graphql-compiler

Turn complex GraphQL queries into optimized database queries.

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Project description

Turn complex GraphQL queries into optimized database queries.

pip install graphql-compiler

Quick Overview

Through the GraphQL compiler, users can write powerful queries that uncover deep relationships in the data while not having to worry about the underlying database query language. The GraphQL compiler turns read-only queries written in GraphQL syntax to different query languages.

Furthermore, the GraphQL compiler validates queries through the use of a GraphQL schema that specifies the underlying schema of the database. We we can currently autogenerate GraphQL schemas from OrientDB databases, (see End-to-End Example) and from SQL databases, (see End-to-End SQL Example).

For a more detailed overview and getting started guide, please see our blog post.

Features

Databases and Query Languages: We currently support a single database, OrientDB version 2.2.28+, and two query languages that OrientDB supports: the OrientDB dialect of gremlin, and OrientDB’s own custom SQL-like query language that we refer to as MATCH, after the name of its graph traversal operator. With OrientDB, MATCH should be the preferred choice for most users, since it tends to run faster than gremlin, and has other desirable properties. See the Execution model section for more details.

Support for relational databases including PostgreSQL, MySQL, SQLite, and Microsoft SQL Server is a work in progress. A subset of compiler features are available for these databases. See the SQL section for more details.
GraphQL Language Features: We prioritized and implemented a subset of all functionality supported by the GraphQL language. We hope to add more functionality over time.

End-to-End Example

Even though this example specifically targets an OrientDB database, it is meant to be a generic end-to-end example of how to use the GraphQL compiler.

from graphql.utils.schema_printer import print_schema
from graphql_compiler import (
    get_graphql_schema_from_orientdb_schema_data, graphql_to_match
)
from graphql_compiler.schema_generation.orientdb.utils import ORIENTDB_SCHEMA_RECORDS_QUERY

# Step 1: Get schema metadata from hypothetical Animals database.
client = your_function_that_returns_an_orientdb_client()
schema_records = client.command(ORIENTDB_SCHEMA_RECORDS_QUERY)
schema_data = [record.oRecordData for record in schema_records]

# Step 2: Generate GraphQL schema from metadata.
schema, type_equivalence_hints = get_graphql_schema_from_orientdb_schema_data(schema_data)

print(print_schema(schema))
# schema {
#    query: RootSchemaQuery
# }
#
# directive @filter(op_name: String!, value: [String!]!) on FIELD | INLINE_FRAGMENT
#
# directive @tag(tag_name: String!) on FIELD
#
# directive @output(out_name: String!) on FIELD
#
# directive @output_source on FIELD
#
# directive @optional on FIELD
#
# directive @recurse(depth: Int!) on FIELD
#
# directive @fold on FIELD
#
# type Animal {
#     name: String
#     net_worth: Int
#     limbs: Int
# }
#
# type RootSchemaQuery{
#     Animal: [Animal]
# }

# Step 3: Write GraphQL query that returns the names of all animals with a certain net worth.
# Note that we prefix net_worth with '$' and surround it with quotes to indicate it's a parameter.
graphql_query = '''
{
    Animal {
        name @output(out_name: "animal_name")
        net_worth @filter(op_name: "=", value: ["$net_worth"])
    }
}
'''
parameters = {
    'net_worth': '100',
}

# Step 4: Use autogenerated GraphQL schema to compile query into the target database language.
compilation_result = graphql_to_match(schema, graphql_query, parameters, type_equivalence_hints)
print(compilation_result.query)
# SELECT Animal___1.name AS `animal_name`
# FROM  ( MATCH  { class: Animal, where: ((net_worth = decimal("100"))), as: Animal___1 }
# RETURN $matches)

