refinements and additional information
Signed-off-by: Richard Jones <richard@dagger.io>
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Richard Jones
Solomon Hykes
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@ -6,17 +6,15 @@ sidebar_label: What is Cue?
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# What is Cue?
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CUE is a powerful configuration language created by Marcel van Lohuizen who co-created the Borg Configuration Language (BCL)—the [language used to deploy all applications at Google](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43438.pdf). It is a superset of JSON, with additional features to make declarative, data-driven programming as pleasant and productive as regular imperative programming.
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CUE is a powerful configuration language created by Marcel van Lohuizen who co-created the Borg Configuration Language (BCL)—the [language used to deploy all applications at Google](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43438.pdf). CUE is the result of years of experience writing configuration languages at Google, and seeks to improve the developer experience while avoiding some nasty pitfalls. It is a superset of JSON, with additional features to make declarative, data-driven programming as pleasant and productive as regular imperative programming.
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## The Need for a Configuration Language
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For decades, developers, engineers, and system administrators alike have used some combination of `INI`, `ENV`, `YAML`, `XML`, and `JSON` (as well as custom formats such as for Apache, Nginx, etc) to describe configurations, resources, operations, variables, parameters, state, etc. While these technically work, they are merely _data formats_ and not languages.
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For decades, developers, engineers, and system administrators alike have used some combination of `INI`, `ENV`, `YAML`, `XML`, and `JSON` (as well as custom formats such as those for Apache, Nginx, et al) to describe configurations, resources, operations, variables, parameters, state, etc. While these examples work fine for storing data, they are merely _data formats_, not languages, and as such they each lack the ability to execute logic and operate on data directly.
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As a _data format_ they each lack the ability to express logic and operate on data directly. Simple—yet powerful!—things like if statements, for loops, comprehensions, and string interpolation, among others are just not possible in these languages _directly_—that is to say not without the use of a secondary process. The result is that variables or parameters must be injected, and any logic handled by a templating language (such as Jinja) or a DSL (Domain-specific Language). Often templating languages and DSLs are used in conjuction.
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Simple—yet powerful!—things like if statements, for loops, comprehensions, and string interpolation, among others are just not possible in these formats without the use of a separate process for execution. The result is that variables or parameters must be injected, and any logic executed by a templating language (such as Jinja) or executed by a separate engine instructed by a DSL (Domain-specific Language). Often templating languages and DSLs are used in conjuction and while this technically works, the results are that we end up with code bases, or even single files, that are overly verbose, that intersperse templating languages with various DSLs (and sometimes multiple DSLs that feed ouput from one to the input of another!), that create rigid structures without enforcing schemas (not without more effort), thereby making the code challenging to reason about, difficult to maintain, brittle, and perhaps worst of all, _prone to side effects_.
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Again, while this technically works, the results are that we end up with code bases, or even single files, that intersperse templating languages with various DSLs, thereby making the code difficult to maintain, challenging to reason about, brittle, and perhaps worst of all _prone to side effects_.
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A configuration _language_ such as CUE, allows us to both specify data, as well as _act_ on that data with any logic necessary to achieve the desired output. Furthermore, and perhaps most importantly, CUE allows us to not only specify data as concrete values, but also the _types_ those concrete values can or must be. It gives us the ability to define a _schema_ but unlike doing so with say JSON Schema, CUE can both _define_ and _enforce_ the schema, whereas JSON Schema is merely a definition that requires some other process to enforce it.
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A _configuration language_ such as CUE, allows us to both _specify_ data as well as _act_ upon that data with any logic necessary to achieve the desired output. Furthermore, and perhaps most importantly, CUE allows us to not only specify data as concrete values, but also specify the _types_ those concrete values must be as well as any _constraints_ such as min and max for example. It gives us the ability to define a _schema_ but unlike doing so with say JSON Schema, CUE can both _define_ and _enforce_ the schema, whereas JSON Schema is merely a definition that requires some other process to enforce it.
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For a deeper dive on the problems with configuration as code, check out [The Configuration Complexity Curse](https://blog.cedriccharly.com/post/20191109-the-configuration-complexity-curse/) and [How Cue Wins](https://blog.cedriccharly.com/post/20210523-how-cue-wins/).
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@ -25,88 +23,186 @@ For a deeper dive on the problems with configuration as code, check out [The Con
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We won't attempt to rewrite the [CUE documentation](https://cuelang.org/docs/) or replicate some excellent [CUE tutorials](https://cuetorials.com/), but instead give you enough understanding of CUE to move through the dagger tutorials.
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At least for our purposes here you need to understand a few key concepts, each of which will be explained in more detail below:
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1. Superset of JSON
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1. Cue is a superset of JSON
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2. Types _are_ values
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3. Concrete values, no overrides
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5. Definitions and schemas
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6. Disjunctions, Conjunctions, and Unification
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3. Concrete values
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4. Constraints, definitions, and schemas
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5. Unification
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6. Default values and the nature of inheritance
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7. Packages
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It would be most helpful to [install CUE](https://github.com/cue-lang/cue#download-and-install), but if you prefer you can also try these examples in the [CUE playground](https://cuelang.org/play/).
