Rust  Language

Rust Language


What is Rust and why is it so popular?

Rust has been Stack Overflow’s most loved language for four years in a row, indicating that many of those who have had the opportunity to use Rust have fallen in love with it. However, the roughly 97% of survey respondents who haven’t used Rust may wonder, “What’s the deal with Rust?”

The short answer is that Rust solves pain points present in many other languages, providing a solid step forward with a limited number of downsides.

I’ll show a sample of what Rust offers to users of other programming languages and what the current ecosystem looks like. It’s not all roses in Rust-land, so I talk about the downsides, too. 

Coming from dynamically-typed languages

The arguments between programmers who prefer dynamic versus static type systems are likely to endure for decades more, but it’s hard to argue about the benefits of static types. You only need to look at the rise of languages like TypeScript or features like Python’s type hints as people have become frustrated with the current state of dynamic typing in today’s larger codebases. Statically-typed languages allow for compiler-checked constraints on the data and its behavior, alleviating cognitive overhead and misunderstandings.

This isn’t to say that all static type systems are equivalent. Many statically-typed languages have a large asterisk next to them: they allow for the concept of NULL. This means any value may be what it says or nothing, effectively creating a second possible type for every type. Like Haskell and some other modern programming languages, Rust encodes this possibility using an optional type, and the compiler requires you to handle the None case. This prevents occurrences of the dreaded TypeError: Cannot read property 'foo' of null runtime error (or language equivalent), instead promoting it to a compile time error you can resolve before a user ever sees it. Here’s an example of a function to greet someone whether or not we know their name; if we had forgotten the None case in the match or tried to use name as if it was an always-present String value, the compiler would complain.

fn greet_user(name: Option<String>) {
    match name {
        Some(name) => println!("Hello there, {}!", name),
        None => println!("Well howdy, stranger!"),

Rust’s static typing does its best to get out of the programmer’s way while encouraging long-term maintainability. Some statically-typed languages place a large burden on the programmer, requiring them to repeat the type of a variable multiple times, which hinders readability and refactoring. Other statically-typed languages allow whole-program type inference. While convenient during initial development, this reduces the ability of the compiler to provide useful error information when types no longer match. Rust learns from both of these styles and requires top-level items like function arguments and constants to have explicit types, while allowing type inference inside of function bodies. In this example, the Rust compiler can infer the type of twice, 2, and 1 because the val parameter and the return type are declared as 32-bit signed integers.

fn simple_math(val: i32) -> i32 {
    let twice = val * 2;
    twice - 1

Coming from garbage-collected languages

One of the biggest benefits of using a systems programming language is the ability to have control over low-level details.

Rust gives you the choice of storing data on the stack or on the heap and determines at compile time when memory is no longer needed and can be cleaned up. This allows efficient usage of memory as well as more performant memory access. Tilde, an early production user of Rust in their Skylight product, found they were able to reduce their memory usage from 5GiB to 50MiB by rewriting certain Java HTTP endpoints in idiomatic Rust. Savings like this quickly add up when cloud providers charge premium prices for increased memory or additional nodes.

Without the need to have a garbage collector continuously running, Rust projects are well-suited to be used as libraries by other programming languages via foreign-function interfaces. This allows existing projects to replace performance-critical pieces with speedy Rust code without the memory safety risks inherent with other systems programming languages. Some projects have even been incrementally rewritten in Rust using these techniques.

With direct access to hardware and memory, Rust is an ideal language for embedded and bare-metal development. You can write extremely low-level code, such as operating system kernels or microcontroller applications. Rust’s core types and functions as well as reusable library code shine in these especially challenging environments.


Coming from other systems programming languages

To many people, Rust is largely viewed as an alternative to other systems programming languages, like C or C++. The biggest benefit Rust can provide compared to these languages is the borrow checker. This is the part of the compiler responsible for ensuring that references do not outlive the data they refer to, and it helps eliminate entire classes of bugs caused by memory unsafety.

