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https://github.com/google/mangle


https://github.com/google/mangle

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README 

        
# Mangle



Mangle is a programming language for deductive database programming. It

is an extension of Datalog, with various extensions like aggregation, function

calls and optional type-checking.



Deductive database programming is useful for bringing data from multiple

data sources together since it enables us to represent and query that data in

a uniform way. It can also be used to model domain knowledge, similar

to machine-readable ontology but without being restricted to binary

predicates.



Datalog is an expressive declarative language similar to relational calculus

(think SQL and relational views). Unlike relational calculus, it also supports

recursive rules and program structuring in a straightforward way.



Mangle contains Datalog as a fragment and adds extensions that make its use

more practical. Some of the good properties like guaranteed termination are

lost when such extensions are used.



The goal of Mangle as an open source project is to convey the concepts in

a way that is accessible to developers and lends itself to easy experimentation.

This repository contains an implementation of Mangle as a go library that can be

easily embedded into applications.



Check out the [docs](docs/README.md) and the

[GitHub discussions](https://github.com/google/mangle/discussions) for more

information. There is also a Q&A section.



For an example how to use Mangle library in a database-like grpc service,

see the separate [Mangle demo service repo](https://github.com/burakemir/mangle-service).



This is not an officially supported Google product. 



## Table of Contents

- [Examples](#examples)

- [Building](#building)



## Examples



### Simple Queries



Imagine you were asked to spot software affected by the

[log4j vulnerability discovered in late 2021](https://www.cisa.gov/uscert/apache-log4j-vulnerability-guidance).

We want to look for projects that contain a Java archive (jar file) of

log4j that is not updated to the patched version.



```prolog

projects_with_vulnerable_log4j(P) :-

  projects(P),

  contains_jar(P, "log4j", Version),

  Version != "2.17.1",

  Version != "2.12.4",

  Version != "2.3.2".

```



This is a Mangle *rule*: conceptually, the implementation retrieve all

possible values for variables `P` and `Version` that make all the subgoals true.



Simple Mangle rules like this correspond to select-project-join relational

queries. The same query in SQL would look like this:



```sql

SELECT projects.id as P

FROM projects JOIN contains_jar ON projects.id = contains_jar.project_id

WHERE contains_jar.version NOT IN ("2.17.1", "2.12.4", "2.3.2")

```



Unlike SQL, our Mangle rule `projects_with_vulnerable_log4j` has a name

and can be referenced in other queries.



(If translating non-recursive Datalog into SQL queries sounds interesting, you

should check out the [Logica](https://logica.dev/) open source project.)



### Aggregation



In practice, querying is rarely enough and we also need grouping and

aggregation.



```

count_projects_with_vulnerable_log4j(Num) :-

  projects_with_vulnerable_log4j(P) |> do fn:group_by(), let Num = fn:Count().

```



### Recursive Queries



The example does not specify what `contains_jar` does. Here is a possible

implementation for `contains_jar` that walks a dependency graph.

This shows that Mangle rules can be recursive. 



```

contains_jar(P, Name, Version) :-

  contains_jar_directly(P, Name, Version).



contains_jar(P, Name, Version) :-

  project_depends(P, Q),

  contains_jar(Q, Name, Version).

```



The two rules correspond to two cases in which a project may "contain" a jar:

either directly, or through some dependency.



### Knowledge Graphs, Property Graphs



In requirements engineering, one needs to captures real world concepts in a

domain model and controlled vocabulary. Description logics use

roles to describe how concepts interact, but these relationships are always

binary. Mangle can represent binary predicates, but also arbitrary n-ary

relations. Moreover it also has support for structured data.



```

one_or_two_leg_trip(Codes, Start, Destination, Price) :-

  direct_conn(Code, Start, Destination, Price)

  |> let Codes = [Code].



one_or_two_leg_trip(Codes, Start, Destination, Price) :-

  direct_conn(FirstCode, Start, Connecting, FirstLegPrice).

  direct_conn(SecondCode, Connecting, Destination, SecondLegPrice)

  |> let Code = [FirstCode, SecondCode],

     let Price = fn:plus(FirstLegPrice, SecondLegPrice).



```



```mermaid

graph LR

    /zurich -->|/code/ZL 
 60 CHF| /lausanne

    /zurich -->|/code/ZB 
 30 CHF| /bern

    /bern -->|/code/BL 
 30 CHF| /lausanne

```



## Building & Testing



Get the dependencies (see [go.mod](go.mod)), build the library, run tests:



```

go get -t ./...

go build ./...

go test ./...

```



### Regenerating the parser sources



If you want to regenerate the parser sources, you need to set up ANTLR first.

This requires a Java runtime environment.



```

wget http://www.antlr.org/download/antlr-4.13.2-complete.jar

alias antlr='java -jar $PWD/antlr-4.13.2-complete.jar'

antlr -Dlanguage=Go -package gen -o ./ parse/gen/Mangle.g4 -visitor

```



## Contributing



The Mangle maintainers welcome external contributions to spec, documentation

and this implementation (see [CONTRIBUTING.md](CONTRIBUTING.md)) and also other

implementations. Pull requests will be handled

[like for tensorflow](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md),

to ensure our internal usage and tests will pass.