78% faster than v2.
strings.Builder is the recommended solution to concatenate a list of strings. Usually, this solution should be used within a loop. Indeed, if we just have to concate- nate a few strings (such as a name and a surname), using strings.Builder is not rec- ommended as doing so will make the code a bit less readable than using the += operator or fmt.Sprintf.
Source code
"},{"location":"mistakes/#useless-string-conversions-40","title":"Useless string conversions (#40)","text":"TL;DR: Remembering that the bytes package offers the same operations as the strings package can help avoid extra byte/string conversions.
When choosing to work with a string or a []byte, most programmers tend to favor strings for convenience. But most I/O is actually done with []byte. For example, io.Reader, io.Writer, and io.ReadAll work with []byte, not strings.
When we\u2019re wondering whether we should work with strings or []byte, let\u2019s recall that working with []byte isn\u2019t necessarily less convenient. Indeed, all the exported functions of the strings package also have alternatives in the bytes package: Split, Count, Contains, Index, and so on. Hence, whether we\u2019re doing I/O or not, we should first check whether we could implement a whole workflow using bytes instead of strings and avoid the price of additional conversions.
Source code
"},{"location":"mistakes/#substring-and-memory-leaks-41","title":"Substring and memory leaks (#41)","text":"TL;DR: Using copies instead of substrings can prevent memory leaks, as the string returned by a substring operation will be backed by the same byte array.
In mistake #26, \u201cSlices and memory leaks,\u201d we saw how slicing a slice or array may lead to memory leak situations. This principle also applies to string and substring opera- tions.
We need to keep two things in mind while using the substring operation in Go. First, the interval provided is based on the number of bytes, not the number of runes. Second, a substring operation may lead to a memory leak as the resulting substring will share the same backing array as the initial string. The solutions to prevent this case from happening are to perform a string copy manually or to use strings.Clone from Go 1.18.
Source code
"},{"location":"mistakes/#functions-and-methods","title":"Functions and Methods","text":""},{"location":"mistakes/#not-knowing-which-type-of-receiver-to-use-42","title":"Not knowing which type of receiver to use (#42)","text":"TL;DR: The decision whether to use a value or a pointer receiver should be made based on factors such as the type, whether it has to be mutated, whether it contains a field that can\u2019t be copied, and how large the object is. When in doubt, use a pointer receiver.
Choosing between value and pointer receivers isn\u2019t always straightforward. Let\u2019s dis- cuss some of the conditions to help us choose.
A receiver must be a pointer
If the method needs to mutate the receiver. This rule is also valid if the receiver is a slice and a method needs to append elements: ```cgo type slice []int
func (s slice) add(element int) { s = append(*s, element) } ```
If the method receiver contains a field that cannot be copied: for example, a type part of the sync package (see #74, \u201cCopying a sync type\u201d). A receiver should be a pointer
If the receiver is a large object. Using a pointer can make the call more effi- cient, as doing so prevents making an extensive copy. When in doubt about how large is large, benchmarking can be the solution; it\u2019s pretty much impossible to state a specific size, because it depends on many factors. A receiver must be a value
If we have to enforce a receiver\u2019s immutability. If the receiver is a map, function, or channel. Otherwise, a compilation error occurs. A receiver should be a value
If the receiver is a slice that doesn\u2019t have to be mutated. If the receiver is a small array or struct that is naturally a value type without mutable fields, such as time.Time. If the receiver is a basic type such as int, float64, or string. Of course, it\u2019s impossible to be exhaustive, as there will always be edge cases, but this section\u2019s goal was to provide guidance to cover most cases. By default, we can choose to go with a value receiver unless there\u2019s a good reason not to do so. In doubt, we should use a pointer receiver.
Source code
"},{"location":"mistakes/#never-using-named-result-parameters-43","title":"Never using named result parameters (#43)","text":"TL;DR: Using named result parameters can be an efficient way to improve the readability of a function/method, especially if multiple result parameters have the same type. In some cases, this approach can also be convenient because named result parameters are initialized to their zero value. But be cautious about potential side effects.
When we return parameters in a function or a method, we can attach names to these parameters and use them as regular variables. When a result parameter is named, it\u2019s initialized to its zero value when the function/method begins. With named result parameters, we can also call a naked return statement (without argu- ments). In that case, the current values of the result parameters are used as the returned values.
Here\u2019s an example that uses a named result parameter b:
func f(a int) (b int) {\n b = a\n return\n}\nIn this example, we attach a name to the result parameter: b. When we call return without arguments, it returns the current value of b.
In some cases, named result parameters can also increase readability: for example, if two parameters have the same type. In other cases, they can also be used for convenience. Therefore, we should use named result parameters sparingly when there\u2019s a clear benefit.
Source code
"},{"location":"mistakes/#unintended-side-effects-with-named-result-parameters-44","title":"Unintended side effects with named result parameters (#44)","text":"TL;DR: See #43.
We mentioned why named result parameters can be useful in some situations. But as these result parameters are initialized to their zero value, using them can sometimes lead to subtle bugs if we\u2019re not careful enough. For example, can you spot what\u2019s wrong with this code?
func (l loc) getCoordinates(ctx context.Context, address string) (\n lat, lng float32, err error) {\n isValid := l.validateAddress(address) (1)\n if !isValid {\n return 0, 0, errors.New(\"invalid address\")\n }\n\n if ctx.Err() != nil { (2)\n return 0, 0, err\n }\n\n // Get and return coordinates\n}\nThe error might not be obvious at first glance. Here, the error returned in the if ctx.Err() != nil scope is err. But we haven\u2019t assigned any value to the err variable. It\u2019s still assigned to the zero value of an error type: nil. Hence, this code will always return a nil error.
When using named result parameters, we must recall that each parameter is initial- ized to its zero value. As we have seen in this section, this can lead to subtle bugs that aren\u2019t always straightforward to spot while reading code. Therefore, let\u2019s remain cau- tious when using named result parameters, to avoid potential side effects.
Source code
"},{"location":"mistakes/#returning-a-nil-receiver-45","title":"Returning a nil receiver (#45)","text":"TL;DR: When returning an interface, be cautious about returning not a nil pointer but an explicit nil value. Otherwise, unintended consequences may result because the caller will receive a non-nil value.
Source code
"},{"location":"mistakes/#using-a-filename-as-a-function-input-46","title":"Using a filename as a function input (#46)","text":"TL;DR: Designing functions to receive io.Reader types instead of filenames improves the reusability of a function and makes testing easier.
Accepting a filename as a function input to read from a file should, in most cases, be considered a code smell (except in specific functions such as os.Open). Indeed, it makes unit tests more complex because we may have to create multiple files. It also reduces the reusability of a function (although not all functions are meant to be reused). Using the io.Reader interface abstracts the data source. Regardless of whether the input is a file, a string, an HTTP request, or a gRPC request, the imple- mentation can be reused and easily tested.
Source code
"},{"location":"mistakes/#ignoring-how-defer-arguments-and-receivers-are-evaluated-argument-evaluation-pointer-and-value-receivers-47","title":"Ignoring how defer arguments and receivers are evaluated (argument evaluation, pointer, and value receivers) (#47)","text":"TL;DR: Passing a pointer to a defer function and wrapping a call inside a closure are two possible solutions to overcome the immediate evaluation of arguments and receivers.
In a defer function the arguments are evaluated right away, not once the surrounding function returns. For example, in this code, we always call notify and incrementCounter with the same status: an empty string.
const (\n StatusSuccess = \"success\"\n StatusErrorFoo = \"error_foo\"\n StatusErrorBar = \"error_bar\"\n)\n\nfunc f() error {\n var status string\n defer notify(status)\n defer incrementCounter(status)\n\n if err := foo(); err != nil {\n status = StatusErrorFoo\n return err\n }\n\n if err := bar(); err != nil {\n status = StatusErrorBar\n return err\n }\n\n status = StatusSuccess <5>\n return nil\n}\nIndeed, we call notify(status) and incrementCounter(status) as defer functions. Therefore, Go will delay these calls to be executed once f returns with the current value of status at the stage we used defer, hence passing an empty string.
Two leading options if we want to keep using defer.
The first solution is to pass a string pointer:
func f() error {\n var status string\n defer notify(&status) \n defer incrementCounter(&status)\n\n // The rest of the function unchanged\n if err := foo(); err != nil {\n status = StatusErrorFoo\n return err\n }\n\n if err := bar(); err != nil {\n status = StatusErrorBar\n return err\n }\n\n status = StatusSuccess\n return nil\n}\nUsing defer evaluates the arguments right away: here, the address of status. Yes, status itself is modified throughout the function, but its address remains constant, regardless of the assignments. Hence, if notify or incrementCounter uses the value referenced by the string pointer, it will work as expected. But this solution requires changing the signature of the two functions, which may not always be possible.
There\u2019s another solution: calling a closure (an anonymous function value that references variables from outside its body) as a defer statement:
func f() error {\n var status string\n defer func() {\n notify(status)\n incrementCounter(status)\n }()\n\n // The rest of the function unchanged\n}\nHere, we wrap the calls to both notify and incrementCounter within a closure. This closure references the status variable from outside its body. Therefore, status is evaluated once the closure is executed, not when we call defer. This solution also works and doesn\u2019t require notify and incrementCounter to change their signature.
Let's also note this behavior applies with method receiver: the receiver is evaluated immediately.
Source code
"},{"location":"mistakes/#error-management","title":"Error Management","text":""},{"location":"mistakes/#panicking-48","title":"Panicking (#48)","text":"TL;DR: Using panic is an option to deal with errors in Go. However, it should only be used sparingly in unrecoverable conditions: for example, to signal a programmer error or when you fail to load a mandatory dependency.
In Go, panic is a built-in function that stops the ordinary flow:
func main() {\n fmt.Println(\"a\")\n panic(\"foo\")\n fmt.Println(\"b\")\n}\nThis code prints a and then stops before printing b:
a\npanic: foo\n\ngoroutine 1 [running]:\nmain.main()\n main.go:7 +0xb3\nPanicking in Go should be used sparingly. There are two prominent cases, one to signal a programmer error (e.g., sql.Register that panics if the driver is nil or has already been register) and another where our application fails to create a man- datory dependency. Hence, exceptional conditions that lead us to stop the application. In most other cases, error management should be done with a function that returns a proper error type as the last return argument.
Source code
"},{"location":"mistakes/#ignoring-when-to-wrap-an-error-49","title":"Ignoring when to wrap an error (#49)","text":"TL;DR: Wrapping an error allows you to mark an error and/or provide additional context. However, error wrapping creates potential coupling as it makes the source error available for the caller. If you want to prevent that, don\u2019t use error wrapping.
Since Go 1.13, the %w directive allows us to wrap errors conveniently. Error wrapping is about wrapping or packing an error inside a wrapper container that also makes the source error available. In general, the two main use cases for error wrapping are the following:
Adding additional context to an error Marking an error as a specific error When handling an error, we can decide to wrap it. Wrapping is about adding additional context to an error and/or marking an error as a specific type. If we need to mark an error, we should create a custom error type. However, if we just want to add extra context, we should use fmt.Errorf with the %w directive as it doesn\u2019t require creating a new error type. Yet, error wrapping creates potential coupling as it makes the source error available for the caller. If we want to prevent it, we shouldn\u2019t use error wrapping but error transformation, for example, using fmt.Errorf with the %v directive.
Source code
"},{"location":"mistakes/#comparing-an-error-type-inaccurately-50","title":"Comparing an error type inaccurately (#50)","text":"TL;DR: If you use Go 1.13 error wrapping with the %w directive and fmt.Errorf, comparing an error against a type has to be done using errors.As. Otherwise, if the returned error you want to check is wrapped, it will fail the checks.
Source code
"},{"location":"mistakes/#comparing-an-error-value-inaccurately-51","title":"Comparing an error value inaccurately (#51)","text":"TL;DR: If you use Go 1.13 error wrapping with the %w directive and fmt.Errorf, comparing an error against or a value has to be done using errors.As. Otherwise, if the returned error you want to check is wrapped, it will fail the checks.
A sentinel error is an error defined as a global variable:
import \"errors\"\n\nvar ErrFoo = errors.New(\"foo\")\nIn general, the convention is to start with Err followed by the error type: here, ErrFoo. A sentinel error conveys an expected error, an error that clients will expect to check. As general guidelines:
Expected errors should be designed as error values (sentinel errors): var ErrFoo = errors.New(\"foo\"). Unexpected errors should be designed as error types: type BarError struct { ... }, with BarError implementing the error interface. If we use error wrapping in our application with the %w directive and fmt.Errorf, checking an error against a specific value should be done using errors.Is instead of ==. Thus, even if the sentinel error is wrapped, errors.Is can recursively unwrap it and compare each error in the chain against the provided value.
Source code
"},{"location":"mistakes/#handling-an-error-twice-52","title":"Handling an error twice (#52)","text":"TL;DR: In most situations, an error should be handled only once. Logging an error is handling an error. Therefore, you have to choose between logging or returning an error. In many cases, error wrapping is the solution as it allows you to provide additional context to an error and return the source error.
Handling an error multiple times is a mistake made frequently by developers, not spe- cifically in Go. This can cause situations where the same error is logged multiple times make debugging harder.
Let's reming us that handling an error should be done only once. Logging an error is handling an error. Hence, we should either log or return an error. By doing this, we simplify our code and gain better insights into the error situation. Using error wrap- ping is the most convenient approach as it allows us to propagate the source error and add context to an error.