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### A Superset of JSON
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### Cue is a Superset of JSON
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What you can express in JSON, you can express in CUE, but not everything in CUE can be expressed in JSON. CUE also supports a "lite" version of JSON where certain characters can be eliminated entirely. Take a look at the following code:
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```cue
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{
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"Person": {
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"Name": "Bob Smith"
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"Bob": {
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"Name": "Bob Smith",
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"Age": 42
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}
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}
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Person: Name: "Bob Smith"
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Bob: Name: "Bob Smith"
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```
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In this example we see that in CUE we have declared a top-level key _Person_ twice: once in a more verbose JSON style with brackets and quotes, and again with a "lite" style without the extra characters; notice also that CUE supports _short hand_: when you are defining a single key within an object, you don't need the curly braces, you can write it as a colon-separated path. [Try it in the CUE playground](https://cuelang.org/play/?id=wOVTT8MmvKx#cue@export@yaml), and notice the output (you can choose different formats). Person is declared twice, but is only output once because CUE is automatically _unifying_ the two declarations of Person. It’s ok to declare the same field multiple times, _so long as we provide the same value_. See [Concrete Values, No Overrides](#concreteness-no-overrides) below.
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In this example we see that in CUE we have declared a top-level key _Bob_ twice: once in a more verbose JSON style with brackets, quotes, and commas, and again with a "lite" style without the extra characters. Notice also that CUE supports _short hand_: when you are targeting a single key within an object, you don't need the curly braces, you can write it as a colon-separated path. [Try it in the CUE playground](https://cuelang.org/play/?id=qXGPCDqQdtp#cue@export@yaml), and notice the output (you can choose different formats). The top-level Bob key is declared twice, but is only output once because CUE is automatically _unifying_ the two declarations. It’s ok to declare the same field multiple times, _so long as we provide the same value_. See [Concrete Values](#concrete-values) below.
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### Types _are_ Values
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In the previous example we defined the Person’s name as the string literal _"Bob Smith"_ which is a _concrete value_ (more on this later). But nothing so far _enforces_ a type. Nothing prevents us from setting the `Name` field to an `int`, but if this data were to be fed to an API that expects a `string` we'd have an error. Let's define some types:
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In the previous example we defined the `Name` as the string literal _"Bob Smith"_ which is a _concrete value_ (more on this later). But nothing so far _enforces a type_. Nothing prevents us from setting the `Name` field to an `int`, but if this data were to be fed to an API that expects a `string` we'd get an error. Let's define some types:
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```cue
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Person: {
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Bob: {
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Name: string
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Age: int
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}
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Person: {
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Bob: {
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Name: "Bob Smith"
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Age: 42
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}
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```
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Here we’ve defined the `Name` field as a `string` and the `Age` field as an `int`. Notice how `string` and `int` _are not_ within quotes. This is what we mean when we say "types _are_ values". This will be quite familiar to anyone who has written Go or some other strongly-typed language. With these types defined CUE will now _enforce_ them, so any attempt to provide say an integer for the Name or a string for the Age will result in an error. It’s worth noting here that the output from this example is the result of _implicit uinification_; we’ll talk about _explicit unification_ later. [Try it in the CUE playground](https://cuelang.org/play/?id=p12E8vjFTsc#cue@export@yaml).
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Here we’ve defined the Person’s `Name` field as a `string` and the `Age` field as an `int`. CUE will enforce these types, so any attempt to provide say an integer for the Name or a string for the Age will result in an error. It’s worth noting here that the output from this example is the result of _implicit uinification_; we’ll talk about _explicit unification_ later. [Try it in the CUE playground](https://cuelang.org/play/?id=p12E8vjFTsc#cue@export@yaml).
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### Concrete Values, No Overrides
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### Concrete Values
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CUE is ultimately used to export data, and is most useful when that data has been validated against a strong, well-defined schema. In order for CUE to export anything, we must provide _concrete values_ for all defined fields not marked as optional.
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In the previous examples we have provided concrete values: "Bob Smith" as a string, and 42 as an int. Were we to leave a required field simply defined as a type without a concrete value, CUE will return an error. Try removing a concrete value from the previous example and note the error you get. CUE will complain of an _incomplete value_.
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CUE also prevents you from specifying conflicting values. In other words, CUE does not allow one declaration to override any other. The following example would result in an error:
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In the previous examples we have provided concrete values: "Bob Smith" as a string, and 42 as an int. Were we to leave a required field simply defined as a type without a concrete value, CUE will return an error.
