Learn Faunadb, Lodash and Rust

In this course, Chris takes a README-driven approach to build out a Rust CLI that supports content authoring for a digital garden.

Specifically, you'll work through the first command for the CLI, writing files. You start by initializing a new Cargo binary crate through creating the `write` command that allows you to create files (and their contents) to the folder that you set. 

Step by step you'll learn about:
- Handling user input
- Error handling
- Rust Move semantics
- Rust Copy semantics
- Integration testing

...and more!

Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.

rust

Creating a Digital Garden CLI with Rust

[Rustlings](https://github.com/rust-lang/rustlings) is a set of exercises that serves as a great introduction to the Rust language. It covers many concepts from the [Rust book](https://doc.rust-lang.org/book/) by asking you to work through sets of exercises.

This collection walks you through Rustlings in a way that explains what's going on in each exercise. We cover everything from basic types to generics and threading.

Learning Rust by Working Through the Rustlings Exercises

Rust is a modern language that is close to the metal. It's got the performance of C, but with a more friendly syntax and community.

It's also memory safe, and ensures your applications are bug free by catching errors at compile time instead of runtime.

These features make Rust a great choice for developing code for embedded systems, web servers, and CLI apps. You can even compile Rust into WebAssembly in order to run it in-browser!

But as the saying goes, "before you learn to run, you must learn to walk".

In this course, you will write your first Rust application.

Starting from “Hello World” you’ll quickly move on to learning about Rust’s types, configuration files, and the `cargo` package manager along with classics like functions, loops, and handling user input.

More Rust-specific concepts like macros and pattern matching will also be introduced.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/write-your-first-program-with-the-rust-language).

Next Steps: [Basic Types in Rust](https://egghead.io/playlists/data-types-in-rust-ab72)

Write Your First Program with the Rust Language

This application serves as an introduction to building products with a JAMStack and Serverless approach. We cover everything from the beginning:

* starting with the Gatsby client
* handling authentication with Netlify Identity
* Shipping a GraphQL server on Netlify Functions
* Building in access control with a serverless capable database

It uses tools that remove as many of the barriers as possible while also allowing upgrading in the future. Netlify Functions can grow into AWS Lambda, Netlify Identity can grow into Auth0 or Cognito, and FaunaDB can grow into DynamoDB.

Gatsby is a blazing fast static site generator built with React. Gatsby gives you all of the modern JavaScript tools you want; set up and ready to go out of the box.

gatsby

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

This collection contains videos that explore the basic types in Rust, including **Fixed size Integer**, **Pointer sized Integer**, **Boolean**, **Strings**, **Characters**, **Arrays**, **Tuples** and more.

If you're just starting out learning Rust and [wrote your first program](https://egghead.io/courses/write-your-first-program-with-the-rust-language), this collection is the right place to get more familiar with the language!

Basic Types in Rust

This collection is a sequel to the [Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB](https://egghead.io/playlists/building-a-serverless-jamstack-todo-app-with-netlify-gatsby-graphql-and-faunadb-53bb) collection. In this collection we take the TODO application we built and convert it to run using Netlify Identity, AWS Lambda (using the serverless framework) and DynamoDB. We cover

* Fauna vs DynamoDB and when to use each
* Setting up AWS accounts
* Creating DynamoDB tables and data modeling differences between Fauna and Dynamo
* Converting our Netlify Functions deployment to a Serverless Framework deployment
* Implementing Custom authorizer functions on AWS

It uses tools that remove as many of the barriers as possible. Netlify Functions grows into Serverless Framework adn AWS Lambda, Netlify Identity is kept around, and FaunaDB can grows into DynamoDB.



FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

faunadb

Converting a Serverless App to run on AWS Lambda and DynamoDB with Serverless Framework

Many languages work on a compilation story to WebAssembly, but Rust is already ready for primetime. Why?


- Rust is a stable language and great replacement for C and C++. 
- Due to the reference and borrowing concept, Rust doesn’t need a Garbage Collector and can be used for a production-ready WebAssembly project. 
- The Rust/Wasm team has built some sophisticated tooling for straightforward interoperability between Rust and JavaScript.

This course gives you the tools to compile to WebAssembly with Rust. Over 11 lessons, we’ll set up a project and explore various paths of communication and interoperability between Rust and JavaScript.

Using WebAssembly with Rust

FaunaDB is a global serverless database that gives you ubiquitous, low latency access to app data, without sacrificing data correctness. It eliminates layers of app code for manually handling data anomalies, security, and scale, creating a friendlier dev experience for you and better app experience for your users.

Fauna is inspired by [Calvin](https://blog.acolyer.org/2019/03/29/calvin-fast-distributed-transactions-for-partitioned-database-systems/), has [built-in GraphQL integration](https://docs.fauna.com/fauna/current/api/graphql/), and a query language based on lambda calculus. Be sure to check out the [Jepsen analysis](https://jepsen.io/analyses/faunadb-2.5.4) and the associated [chart of consistency models](https://jepsen.io/consistency)

The Complete Guide to FaunaDB

[Lodash](https://lodash.com/docs/) is a collection of utilities that have become a staple of modern web development. This collection will show you how to re-create its most common methods using simple JavaScript techniques available in any modern web browser. In addition to re-building tools like `_.merge`, `_.get` and `_.debounce` from scratch you will also learn how to replace them completely in some cases with modern syntax such as [optional chaining](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Optional_chaining), [nullish coalescing](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Nullish_coalescing_operator) and [Object.fromEntries](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/fromEntries).