Unlike many existing systems programming languages, Rust doesn’t require that you spend all of your time mired in nitty-gritty details. Rust strives to have as many zero-cost abstractions as possible—abstractions that are as equally as performant as the equivalent hand-written code. In this example, we show how iterators, a primary Rust abstraction, can be used to succinctly create a vector containing the first ten square numbers.

let squares: Vec<_> = (0..10).map(|i| i * i).collect();

When safe Rust isn’t able to express some concept, you can use unsafe Rust. This unlocks a few extra powers, but in exchange the programmer is now responsible for ensuring that the code is truly safe. This unsafe code can then be wrapped in higher-level abstractions which guarantee that all uses of the abstraction are safe.

Using unsafe code should be a calculated decision, as using it correctly requires as much thought and care as any other language where you are responsible for avoiding undefined behavior. Minimizing unsafe code is the best way to minimize the possibilities for segfaults and vulnerabilities due to memory unsafety.

Systems programming languages have the implicit expectation that they will be around effectively forever. While some modern development doesn’t require that amount of longevity, many businesses want to know that their fundamental code base will be usable for the foreseeable future. Rust recognizes this and has made conscious design decisions around backwards compatibility and stability; it’s a language designed for the next 40 years.

The Rust ecosystem

The Rust experience is larger than a language specification and a compiler; many aspects of creating and maintaining production-quality software are treated as first-class citizens. Multiple concurrent Rust toolchains can be installed and managed via rustup. Rust installations come with Cargo, a command line tool to manage dependencies, run tests, generate documentation, and more. Because dependencies, tests, and documentation are available by default, their usage is prevalent. is the community site for sharing and discovering Rust libraries. Any library published to will have its documentation built and published on

In addition to the built-in tools, the Rust community has created a large number of development tools. Benchmarking, fuzzing, and property-based testing are all easily accessible and well-used in projects. Extra compiler lints are available from Clippy and automatic idiomatic formatting is provided by rustfmt. IDE support is healthy and growing more capable every day.

Going beyond technical points, Rust has a vibrant, welcoming community. There are several official and unofficial avenues for people to get help, such as the chat, the user’s forum, the Rust subreddit, and, of course, Stack Overflow questions and answers and chatroom. Rust has a code of conduct enforced by an awesome moderation team to make sure that the official spaces are welcoming, and most unofficial spaces also observe something similar.

Offline, Rust has multiple conferences across the globe, such as RustConf, Rust Belt Rust, RustFest, Rust Latam, RustCon Asia, and more.

It’s not all roses

Rust’s strong type system and emphasis on memory safety—all enforced at compile time—mean that it’s extremely common to get errors when compiling your code. This can be a frustrating feeling for programmers not used to such an opinionated programming language. However, the Rust developers have spent a large amount of time working to improve the error messages to ensure that they are clear and actionable. Don’t let your eyes gloss over while reading Rust errors!

It’s especially common to hear someone complain that they’ve been “fighting the borrow checker.” While these errors can be disheartening, it’s important to recognize that each of the locations identified had the potential to introduce bugs and potential vulnerabilities in a language that didn’t perform the same checks.

In this example, we create a mutable string containing a name, then take a reference to the first three bytes of the name. While that reference is outstanding, we attempt to mutate the string by clearing it. There’s now no guarantee that the reference points to valid data and dereferencing it could lead to undefined behavior, so the compiler stops us:

fn no_mutable_aliasing() {
    let mut name = String::from("Vivian");
    let nickname = &name[..3];
    println!("Hello there, {}!", nickname);
error[E0502]: cannot borrow `name` as mutable because it is also borrowed as immutable
3 |     let nickname = &name[..3];
  |                     ---- immutable borrow occurs here
4 |     name.clear();
  |     ^^^^^^^^^^^^ mutable borrow occurs here
5 |     println!("Hello there, {}!", nickname);
  |                                  -------- immutable borrow later used here

For more information about this error, try `rustc --explain E0502`.

Helpfully, the error message incorporates our code and tries its hardest to explain the problem, pointing out exact locations.

Prototyping solutions in Rust can be challenging due to its statically-typed nature and because Rust requires covering 100% of the conditions, not just 99%. Edge cases must have applicable code, even when the programmer doesn’t yet know what the happy path should do. During early development, these edge cases can often be addressed by causing the program to crash, and then rigorous error handling can be added at a later point. This is a different workflow than in languages such as Ruby, where developers often try out code in a REPL and then move that to a prototype without considering error cases at all.