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```cue
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Person: {
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Bob: {
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Name: string
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Age: int
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}
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Bob: {
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Name: "Bob Smith"
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Age: 42
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//Age: is considered "incomplete" because no concrete value is defined
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}
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Person: {
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Name: "Bob Something Else"
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Age: 42
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}
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```
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[Try it in the CUE playground](https://cuelang.org/play/?id=cflqGMQsbLo#cue@export@yaml) and see that CUE will complain of an _incomplete value_.
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### Definitions and Schemas
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### Definitions
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In a real-world scenario we’d likely need to define more than one person, and ensure that each one satisfies the schema. That's where `Definitions` come in handy.
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```cue
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#Person: {
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Name: string
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Age: int
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Nickname?: string
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Email: string
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Age?: int
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}
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Bob: #Person & {
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Name: "Bob Smith"
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Age: 42
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Email: "bob@smith.com"
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// Age is now optional
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}
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```
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In this example we’ve declared that `#Person` is a _definition_, as denoted by the `#` sign. By doing so we have _constrained_ the Person object to a specific set of fields, each a specific type. Definitions by default are _closed_ meaning that a `#Person` cannot contain any fields not specified in the definition. You will also notice that `Nickname?` contains a `?` which denotes that this field is _optional_.
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In this example we’ve declared that `#Person` is a _definition_, as denoted by the `#` sign. By doing so, we have _constrained_ the Person object to a specific set of fields, each a specific type. Definitions by default are _closed_ meaning that a `#Person` cannot contain any fields not specified in the definition. You will also notice that `Age?` now contains a `?` which denotes that this field as being _optional_.
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Definitions themselves are _not_ exported to final output. To get concrete output, we’ve declared that the field `Bob` _is_ a `#Person`, and using the single `&` we _unified_ the `#Person` definition with an object whose _concrete values satisfy the constraints_.
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Definitions themselves are _not_ exported to final output. To get concrete output, we’ve declared that the field `Bob` _is_ a `#Person`, and using the _single_ `&` (not the same as logical AND via `&&`!) we _unified_ the `#Person` definition with an object whose _concrete values satisfy the constraints_ defined by that definition.
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You can think of _definitions_ as a logical set of related _constraints_ and a _schema_ as a larger collective of contraints, not all of which need to be definitions.
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### Unification
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Unification is really at the core of what makes CUE what it is. If values are the fuel, unification is the engine. It is through unification that we can both define constraints and compute concrete values. Let's take a look at some examples to see this idea in action:
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```cue
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import (
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"strings"
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)
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#Person: {
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// further constrain to a min and max length
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Name: string & strings.MinRunes(3) & strings.MaxRunes(22)
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// we don't need string because the regex handles that
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Email: =~"^[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$"
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// further constrain to realistic ages
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Age?: int & >0 & <140
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}
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Bob: #Person & {
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Name: "Bob Smith"
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Email: "bob@smith.com"
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Age: 42
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}
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// output in YAML:
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Bob:
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Name: Bob Smith
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Email: bob@smith.com
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Age: 42
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```
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The output here is a product of _*unifying*_ the `#Person` _definition_ with and object that contains _concrete values_ each of which is the product of unifying the concrete value with the _types_ and _contraints_ declared by the field in the defintion.
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### Default Values and the Nature of Inheritance
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When unifying objects, or _structs_ as we like to call them, a form of merging happens where fields are unified recursively, but unlike for example merging JSON objects in JavaScript, differing values will _not override_ but result in an error. This is partially due to the _commutative_ nature of CUE (if order doesn't matter how would you choose one value over another?), but it is primarily due to the fact that overrides too easily result in unwanted and difficult to debug side effects. Let's take a look at another example:
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```cue
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import (
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"strings"
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)
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#Person: {
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// further constrain to a min and max length
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Name: string & strings.MinRunes(3) & strings.MaxRunes(22)
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// we don't need string because the regex handles that
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Email: =~"^[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$"
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// further constrain to realistic ages
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Age?: int & >0 & <140
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// Job is optional and a string
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Job?: string
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}
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Bob: #Engineer & {
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Name: "Bob Smith"
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Email: "bob@smith.com"
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Age: 42
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// Job: "Carptenter" // would result in an error
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}
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// output in YAML:
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Bob:
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Name: Bob Smith
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Email: bob@smith.com
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Age: 42
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Job: Engineer
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```
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While it's possible for Bob to inherit his job from `#Engineer` which in turn inherits contraints from `#Person`, it it not possible to override that value. [Try it in the CUE playground](https://tip.cuelang.org/play/?id=96IBeFxXgfS#cue@export@yaml) and uncomment the Job field in Bob and see that CUE returns an error.
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In the above example if you needed the Bob object to have a different job, it would either need to be unified with a different type OR the `#Engineer: Job: ` field would need a looser constraint with a _default value_. Try changing the Job field to the following:
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```cue
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#Engineer: #Person & {
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Job: string | *"Engineer" // can still be any string, but *defaults* to "Engineer"
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}
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```
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Bob can inherit the _default value_ but is now allowed to specify a different job.
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