Source code
"},{"location":"mistakes/#not-handling-an-error-53","title":"Not handling an error (#53)","text":"TL;DR: Ignoring an error, whether during a function call or in a defer function, should be done explicitly using the blank identifier. Otherwise, future readers may be confused about whether it was intentional or a miss.
Source code
"},{"location":"mistakes/#not-handling-defer-errors-54","title":"Not handling defer errors (#54)","text":"TL;DR: In many cases, you shouldn\u2019t ignore an error returned by a defer function. Either handle it directly or propagate it to the caller, depending on the context. If you want to ignore it, use the blank identifier.
Consider the following code:
func f() {\n // ...\n notify() // Error handling is omitted\n}\n\nfunc notify() error {\n // ...\n}\nFrom a maintainability perspective, the code can lead to some issues. Let\u2019s consider a new reader looking at it. This reader notices that notify returns an error but that the error isn\u2019t handled by the parent function. How can they guess whether or not handling the error was intentional? How can they know whether the previous developer forgot to handle it or did it purposely?
For these reasons, when we want to ignore an error, there's only one way to do it, using the blank identifier (_):
_ = notify\nIn terms of compilation and run time, this approach doesn\u2019t change anything compared to the first piece of code. But this new version makes explicit that we aren\u2019t interested in the error. Also, we can add a comment that indicates the rationale for why an error is ignored:
// At-most once delivery.\n// Hence, it's accepted to miss some of them in case of errors.\n_ = notify()\nSource code
"},{"location":"mistakes/#concurrency-foundations","title":"Concurrency: Foundations","text":""},{"location":"mistakes/#mixing-up-concurrency-and-parallelism-55","title":"Mixing up concurrency and parallelism (#55)","text":"TL;DR: Understanding the fundamental differences between concurrency and parallelism is a cornerstone of the Go developer\u2019s knowledge. Concurrency is about structure, whereas parallelism is about execution.
"},{"location":"mistakes/#thinking-concurrency-is-always-faster-56","title":"Thinking concurrency is always faster (#56)","text":"TL;DR: To be a proficient developer, you must acknowledge that concurrency isn\u2019t always faster. Solutions involving parallelization of minimal workloads may not necessarily be faster than a sequential implementation. Benchmarking sequential versus concurrent solutions should be the way to validate assumptions.
Source code
"},{"location":"mistakes/#being-puzzled-about-when-to-use-channels-or-mutexes-57","title":"Being puzzled about when to use channels or mutexes (#57)","text":"TL;DR: Being aware of goroutine interactions can also be helpful when deciding between channels and mutexes. In general, parallel goroutines require synchronization and hence mutexes. Conversely, concurrent goroutines generally require coordination and orchestration and hence channels.
"},{"location":"mistakes/#not-understanding-race-problems-data-races-vs-race-conditions-and-the-go-memory-model-58","title":"Not understanding race problems (data races vs. race conditions and the Go memory model) (#58)","text":"TL;DR: Being proficient in concurrency also means understanding that data races and race conditions are different concepts. Data races occur when multiple goroutines simultaneously access the same memory location and at least one of them is writing. Meanwhile, being data-race-free doesn\u2019t necessarily mean deterministic execution. When a behavior depends on the sequence or the timing of events that can\u2019t be controlled, this is a race condition.
Understanding the Go memory model and the underlying guarantees in terms of ordering and synchronization is essential to prevent possible data races and/or race conditions.
Source code
"},{"location":"mistakes/#not-understanding-the-concurrency-impacts-of-a-workload-type-59","title":"Not understanding the concurrency impacts of a workload type (#59)","text":"TL;DR: When creating a certain number of goroutines, consider the workload type. Creating CPU-bound goroutines means bounding this number close to the GOMAXPROCS variable (based by default on the number of CPU cores on the host). Creating I/O-bound goroutines depends on other factors, such as the external system.
Source code
"},{"location":"mistakes/#misunderstanding-go-contexts-60","title":"Misunderstanding Go contexts (#60)","text":"TL;DR: Go contexts are also one of the cornerstones of concurrency in Go. A context allows you to carry a deadline, a cancellation signal, and/or a list of keys-values.
Source code
"},{"location":"mistakes/#concurrency-practice","title":"Concurrency: Practice","text":""},{"location":"mistakes/#propagating-an-inappropriate-context-61","title":"Propagating an inappropriate context (#61)","text":"TL;DR: Understanding the conditions when a context can be canceled should matter when propagating it: for example, an HTTP handler canceling the context when the response has been sent.
Source code
"},{"location":"mistakes/#starting-a-goroutine-without-knowing-when-to-stop-it-62","title":"Starting a goroutine without knowing when to stop it (#62)","text":"TL;DR: Avoiding leaks means being mindful that whenever a goroutine is started, you should have a plan to stop it eventually.
Source code
"},{"location":"mistakes/#not-being-careful-with-goroutines-and-loop-variables-63","title":"Not being careful with goroutines and loop variables (#63)","text":"TL;DR: To avoid bugs with goroutines and loop variables, create local variables or call functions instead of closures.
Source code
"},{"location":"mistakes/#expecting-a-deterministic-behavior-using-select-and-channels-64","title":"Expecting a deterministic behavior using select and channels (#64)","text":"TL;DR: Understanding that select with multiple channels chooses the case randomly if multiple options are possible prevents making wrong assumptions that can lead to subtle concurrency bugs.
Source code
"},{"location":"mistakes/#not-using-notification-channels-65","title":"Not using notification channels (#65)","text":"TL;DR: Send notifications using a chan struct{} type.
"},{"location":"mistakes/#not-using-nil-channels-66","title":"Not using nil channels (#66)","text":"TL;DR: Using nil channels should be part of your concurrency toolset because it allows you to remove cases from select statements, for example.
Source code
"},{"location":"mistakes/#being-puzzled-about-channel-size-67","title":"Being puzzled about channel size (#67)","text":"TL;DR: Carefully decide on the right channel type to use, given a problem. Only unbuffered channels provide strong synchronization guarantees.
You should have a good reason to specify a channel size other than one for buffered channels.
"},{"location":"mistakes/#forgetting-about-possible-side-effects-with-string-formatting-etcd-data-race-example-and-deadlock-68","title":"Forgetting about possible side effects with string formatting (etcd data race example and deadlock) (#68)","text":"TL;DR: Being aware that string formatting may lead to calling existing functions means watching out for possible deadlocks and other data races.
Source code
"},{"location":"mistakes/#creating-data-races-with-append-69","title":"Creating data races with append (#69)","text":"TL;DR: Calling append isn\u2019t always data-race-free; hence, it shouldn\u2019t be used concurrently on a shared slice.
Source code
"},{"location":"mistakes/#using-mutexes-inaccurately-with-slices-and-maps-70","title":"Using mutexes inaccurately with slices and maps (#70)","text":"TL;DR: Remembering that slices and maps are pointers can prevent common data races.
Source code
"},{"location":"mistakes/#misusing-syncwaitgroup-71","title":"Misusing sync.WaitGroup (#71)","text":"TL;DR: To accurately use sync.WaitGroup, call the Add method before spinning up goroutines.
Source code
"},{"location":"mistakes/#forgetting-about-synccond-72","title":"Forgetting about sync.Cond (#72)","text":"TL;DR: You can send repeated notifications to multiple goroutines with sync.Cond.
Source code
"},{"location":"mistakes/#not-using-errgroup-73","title":"Not using errgroup (#73)","text":"TL;DR: You can synchronize a group of goroutines and handle errors and contexts with the errgroup package.
Source code
"},{"location":"mistakes/#copying-a-sync-type-74","title":"Copying a sync type (#74)","text":"TL;DR: sync types shouldn\u2019t be copied.
Source code
"},{"location":"mistakes/#standard-library","title":"Standard Library","text":""},{"location":"mistakes/#providing-a-wrong-time-duration-75","title":"Providing a wrong time duration (#75)","text":"TL;DR: Remain cautious with functions accepting a time.Duration. Even though passing an integer is allowed, strive to use the time API to prevent any possible confusion.
Source code
"},{"location":"mistakes/#timeafter-and-memory-leaks-76","title":"time.After and memory leaks (#76)","text":"TL;DR: Avoiding calls to time.After in repeated functions (such as loops or HTTP handlers) can avoid peak memory consumption. The resources created by time.After are released only when the timer expires.
Source code
"},{"location":"mistakes/#json-handling-common-mistakes-77","title":"JSON handling common mistakes (#77)","text":" Unexpected behavior because of type embedding Be careful about using embedded fields in Go structs. Doing so may lead to sneaky bugs like an embedded time.Time field implementing the json.Marshaler interface, hence overriding the default marshaling behavior.
Source code
JSON and the monotonic clock When comparing two time.Time structs, recall that time.Time contains both a wall clock and a monotonic clock, and the comparison using the == operator is done on both clocks.
Source code
To avoid wrong assumptions when you provide a map while unmarshaling JSON data, remember that numerics are converted to float64 by default.
Source code
"},{"location":"mistakes/#common-sql-mistakes-78","title":"Common SQL mistakes (#78)","text":" Forgetting that sql.Open doesn't necessarily establish connections to a database Call the Ping or PingContext method if you need to test your configuration and make sure a database is reachable.
Source code
Forgetting about connections pooling Configure the database connection parameters for production-grade applications.
Not using prepared statements Using SQL prepared statements makes queries more efficient and more secure.
Source code
Deal with nullable columns in tables using pointers or sql.NullXXX types.
Source code
Not handling rows iteration errors Call the Err method of sql.Rows after row iterations to ensure that you haven\u2019t missed an error while preparing the next row.
Source code
"},{"location":"mistakes/#not-closing-transient-resources-http-body-sqlrows-and-osfile-79","title":"Not closing transient resources (HTTP body, sql.Rows, and os.File) (#79)","text":"TL;DR: Eventually close all structs implementing io.Closer to avoid possible leaks.
Source code
"},{"location":"mistakes/#forgetting-the-return-statement-after-replying-to-an-http-request-80","title":"Forgetting the return statement after replying to an HTTP request (#80)","text":"TL;DR: To avoid unexpected behaviors in HTTP handler implementations, make sure you don\u2019t miss the return statement if you want a handler to stop after http.Error.
Source code
"},{"location":"mistakes/#using-the-default-http-client-and-server-81","title":"Using the default HTTP client and server (#81)","text":"TL;DR: For production-grade applications, don\u2019t use the default HTTP client and server implementations. These implementations are missing timeouts and behaviors that should be mandatory in production.
Source code
"},{"location":"mistakes/#testing","title":"Testing","text":""},{"location":"mistakes/#not-categorizing-tests-build-tags-environment-variables-and-short-mode-82","title":"Not categorizing tests (build tags, environment variables, and short mode) (#82)","text":"TL;DR: Categorizing tests using build flags, environment variables, or short mode makes the testing process more efficient. You can create test categories using build flags or environment variables (for example, unit versus integration tests) and differentiate short from long-running tests to decide which kinds of tests to execute.
Source code
"},{"location":"mistakes/#not-enabling-the-race-flag-83","title":"Not enabling the race flag (#83)","text":"TL;DR: Enabling the -race flag is highly recommended when writing concurrent applications. Doing so allows you to catch potential data races that can lead to software bugs.
"},{"location":"mistakes/#not-using-test-execution-modes-parallel-and-shuffle-84","title":"Not using test execution modes (parallel and shuffle) (#84)","text":"TL;DR: Using the -parallel flag is an efficient way to speed up tests, especially long-running ones. Use the -shuffle flag to help ensure that a test suite doesn\u2019t rely on wrong assumptions that could hide bugs.
"},{"location":"mistakes/#not-using-table-driven-tests-85","title":"Not using table-driven tests (#85)","text":"TL;DR: Table-driven tests are an efficient way to group a set of similar tests to prevent code duplication and make future updates easier to handle.
Source code
"},{"location":"mistakes/#sleeping-in-unit-tests-86","title":"Sleeping in unit tests (#86)","text":"TL;DR: Avoid sleeps using synchronization to make a test less flaky and more robust. If synchronization isn\u2019t possible, consider a retry approach.
Source code
"},{"location":"mistakes/#not-dealing-with-the-time-api-efficiently-87","title":"Not dealing with the time API efficiently (#87)","text":"TL;DR: Understanding how to deal with functions using the time API is another way to make a test less flaky. You can use standard techniques such as handling the time as part of a hidden dependency or asking clients to provide it.
Source code
"},{"location":"mistakes/#not-using-testing-utility-packages-httptest-and-iotest-88","title":"Not using testing utility packages (httptest and iotest) (#88)","text":" The httptest package is helpful for dealing with HTTP applications. It provides a set of utilities to test both clients and servers. Source code
The iotest package helps write io.Reader and test that an application is tolerant to errors. Source code
"},{"location":"mistakes/#writing-inaccurate-benchmarks-89","title":"Writing inaccurate benchmarks (#89)","text":" Not resetting or pausing the timer Use time methods to preserve the accuracy of a benchmark.
Source code
Making wrong assumptions about micro-benchmarks Increasing benchtime or using tools such as benchstat can be helpful when dealing with micro-benchmarks.
Be careful with the results of a micro-benchmark if the system that ends up running the application is different from the one running the micro-benchmark.
Source code
Not being careful about compiler optimizations Make sure the function under test leads to a side effect, to prevent compiler optimizations from fooling you about the benchmark results.
Source code
Being fooled by the observer effect To prevent the observer effect, force a benchmark to re-create the data used by a CPU-bound function.