Rust is still relatively new, which means that some desired libraries may not be available yet. The upside is there’s plenty of fertile ground to develop these needed libraries, perhaps even taking advantage of recent developments in the relevant computer science fields. Due to this and Rust’s capabilities, some of Rust’s libraries, such as the regex crate, are the best-in-breed across any language.

While Rust has a strong commitment to stability and backwards compatibility, that doesn’t imply the language is finalized. A specific problem may not have access to language features that would make it simpler to express or perhaps even possible to express. As an example, Rust has had asynchronous futures for over three years, but stable async / await support in the language itself is only a few months old.

The Rust compiler is built on top of LLVM, which means that the number of target platforms will be smaller than C or C++.

Original article source at

#rust #programming #developer 

What is GEEK

Buddha Community

What is Rust and why is it so popular?

Serde Rust: Serialization Framework for Rust


*Serde is a framework for serializing and deserializing Rust data structures efficiently and generically.*

You may be looking for:

Serde in action

Click to show Cargo.toml. Run this code in the playground.


# The core APIs, including the Serialize and Deserialize traits. Always
# required when using Serde. The "derive" feature is only required when
# using #[derive(Serialize, Deserialize)] to make Serde work with structs
# and enums defined in your crate.
serde = { version = "1.0", features = ["derive"] }

# Each data format lives in its own crate; the sample code below uses JSON
# but you may be using a different one.
serde_json = "1.0"


use serde::{Serialize, Deserialize};

#[derive(Serialize, Deserialize, Debug)]
struct Point {
    x: i32,
    y: i32,

fn main() {
    let point = Point { x: 1, y: 2 };

    // Convert the Point to a JSON string.
    let serialized = serde_json::to_string(&point).unwrap();

    // Prints serialized = {"x":1,"y":2}
    println!("serialized = {}", serialized);

    // Convert the JSON string back to a Point.
    let deserialized: Point = serde_json::from_str(&serialized).unwrap();

    // Prints deserialized = Point { x: 1, y: 2 }
    println!("deserialized = {:?}", deserialized);

Getting help

Serde is one of the most widely used Rust libraries so any place that Rustaceans congregate will be able to help you out. For chat, consider trying the #rust-questions or #rust-beginners channels of the unofficial community Discord (invite:, the #rust-usage or #beginners channels of the official Rust Project Discord (invite:, or the #general stream in Zulip. For asynchronous, consider the [rust] tag on StackOverflow, the /r/rust subreddit which has a pinned weekly easy questions post, or the Rust Discourse forum. It's acceptable to file a support issue in this repo but they tend not to get as many eyes as any of the above and may get closed without a response after some time.

Download Details:
Author: serde-rs
Source Code:
License: View license

#rust  #rustlang 

张 小龙


Rust vs Go - Which Is More Popular?

Go and Rust are two of the hottest compiled programming languages. I develop in Go full-time and love it, and I’m learning more about Rust recently – its an exciting language. Let’s explore some differences between the two and look at which is growing faster in the popularity polls.

#Engineering #Golang #Programming #Rust #comparison #go #popular #rust

Awesome  Rust

Awesome Rust


Serde JSON: JSON Support for Serde Framework

Serde JSON

Serde is a framework for serializing and deserializing Rust data structures efficiently and generically.

serde_json = "1.0"

You may be looking for:

JSON is a ubiquitous open-standard format that uses human-readable text to transmit data objects consisting of key-value pairs.

    "name": "John Doe",
    "age": 43,
    "address": {
        "street": "10 Downing Street",
        "city": "London"
    "phones": [
        "+44 1234567",
        "+44 2345678"

There are three common ways that you might find yourself needing to work with JSON data in Rust.

  • As text data. An unprocessed string of JSON data that you receive on an HTTP endpoint, read from a file, or prepare to send to a remote server.
  • As an untyped or loosely typed representation. Maybe you want to check that some JSON data is valid before passing it on, but without knowing the structure of what it contains. Or you want to do very basic manipulations like insert a key in a particular spot.
  • As a strongly typed Rust data structure. When you expect all or most of your data to conform to a particular structure and want to get real work done without JSON's loosey-goosey nature tripping you up.

Serde JSON provides efficient, flexible, safe ways of converting data between each of these representations.

