mgmt/docs/language-guide.md

Language guide

Overview

The mgmt tool has various frontends, each of which may produce a stream of between zero or more graphs that are passed to the engine for desired state application. In almost all scenarios, you're going to want to use the language frontend. This guide describes some of the internals of the language.

Theory

The mgmt language is a declarative (immutable) functional, reactive programming language. It is implemented in golang. A longer introduction to the language is coming soon!

Types

All expressions must have a type. A composite type such as a list of strings ([]str) is different from a list of integers ([]int).

There is a variant type in the language's type system, but it is only used internally and only appears briefly when needed for type unification hints during static polymorphic function generation. This is an advanced topic which is not required for normal usage of the software.

The implementation of the internal types can be found in lang/types/.

bool

A true or false value.

str

Any "string!" enclosed in quotes.

int

A number like 42 or -13. Integers are represented internally as golang's int64.

float

A floating point number like: 3.1415926. Float's are represented internally as golang's float64.

list

An ordered collection of values of the same type, eg: [6, 7, 8, 9,]. It is worth mentioning that empty lists have a type, although without type hints it can be impossible to infer the item's type.

map

An unordered set of unique keys of the same type and corresponding value pairs of another type, eg: {"boiling" => 100, "freezing" => 0, "room" => "25", "house" => 22, "canada" => -30,}. That is to say, all of the keys must have the same type, and all of the values must have the same type. You can use any type for either, although it is probably advisable to avoid using very complex types as map keys.

struct

An ordered set of field names and corresponding values, each of their own type, eg: struct{answer => "42", james => "awesome", is_mgmt_awesome => true,}. These are useful for combining more than one type into the same value. Note the syntactical difference between these and map's: the key's in map's have types, and as a result, string keys are enclosed in quotes, whereas struct fields are not string values, and as such are bare and specified without quotes.

func

An ordered set of optionally named, differently typed input arguments, and a return type, eg: func(s str) int or: func(bool, []str, {str: float}) struct{foo str; bar int}.

Expressions

Expressions, and the Expr interface need to be better documented. For now please consume lang/interfaces/ast.go. These docs will be expanded on when things are more certain to be stable.

Statements

Statements, and the Stmt interface need to be better documented. For now please consume lang/interfaces/ast.go. These docs will be expanded on when things are more certain to be stable.

Stages

The mgmt compiler runs in a number of stages. In order of execution they are:

• Lexing
• Parsing
• Interpolation
• Scope propagation
• Type unification
• Function graph generation
• Function engine creation and validation

All of the above needs to be done every time the source code changes. After this point, the function engine runs and produces events. On every event, we "interpret" which produces a resource graph. This series of resource graphs are passed to the engine as they are produced.

What follows are some notes about each step.

Lexing

Lexing is done using nex. It is a pure-golang implementation which is similar to Lex or Flex, but which produces golang code instead of C. It integrates reasonably well with golang's yacc which is used for parsing. The token definitions are in: lang/lexer.nex. Lexing and parsing run together by calling the LexParse method.

Parsing

The parser used is golang's implementation of yacc. The documentation is quite abysmal, so it's helpful to rely on the documentation from standard yacc and trial and error. One small advantage yacc has over standard yacc is that it can produce error messages from examples. The best documentation is to examine the source. There is a short write up available here. The yacc file exists at: lang/parser.y. Lexing and parsing run together by calling the LexParse method.

Interpolation

Interpolation is used to transform the AST (which was produced from lexing and parsing) into one which is either identical or different. It expands strings which might contain expressions to be interpolated (eg: "the answer is: ${foo}") and can be used for other scenarios in which one statement or expression would be better represented by a larger AST. Most nodes in the AST simply return their own node address, and do not modify the AST.

Scope propagation

Scope propagation passes the parent scope (starting with the top-level, built-in scope) down through the AST. This is necessary so that children nodes can access variables in the scope if needed. Most AST node's simply pass on the scope without making any changes. The ExprVar node naturally consumes scope's and the StmtProg node cleverly passes the scope through in the order expected for the out-of-order bind logic to work.

Type unification

Each expression must have a known type. The unpleasant option is to force the programmer to specify by annotation every type throughout their whole program so that each Expr node in the AST knows what to expect. Type annotation is allowed in situations when you want to explicitly specify a type, or when the compiler cannot deduce it, however, most of it can usually be inferred.