Source code
"},{"location":"mistakes/#not-exploring-all-the-go-testing-features-90","title":"Not exploring all the Go testing features (#90)","text":" Use code coverage with the -coverprofile flag to quickly see which part of the code needs more attention.
Testing from a different package Place unit tests in a different package to enforce writing tests that focus on an exposed behavior, not internals.
Source code
Handling errors using the *testing.T variable instead of the classic if err != nil makes code shorter and easier to read.
Source code
You can use setup and teardown functions to configure a complex environment, such as in the case of integration tests.
Source code
"},{"location":"mistakes/#not-using-fuzzing-community-mistake","title":"Not using fuzzing (community mistake)","text":"TL;DR: Fuzzing is an efficient strategy to detect random, unexpected, or malformed inputs to complex functions and methods in order to discover vulnerabilities, bugs, or even potential crashes.
Credits: @jeromedoucet
"},{"location":"mistakes/#optimizations","title":"Optimizations","text":""},{"location":"mistakes/#not-understanding-cpu-caches-91","title":"Not understanding CPU caches (#91)","text":" Understanding how to use CPU caches is important for optimizing CPU-bound applications because the L1 cache is about 50 to 100 times faster than the main memory.
Being conscious of the cache line concept is critical to understanding how to organize data in data-intensive applications. A CPU doesn\u2019t fetch memory word by word; instead, it usually copies a memory block to a 64-byte cache line. To get the most out of each individual cache line, enforce spatial locality.
Source code
Slice of structs vs. struct of slices Source code
Making code predictable for the CPU can also be an efficient way to optimize certain functions. For example, a unit or constant stride is predictable for the CPU, but a non-unit stride (for example, a linked list) isn\u2019t predictable.
Source code
To avoid a critical stride, hence utilizing only a tiny portion of the cache, be aware that caches are partitioned.
"},{"location":"mistakes/#writing-concurrent-code-that-leads-to-false-sharing-92","title":"Writing concurrent code that leads to false sharing (#92)","text":"TL;DR: Knowing that lower levels of CPU caches aren\u2019t shared across all the cores helps avoid performance-degrading patterns such as false sharing while writing concurrency code. Sharing memory is an illusion.
Source code
"},{"location":"mistakes/#not-taking-into-account-instruction-level-parallelism-93","title":"Not taking into account instruction-level parallelism (#93)","text":"TL;DR: Use instruction-level parallelism (ILP) to optimize specific parts of your code to allow a CPU to execute as many parallel instructions as possible. Identifying data hazards is one of the main steps.
Source code
"},{"location":"mistakes/#not-being-aware-of-data-alignment-94","title":"Not being aware of data alignment (#94)","text":"TL;DR: You can avoid common mistakes by remembering that in Go, basic types are aligned with their own size. For example, keep in mind that reorganizing the fields of a struct by size in descending order can lead to more compact structs (less memory allocation and potentially a better spatial locality).
Source code
"},{"location":"mistakes/#not-understanding-stack-vs-heap-95","title":"Not understanding stack vs. heap (#95)","text":"TL;DR: Understanding the fundamental differences between heap and stack should also be part of your core knowledge when optimizing a Go application. Stack allocations are almost free, whereas heap allocations are slower and rely on the GC to clean the memory.
Source code
"},{"location":"mistakes/#not-knowing-how-to-reduce-allocations-api-change-compiler-optimizations-and-syncpool-96","title":"Not knowing how to reduce allocations (API change, compiler optimizations, and sync.Pool) (#96)","text":"TL;DR: Reducing allocations is also an essential aspect of optimizing a Go application. This can be done in different ways, such as designing the API carefully to prevent sharing up, understanding the common Go compiler optimizations, and using sync.Pool.
Source code
"},{"location":"mistakes/#not-relying-on-inlining-97","title":"Not relying on inlining (#97)","text":"TL;DR: Use the fast-path inlining technique to efficiently reduce the amortized time to call a function.
"},{"location":"mistakes/#not-using-go-diagnostics-tooling-98","title":"Not using Go diagnostics tooling (#98)","text":"TL;DR: Rely on profiling and the execution tracer to understand how an application performs and the parts to optimize.
"},{"location":"mistakes/#not-understanding-how-the-gc-works-99","title":"Not understanding how the GC works (#99)","text":"TL;DR: Understanding how to tune the GC can lead to multiple benefits such as handling sudden load increases more efficiently.
"},{"location":"mistakes/#not-understanding-the-impacts-of-running-go-in-docker-and-kubernetes-100","title":"Not understanding the impacts of running Go in Docker and Kubernetes (#100)","text":"TL;DR: To help avoid CPU throttling when deployed in Docker and Kubernetes, keep in mind that Go isn\u2019t CFS-aware.
"},{"location":"chinese/presentation/","title":"Presentation","text":"TODO
"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"\ud83d\udcd6 Book Presentation","text":"Source code and community space of \ud83d\udcd6 100 Go Mistakes and How to Avoid Them, published by Manning in August 2022.
"},{"location":"#book-description","title":"Book Description","text":"If you're a Go developer looking to improve your skills, this book is for you. With a focus on practical examples, 100 Go Mistakes and How to Avoid Them covers a wide range of topics from concurrency and error handling to testing and code organization. You'll learn to write more idiomatic, efficient, and maintainable code and become a proficient Go developer.
"},{"location":"#quotes","title":"Quotes","text":"This should be the required reading for all Golang developers before they touch code in Production... It's the Golang equivalent of the legendary 'Effective Java' by Joshua Bloch.
\u2013 Neeraj Shah
Not having this will be the 101st mistake a Go programmer could make.
\u2013 Anupam Sengupta
"},{"location":"#where-to-buy","title":"Where to Buy?","text":"100 Go Mistakes and How to Avoid Them (\ud83c\uddec\ud83c\udde7 edition: paper, digital, or audiobook):
Manning O\u2019Reilly Amazon: .com, .co.uk, .de, .fr, .in, .co.jp, .es, .it, .com.br Go\u8a00\u8a9e100Tips \u958b\u767a\u8005\u306b\u3042\u308a\u304c\u3061\u306a\u9593\u9055\u3044\u3078\u306e\u5bfe\u51e6\u6cd5 (\ud83c\uddef\ud83c\uddf5 edition):
"},{"location":"#about-the-author","title":"About the Author","text":"Teiva Harsanyi is a senior software engineer at Google. He has worked in various domains, including insurance, transportation, and safety-critical industries like air traffic management. He is passionate about Go and how to design and implement reliable systems.
"},{"location":"#external-resources","title":"External Resources","text":""},{"location":"#english","title":"English","text":" Book Review: 100 Go Mistakes and How to Avoid Them: Post, YouTube How to make mistakes in Go - Go Time #190: Episode, Spotify 8LU - 100% Test Coverage Some Tips I learned from 100 Mistakes in Go What can be summarized from 100 Go Mistakes? "},{"location":"#chinese","title":"Chinese","text":" \u6df1\u5ea6\u9605\u8bfb\u4e4b\u300a100 Go Mistakes and How to Avoid Them 100 Go Mistakes \u968f\u8bb0 \u6211\u4e3a\u4ec0\u4e48\u653e\u5f03Go\u8bed\u8a00\uff1f "},{"location":"#japanese","title":"Japanese","text":" \u6700\u8fd1\u8aad\u3093\u3060Go\u8a00\u8a9e\u306e\u672c\u306e\u7d39\u4ecb\uff1a100 Go Mistakes and How to Avoid Them \u300e100 Go Mistakes and How to Avoid Them\u300f\u3092\u8aad\u3080 100 Go Mistakes \u968f\u8bb0 - 01 Code and project organization "},{"location":"#portuguese","title":"Portuguese","text":" Um \u00d3TIMO livro para programadores Go "},{"location":"jobs/","title":"\u2764\ufe0f Go Jobs","text":"Is your company hiring? Sponsor this repository and let Go developers know your opportunities in this section (traffic: +400 unique visitor per week).
For example:
"},{"location":"jobs/#company-remote-usa-96000-to-120000-a-year","title":"[Company] Remote (USA) - $96,000 to $120,000 a year","text":""},{"location":"jobs/#job-description","title":"Job Description","text":"...
"},{"location":"jobs/#qualifications","title":"Qualifications","text":"...
"},{"location":"mistakes/","title":"\ud83d\udca1 Go Mistakes","text":"This section contains a summary of the 100 mistakes in the book. Meanwhile, it's also a section open to the community. If you believe that a mistake should be added, please create a community mistake issue.
Note: You're currently viewing a new version that I'm enriching with significantly more content. Yet, this version is still under development; please be gentle if you find an issue, and feel free to create a PR.
"},{"location":"mistakes/#code-and-project-organization","title":"Code and Project Organization","text":""},{"location":"mistakes/#unintended-variable-shadowing-1","title":"Unintended variable shadowing (#1)","text":"TL;DR: Avoiding shadowed variables can help prevent mistakes like referencing the wrong variable or confusing readers.
Variable shadowing occurs when a variable name is redeclared in an inner block, but this practice is prone to mistakes. Imposing a rule to forbid shadowed variables depends on personal taste. For example, sometimes it can be convenient to reuse an existing variable name like err for errors. Yet, in general, we should remain cautious because we now know that we can face a scenario where the code compiles, but the variable that receives the value is not the one expected.
Source code
"},{"location":"mistakes/#unnecessary-nested-code-2","title":"Unnecessary nested code (#2)","text":"TL;DR: Avoiding nested levels and keeping the happy path aligned on the left makes building a mental code model easier.
In general, the more nested levels a function requires, the more complex it is to read and understand. Let\u2019s see some different applications of this rule to optimize our code for readability:
When an if block returns, we should omit the else block in all cases. For example, we shouldn\u2019t write: go if foo() { // ... return true } else { // ... }
Instead, we omit the else block like this:
go if foo() { // ... return true } // ... * We can also follow this logic with a non-happy path:
go if s != \"\" { // ... } else { return errors.New(\"empty string\") }
Here, an empty s represents the non-happy path. Hence, we should flip the condition like so:
go if s == \"\" { return errors.New(\"empty string\") } // ...
Writing readable code is an important challenge for every developer. Striving to reduce the number of nested blocks, aligning the happy path on the left, and returning as early as possible are concrete means to improve our code\u2019s readability.
Source code
"},{"location":"mistakes/#misusing-init-functions-3","title":"Misusing init functions (#3)","text":"TL;DR: When initializing variables, remember that init functions have limited error handling and make state handling and testing more complex. In most cases, initializations should be handled as specific functions.
An init function is a function used to initialize the state of an application. It takes no arguments and returns no result (a func() function). When a package is initialized, all the constant and variable declarations in the package are evaluated. Then, the init functions are executed.
Init functions can lead to some issues: * They can limit error management. * They can complicate how to implement tests (for example, an external dependency must be set up, which may not be necessary for the scope of unit tests). * If the initialization requires us to set a state, that has to be done through global variables.
We should be cautious with init functions. They can be helpful in some situations, however, such as defining static configuration. Otherwise, and in most cases, we should handle initializations through ad hoc functions.
Source code
"},{"location":"mistakes/#overusing-getters-and-setters-4","title":"Overusing getters and setters (#4)","text":"TL;DR: Forcing the use of getters and setters isn\u2019t idiomatic in Go. Being pragmatic and finding the right balance between efficiency and blindly following certain idioms should be the way to go.
Data encapsulation refers to hiding the values or state of an object. Getters and setters are means to enable encapsulation by providing exported methods on top of unexported object fields.
In Go, there is no automatic support for getters and setters as we see in some languages. It is also considered neither mandatory nor idiomatic to use getters and setters to access struct fields. We shouldn\u2019t overwhelm our code with getters and setters on structs if they don\u2019t bring any value. We should be pragmatic and strive to find the right balance between efficiency and following idioms that are sometimes considered indisputable in other programming paradigms.
Remember that Go is a unique language designed for many characteristics, including simplicity. However, if we find a need for getters and setters or, as mentioned, foresee a future need while guaranteeing forward compatibility, there\u2019s nothing wrong with using them.
"},{"location":"mistakes/#interface-pollution-5","title":"Interface pollution (#5)","text":"TL;DR: Abstractions should be discovered, not created. To prevent unnecessary complexity, create an interface when you need it and not when you foresee needing it, or if you can at least prove the abstraction to be a valid one.
An interface provides a way to specify the behavior of an object. We use interfaces to create common abstractions that multiple objects can implement. What makes Go interfaces so different is that they are satisfied implicitly. There is no explicit keyword like implements to mark that an object X implements interface Y.
In general, we can define three main use cases where interfaces are generally considered as bringing value: factoring out a common behavior, creating some decoupling, and restricting a type to a certain behavior. Yet, this list isn't exhaustive and will also depend on the context we face.
In many occasions, interfaces are made to create abstractions. And the main caveat when programming meets abstractions is remembering that abstractions should be discovered, not created. What does this mean? It means we shouldn\u2019t start creating abstractions in our code if there is no immediate reason to do so. We shouldn\u2019t design with interfaces but wait for a concrete need. Said differently, we should create an interface when we need it, not when we foresee that we could need it. What\u2019s the main problem if we overuse interfaces? The answer is that they make the code flow more complex. Adding a useless level of indirection doesn\u2019t bring any value; it creates a worthless abstraction making the code more difficult to read, understand, and reason about. If we don\u2019t have a strong reason for adding an interface and it\u2019s unclear how an interface makes a code better, we should challenge this interface\u2019s purpose. Why not call the implementation directly?