Definitions

Vertex field: A field corresponding to a vertex in the graph. In the below example, Animal and out_Entity_Related are vertex fields. The Animal field is the field at which querying starts, and is therefore the root vertex field. In any scope, fields with the prefix out_ denote vertex fields connected by an outbound edge, whereas ones with the prefix in_ denote vertex fields connected by an inbound edge.
```
{
    Animal {
        name @output(out_name: "name")
        out_Entity_Related {
            ... on Species {
                description @output(out_name: "description")
            }
        }
    }
}
```
Property field: A field corresponding to a property of a vertex in the graph. In the above example, the name and description fields are property fields. In any given scope, property fields must appear before vertex fields.
Result set: An assignment of vertices in the graph to scopes (locations) in the query. As the database processes the query, new result sets may be created (e.g. when traversing edges), and result sets may be discarded when they do not satisfy filters or type coercions. After all parts of the query are processed by the database, all remaining result sets are used to form the query result, by taking their values at all properties marked for output.
Scope: The part of a query between any pair of curly braces. The compiler infers the type of each scope. For example, in the above query, the scope beginning with Animal { is of type Animal, the one beginning with out_Entity_Related { is of type Entity, and the one beginning with ... on Species { is of type Species.
Type coercion: An operation that produces a new scope of narrower type than the scope in which it exists. Any result sets that cannot satisfy the narrower type are filtered out and not returned. In the above query, ... on Species is a type coercion which takes its enclosing scope of type Entity, and coerces it into a narrower scope of type Species. This is possible since Entity is an interface, and Species is a type that implements the Entity interface.

Directives

@optional

Without this directive, when a query includes a vertex field, any results matching that query must be able to produce a value for that vertex field. Applied to a vertex field, this directive prevents result sets that are unable to produce a value for that field from being discarded, and allowed to continue processing the remainder of the query.

Example Use

{
    Animal {
        name @output(out_name: "name")
        out_Animal_ParentOf @optional {
            name @output(out_name: "child_name")
        }
    }
}

For each Animal:

if it is a parent of another animal, at least one row containing the parent and child animal’s names, in the name and child_name columns respectively;
if it is not a parent of another animal, a row with its name in the name column, and a null value in the child_name column.

Constraints and Rules

@optional can only be applied to vertex fields, except the root vertex field.
It is allowed to expand vertex fields within an @optional scope. However, doing so is currently associated with a performance penalty in MATCH. For more detail, see: Expanding @optional vertex fields.
@recurse, @fold, or @output_source may not be used at the same vertex field as @optional.
@output_source and @fold may not be used anywhere within a scope marked @optional.

If a given result set is unable to produce a value for a vertex field marked @optional, any fields marked @output within that vertex field return the null value.

When filtering (via @filter) or type coercion (via e.g. ... on Animal) are applied at or within a vertex field marked @optional, the @optional is given precedence:

If a given result set cannot produce a value for the optional vertex field, it is preserved: the @optional directive is applied first, and no filtering or type coercion can happen.
If a given result set is able to produce a value for the optional vertex field, the @optional does not apply, and that value is then checked against the filtering or type coercion. These subsequent operations may then cause the result set to be discarded if it does not match.

For example, suppose we have two Person vertices with names Albert and Betty such that there is a Person_Knows edge from Albert to Betty.

Then the following query:

{
  Person {
    out_Person_Knows @optional {
      name @filter(op_name: "=", value: ["$name"])
    }
    name @output(out_name: "person_name")
  }
}

with runtime parameter

{
  "name": "Charles"
}

would output an empty list because the Person_Knows edge from Albert to Betty satisfies the @optional directive, but Betty doesn’t match the filter checking for a node with name Charles.

However, if no such Person_Knows edge existed from Albert, then the output would be

{
  name: 'Albert'
}

because no such edge can satisfy the @optional directive, and no filtering happens.

@output

Denotes that the value of a property field should be included in the output. Its out_name argument specifies the name of the column in which the output value should be returned.

Example Use

{
    Animal {
        name @output(out_name: "animal_name")
    }
}

This query returns the name of each Animal in the graph, in a column named animal_name.

Constraints and Rules

@output can only be applied to property fields.
The value provided for out_name may only consist of upper or lower case letters (A-Z, a-z), or underscores (_).
The value provided for out_name cannot be prefixed with ___ (three underscores). This namespace is reserved for compiler internal use.
For any given query, all out_name values must be unique. In other words, output columns must have unique names.

If the property field marked @output exists within a scope marked @optional, result sets that are unable to assign a value to the optional scope return the value null as the output of that property field.

@fold

Applying @fold on a scope “folds” all outputs from within that scope: rather than appearing on separate rows in the query result, the folded outputs are coalesced into parallel lists starting at the scope marked @fold.