Operating on untyped JSON values

Any valid JSON data can be manipulated in the following recursive enum representation. This data structure is serde_json::Value.

enum Value {
    Object(Map<String, Value>),

A string of JSON data can be parsed into a serde_json::Value by the serde_json::from_str function. There is also from_slice for parsing from a byte slice &[u8] and from_reader for parsing from any io::Read like a File or a TCP stream.

use serde_json::{Result, Value};

fn untyped_example() -> Result<()> {
    // Some JSON input data as a &str. Maybe this comes from the user.
    let data = r#"
            "name": "John Doe",
            "age": 43,
            "phones": [
                "+44 1234567",
                "+44 2345678"

    // Parse the string of data into serde_json::Value.
    let v: Value = serde_json::from_str(data)?;

    // Access parts of the data by indexing with square brackets.
    println!("Please call {} at the number {}", v["name"], v["phones"][0]);


The result of square bracket indexing like v["name"] is a borrow of the data at that index, so the type is &Value. A JSON map can be indexed with string keys, while a JSON array can be indexed with integer keys. If the type of the data is not right for the type with which it is being indexed, or if a map does not contain the key being indexed, or if the index into a vector is out of bounds, the returned element is Value::Null.

When a Value is printed, it is printed as a JSON string. So in the code above, the output looks like Please call "John Doe" at the number "+44 1234567". The quotation marks appear because v["name"] is a &Value containing a JSON string and its JSON representation is "John Doe". Printing as a plain string without quotation marks involves converting from a JSON string to a Rust string with as_str() or avoiding the use of Value as described in the following section.

The Value representation is sufficient for very basic tasks but can be tedious to work with for anything more significant. Error handling is verbose to implement correctly, for example imagine trying to detect the presence of unrecognized fields in the input data. The compiler is powerless to help you when you make a mistake, for example imagine typoing v["name"] as v["nmae"] in one of the dozens of places it is used in your code.

Parsing JSON as strongly typed data structures

Serde provides a powerful way of mapping JSON data into Rust data structures largely automatically.

use serde::{Deserialize, Serialize};
use serde_json::Result;

#[derive(Serialize, Deserialize)]
struct Person {
    name: String,
    age: u8,
    phones: Vec<String>,

fn typed_example() -> Result<()> {
    // Some JSON input data as a &str. Maybe this comes from the user.
    let data = r#"
            "name": "John Doe",
            "age": 43,
            "phones": [
                "+44 1234567",
                "+44 2345678"

    // Parse the string of data into a Person object. This is exactly the
    // same function as the one that produced serde_json::Value above, but
    // now we are asking it for a Person as output.
    let p: Person = serde_json::from_str(data)?;

    // Do things just like with any other Rust data structure.
    println!("Please call {} at the number {}",, p.phones[0]);


This is the same serde_json::from_str function as before, but this time we assign the return value to a variable of type Person so Serde will automatically interpret the input data as a Person and produce informative error messages if the layout does not conform to what a Person is expected to look like.

Any type that implements Serde's Deserialize trait can be deserialized this way. This includes built-in Rust standard library types like Vec<T> and HashMap<K, V>, as well as any structs or enums annotated with #[derive(Deserialize)].

Once we have p of type Person, our IDE and the Rust compiler can help us use it correctly like they do for any other Rust code. The IDE can autocomplete field names to prevent typos, which was impossible in the serde_json::Value representation. And the Rust compiler can check that when we write p.phones[0], then p.phones is guaranteed to be a Vec<String> so indexing into it makes sense and produces a String.

The necessary setup for using Serde's derive macros is explained on the Using derive page of the Serde site.

Constructing JSON values

Serde JSON provides a json! macro to build serde_json::Value objects with very natural JSON syntax.

use serde_json::json;

fn main() {
    // The type of `john` is `serde_json::Value`
    let john = json!({
        "name": "John Doe",
        "age": 43,
        "phones": [
            "+44 1234567",
            "+44 2345678"

    println!("first phone number: {}", john["phones"][0]);

    // Convert to a string of JSON and print it out
    println!("{}", john.to_string());

The Value::to_string() function converts a serde_json::Value into a String of JSON text.