We should be cautious when creating abstractions in our code (abstractions should be discovered, not created). It\u2019s common for us, software developers, to overengineer our code by trying to guess what the perfect level of abstraction is, based on what we think we might need later. This process should be avoided because, in most cases, it pollutes our code with unnecessary abstractions, making it more complex to read.
Don\u2019t design with interfaces, discover them.
\u2013 Rob Pike
Let\u2019s not try to solve a problem abstractly but solve what has to be solved now. Last, but not least, if it\u2019s unclear how an interface makes the code better, we should probably consider removing it to make our code simpler.
Source code
"},{"location":"mistakes/#interface-on-the-producer-side-6","title":"Interface on the producer side (#6)","text":"TL;DR: Keeping interfaces on the client side avoids unnecessary abstractions.
Interfaces are satisfied implicitly in Go, which tends to be a gamechanger compared to languages with an explicit implementation. In most cases, the approach to follow is similar to what we described in the previous section: abstractions should be discovered, not created. This means that it\u2019s not up to the producer to force a given abstraction for all the clients. Instead, it\u2019s up to the client to decide whether it needs some form of abstraction and then determine the best abstraction level for its needs.
An interface should live on the consumer side in most cases. However, in particular contexts (for example, when we know\u2014not foresee\u2014that an abstraction will be helpful for consumers), we may want to have it on the producer side. If we do, we should strive to keep it as minimal as possible, increasing its reusability potential and making it more easily composable.
Source code
"},{"location":"mistakes/#returning-interfaces-7","title":"Returning interfaces (#7)","text":"TL;DR: To prevent being restricted in terms of flexibility, a function shouldn\u2019t return interfaces but concrete implementations in most cases. Conversely, a function should accept interfaces whenever possible.
In most cases, we shouldn\u2019t return interfaces but concrete implementations. Otherwise, it can make our design more complex due to package dependencies and can restrict flexibility because all the clients would have to rely on the same abstraction. Again, the conclusion is similar to the previous sections: if we know (not foresee) that an abstraction will be helpful for clients, we can consider returning an interface. Otherwise, we shouldn\u2019t force abstractions; they should be discovered by clients. If a client needs to abstract an implementation for whatever reason, it can still do that on the client\u2019s side.
"},{"location":"mistakes/#any-says-nothing-8","title":"any says nothing (#8)","text":"TL;DR: Only use any if you need to accept or return any possible type, such as json.Marshal. Otherwise, any doesn\u2019t provide meaningful information and can lead to compile-time issues by allowing a caller to call methods with any data type.
The any type can be helpful if there is a genuine need for accepting or returning any possible type (for instance, when it comes to marshaling or formatting). In general, we should avoid overgeneralizing the code we write at all costs. Perhaps a little bit of duplicated code might occasionally be better if it improves other aspects such as code expressiveness.
Source code
"},{"location":"mistakes/#being-confused-about-when-to-use-generics-9","title":"Being confused about when to use generics (#9)","text":"TL;DR: Relying on generics and type parameters can prevent writing boilerplate code to factor out elements or behaviors. However, do not use type parameters prematurely, but only when you see a concrete need for them. Otherwise, they introduce unnecessary abstractions and complexity.
Source code
"},{"location":"mistakes/#not-being-aware-of-the-possible-problems-with-type-embedding-10","title":"Not being aware of the possible problems with type embedding (#10)","text":"TL;DR: Using type embedding can also help avoid boilerplate code; however, ensure that doing so doesn\u2019t lead to visibility issues where some fields should have remained hidden.
When creating a struct, Go offers the option to embed types. But this can sometimes lead to unexpected behaviors if we don\u2019t understand all the implications of type embedding. Throughout this section, we look at how to embed types, what these bring, and the possible issues.
In Go, a struct field is called embedded if it\u2019s declared without a name. For example,
type Foo struct {\n Bar // Embedded field\n}\n\ntype Bar struct {\n Baz int\n}\nIn the Foo struct, the Bar type is declared without an associated name; hence, it\u2019s an embedded field.
We use embedding to promote the fields and methods of an embedded type. Because Bar contains a Baz field, this field is promoted to Foo. Therefore, Baz becomes available from Foo.
What can we say about type embedding? First, let\u2019s note that it\u2019s rarely a necessity, and it means that whatever the use case, we can probably solve it as well without type embedding. Type embedding is mainly used for convenience: in most cases, to promote behaviors.
If we decide to use type embedding, we need to keep two main constraints in mind: * It shouldn\u2019t be used solely as some syntactic sugar to simplify accessing a field (such as Foo.Baz() instead of Foo.Bar.Baz()). If this is the only rationale, let\u2019s not embed the inner type and use a field instead. * It shouldn\u2019t promote data (fields) or a behavior (methods) we want to hide from the outside: for example, if it allows clients to access a locking behavior that should remain private to the struct.
Using type embedding consciously by keeping these constraints in mind can help avoid boilerplate code with additional forwarding methods. However, let\u2019s make sure we don\u2019t do it solely for cosmetics and not promote elements that should remain hidden.
Source code
"},{"location":"mistakes/#not-using-the-functional-options-pattern-11","title":"Not using the functional options pattern (#11)","text":"TL;DR: To handle options conveniently and in an API-friendly manner, use the functional options pattern.
Although there are different implementations with minor variations, the main idea is as follows: * An unexported struct holds the configuration: options. * Each option is a function that returns the same type: type Option func(options *options) error. For example, WithPort accepts an int argument that represents the port and returns an Option type that represents how to update the options struct.
type options struct {\n port *int\n}\n\ntype Option func(options *options) error\n\nfunc WithPort(port int) Option {\n return func(options *options) error {\n if port < 0 {\n return errors.New(\"port should be positive\")\n }\n options.port = &port\n return nil\n }\n}\n\nfunc NewServer(addr string, opts ...Option) ( *http.Server, error) { <1>\n var options options <2>\n for _, opt := range opts { <3>\n err := opt(&options) <4>\n if err != nil {\n return nil, err\n }\n }\n\n // At this stage, the options struct is built and contains the config\n // Therefore, we can implement our logic related to port configuration\n var port int\n if options.port == nil {\n port = defaultHTTPPort\n } else {\n if *options.port == 0 {\n port = randomPort()\n } else {\n port = *options.port\n }\n }\n\n // ...\n}\nThe functional options pattern provides a handy and API-friendly way to handle options. Although the builder pattern can be a valid option, it has some minor downsides (having to pass a config struct that can be empty or a less handy way to handle error management) that tend to make the functional options pattern the idiomatic way to deal with these kind of problems in Go.
Source code
"},{"location":"mistakes/#project-misorganization-project-structure-and-package-organization-12","title":"Project misorganization (project structure and package organization) (#12)","text":"TL;DR: Following a layout such as project-layout can be a good way to start structuring Go projects, especially if you are looking for existing conventions to standardize a new project.
Regarding the overall organization, there are different schools of thought. For example, should we organize our application by context or by layer? It depends on our preferences. We may favor grouping code per context (such as the customer context, the contract context, etc.), or we may favor following hexagonal architecture principles and group per technical layer. If the decision we make fits our use case, it cannot be a wrong decision, as long as we remain consistent with it.
Regarding packages, there are multiple best practices that we should follow. First, we should avoid premature packaging because it might cause us to overcomplicate a project. Sometimes, it\u2019s better to use a simple organization and have our project evolve when we understand what it contains rather than forcing ourselves to make the perfect structure up front. Granularity is another essential thing to consider. We should avoid having dozens of nano packages containing only one or two files. If we do, it\u2019s because we have probably missed some logical connections across these packages, making our project harder for readers to understand. Conversely, we should also avoid huge packages that dilute the meaning of a package name.
Package naming should also be considered with care. As we all know (as developers), naming is hard. To help clients understand a Go project, we should name our packages after what they provide, not what they contain. Also, naming should be meaningful. Therefore, a package name should be short, concise, expressive, and, by convention, a single lowercase word.
Regarding what to export, the rule is pretty straightforward. We should minimize what should be exported as much as possible to reduce the coupling between packages and keep unnecessary exported elements hidden. If we are unsure whether to export an element or not, we should default to not exporting it. Later, if we discover that we need to export it, we can adjust our code. Let\u2019s also keep in mind some exceptions, such as making fields exported so that a struct can be unmarshaled with encoding/json.
Organizing a project isn\u2019t straightforward, but following these rules should help make it easier to maintain. However, remember that consistency is also vital to ease maintainability. Therefore, let\u2019s make sure that we keep things as consistent as possible within a codebase.
"},{"location":"mistakes/#creating-utility-packages-13","title":"Creating utility packages (#13)","text":"TL;DR: Naming is a critical piece of application design. Creating packages such as common, util, and shared doesn\u2019t bring much value for the reader. Refactor such packages into meaningful and specific package names.
Also, bear in mind that naming a package after what it provides and not what it contains can be an efficient way to increase its expressiveness.
Source code
"},{"location":"mistakes/#ignoring-package-name-collisions-14","title":"Ignoring package name collisions (#14)","text":"TL;DR: To avoid naming collisions between variables and packages, leading to confusion or perhaps even bugs, use unique names for each one. If this isn\u2019t feasible, use an import alias to change the qualifier to differentiate the package name from the variable name, or think of a better name.
Package collisions occur when a variable name collides with an existing package name, preventing the package from being reused. We should prevent variable name collisions to avoid ambiguity. If we face a collision, we should either find another meaningful name or use an import alias.
"},{"location":"mistakes/#missing-code-documentation-15","title":"Missing code documentation (#15)","text":"TL;DR: To help clients and maintainers understand your code\u2019s purpose, document exported elements.
Documentation is an important aspect of coding. It simplifies how clients can consume an API but can also help in maintaining a project. In Go, we should follow some rules to make our code idiomatic:
First, every exported element must be documented. Whether it is a structure, an interface, a function, or something else, if it\u2019s exported, it must be documented. The convention is to add comments, starting with the name of the exported element.
As a convention, each comment should be a complete sentence that ends with punctuation. Also bear in mind that when we document a function (or a method), we should highlight what the function intends to do, not how it does it; this belongs to the core of a function and comments, not documentation. Furthermore, the documentation should ideally provide enough information that the consumer does not have to look at our code to understand how to use an exported element.
When it comes to documenting a variable or a constant, we might be interested in conveying two aspects: its purpose and its content. The former should live as code documentation to be useful for external clients. The latter, though, shouldn\u2019t necessarily be public.
To help clients and maintainers understand a package\u2019s scope, we should also document each package. The convention is to start the comment with // Package followed by the package name. The first line of a package comment should be concise. That\u2019s because it will appear in the package. Then, we can provide all the information we need in the following lines.
Documenting our code shouldn\u2019t be a constraint. We should take the opportunity to make sure it helps clients and maintainers to understand the purpose of our code.
"},{"location":"mistakes/#not-using-linters-16","title":"Not using linters (#16)","text":"TL;DR: To improve code quality and consistency, use linters and formatters.
A linter is an automatic tool to analyze code and catch errors. The scope of this section isn\u2019t to give an exhaustive list of the existing linters; otherwise, it will become deprecated pretty quickly. But we should understand and remember why linters are essential for most Go projects.
However, if you\u2019re not a regular user of linters, here is a list that you may want to use daily: * https://golang.org/cmd/vet/\u2014A standard Go analyzer * https://github.com/kisielk/errcheck\u2014An error checker * https://github.com/fzipp/gocyclo\u2014A cyclomatic complexity analyzer * https://github.com/jgautheron/goconst\u2014A repeated string constants analyzer * Besides linters, we should also use code formatters to fix code style. Here is a list of some code formatters for you to try: * https://golang.org/cmd/gofmt/\u2014A standard Go code formatter * https://godoc.org/golang.org/x/tools/cmd/goimports\u2014A standard Go imports formatter * Meanwhile, we should also look at golangci-lint (https://github.com/golangci/ golangci-lint). It\u2019s a linting tool that provides a facade on top of many useful linters and formatters. Also, it allows running the linters in parallel to improve analysis speed, which is quite handy.
Linters and formatters are a powerful way to improve the quality and consistency of our codebase. Let\u2019s take the time to understand which one we should use and make sure we automate their execution (such as a CI or Git precommit hook).
"},{"location":"mistakes/#data-types","title":"Data Types","text":""},{"location":"mistakes/#creating-confusion-with-octal-literals-17","title":"Creating confusion with octal literals (#17)","text":"TL;DR: When reading existing code, bear in mind that integer literals starting with 0 are octal numbers. Also, to improve readability, make octal integers explicit by prefixing them with 0o.
Octal numbers start with a 0 (e.g., 010 is equal to 8 in base 10). To improve readability and avoid potential mistakes for future code readers, we should make octal numbers explicit using the 0o prefix (e.g., 0o10).
We should also note the other integer literal representations: * Binary\u2014Uses a 0b or 0B prefix (for example, 0b100 is equal to 4 in base 10) * Hexadecimal\u2014Uses an 0x or 0X prefix (for example, 0xF is equal to 15 in base 10) * Imaginary\u2014Uses an i suffix (for example, 3i)
We can also use an underscore character (_) as a separator for readability. For example, we can write 1 billion this way: 1_000_000_000. We can also use the under- score character with other representations (for example, 0b00_00_01).
Source code
"},{"location":"mistakes/#neglecting-integer-overflows-18","title":"Neglecting integer overflows (#18)","text":"TL;DR: Because integer overflows and underflows are handled silently in Go, you can implement your own functions to catch them.