This collection should interest the following groups:
- Those who want to better understand common JavaScript patterns
- Engineers who want to reduce their dependence on Lodash (for performance or other reasons)
- Candidates preparing for a technical job interview (where it is common to be asked how to implement some of these methods).

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Recreating Popular JavaScript Utility Methods from Lodash

FaunaDB is a Calvin inspired hosted database marketed towards JAMStack and serverless users. It is geo-replicated across multiple providers (AWS, GCP, and soon Azure), uses a lambda calculus based query language (FQL) or the built-in GraphQL support, and is quick to get started with. Don't worry about the vocabulary here (Consistency, lambda calculus, etc), FaunaDB can be quickly put to work without reading any papers and without knowing what lambda calculus is.

* [Calvin Paper](http://cs.yale.edu/homes/thomson/publications/calvin-sigmod12.pdf) (warning: pdf link)
  - [Morning Paper Overview of Calvin Paper](https://blog.acolyer.org/2019/03/29/calvin-fast-distributed-transactions-for-partitioned-database-systems/) if you don't want to read the whole paper
* [jepsen report](https://jepsen.io/analyses/faunadb-2.5.4)

An Introduction to FaunaDB

FQL is the main query language for interacting with FaunaDB. It supports all JSON types, plus some additional types like Ref, Date, and Set. FQL is based in expressions and as such everything returns a value. This makes it easy to compose logic into larger queries.

Code shown in the shell:

```
Match(Index("all_customers"))
```

Overview of the FaunaDB Query Language

When working with FQL it's often the case that you'll encounter `Ref`s. `Ref`s don't have any fields on them though! So how do we access the fields? `Get`.

```
Collections()
```

```
Collection("customers")
```

```
Ref(Collection("customers"), "244075679454331401")
```

```
Get(Ref(Collection("customers"), "244075679454331401"))
```

```
Get(Collection("customers"))
```



Refs and Get in FaunaDB

FQL Page objects have additional fields like `before`, and `data`, that may make it seem like you need to `Select` to get at the underlying array of results. However, FQL supports passing Page objects directly to functions that operate on arrays and the function will transparently operate on the contained `data` field.

```
Paginate(Match(Index("all_customers")))
```

```
Drop(1, Paginate(Match(Index("all_customers"))))
```

```
Take(1, Paginate(Match(Index("all_customers"))))
```

FQL Page objects can be arguments to array functions

`Ref`s are opaque. To access the fields on a document associated with a Ref, we first need to run `Get` on our results and then we can access fields using `Select`

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Get(Var("X")))
)
```

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Select(["data", "firstName"], Get(Var("X"))))
)
```

Accessing fields through Refs with Select

We walk through creating a new Database in the FaunaDB console complete with some demo data. We do some exploration of the data, introducing concepts like Collections and Indexes, before showing an example FQL query that runs against the database. This sets us up to talk about FQL in future videos

Creating your first FaunaDB database

Overview of the FaunaDB Query Language (FQL)

When working with FQL it's often the case that you'll encounter Refs. Refs don't have any fields on them though! So how do we access the fields? Get.

```
Collections()
```

```
Collection("customers")
```

```
Ref(Collection("customers"), "244075679454331401")
```

```
Get(Ref(Collection("customers"), "244075679454331401"))
```

```
Get(Collection("customers"))
```

Getting fields out of Refs with Get

FQL Page objects have additional fields like before, and data, that may make it seem like you need to Select to get at the underlying array of results. However, FQL supports passing Page objects directly to functions that operate on arrays and the function will transparently operate on the contained data field.

```
Paginate(Match(Index("all_customers")))
```

```
Drop(1, Paginate(Match(Index("all_customers"))))
```

```
Take(1, Paginate(Match(Index("all_customers"))))
```

FQL Page objects can be arguments to Array functions

Lambdas allow you to use functions like Map or Filter effectively in FQL. In this lesson we should the Lambda syntax by operating on some paginated results.

```
Paginate(Match(Index("all_customers")))
```

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Get(Var("X")))
)
```

Using FQL Lambda in Array functions

`Ref`s are opaque. To access the fields on a document associated with a `Ref`, we first need to run `Get` on our results and then we can access fields using `Select`

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Get(Var("X")))
)
```

```
Map(
  Paginate(Match(Index("all_customers"))),
  Lambda("X", Select(["data", "firstName"], Get(Var("X"))))
)
```

Topics

Instructors

Content Type

Free or Pro

Creating a Digital Garden CLI with Rust

Learning Rust by Working Through the Rustlings Exercises

Write Your First Program with the Rust Language

Building a Serverless JAMStack Todo app with Netlify, Gatsby, GraphQL, and FaunaDB

Basic Types in Rust

Converting a Serverless App to run on AWS Lambda and DynamoDB with Serverless Framework

Using WebAssembly with Rust

The Complete Guide to FaunaDB

Recreating Popular JavaScript Utility Methods from Lodash

An Introduction to FaunaDB

Overview of the FaunaDB Query Language

Refs and Get in FaunaDB

FQL Page objects can be arguments to array functions

Accessing fields through Refs with Select

An Introduction to FaunaDB

Creating your first FaunaDB database

Overview of the FaunaDB Query Language (FQL)

Getting fields out of Refs with Get

FQL Page objects can be arguments to Array functions

Using FQL Lambda in Array functions

Accessing fields through Refs with Select