It is also possible to output or apply filters to the number of results captured in a @fold. The _x_count meta field that is available within @fold scopes represents the number of elements in the fold, and may be filtered or output as usual. As _x_count represents a count of elements, marking it @output will produce an integer value. See the _x_count section for more details.

Example Use

{
    Animal {
        name @output(out_name: "animal_name")
        out_Entity_Related @fold {
            ... on Location {
                _x_count @output(out_name: "location_count")
                name @output(out_name: "location_names")
            }
        }
    }
}

Each returned row has three columns: animal_name with the name of each Animal in the graph, location_count with the related locations for that Animal, and location_names with a list of the names of all related locations of the Animal named animal_name. If a given Animal has no related locations, its location_names list is empty and the location_count value is 0.

Constraints and Rules

@fold can only be applied to vertex fields, except the root vertex field.
May not exist at the same vertex field as @recurse, @optional, or @output_source.
Any scope that is either marked with @fold or is nested within a @fold marked scope, may expand at most one vertex field.
“No no-op @fold scopes”: within any @fold scope, there must either be at least one field that is marked @output, or there must be a @filter applied to the _x_count field.
All @output fields within a @fold traversal must be present at the innermost scope. It is invalid to expand vertex fields within a @fold after encountering an @output directive.
@tag, @recurse, @optional, @output_source and @fold may not be used anywhere within a scope marked @fold.
The _x_count meta field may only appear at the innermost scope of a @fold marked scope.
Marking the _x_count meta field with an @output produces an integer value corresponding to the number of results within that fold.
Marking for @output any field other than the _x_count meta field produces a list of results, where the number of elements in that list is equal to the value of the _x_count meta field, if it were selected for output.
If multiple fields (other than _x_count) are marked @output, the resulting output lists are parallel: the ith element of each such list is the value of the corresponding field of the ith element of the @fold, for some fixed order of elements in that @fold. The order of elements within the output of a @fold is only fixed for a particular execution of a given query, for the results of a given @fold that are part of a single result set. There is no guarantee of consistent ordering of elements for the same @fold in any of the following situations:
- across two or more result sets that are both the result of the execution of the same query;
- across different executions of the same query, or
- across different queries that contain the same @fold scope.
Use of type coercions or @filter at or within the vertex field marked @fold is allowed. The order of operations is conceptually as follows:
- First, type coercions and filters (except @filter on the _x_count meta field) are applied, and any data that does not satisfy such coercions and filters is discarded. At this point, the size of the fold (i.e. its number of results) is fixed.
- Then, any @filter directives on the _x_count meta field are applied, allowing filtering of result sets based on the fold size. Any result sets that do not match these filters are discarded.
- Finally, if the result set was not discarded by the previous step, @output directives are processed, selecting folded data for output.
If the compiler is able to prove that a type coercion in the @fold scope is actually a no-op, it may optimize it away. See the Optional type_equivalence_hints compilation parameter section for more details.

Example

The following GraphQL is not allowed and will produce a GraphQLCompilationError. This query is invalid for two separate reasons:

It expands vertex fields after an @output directive (outputting animal_name)
The in_Animal_ParentOf scope, which is within a scope marked @fold, expands two vertex fields instead of at most one.

{
    Animal {
        out_Animal_ParentOf @fold {
            name @output(out_name: "animal_name")
            in_Animal_ParentOf {
                out_Animal_OfSpecies {
                    uuid @output(out_name: "species_id")
                }
                out_Entity_Related {
                    ... on Animal {
                        name @output(out_name: "relative_name")
                    }
                }
            }
        }
    }
}

The following GraphQL query is similarly not allowed and will produce a GraphQLCompilationError, since the _x_count field is not within the innermost scope in the @fold.