In Go, an integer overflow that can be detected at compile time generates a compila- tion error. For example,
var counter int32 = math.MaxInt32 + 1\nconstant 2147483648 overflows int32\nHowever, at run time, an integer overflow or underflow is silent; this does not lead to an application panic. It is essential to keep this behavior in mind, because it can lead to sneaky bugs (for example, an integer increment or addition of positive integers that leads to a negative result).
Source code
"},{"location":"mistakes/#not-understanding-floating-points-19","title":"Not understanding floating-points (#19)","text":"TL;DR: Making floating-point comparisons within a given delta can ensure that your code is portable. When performing addition or subtraction, group the operations with a similar order of magnitude to favor accuracy. Also, perform multiplication and division before addition and subtraction.
In Go, there are two floating-point types (if we omit imaginary numbers): float32 and float64. The concept of a floating point was invented to solve the major problem with integers: their inability to represent fractional values. To avoid bad surprises, we need to know that floating-point arithmetic is an approximation of real arithmetic.
For that, we\u2019ll look at a multiplication example:
var n float32 = 1.0001\nfmt.Println(n * n)\nWe may expect this code to print the result of 1.0001 * 1.0001 = 1.00020001, right? However, running it on most x86 processors prints 1.0002, instead.
Because Go\u2019s float32 and float64 types are approximations, we have to bear a few rules in mind: * When comparing two floating-point numbers, check that their difference is within an acceptable range. * When performing additions or subtractions, group operations with a similar order of magnitude for better accuracy. * To favor accuracy, if a sequence of operations requires addition, subtraction, multiplication, or division, perform the multiplication and division operations first.
Source code
"},{"location":"mistakes/#not-understanding-slice-length-and-capacity-20","title":"Not understanding slice length and capacity (#20)","text":"TL;DR: Understanding the difference between slice length and capacity should be part of a Go developer\u2019s core knowledge. The slice length is the number of available elements in the slice, whereas the slice capacity is the number of elements in the backing array.
Source code
"},{"location":"mistakes/#inefficient-slice-initialization-21","title":"Inefficient slice initialization (#21)","text":"TL;DR: When creating a slice, initialize it with a given length or capacity if its length is already known. This reduces the number of allocations and improves performance.
While initializing a slice using make, we can provide a length and an optional capacity. Forgetting to pass an appropriate value for both of these parameters when it makes sense is a widespread mistake. Indeed, it can lead to multiple copies and additional effort for the GC to clean the temporary backing arrays. Performance-wise, there\u2019s no good reason not to give the Go runtime a helping hand.
Our options are to allocate a slice with either a given capacity or a given length. Of these two solutions, we have seen that the second tends to be slightly faster. But using a given capacity and append can be easier to implement and read in some contexts.
Source code
"},{"location":"mistakes/#being-confused-about-nil-vs-empty-slice-22","title":"Being confused about nil vs. empty slice (#22)","text":"TL;DR: To prevent common confusions such as when using the encoding/json or the reflect package, you need to understand the difference between nil and empty slices. Both are zero-length, zero-capacity slices, but only a nil slice doesn\u2019t require allocation.
In Go, there is a distinction between nil and empty slices. A nil slice is equals to nil, whereas an empty slice has a length of zero. A nil slice is empty, but an empty slice isn\u2019t necessarily nil. Meanwhile, a nil slice doesn\u2019t require any allocation. We have seen throughout this section how to initialize a slice depending on the con- text by using
var s []string if we aren\u2019t sure about the final length and the slice can be empty[]string(nil) as syntactic sugar to create a nil and empty slicemake([]string, length) if the future length is known The last option, []string{}, should be avoided if we initialize the slice without ele- ments. Finally, let\u2019s check whether the libraries we use make the distinctions between nil and empty slices to prevent unexpected behaviors.
Source code
"},{"location":"mistakes/#not-properly-checking-if-a-slice-is-empty-23","title":"Not properly checking if a slice is empty (#23)","text":"TL;DR: To check if a slice doesn\u2019t contain any element, check its length. This check works regardless of whether the slice is nil or empty. The same goes for maps. To design unambiguous APIs, you shouldn\u2019t distinguish between nil and empty slices.
To determine whether a slice has elements, we can either do it by checking if the slice is nil or if its length is equal to 0. Checking the length is the best option to follow as it will cover both if the slice is empty or is the slice is nil.
Meanwhile, when designing interfaces, we should avoid distinguishing nil and empty slices, which leads to subtle programming errors. When returning slices, it should make neither a seman- tic nor a technical difference if we return a nil or empty slice. Both should mean the same thing for the callers. This principle is the same with maps. To check if a map is empty, check its length, not whether it\u2019s nil.
Source code
"},{"location":"mistakes/#not-making-slice-copies-correctly-24","title":"Not making slice copies correctly (#24)","text":"TL;DR: To copy one slice to another using the copy built-in function, remember that the number of copied elements corresponds to the minimum between the two slice\u2019s lengths.
Copying elements from one slice to another is a reasonably frequent operation. When using copy, we must recall that the number of elements copied to the destina- tion corresponds to the minimum between the two slices\u2019 lengths. Also bear in mind that other alternatives exist to copy a slice, so we shouldn\u2019t be surprised if we find them in a codebase.
Source code
"},{"location":"mistakes/#unexpected-side-effects-using-slice-append-25","title":"Unexpected side effects using slice append (#25)","text":"TL;DR: Using copy or the full slice expression is a way to prevent append from creating conflicts if two different functions use slices backed by the same array. However, only a slice copy prevents memory leaks if you want to shrink a large slice.
When using slicing, we must remember that we can face a situation leading to unintended side effects. If the resulting slice has a length smaller than its capacity, append can mutate the original slice. If we want to restrict the range of possible side effects, we can use either a slice copy or the full slice expression, which prevents us from doing a copy.
[!NOTE] s[low:high:max] (full slice expression): This statement creates a slice similar to the one created with s[low:high], except that the resulting slice\u2019s capacity is equal to max - low.
Source code
"},{"location":"mistakes/#slice-and-memory-leaks-26","title":"Slice and memory leaks (#26)","text":"TL;DR: Working with a slice of pointers or structs with pointer fields, you can avoid memory leaks by marking as nil the elements excluded by a slicing operation.
"},{"location":"mistakes/#leaking-capacity","title":"Leaking capacity","text":"Remember that slicing a large slice or array can lead to potential high memory consumption. The remaining space won\u2019t be reclaimed by the GC, and we can keep a large backing array despite using only a few elements. Using a slice copy is the solution to prevent such a case.
Source code
"},{"location":"mistakes/#slice-and-pointers","title":"Slice and pointers","text":"When we use the slicing operation with pointers or structs with pointer fields, we need to know that the GC won\u2019t reclaim these elements. In that case, the two options are to either perform a copy or explicitly mark the remaining elements or their fields to nil.
Source code
"},{"location":"mistakes/#inefficient-map-initialization-27","title":"Inefficient map initialization (#27)","text":"TL;DR: When creating a map, initialize it with a given length if its length is already known. This reduces the number of allocations and improves performance.
A map provides an unordered collection of key-value pairs in which all the keys are dis- tinct. In Go, a map is based on the hash table data structure. Internally, a hash table is an array of buckets, and each bucket is a pointer to an array of key-value pairs.
If we know up front the number of elements a map will contain, we should create it by providing an initial size. Doing this avoids potential map growth, which is quite heavy computation-wise because it requires reallocating enough space and rebalancing all the elements.
Source code
"},{"location":"mistakes/#map-and-memory-leaks-28","title":"Map and memory leaks (#28)","text":"TL;DR: A map can always grow in memory, but it never shrinks. Hence, if it leads to some memory issues, you can try different options, such as forcing Go to recreate the map or using pointers.
Source code
"},{"location":"mistakes/#comparing-values-incorrectly-29","title":"Comparing values incorrectly (#29)","text":"TL;DR: To compare types in Go, you can use the == and != operators if two types are comparable: Booleans, numerals, strings, pointers, channels, and structs are composed entirely of comparable types. Otherwise, you can either use reflect.DeepEqual and pay the price of reflection or use custom implementations and libraries.
It\u2019s essential to understand how to use == and != to make comparisons effectively. We can use these operators on operands that are comparable: * Booleans\u2014Compare whether two Booleans are equal. * Numerics (int, float, and complex types)\u2014Compare whether two numerics are equal. * Strings\u2014Compare whether two strings are equal. * Channels\u2014Compare whether two channels were created by the same call to make or if both are nil. * Interfaces\u2014Compare whether two interfaces have identical dynamic types and equal dynamic values or if both are nil. * Pointers\u2014Compare whether two pointers point to the same value in memory or if both are nil. * _Structs and arrays)\u2014Compare whether they are composed of similar types.
[!NOTE] We can also use the ?, >=, <, and > operators with numeric types to compare values and with strings to compare their lexical order.
If operands are not comparable (e.g., slices and maps), we have to use other options such as reflection. Reflection is a form of metaprogramming, and it refers to the ability of an applica- tion to introspect and modify its structure and behavior. For example, in Go, we can use reflect.DeepEqual. This function reports whether two elements are deeply equal by recursively traversing two values. The elements it accepts are basic types plus arrays, structs, slices, maps, pointers, interfaces, and functions. Yet, the main catch is the performance penalty.
If performance is crucial at run time, implementing our custom method might be the best solution. One additional note: we must remember that the standard library has some exist- ing comparison methods. For example, we can use the optimized bytes.Compare function to compare two slices of bytes. Before implementing a custom method, we need to make sure we don\u2019t reinvent the wheel.
Source code
"},{"location":"mistakes/#control-structures","title":"Control Structures","text":""},{"location":"mistakes/#ignoring-that-elements-are-copied-in-range-loops-30","title":"Ignoring that elements are copied in range loops (#30)","text":"TL;DR: The value element in a range loop is a copy. Therefore, to mutate a struct, for example, access it via its index or via a classic for loop (unless the element or the field you want to modify is a pointer).
A range loop allows iterating over different data structures: * String * Array * Pointer to an array * Slice * Map * Receiving channel
Compared to a classic for loop, a range loop is a convenient way to iterate over all the elements of one of these data structures, thanks to its concise syntax.
Yet, we should remember that the value element in a range loop is a copy. Therefore, if the value is a struct we need to mutate, we will only update the copy, not the element itself, unless the value or field we modify is a pointer. The favored options are to access the element via the index using a range loop or a classic for loop.
Source code
"},{"location":"mistakes/#ignoring-how-arguments-are-evaluated-in-range-loops-channels-and-arrays-31","title":"Ignoring how arguments are evaluated in range loops (channels and arrays) (#31)","text":"TL;DR: Understanding that the expression passed to the range operator is evaluated only once before the beginning of the loop can help you avoid common mistakes such as inefficient assignment in channel or slice iteration.
The range loop evaluates the provided expression only once, before the beginning of the loop, by doing a copy (regardless of the type). We should remember this behavior to avoid common mistakes that might, for example, lead us to access the wrong element. For example:
a := [3]int{0, 1, 2}\nfor i, v := range a {\n a[2] = 10\n if i == 2 {\n fmt.Println(v)\n }\n}\nThis code updates the last index to 10. However, if we run this code, it does not print 10; it prints 2.
Source code
"},{"location":"mistakes/#ignoring-the-impacts-of-using-pointer-elements-in-range-loops-32","title":"Ignoring the impacts of using pointer elements in range loops (#32)","text":"TL;DR: Using a local variable or accessing an element using an index, you can prevent mistakes while copying pointers inside a loop.
When iterating over a data structure using a range loop, we must recall that all the values are assigned to a unique variable with a single unique address. Therefore, if we store a pointer referencing this variable during each iteration, we will end up in a situ- ation where we store the same pointer referencing the same element: the latest one. We can overcome this issue by forcing the creation of a local variable in the loop\u2019s scope or creating a pointer referencing a slice element via its index. Both solutions are fine.
Source code
"},{"location":"mistakes/#making-wrong-assumptions-during-map-iterations-ordering-and-map-insert-during-iteration-33","title":"Making wrong assumptions during map iterations (ordering and map insert during iteration) (#33)","text":"TL;DR: To ensure predictable outputs when using maps, remember that a map data structure: * Doesn\u2019t order the data by keys * Doesn\u2019t preserve the insertion order * Doesn\u2019t have a deterministic iteration order * Doesn\u2019t guarantee that an element added during an iteration will be produced during this iteration
Source code
"},{"location":"mistakes/#ignoring-how-the-break-statement-works-34","title":"Ignoring how the break statement works (#34)","text":"TL;DR: Using break or continue with a label enforces breaking a specific statement. This can be helpful with switch or select statements inside loops.
A break statement is commonly used to terminate the execution of a loop. When loops are used in conjunction with switch or select, developers frequently make the mistake of breaking the wrong statement. For example:
for i := 0; i < 5; i++ {\n fmt.Printf(\"%d \", i)\n\n switch i {\n default:\n case 2:\n break\n }\n}\nThe break statement doesn\u2019t terminate the for loop: it terminates the switch statement, instead. Hence, instead of iterating from 0 to 2, this code iterates from 0 to 4: 0 1 2 3 4.
One essential rule to keep in mind is that a break statement terminates the execu- tion of the innermost for, switch, or select statement. In the previous example, it terminates the switch statement.