One neat thing about the json! macro is that variables and expressions can be interpolated directly into the JSON value as you are building it. Serde will check at compile time that the value you are interpolating is able to be represented as JSON.

let full_name = "John Doe";
let age_last_year = 42;

// The type of `john` is `serde_json::Value`
let john = json!({
    "name": full_name,
    "age": age_last_year + 1,
    "phones": [
        format!("+44 {}", random_phone())

This is amazingly convenient, but we have the problem we had before with Value: the IDE and Rust compiler cannot help us if we get it wrong. Serde JSON provides a better way of serializing strongly-typed data structures into JSON text.

Creating JSON by serializing data structures

A data structure can be converted to a JSON string by serde_json::to_string. There is also serde_json::to_vec which serializes to a Vec<u8> and serde_json::to_writer which serializes to any io::Write such as a File or a TCP stream.

use serde::{Deserialize, Serialize};
use serde_json::Result;

#[derive(Serialize, Deserialize)]
struct Address {
    street: String,
    city: String,

fn print_an_address() -> Result<()> {
    // Some data structure.
    let address = Address {
        street: "10 Downing Street".to_owned(),
        city: "London".to_owned(),

    // Serialize it to a JSON string.
    let j = serde_json::to_string(&address)?;

    // Print, write to a file, or send to an HTTP server.
    println!("{}", j);


Any type that implements Serde's Serialize trait can be serialized this way. This includes built-in Rust standard library types like Vec<T> and HashMap<K, V>, as well as any structs or enums annotated with #[derive(Serialize)].


It is fast. You should expect in the ballpark of 500 to 1000 megabytes per second deserialization and 600 to 900 megabytes per second serialization, depending on the characteristics of your data. This is competitive with the fastest C and C++ JSON libraries or even 30% faster for many use cases. Benchmarks live in the serde-rs/json-benchmark repo.

Getting help

Serde is one of the most widely used Rust libraries, so any place that Rustaceans congregate will be able to help you out. For chat, consider trying the #rust-questions or #rust-beginners channels of the unofficial community Discord (invite:, the #rust-usage or #beginners channels of the official Rust Project Discord (invite:, or the #general stream in Zulip. For asynchronous, consider the [rust] tag on StackOverflow, the /r/rust subreddit which has a pinned weekly easy questions post, or the Rust Discourse forum. It's acceptable to file a support issue in this repo, but they tend not to get as many eyes as any of the above and may get closed without a response after some time.

No-std support

As long as there is a memory allocator, it is possible to use serde_json without the rest of the Rust standard library. This is supported on Rust 1.36+. Disable the default "std" feature and enable the "alloc" feature:

serde_json = { version = "1.0", default-features = false, features = ["alloc"] }

For JSON support in Serde without a memory allocator, please see the serde-json-core crate.


#rust  #rustlang  #encode   #json 

Rust Lang Course For Beginner In 2021: Guessing Game

 What we learn in this chapter:
- Rust number types and their default
- First exposure to #Rust modules and the std::io module to read input from the terminal
- Rust Variable Shadowing
- Rust Loop keyword
- Rust if/else
- First exposure to #Rust match keyword

=== Content:
00:00 - Intro & Setup
02:11 - The Plan
03:04 - Variable Secret
04:03 - Number Types
05:45 - Mutability recap
06:22 - Ask the user
07:45 - First intro to module std::io
08:29 - Rust naming conventions
09:22 - Read user input io:stdin().read_line(&mut guess)
12:46 - Break & Understand
14:20 - Parse string to number
17:10 - Variable Shadowing
18:46 - If / Else - You Win, You Loose
19:28 - Loop
20:38 - Match
23:19 - Random with rand
26:35 - Run it all
27:09 - Conclusion and next episode


Lydia  Kessler

Lydia Kessler


ULTIMATE Rust Lang Tutorial! - Publishing a Rust Crate

The ultimate Rust lang tutorial. Follow along as we go through the Rust lang book chapter by chapter.

📝Get the FREE Rust Cheatsheet:

The Rust book:​​

0:00​ Intro
0:43 Release Profiles
3:00 Documentation Comments
4:32 Commonly Used Sections
5:04 Documentation Comments as Tests
5:50 Commenting Contained Items
6:29 Exporting a Public API
8:44 Setting up Account
9:54 Adding Metadata to a New Create
12:14 Publishing to
12:49 Removing Version from
13:37 Outro

#letsgetrusty​​ #rust​lang​ #tutorial

#rust #rust lang #rust crate