For type inferrence to work, each node in the AST implements a Unify method which is able to return a list of invariants that must hold true. This starts at the top most AST node, and gets called through to it's children to assemble a giant list of invariants. The invariants can take different forms. They can specify that a particular expression must have a particular type, or they can specify that two expressions must have the same types. More complex invariants allow you to specify relationships between different types and expressions. Furthermore, invariants can allow you to specify that only one invariant out of a set must hold true.

Once the list of invariants has been collected, they are run through an invariant solver. The solver can return either return successfully or with an error. If the solver returns successfully, it means that it has found a trivial mapping between every expression and it's corresponding type. At this point it is a simple task to run SetType on every expression so that the types are known. If the solver returns in error, it is usually due to one of two possibilities:

1. Ambiguity

   The solver does not have enough information to make a definitive or unique determination about the expression to type mappings. The set of invariants is ambiguous, and we cannot continue. An error will be returned to the programmer. In this scenario the user will probably need to add a type annotation, possibly because of a design bug in the user's program.

2. Conflict

   The solver has conflicting information that cannot be reconciled. In this situation an explicit conflict has been found. If two invariants are found which both expect a particular expression to have different types, then it is not possible to find a valid solution. This almost always happens if the user has made a type error in their program.

Only one solver currently exists, but it is possible to easily plug in an alternate implementation if someone more skilled in the art of solver design would like to propose a more logical or performant variant.

Function graph generation

At this point we have a fully type AST. The AST must now be transformed into a directed, acyclic graph (DAG) data structure that represents the flow of data as necessary for everything to be reactive. Note that this graph is different from the resource graph which is produced and sent to the engine. It is just a coincidence that both happen to be DAG's. (You don't freak out when you see a list data structure show up in more than one place, do you?)

To produce this graph, each node has a Graph method which it can call. This starts at the top most node, and is called down through the AST. The edges in the graphs must represent the individual expression values which are passed from node to node. The names of the edges must match the function type argument names which are used in the definition of the corresponding function. These corresponding functions must exist for each expression node and are produced by calling that expression's Func method. These are usually called by the function engine during function creation and validation.

Function engine creation and validation

Finally we have a graph of the data flows. The function engine must first initialize which creates references to each of the necessary function implementations, and gets information about each one. It then needs to be type checked to ensure that the data flows all correctly match what is expected. If you were to pass an int to a function expecting a bool, this would be a problem. If all goes well, the program should get run shortly.

Function engine running and interpret

At this point the function engine runs. It produces a stream of events which cause the Output() method of the top-level program to run, which produces the list of resources and edges. These are then transformed into the resource graph which is passed to the engine.

Function API

If you'd like to create a built-in, core function, you'll need to implement the function API interface named Func. It can be found in lang/interfaces/func.go. Your function must have a specific type. For example, a simple math function might have a signature of func(x int, x int) int. As you can see, all the types are known before compile time.

What follows are each of the method signatures and a description of each. Failure to implement the API correctly can cause the function graph engine to block, or the program to panic.

Info

Info() *Info

The Info method must return a struct containing some information about your function. The struct has the following type:

type Info struct {
	Sig  *types.Type // the signature of the function, must be KindFunc
}

You must implement this correctly. Other fields in the Info struct may be added in the future. This method is usually called before any other, and should not depend on any other method being called first. Other methods must not depend on this method being called first.

Example

func (obj *FooFunc) Info() *interfaces.Info {
	return &interfaces.Info{
		Sig: types.NewType("func(a str, b int) float"),
	}
}

Init

Init(*Init) error

Init is called by the function graph engine to create an implementation of this function. It is passed in a struct of the following form:

type Init struct {
	Hostname string // uuid for the host
	Input  chan types.Value // Engine will close `input` chan
	Output chan types.Value // Stream must close `output` chan
	World  resources.World
	Debug  bool
	Logf   func(format string, v ...interface{})
}

These values and references may be used (wisely) inside your function. Input will contain a channel of input structs matching the expected input signature for your function. Output will be the channel which you must send values to whenever a new value should be produced. This must be done in the Stream() function. You may carefully use World to access functionality provided by the engine. You may use Logf to log informational messages, however there is no guarantee that they will be displayed to the user. Debug specifies whether the function is running in a user-requested debug mode. This might cause you to want to print more log messages for example. You will need to save references to any or all of these info fields that you wish to use in the struct implementing this Func interface. At a minimum you will need to save Output as a minimum of one value must be produced.