To break the loop instead of the switch statement, the most idiomatic way is to use a label:
loop:\n for i := 0; i < 5; i++ {\n fmt.Printf(\"%d \", i)\n\n switch i {\n default:\n case 2:\n break loop\n }\n }\nHere, we associate the loop label with the for loop. Then, because we provide the loop label to the break statement, it breaks the loop, not the switch. Therefore, this new version will print 0 1 2, as we expected.
Source code
"},{"location":"mistakes/#using-defer-inside-a-loop-35","title":"Using defer inside a loop (#35)","text":"TL;DR: Extracting loop logic inside a function leads to executing a defer statement at the end of each iteration.
The defer statement delays a call\u2019s execution until the surrounding function returns. It\u2019s mainly used to reduce boilerplate code. For example, if a resource has to be closed eventually, we can use defer to avoid repeating the closure calls before every single return.
One common mistake with defer is to forget that it schedules a function call when the surrounding function returns. For example:
func readFiles(ch <-chan string) error {\n for path := range ch {\n file, err := os.Open(path)\n if err != nil {\n return err\n }\n\n defer file.Close()\n\n // Do something with file\n }\n return nil\n}\nThe defer calls are executed not during each loop iteration but when the readFiles function returns. If readFiles doesn\u2019t return, the file descriptors will be kept open forever, causing leaks.
One common option to fix this problem is to create a surrounding function after defer, called during each iteration:
func readFiles(ch <-chan string) error {\n for path := range ch {\n if err := readFile(path); err != nil {\n return err\n }\n }\n return nil\n}\n\nfunc readFile(path string) error {\n file, err := os.Open(path)\n if err != nil {\n return err\n }\n\n defer file.Close()\n\n // Do something with file\n return nil\n}\nAnother solution is to make the readFile function a closure but intrinsically, this remains the same solution: adding another surrounding function to execute the defer calls during each iteration.
Source code
"},{"location":"mistakes/#strings","title":"Strings","text":""},{"location":"mistakes/#not-understanding-the-concept-of-rune-36","title":"Not understanding the concept of rune (#36)","text":"TL;DR: Understanding that a rune corresponds to the concept of a Unicode code point and that it can be composed of multiple bytes should be part of the Go developer\u2019s core knowledge to work accurately with strings.
As runes are everywhere in Go, it's important to understand the following:
A charset is a set of characters, whereas an encoding describes how to translate a charset into binary. In Go, a string references an immutable slice of arbitrary bytes. Go source code is encoded using UTF-8. Hence, all string literals are UTF-8 strings. But because a string can contain arbitrary bytes, if it\u2019s obtained from somewhere else (not the source code), it isn\u2019t guaranteed to be based on the UTF-8 encoding. A rune corresponds to the concept of a Unicode code point, meaning an item represented by a single value. Using UTF-8, a Unicode code point can be encoded into 1 to 4 bytes. Using len() on a string in Go returns the number of bytes, not the number of runes. Source code
"},{"location":"mistakes/#inaccurate-string-iteration-37","title":"Inaccurate string iteration (#37)","text":"TL;DR: Iterating on a string with the range operator iterates on the runes with the index corresponding to the starting index of the rune\u2019s byte sequence. To access a specific rune index (such as the third rune), convert the string into a []rune.
Iterating on a string is a common operation for developers. Perhaps we want to per- form an operation for each rune in the string or implement a custom function to search for a specific substring. In both cases, we have to iterate on the different runes of a string. But it\u2019s easy to get confused about how iteration works.
For example, consider the following example:
s := \"h\u00eallo\"\nfor i := range s {\n fmt.Printf(\"position %d: %c\\n\", i, s[i])\n}\nfmt.Printf(\"len=%d\\n\", len(s))\nposition 0: h\nposition 1: \u00c3\nposition 3: l\nposition 4: l\nposition 5: o\nlen=6\nLet's highlight three points that might be confusing:
The second rune is \u00c3 in the output instead of \u00ea. We jumped from position 1 to position 3: what is at position 2? len returns a count of 6, whereas s contains only 5 runes. Let\u2019s start with the last observation. We already mentioned that len returns the num- ber of bytes in a string, not the number of runes. Because we assigned a string literal to s, s is a UTF-8 string. Meanwhile, the special character \"\u00ea\" isn\u2019t encoded in a single byte; it requires 2 bytes. Therefore, calling len(s) returns 6.
Meanwhile, in the previous example, we have to understand that we don't iterate over each rune; instead, we iterate over each starting index of a rune:
Printing s[i] doesn\u2019t print the ith rune; it prints the UTF-8 representation of the byte at index i. Hence, we printed \"h\u00c3llo\" instead of \"h\u00eallo\".
If we want to print all the different runes, we can either use the value element of the range operator:
s := \"h\u00eallo\"\nfor i, r := range s {\n fmt.Printf(\"position %d: %c\\n\", i, r)\n}\nOr, we can convert the string into a slice of runes and iterate over it:
s := \"h\u00eallo\"\nrunes := []rune(s)\nfor i, r := range runes {\n fmt.Printf(\"position %d: %c\\n\", i, r)\n}\nNote that this solution introduces a run-time overhead compared to the previous one. Indeed, converting a string into a slice of runes requires allocating an additional slice and converting the bytes into runes: an O(n) time complexity with n the number of bytes in the string. Therefore, if we want to iterate over all the runes, we should use the first solution.
However, if we want to access the ith rune of a string with the first option, we don\u2019t have access to the rune index; rather, we know the starting index of a rune in the byte sequence.
s := \"h\u00eallo\"\nr := []rune(s)[4]\nfmt.Printf(\"%c\\n\", r) // o\nSource code
"},{"location":"mistakes/#misusing-trim-functions-38","title":"Misusing trim functions (#38)","text":"TL;DR: strings.TrimRight/strings.TrimLeft removes all the trailing/leading runes contained in a given set, whereas strings.TrimSuffix/strings.TrimPrefix returns a string without a provided suffix/prefix.
For example:
fmt.Println(strings.TrimRight(\"123oxo\", \"xo\"))\nThe example prints 123:
Conversely, strings.TrimLeft removes all the leading runes contained in a set.
On the other side, strings.TrimSuffix / strings.TrimPrefix returns a string without the provided trailing suffix / prefix.
Source code
"},{"location":"mistakes/#under-optimized-strings-concatenation-39","title":"Under-optimized strings concatenation (#39)","text":"TL;DR: Concatenating a list of strings should be done with strings.Builder to prevent allocating a new string during each iteration.
Let\u2019s consider a concat function that concatenates all the string elements of a slice using the += operator:
func concat(values []string) string {\n s := \"\"\n for _, value := range values {\n s += value\n }\n return s\n}\nDuring each iteration, the += operator concatenates s with the value string. At first sight, this function may not look wrong. But with this implementation, we forget one of the core characteristics of a string: its immutability. Therefore, each iteration doesn\u2019t update s; it reallocates a new string in memory, which significantly impacts the performance of this function.
Fortunately, there is a solution to deal with this problem, using strings.Builder:
func concat(values []string) string {\n sb := strings.Builder{}\n for _, value := range values {\n _, _ = sb.WriteString(value)\n }\n return sb.String()\n}\nDuring each iteration, we constructed the resulting string by calling the WriteString method that appends the content of value to its internal buffer, hence minimizing memory copying.
[!NOTE] WriteString returns an error as the second output, but we purposely ignore it. Indeed, this method will never return a non-nil error. So what\u2019s the purpose of this method returning an error as part of its signature? strings.Builder imple- ments the io.StringWriter interface, which contains a single method: WriteString(s string) (n int, err error). Hence, to comply with this interface, WriteString must return an error.
Internally, strings.Builder holds a byte slice. Each call to WriteString results in a call to append on this slice. There are two impacts. First, this struct shouldn\u2019t be used concurrently, as the calls to append would lead to race conditions. The second impact is something that we saw in mistake #21, \"Inefficient slice initialization\": if the future length of a slice is already known, we should preallocate it. For that purpose, strings.Builder exposes a method Grow(n int) to guarantee space for another n bytes:
func concat(values []string) string {\n total := 0\n for i := 0; i < len(values); i++ {\n total += len(values[i])\n }\n\n sb := strings.Builder{}\n sb.Grow(total) (2)\n for _, value := range values {\n _, _ = sb.WriteString(value)\n }\n return sb.String()\n}\nLet\u2019s run a benchmark to compare the three versions (v1 using +=; v2 using strings.Builder{} without preallocation; and v3 using strings.Builder{} with pre- allocation). The input slice contains 1,000 strings, and each string contains 1,000 bytes:
BenchmarkConcatV1-4 16 72291485 ns/op\nBenchmarkConcatV2-4 1188 878962 ns/op\nBenchmarkConcatV3-4 5922 190340 ns/op\nAs we can see, the latest version is by far the most efficient: 99% faster than v1 and 78% faster than v2.
strings.Builder is the recommended solution to concatenate a list of strings. Usually, this solution should be used within a loop. Indeed, if we just have to concate- nate a few strings (such as a name and a surname), using strings.Builder is not rec- ommended as doing so will make the code a bit less readable than using the += operator or fmt.Sprintf.
Source code
"},{"location":"mistakes/#useless-string-conversions-40","title":"Useless string conversions (#40)","text":"TL;DR: Remembering that the bytes package offers the same operations as the strings package can help avoid extra byte/string conversions.
When choosing to work with a string or a []byte, most programmers tend to favor strings for convenience. But most I/O is actually done with []byte. For example, io.Reader, io.Writer, and io.ReadAll work with []byte, not strings.
When we\u2019re wondering whether we should work with strings or []byte, let\u2019s recall that working with []byte isn\u2019t necessarily less convenient. Indeed, all the exported functions of the strings package also have alternatives in the bytes package: Split, Count, Contains, Index, and so on. Hence, whether we\u2019re doing I/O or not, we should first check whether we could implement a whole workflow using bytes instead of strings and avoid the price of additional conversions.
Source code
"},{"location":"mistakes/#substring-and-memory-leaks-41","title":"Substring and memory leaks (#41)","text":"TL;DR: Using copies instead of substrings can prevent memory leaks, as the string returned by a substring operation will be backed by the same byte array.
In mistake #26, \u201cSlices and memory leaks,\u201d we saw how slicing a slice or array may lead to memory leak situations. This principle also applies to string and substring opera- tions.
We need to keep two things in mind while using the substring operation in Go. First, the interval provided is based on the number of bytes, not the number of runes. Second, a substring operation may lead to a memory leak as the resulting substring will share the same backing array as the initial string. The solutions to prevent this case from happening are to perform a string copy manually or to use strings.Clone from Go 1.18.
Source code
"},{"location":"mistakes/#functions-and-methods","title":"Functions and Methods","text":""},{"location":"mistakes/#not-knowing-which-type-of-receiver-to-use-42","title":"Not knowing which type of receiver to use (#42)","text":"TL;DR: The decision whether to use a value or a pointer receiver should be made based on factors such as the type, whether it has to be mutated, whether it contains a field that can\u2019t be copied, and how large the object is. When in doubt, use a pointer receiver.
Choosing between value and pointer receivers isn\u2019t always straightforward. Let\u2019s dis- cuss some of the conditions to help us choose.
A receiver must be a pointer
If the method needs to mutate the receiver. This rule is also valid if the receiver is a slice and a method needs to append elements: ```cgo type slice []int
func (s slice) add(element int) { s = append(*s, element) } ```
If the method receiver contains a field that cannot be copied: for example, a type part of the sync package (see #74, \u201cCopying a sync type\u201d). A receiver should be a pointer
If the receiver is a large object. Using a pointer can make the call more effi- cient, as doing so prevents making an extensive copy. When in doubt about how large is large, benchmarking can be the solution; it\u2019s pretty much impossible to state a specific size, because it depends on many factors. A receiver must be a value
If we have to enforce a receiver\u2019s immutability. If the receiver is a map, function, or channel. Otherwise, a compilation error occurs. A receiver should be a value
If the receiver is a slice that doesn\u2019t have to be mutated. If the receiver is a small array or struct that is naturally a value type without mutable fields, such as time.Time. If the receiver is a basic type such as int, float64, or string. Of course, it\u2019s impossible to be exhaustive, as there will always be edge cases, but this section\u2019s goal was to provide guidance to cover most cases. By default, we can choose to go with a value receiver unless there\u2019s a good reason not to do so. In doubt, we should use a pointer receiver.
Source code
"},{"location":"mistakes/#never-using-named-result-parameters-43","title":"Never using named result parameters (#43)","text":"TL;DR: Using named result parameters can be an efficient way to improve the readability of a function/method, especially if multiple result parameters have the same type. In some cases, this approach can also be convenient because named result parameters are initialized to their zero value. But be cautious about potential side effects.
When we return parameters in a function or a method, we can attach names to these parameters and use them as regular variables. When a result parameter is named, it\u2019s initialized to its zero value when the function/method begins. With named result parameters, we can also call a naked return statement (without argu- ments). In that case, the current values of the result parameters are used as the returned values.
Here\u2019s an example that uses a named result parameter b:
func f(a int) (b int) {\n b = a\n return\n}\nIn this example, we attach a name to the result parameter: b. When we call return without arguments, it returns the current value of b.
In some cases, named result parameters can also increase readability: for example, if two parameters have the same type. In other cases, they can also be used for convenience. Therefore, we should use named result parameters sparingly when there\u2019s a clear benefit.
Source code
"},{"location":"mistakes/#unintended-side-effects-with-named-result-parameters-44","title":"Unintended side effects with named result parameters (#44)","text":"TL;DR: See #43.