{
    Animal {
        out_Animal_ParentOf @fold {
            _x_count @output(out_name: "related_count")
            out_Entity_Related {
                ... on Animal {
                    name @output(out_name: "related_name")
                }
            }
        }
    }
}

Moving the _x_count field to the innermost scope results in the following valid use of @fold:

{
    Animal {
        out_Animal_ParentOf @fold {
            out_Entity_Related {
                ... on Animal {
                    _x_count @output(out_name: "related_count")
                    name @output(out_name: "related_name")
                }
            }
        }
    }
}

Here is an example of query whose @fold does not output any data; it returns the names of all animals that have more than count children whose names contain the substring substr:

{
    Animal {
        name @output(out_name: "animal_name")
        out_Animal_ParentOf @fold {
            _x_count @filter(op_name: ">=", value: ["$count"])
            name @filter(op_name: "has_substring", value: ["$substr"])
        }
    }
}

@tag

The @tag directive enables filtering based on values encountered elsewhere in the same query. Applied on a property field, it assigns a name to the value of that property field, allowing that value to then be used as part of a @filter directive.

To supply a tagged value to a @filter directive, place the tag name (prefixed with a % symbol) in the @filter’s value array. See Passing parameters for more details.

Example Use

{
    Animal {
        name @tag(tag_name: "parent_name")
        out_Animal_ParentOf {
            name @filter(op_name: "<", value: ["%parent_name"])
                 @output(out_name: "child_name")
        }
    }
}

Each row returned by this query contains, in the child_name column, the name of an Animal that is the child of another Animal, and has a name that is lexicographically smaller than the name of its parent.

Constraints and Rules

@tag can only be applied to property fields.
The value provided for tag_name may only consist of upper or lower case letters (A-Z, a-z), or underscores (_).
For any given query, all tag_name values must be unique.
Cannot be applied to property fields within a scope marked @fold.
Using a @tag and a @filter that references the tag within the same vertex is allowed, so long as the two do not appear on the exact same property field.

@filter

Allows filtering of the data to be returned, based on any of a set of filtering operations. Conceptually, it is the GraphQL equivalent of the SQL WHERE keyword.

See Supported filtering operations for details on the various types of filtering that the compiler currently supports. These operations are currently hardcoded in the compiler; in the future, we may enable the addition of custom filtering operations via compiler plugins.

Multiple @filter directives may be applied to the same field at once. Conceptually, it is as if the different @filter directives were joined by SQL AND keywords.

Using a @tag and a @filter that references the tag within the same vertex is allowed, so long as the two do not appear on the exact same property field.

Passing Parameters

The @filter directive accepts two types of parameters: runtime parameters and tagged parameters.

Runtime parameters are represented with a $ prefix (e.g. $foo), and denote parameters whose values will be known at runtime. The compiler will compile the GraphQL query leaving a spot for the value to fill at runtime. After compilation, the user will have to supply values for all runtime parameters, and their values will be inserted into the final query before it can be executed against the database.

Consider the following query:

{
    Animal {
        name @output(out_name: "animal_name")
        color @filter(op_name: "=", value: ["$animal_color"])
    }
}

It returns one row for every Animal vertex that has a color equal to $animal_color. Each row contains the animal’s name in a column named animal_name. The parameter $animal_color is a runtime parameter – the user must pass in a value (e.g. {"animal_color": "blue"}) that will be inserted into the query before querying the database.

Tagged parameters are represented with a % prefix (e.g. %foo) and denote parameters whose values are derived from a property field encountered elsewhere in the query. If the user marks a property field with a @tag directive and a suitable name, that value becomes available to use as a tagged parameter in all subsequent @filter directives.

Consider the following query:

{
    Animal {
        name @tag(out_name: "parent_name")
        out_Animal_ParentOf {
            name @filter(op_name: "has_substring", value: ["%parent_name"])
                 @output(out_name: "child_name")
        }
    }
}

It returns the names of animals that contain their parent’s name as a substring of their own. The database captures the value of the parent animal’s name as the parent_name tag, and this value is then used as the %parent_name tagged parameter in the child animal’s @filter.