Example

Please see the example functions in
[lang/funcs/public/](https://github.com/purpleidea/mgmt/tree/master/lang/funcs/public/).

Stream

Stream() error

Stream is called by the function engine when it is ready for your function to start accepting input and producing output. You must always produce at least one value. Failure to produce at least one value will probably cause the function engine to hang waiting for your output. This function must close the Output channel when it has no more values to send. The engine will close the Input channel when it has no more values to send. This may or may not influence whether or not you close the Output channel.

Example

Please see the example functions in
[lang/funcs/public/](https://github.com/purpleidea/mgmt/tree/master/lang/funcs/public/).

Close

Close() error

Close asks the particular function to shutdown its Stream() function and return.

Example

Please see the example functions in
[lang/funcs/public/](https://github.com/purpleidea/mgmt/tree/master/lang/funcs/public/).

Polymorphic Function API

For some functions, it might be helpful to be able to implement a function once, but to have multiple polymorphic variants that can be chosen at compile time. For this more advanced topic, you will need to use the Polymorphic Function API. This will help with code reuse when you have a small, finite number of possible type signatures, and also for more complicated cases where you might have an infinite number of possible type signatures. (eg: []str, or [][]str, or [][][]str, etc...)

Suppose you want to implement a function which can assume different type signatures. The mgmt language does not support polymorphic types-- you must use static types throughout the language, however, it is legal to implement a function which can take different specific type signatures based on how it is used. For example, you might wish to add a math function which could take the form of func(x int, x int) int or func(x float, x float) float depending on the input values. You might also want to implement a function which takes an arbitrary number of input arguments (the number must be statically fixed at the compile time of your program though) and which returns a string.

The PolyFunc interface adds additional methods which you must implement to satisfy such a function implementation. If you'd like to implement such a function, then please notify the project authors, and they will expand this section with a longer description of the process.

Examples

What follows are a few examples that might help you understand some of the language details.

Example Foo

TODO: please add an example here!

Example Bar

TODO: please add an example here!

Frequently asked questions

(Send your questions as a patch to this FAQ! I'll review it, merge it, and respond by commit with the answer.)

What is the difference between ExprIf and StmtIf?

The language contains both an if expression, and and if statement. An if expression takes a boolean conditional and it must contain exactly two branches (a then and an else branch) which each contain one expression. The if expression will return the value of one of the two branches based on the conditional.

Example:

# this is an if expression, and both branches must exist
$b = true
$x = if $b {
	42
} else {
	-13
}

The if statement also takes a boolean conditional, but it may have either one or two branches. Branches must only directly contain statements. The if statement does not return any value, but it does produce output when it is evaluated. The output consists primarily of resources (vertices) and edges.

Example:

# this is an if statement, and in this scenario the else branch was omitted
$b = true
if $b {
	file "/tmp/hello" {
		content => "world",
	}
}

I don't like the mgmt language, is there an alternative?

Yes, the language is just one of the available "frontends" that passes a stream of graphs to the engine "backend". While it is the recommended way of using mgmt, you're welcome to either use an alternate frontend, or write your own. To write your own frontend, you must implement the GAPI interface.

I'm an expert in FRP, and you got it all wrong; even the names of things!

I am certainly no expert in FRP, and I've certainly got lots more to learn. One thing FRP experts might notice is that some of the concepts from FRP are either named differently, or are notably absent.

In mgmt, we don't talk about behaviours, events, or signals in the strict FRP definitons of the words. Firstly, because we only support discretized, streams of values with no plan to add continuous semantics. Secondly, because we prefer to use terms which are more natural and relatable to what our target audience is expecting. Our users are more likely to have a background in Physiology, or systems administration than a background in FRP.

Having said that, we hope that the FRP community will engage with us and help improve the parts that we got wrong. Even if that means adding continuous behaviours!

This is brilliant, may I give you a high-five?

Thank you, and yes, probably. "Props" may also be accepted, although patches are preferred. If you can't do either, donations to support the project are welcome too!

Where can I find more information about mgmt?

Additional blog posts, videos and other material is available!.

Suggestions

If you have any ideas for changes or other improvements to the language, please let us know! We're still pre 1.0 and pre 0.1 and happy to change it in order to get it right!