We mentioned why named result parameters can be useful in some situations. But as these result parameters are initialized to their zero value, using them can sometimes lead to subtle bugs if we\u2019re not careful enough. For example, can you spot what\u2019s wrong with this code?
func (l loc) getCoordinates(ctx context.Context, address string) (\n lat, lng float32, err error) {\n isValid := l.validateAddress(address) (1)\n if !isValid {\n return 0, 0, errors.New(\"invalid address\")\n }\n\n if ctx.Err() != nil { (2)\n return 0, 0, err\n }\n\n // Get and return coordinates\n}\nThe error might not be obvious at first glance. Here, the error returned in the if ctx.Err() != nil scope is err. But we haven\u2019t assigned any value to the err variable. It\u2019s still assigned to the zero value of an error type: nil. Hence, this code will always return a nil error.
When using named result parameters, we must recall that each parameter is initial- ized to its zero value. As we have seen in this section, this can lead to subtle bugs that aren\u2019t always straightforward to spot while reading code. Therefore, let\u2019s remain cau- tious when using named result parameters, to avoid potential side effects.
Source code
"},{"location":"mistakes/#returning-a-nil-receiver-45","title":"Returning a nil receiver (#45)","text":"TL;DR: When returning an interface, be cautious about returning not a nil pointer but an explicit nil value. Otherwise, unintended consequences may result because the caller will receive a non-nil value.
Source code
"},{"location":"mistakes/#using-a-filename-as-a-function-input-46","title":"Using a filename as a function input (#46)","text":"TL;DR: Designing functions to receive io.Reader types instead of filenames improves the reusability of a function and makes testing easier.
Accepting a filename as a function input to read from a file should, in most cases, be considered a code smell (except in specific functions such as os.Open). Indeed, it makes unit tests more complex because we may have to create multiple files. It also reduces the reusability of a function (although not all functions are meant to be reused). Using the io.Reader interface abstracts the data source. Regardless of whether the input is a file, a string, an HTTP request, or a gRPC request, the imple- mentation can be reused and easily tested.
Source code
"},{"location":"mistakes/#ignoring-how-defer-arguments-and-receivers-are-evaluated-argument-evaluation-pointer-and-value-receivers-47","title":"Ignoring how defer arguments and receivers are evaluated (argument evaluation, pointer, and value receivers) (#47)","text":"TL;DR: Passing a pointer to a defer function and wrapping a call inside a closure are two possible solutions to overcome the immediate evaluation of arguments and receivers.
In a defer function the arguments are evaluated right away, not once the surrounding function returns. For example, in this code, we always call notify and incrementCounter with the same status: an empty string.
const (\n StatusSuccess = \"success\"\n StatusErrorFoo = \"error_foo\"\n StatusErrorBar = \"error_bar\"\n)\n\nfunc f() error {\n var status string\n defer notify(status)\n defer incrementCounter(status)\n\n if err := foo(); err != nil {\n status = StatusErrorFoo\n return err\n }\n\n if err := bar(); err != nil {\n status = StatusErrorBar\n return err\n }\n\n status = StatusSuccess <5>\n return nil\n}\nIndeed, we call notify(status) and incrementCounter(status) as defer functions. Therefore, Go will delay these calls to be executed once f returns with the current value of status at the stage we used defer, hence passing an empty string.
Two leading options if we want to keep using defer.
The first solution is to pass a string pointer:
func f() error {\n var status string\n defer notify(&status) \n defer incrementCounter(&status)\n\n // The rest of the function unchanged\n if err := foo(); err != nil {\n status = StatusErrorFoo\n return err\n }\n\n if err := bar(); err != nil {\n status = StatusErrorBar\n return err\n }\n\n status = StatusSuccess\n return nil\n}\nUsing defer evaluates the arguments right away: here, the address of status. Yes, status itself is modified throughout the function, but its address remains constant, regardless of the assignments. Hence, if notify or incrementCounter uses the value referenced by the string pointer, it will work as expected. But this solution requires changing the signature of the two functions, which may not always be possible.
There\u2019s another solution: calling a closure (an anonymous function value that references variables from outside its body) as a defer statement:
func f() error {\n var status string\n defer func() {\n notify(status)\n incrementCounter(status)\n }()\n\n // The rest of the function unchanged\n}\nHere, we wrap the calls to both notify and incrementCounter within a closure. This closure references the status variable from outside its body. Therefore, status is evaluated once the closure is executed, not when we call defer. This solution also works and doesn\u2019t require notify and incrementCounter to change their signature.
Let's also note this behavior applies with method receiver: the receiver is evaluated immediately.
Source code
"},{"location":"mistakes/#error-management","title":"Error Management","text":""},{"location":"mistakes/#panicking-48","title":"Panicking (#48)","text":"TL;DR: Using panic is an option to deal with errors in Go. However, it should only be used sparingly in unrecoverable conditions: for example, to signal a programmer error or when you fail to load a mandatory dependency.
In Go, panic is a built-in function that stops the ordinary flow:
func main() {\n fmt.Println(\"a\")\n panic(\"foo\")\n fmt.Println(\"b\")\n}\nThis code prints a and then stops before printing b:
a\npanic: foo\n\ngoroutine 1 [running]:\nmain.main()\n main.go:7 +0xb3\nPanicking in Go should be used sparingly. There are two prominent cases, one to signal a programmer error (e.g., sql.Register that panics if the driver is nil or has already been register) and another where our application fails to create a man- datory dependency. Hence, exceptional conditions that lead us to stop the application. In most other cases, error management should be done with a function that returns a proper error type as the last return argument.
Source code
"},{"location":"mistakes/#ignoring-when-to-wrap-an-error-49","title":"Ignoring when to wrap an error (#49)","text":"TL;DR: Wrapping an error allows you to mark an error and/or provide additional context. However, error wrapping creates potential coupling as it makes the source error available for the caller. If you want to prevent that, don\u2019t use error wrapping.
Since Go 1.13, the %w directive allows us to wrap errors conveniently. Error wrapping is about wrapping or packing an error inside a wrapper container that also makes the source error available. In general, the two main use cases for error wrapping are the following:
Adding additional context to an error Marking an error as a specific error When handling an error, we can decide to wrap it. Wrapping is about adding additional context to an error and/or marking an error as a specific type. If we need to mark an error, we should create a custom error type. However, if we just want to add extra context, we should use fmt.Errorf with the %w directive as it doesn\u2019t require creating a new error type. Yet, error wrapping creates potential coupling as it makes the source error available for the caller. If we want to prevent it, we shouldn\u2019t use error wrapping but error transformation, for example, using fmt.Errorf with the %v directive.
Source code
"},{"location":"mistakes/#comparing-an-error-type-inaccurately-50","title":"Comparing an error type inaccurately (#50)","text":"TL;DR: If you use Go 1.13 error wrapping with the %w directive and fmt.Errorf, comparing an error against a type has to be done using errors.As. Otherwise, if the returned error you want to check is wrapped, it will fail the checks.
Source code
"},{"location":"mistakes/#comparing-an-error-value-inaccurately-51","title":"Comparing an error value inaccurately (#51)","text":"TL;DR: If you use Go 1.13 error wrapping with the %w directive and fmt.Errorf, comparing an error against or a value has to be done using errors.As. Otherwise, if the returned error you want to check is wrapped, it will fail the checks.
A sentinel error is an error defined as a global variable:
import \"errors\"\n\nvar ErrFoo = errors.New(\"foo\")\nIn general, the convention is to start with Err followed by the error type: here, ErrFoo. A sentinel error conveys an expected error, an error that clients will expect to check. As general guidelines:
Expected errors should be designed as error values (sentinel errors): var ErrFoo = errors.New(\"foo\"). Unexpected errors should be designed as error types: type BarError struct { ... }, with BarError implementing the error interface. If we use error wrapping in our application with the %w directive and fmt.Errorf, checking an error against a specific value should be done using errors.Is instead of ==. Thus, even if the sentinel error is wrapped, errors.Is can recursively unwrap it and compare each error in the chain against the provided value.
Source code
"},{"location":"mistakes/#handling-an-error-twice-52","title":"Handling an error twice (#52)","text":"TL;DR: In most situations, an error should be handled only once. Logging an error is handling an error. Therefore, you have to choose between logging or returning an error. In many cases, error wrapping is the solution as it allows you to provide additional context to an error and return the source error.
Handling an error multiple times is a mistake made frequently by developers, not spe- cifically in Go. This can cause situations where the same error is logged multiple times make debugging harder.
Let's reming us that handling an error should be done only once. Logging an error is handling an error. Hence, we should either log or return an error. By doing this, we simplify our code and gain better insights into the error situation. Using error wrap- ping is the most convenient approach as it allows us to propagate the source error and add context to an error.
Source code
"},{"location":"mistakes/#not-handling-an-error-53","title":"Not handling an error (#53)","text":"TL;DR: Ignoring an error, whether during a function call or in a defer function, should be done explicitly using the blank identifier. Otherwise, future readers may be confused about whether it was intentional or a miss.
Source code
"},{"location":"mistakes/#not-handling-defer-errors-54","title":"Not handling defer errors (#54)","text":"TL;DR: In many cases, you shouldn\u2019t ignore an error returned by a defer function. Either handle it directly or propagate it to the caller, depending on the context. If you want to ignore it, use the blank identifier.
Consider the following code:
func f() {\n // ...\n notify() // Error handling is omitted\n}\n\nfunc notify() error {\n // ...\n}\nFrom a maintainability perspective, the code can lead to some issues. Let\u2019s consider a new reader looking at it. This reader notices that notify returns an error but that the error isn\u2019t handled by the parent function. How can they guess whether or not handling the error was intentional? How can they know whether the previous developer forgot to handle it or did it purposely?
For these reasons, when we want to ignore an error, there's only one way to do it, using the blank identifier (_):
_ = notify\nIn terms of compilation and run time, this approach doesn\u2019t change anything compared to the first piece of code. But this new version makes explicit that we aren\u2019t interested in the error. Also, we can add a comment that indicates the rationale for why an error is ignored:
// At-most once delivery.\n// Hence, it's accepted to miss some of them in case of errors.\n_ = notify()\nSource code
"},{"location":"mistakes/#concurrency-foundations","title":"Concurrency: Foundations","text":""},{"location":"mistakes/#mixing-up-concurrency-and-parallelism-55","title":"Mixing up concurrency and parallelism (#55)","text":"TL;DR: Understanding the fundamental differences between concurrency and parallelism is a cornerstone of the Go developer\u2019s knowledge. Concurrency is about structure, whereas parallelism is about execution.
"},{"location":"mistakes/#thinking-concurrency-is-always-faster-56","title":"Thinking concurrency is always faster (#56)","text":"TL;DR: To be a proficient developer, you must acknowledge that concurrency isn\u2019t always faster. Solutions involving parallelization of minimal workloads may not necessarily be faster than a sequential implementation. Benchmarking sequential versus concurrent solutions should be the way to validate assumptions.
Source code
"},{"location":"mistakes/#being-puzzled-about-when-to-use-channels-or-mutexes-57","title":"Being puzzled about when to use channels or mutexes (#57)","text":"TL;DR: Being aware of goroutine interactions can also be helpful when deciding between channels and mutexes. In general, parallel goroutines require synchronization and hence mutexes. Conversely, concurrent goroutines generally require coordination and orchestration and hence channels.
"},{"location":"mistakes/#not-understanding-race-problems-data-races-vs-race-conditions-and-the-go-memory-model-58","title":"Not understanding race problems (data races vs. race conditions and the Go memory model) (#58)","text":"TL;DR: Being proficient in concurrency also means understanding that data races and race conditions are different concepts. Data races occur when multiple goroutines simultaneously access the same memory location and at least one of them is writing. Meanwhile, being data-race-free doesn\u2019t necessarily mean deterministic execution. When a behavior depends on the sequence or the timing of events that can\u2019t be controlled, this is a race condition.
Understanding the Go memory model and the underlying guarantees in terms of ordering and synchronization is essential to prevent possible data races and/or race conditions.
Source code
"},{"location":"mistakes/#not-understanding-the-concurrency-impacts-of-a-workload-type-59","title":"Not understanding the concurrency impacts of a workload type (#59)","text":"TL;DR: When creating a certain number of goroutines, consider the workload type. Creating CPU-bound goroutines means bounding this number close to the GOMAXPROCS variable (based by default on the number of CPU cores on the host). Creating I/O-bound goroutines depends on other factors, such as the external system.
Source code
"},{"location":"mistakes/#misunderstanding-go-contexts-60","title":"Misunderstanding Go contexts (#60)","text":"TL;DR: Go contexts are also one of the cornerstones of concurrency in Go. A context allows you to carry a deadline, a cancellation signal, and/or a list of keys-values.
Source code
"},{"location":"mistakes/#concurrency-practice","title":"Concurrency: Practice","text":""},{"location":"mistakes/#propagating-an-inappropriate-context-61","title":"Propagating an inappropriate context (#61)","text":"TL;DR: Understanding the conditions when a context can be canceled should matter when propagating it: for example, an HTTP handler canceling the context when the response has been sent.
Source code
"},{"location":"mistakes/#starting-a-goroutine-without-knowing-when-to-stop-it-62","title":"Starting a goroutine without knowing when to stop it (#62)","text":"TL;DR: Avoiding leaks means being mindful that whenever a goroutine is started, you should have a plan to stop it eventually.
