FQL basics cookbook

This Fauna documentation cookbook explores the key areas of knowledge needed to become a proficient Fauna developer.

Cookbook topics

Fauna at-a-glance

This section gives you a broad overview of Fauna and the Fauna Query Language to provide context for the topics that follow without going too deep.

This section also sets up a small database as a reference for the exercises in the sections that follow.

Iterate on sets

This section covers basic CRUD operations and the concepts of documents and sets.

Define relationships

This section covers how to establish one-to-one, one-to-many, and many-to-many relationships between documents across collections.

Improve performance with indexes

Learn the fundamentals of indexing to improve performance and reduce costs.

Get query results one page at a time

Learn how to get your data in manageable chunks.

Catch errors at compile time

Learn how to leverage static typing to catch errors at compile time.

Write custom logic with user-defined functions

This section covers user-defined functions (UDFs), which let you write and store custom logic.

How to use the cookbook

This cookbook provides a hands-on learning experience to facilitate learning about Fauna and FQL. Each tutorial is a progression in a graduated learning experience where later tutorials build on what you learned in earlier tutorials. The cookbook terminology defines each top-level section ss a tutorial, and tutorials include exercises that cover a Fauna feature.

After you have set up your environment and verified that you can run queries, work on the tutorials sequentially or start with the topic that matches your immediate interest and experience level.

The examples assume a given database setup so fields with dynamically set values, such as id and ts, might have different values in the database you create. Where applicable, you should replace the values in the examples with your actual database values.

You’re also encouraged to experiment with variations on these examples as a way to gain an even deeper insight into Fauna capabilities.

Some tutorial steps might include variations on the basic operations for added insight and direct you to the documentation, where you can find more detailed information.

Environment setup for running the examples

The easiest way to try the examples as you follow the tutorials is to use the Dashboard. If you’ve already tried the Dashboard quick start, you’re already set up to run the examples. Because the FQL syntax is the same in all contexts, you can also run the same examples programmatically using one of the Supported drivers for your language of choice.

Error detection and reporting

A word should be said about error detection and reporting. FQL validates queries and performs runtime checks, returning informative error messages that aid debugging when an error occurs.

For example, when type checking is enabled an error is raised by the following example because the non-null assertion postfix operator is omitted:

CoffeeBean.byId("366190711733747780").update(
  { Owner: "Healthy Grounds" }
)
QueryCheckError

error: Type `CoffeeBean | NullCoffeeBean` isn't a subtype of `{ update: { Owner: "Healthy Grounds" } => Any, ... }`
at *query*:1:39
  |
1 | CoffeeBean.byId("366190711733747780").update(
  |                                       ^^^^^^
  |
cause: Type `Null` does not have field `update`: use the ! operator to remove the null case
  |
1 | CoffeeBean.byId("366190711733747780").update(
  | ^^^^^^^^^^^^^^^
  |

 

Notice that the error message indicates the location of the error.

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