Source code
"},{"location":"mistakes/#not-being-careful-with-goroutines-and-loop-variables-63","title":"Not being careful with goroutines and loop variables (#63)","text":"TL;DR: To avoid bugs with goroutines and loop variables, create local variables or call functions instead of closures.
Source code
"},{"location":"mistakes/#expecting-a-deterministic-behavior-using-select-and-channels-64","title":"Expecting a deterministic behavior using select and channels (#64)","text":"TL;DR: Understanding that select with multiple channels chooses the case randomly if multiple options are possible prevents making wrong assumptions that can lead to subtle concurrency bugs.
Source code
"},{"location":"mistakes/#not-using-notification-channels-65","title":"Not using notification channels (#65)","text":"TL;DR: Send notifications using a chan struct{} type.
"},{"location":"mistakes/#not-using-nil-channels-66","title":"Not using nil channels (#66)","text":"TL;DR: Using nil channels should be part of your concurrency toolset because it allows you to remove cases from select statements, for example.
Source code
"},{"location":"mistakes/#being-puzzled-about-channel-size-67","title":"Being puzzled about channel size (#67)","text":"TL;DR: Carefully decide on the right channel type to use, given a problem. Only unbuffered channels provide strong synchronization guarantees.
You should have a good reason to specify a channel size other than one for buffered channels.
"},{"location":"mistakes/#forgetting-about-possible-side-effects-with-string-formatting-etcd-data-race-example-and-deadlock-68","title":"Forgetting about possible side effects with string formatting (etcd data race example and deadlock) (#68)","text":"TL;DR: Being aware that string formatting may lead to calling existing functions means watching out for possible deadlocks and other data races.
Source code
"},{"location":"mistakes/#creating-data-races-with-append-69","title":"Creating data races with append (#69)","text":"TL;DR: Calling append isn\u2019t always data-race-free; hence, it shouldn\u2019t be used concurrently on a shared slice.
Source code
"},{"location":"mistakes/#using-mutexes-inaccurately-with-slices-and-maps-70","title":"Using mutexes inaccurately with slices and maps (#70)","text":"TL;DR: Remembering that slices and maps are pointers can prevent common data races.
Source code
"},{"location":"mistakes/#misusing-syncwaitgroup-71","title":"Misusing sync.WaitGroup (#71)","text":"TL;DR: To accurately use sync.WaitGroup, call the Add method before spinning up goroutines.
Source code
"},{"location":"mistakes/#forgetting-about-synccond-72","title":"Forgetting about sync.Cond (#72)","text":"TL;DR: You can send repeated notifications to multiple goroutines with sync.Cond.
Source code
"},{"location":"mistakes/#not-using-errgroup-73","title":"Not using errgroup (#73)","text":"TL;DR: You can synchronize a group of goroutines and handle errors and contexts with the errgroup package.
Source code
"},{"location":"mistakes/#copying-a-sync-type-74","title":"Copying a sync type (#74)","text":"TL;DR: sync types shouldn\u2019t be copied.
Source code
"},{"location":"mistakes/#standard-library","title":"Standard Library","text":""},{"location":"mistakes/#providing-a-wrong-time-duration-75","title":"Providing a wrong time duration (#75)","text":"TL;DR: Remain cautious with functions accepting a time.Duration. Even though passing an integer is allowed, strive to use the time API to prevent any possible confusion.
Source code
"},{"location":"mistakes/#timeafter-and-memory-leaks-76","title":"time.After and memory leaks (#76)","text":"TL;DR: Avoiding calls to time.After in repeated functions (such as loops or HTTP handlers) can avoid peak memory consumption. The resources created by time.After are released only when the timer expires.
Source code
"},{"location":"mistakes/#json-handling-common-mistakes-77","title":"JSON handling common mistakes (#77)","text":" Unexpected behavior because of type embedding Be careful about using embedded fields in Go structs. Doing so may lead to sneaky bugs like an embedded time.Time field implementing the json.Marshaler interface, hence overriding the default marshaling behavior.
Source code
JSON and the monotonic clock When comparing two time.Time structs, recall that time.Time contains both a wall clock and a monotonic clock, and the comparison using the == operator is done on both clocks.
Source code
To avoid wrong assumptions when you provide a map while unmarshaling JSON data, remember that numerics are converted to float64 by default.
Source code
"},{"location":"mistakes/#common-sql-mistakes-78","title":"Common SQL mistakes (#78)","text":" Forgetting that sql.Open doesn't necessarily establish connections to a database Call the Ping or PingContext method if you need to test your configuration and make sure a database is reachable.
Source code
Forgetting about connections pooling Configure the database connection parameters for production-grade applications.
Not using prepared statements Using SQL prepared statements makes queries more efficient and more secure.
Source code
Deal with nullable columns in tables using pointers or sql.NullXXX types.
Source code
Not handling rows iteration errors Call the Err method of sql.Rows after row iterations to ensure that you haven\u2019t missed an error while preparing the next row.
Source code
"},{"location":"mistakes/#not-closing-transient-resources-http-body-sqlrows-and-osfile-79","title":"Not closing transient resources (HTTP body, sql.Rows, and os.File) (#79)","text":"TL;DR: Eventually close all structs implementing io.Closer to avoid possible leaks.
Source code
"},{"location":"mistakes/#forgetting-the-return-statement-after-replying-to-an-http-request-80","title":"Forgetting the return statement after replying to an HTTP request (#80)","text":"TL;DR: To avoid unexpected behaviors in HTTP handler implementations, make sure you don\u2019t miss the return statement if you want a handler to stop after http.Error.
Source code
"},{"location":"mistakes/#using-the-default-http-client-and-server-81","title":"Using the default HTTP client and server (#81)","text":"TL;DR: For production-grade applications, don\u2019t use the default HTTP client and server implementations. These implementations are missing timeouts and behaviors that should be mandatory in production.
Source code
"},{"location":"mistakes/#testing","title":"Testing","text":""},{"location":"mistakes/#not-categorizing-tests-build-tags-environment-variables-and-short-mode-82","title":"Not categorizing tests (build tags, environment variables, and short mode) (#82)","text":"TL;DR: Categorizing tests using build flags, environment variables, or short mode makes the testing process more efficient. You can create test categories using build flags or environment variables (for example, unit versus integration tests) and differentiate short from long-running tests to decide which kinds of tests to execute.
Source code
"},{"location":"mistakes/#not-enabling-the-race-flag-83","title":"Not enabling the race flag (#83)","text":"TL;DR: Enabling the -race flag is highly recommended when writing concurrent applications. Doing so allows you to catch potential data races that can lead to software bugs.
"},{"location":"mistakes/#not-using-test-execution-modes-parallel-and-shuffle-84","title":"Not using test execution modes (parallel and shuffle) (#84)","text":"TL;DR: Using the -parallel flag is an efficient way to speed up tests, especially long-running ones. Use the -shuffle flag to help ensure that a test suite doesn\u2019t rely on wrong assumptions that could hide bugs.
"},{"location":"mistakes/#not-using-table-driven-tests-85","title":"Not using table-driven tests (#85)","text":"TL;DR: Table-driven tests are an efficient way to group a set of similar tests to prevent code duplication and make future updates easier to handle.
Source code
"},{"location":"mistakes/#sleeping-in-unit-tests-86","title":"Sleeping in unit tests (#86)","text":"TL;DR: Avoid sleeps using synchronization to make a test less flaky and more robust. If synchronization isn\u2019t possible, consider a retry approach.
Source code
"},{"location":"mistakes/#not-dealing-with-the-time-api-efficiently-87","title":"Not dealing with the time API efficiently (#87)","text":"TL;DR: Understanding how to deal with functions using the time API is another way to make a test less flaky. You can use standard techniques such as handling the time as part of a hidden dependency or asking clients to provide it.
Source code
"},{"location":"mistakes/#not-using-testing-utility-packages-httptest-and-iotest-88","title":"Not using testing utility packages (httptest and iotest) (#88)","text":" The httptest package is helpful for dealing with HTTP applications. It provides a set of utilities to test both clients and servers. Source code
The iotest package helps write io.Reader and test that an application is tolerant to errors. Source code
"},{"location":"mistakes/#writing-inaccurate-benchmarks-89","title":"Writing inaccurate benchmarks (#89)","text":" Not resetting or pausing the timer Use time methods to preserve the accuracy of a benchmark.
Source code
Making wrong assumptions about micro-benchmarks Increasing benchtime or using tools such as benchstat can be helpful when dealing with micro-benchmarks.
Be careful with the results of a micro-benchmark if the system that ends up running the application is different from the one running the micro-benchmark.
Source code
Not being careful about compiler optimizations Make sure the function under test leads to a side effect, to prevent compiler optimizations from fooling you about the benchmark results.
Source code
Being fooled by the observer effect To prevent the observer effect, force a benchmark to re-create the data used by a CPU-bound function.
Source code
"},{"location":"mistakes/#not-exploring-all-the-go-testing-features-90","title":"Not exploring all the Go testing features (#90)","text":" Use code coverage with the -coverprofile flag to quickly see which part of the code needs more attention.
Testing from a different package Place unit tests in a different package to enforce writing tests that focus on an exposed behavior, not internals.
Source code
Handling errors using the *testing.T variable instead of the classic if err != nil makes code shorter and easier to read.
Source code
You can use setup and teardown functions to configure a complex environment, such as in the case of integration tests.
Source code
"},{"location":"mistakes/#not-using-fuzzing-community-mistake","title":"Not using fuzzing (community mistake)","text":"TL;DR: Fuzzing is an efficient strategy to detect random, unexpected, or malformed inputs to complex functions and methods in order to discover vulnerabilities, bugs, or even potential crashes.
Credits: @jeromedoucet
"},{"location":"mistakes/#optimizations","title":"Optimizations","text":""},{"location":"mistakes/#not-understanding-cpu-caches-91","title":"Not understanding CPU caches (#91)","text":" Understanding how to use CPU caches is important for optimizing CPU-bound applications because the L1 cache is about 50 to 100 times faster than the main memory.
Being conscious of the cache line concept is critical to understanding how to organize data in data-intensive applications. A CPU doesn\u2019t fetch memory word by word; instead, it usually copies a memory block to a 64-byte cache line. To get the most out of each individual cache line, enforce spatial locality.
Source code
Slice of structs vs. struct of slices Source code
Making code predictable for the CPU can also be an efficient way to optimize certain functions. For example, a unit or constant stride is predictable for the CPU, but a non-unit stride (for example, a linked list) isn\u2019t predictable.
Source code
To avoid a critical stride, hence utilizing only a tiny portion of the cache, be aware that caches are partitioned.
"},{"location":"mistakes/#writing-concurrent-code-that-leads-to-false-sharing-92","title":"Writing concurrent code that leads to false sharing (#92)","text":"TL;DR: Knowing that lower levels of CPU caches aren\u2019t shared across all the cores helps avoid performance-degrading patterns such as false sharing while writing concurrency code. Sharing memory is an illusion.
Source code
"},{"location":"mistakes/#not-taking-into-account-instruction-level-parallelism-93","title":"Not taking into account instruction-level parallelism (#93)","text":"TL;DR: Use instruction-level parallelism (ILP) to optimize specific parts of your code to allow a CPU to execute as many parallel instructions as possible. Identifying data hazards is one of the main steps.
Source code
"},{"location":"mistakes/#not-being-aware-of-data-alignment-94","title":"Not being aware of data alignment (#94)","text":"TL;DR: You can avoid common mistakes by remembering that in Go, basic types are aligned with their own size. For example, keep in mind that reorganizing the fields of a struct by size in descending order can lead to more compact structs (less memory allocation and potentially a better spatial locality).
Source code
"},{"location":"mistakes/#not-understanding-stack-vs-heap-95","title":"Not understanding stack vs. heap (#95)","text":"TL;DR: Understanding the fundamental differences between heap and stack should also be part of your core knowledge when optimizing a Go application. Stack allocations are almost free, whereas heap allocations are slower and rely on the GC to clean the memory.
Source code
"},{"location":"mistakes/#not-knowing-how-to-reduce-allocations-api-change-compiler-optimizations-and-syncpool-96","title":"Not knowing how to reduce allocations (API change, compiler optimizations, and sync.Pool) (#96)","text":"TL;DR: Reducing allocations is also an essential aspect of optimizing a Go application. This can be done in different ways, such as designing the API carefully to prevent sharing up, understanding the common Go compiler optimizations, and using sync.Pool.
Source code
"},{"location":"mistakes/#not-relying-on-inlining-97","title":"Not relying on inlining (#97)","text":"TL;DR: Use the fast-path inlining technique to efficiently reduce the amortized time to call a function.
"},{"location":"mistakes/#not-using-go-diagnostics-tooling-98","title":"Not using Go diagnostics tooling (#98)","text":"TL;DR: Rely on profiling and the execution tracer to understand how an application performs and the parts to optimize.
"},{"location":"mistakes/#not-understanding-how-the-gc-works-99","title":"Not understanding how the GC works (#99)","text":"TL;DR: Understanding how to tune the GC can lead to multiple benefits such as handling sudden load increases more efficiently.
"},{"location":"mistakes/#not-understanding-the-impacts-of-running-go-in-docker-and-kubernetes-100","title":"Not understanding the impacts of running Go in Docker and Kubernetes (#100)","text":"TL;DR: To help avoid CPU throttling when deployed in Docker and Kubernetes, keep in mind that Go isn\u2019t CFS-aware.
"},{"location":"chinese/presentation/","title":"Presentation","text":"TODO
"}]}
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