We considered and rejected the idea of allowing literal values (e.g. 123) as @filter parameters, for several reasons:

The GraphQL type of the @filter directive’s value field cannot reasonably encompass all the different types of arguments that people might supply. Even counting scalar types only, there’s already ID, Int, Float, Boolean, String, Date, DateTime... – way too many to include.
Literal values would be used when the parameter’s value is known to be fixed. We can just as easily accomplish the same thing by using a runtime parameter with a fixed value. That approach has the added benefit of potentially reducing the number of different queries that have to be compiled: two queries with different literal values would have to be compiled twice, whereas using two different sets of runtime arguments only requires the compilation of one query.
We were concerned about the potential for accidental misuse of literal values. SQL systems have supported stored procedures and parameterized queries for decades, and yet ad-hoc SQL query construction via simple string interpolation is still a serious problem and is the source of many SQL injection vulnerabilities. We felt that disallowing literal values in the query will drastically reduce both the use and the risks of unsafe string interpolation, at an acceptable cost.

Constraints and Rules

The value provided for op_name may only consist of upper or lower case letters (A-Z, a-z), or underscores (_).
Values provided in the value list must start with either $ (denoting a runtime parameter) or % (denoting a tagged parameter), followed by exclusively upper or lower case letters (A-Z, a-z) or underscores (_).
The @tag directives corresponding to any tagged parameters in a given @filter query must be applied to fields that appear either at the same vertex as the one with the @filter, or strictly before the field with the @filter directive.
“Can’t compare apples and oranges” – the GraphQL type of the parameters supplied to the @filter must match the GraphQL types the compiler infers based on the field the @filter is applied to.
If the @tag corresponding to a tagged parameter originates from within a vertex field marked @optional, the emitted code for the @filter checks if the @optional field was assigned a value. If no value was assigned to the @optional field, comparisons against the tagged parameter from within that field return True.
- For example, assuming %from_optional originates from an @optional scope, when no value is assigned to the @optional field:
  - using @filter(op_name: "=", value: ["%from_optional"]) is equivalent to not having the filter at all;
  - using @filter(op_name: "between", value: ["$lower", "%from_optional"]) is equivalent to @filter(op_name: ">=", value: ["$lower"]).
Using a @tag and a @filter that references the tag within the same vertex is allowed, so long as the two do not appear on the exact same property field.

@recurse

Applied to a vertex field, specifies that the edge connecting that vertex field to the current vertex should be visited repeatedly, up to depth times. The recursion always starts at depth = 0, i.e. the current vertex – see the below sections for a more thorough explanation.

Example Use

Say the user wants to fetch the names of the children and grandchildren of each Animal. That could be accomplished by running the following two queries and concatenating their results:

{
    Animal {
        name @output(out_name: "ancestor")
        out_Animal_ParentOf {
            name @output(out_name: "descendant")
        }
    }
}

{
    Animal {
        name @output(out_name: "ancestor")
        out_Animal_ParentOf {
            out_Animal_ParentOf {
                name @output(out_name: "descendant")
            }
        }
    }
}

If the user then wanted to also add great-grandchildren to the descendants output, that would require yet another query, and so on. Instead of concatenating the results of multiple queries, the user can simply use the @recurse directive. The following query returns the child and grandchild descendants:

{
    Animal {
        name @output(out_name: "ancestor")
        out_Animal_ParentOf {
            out_Animal_ParentOf @recurse(depth: 1) {
                name @output(out_name: "descendant")
            }
        }
    }
}

Each row returned by this query contains the name of an Animal in the ancestor column and the name of its child or grandchild in the descendant column. The out_Animal_ParentOf vertex field marked @recurse is already enclosed within another out_Animal_ParentOf vertex field, so the recursion starts at the “child” level (the out_Animal_ParentOf not marked with @recurse). Therefore, the descendant column contains the names of an ancestor’s children (from depth = 0 of the recursion) and the names of its grandchildren (from depth = 1).

Recursion using this directive is possible since the types of the enclosing scope and the recursion scope work out: the @recurse directive is applied to a vertex field of type Animal and its vertex field is enclosed within a scope of type Animal. Additional cases where recursion is allowed are described in detail below.

The descendant column cannot have the name of the ancestor animal since the @recurse is already within one out_Animal_ParentOf and not at the root Animal vertex field. Similarly, it cannot have descendants that are more than two steps removed (e.g., great-grandchildren), since the depth parameter of @recurse is set to 1.

Now, let’s see what happens when we eliminate the outer out_Animal_ParentOf vertex field and simply have the @recurse applied on the out_Animal_ParentOf in the root vertex field scope:

{
    Animal {
        name @output(out_name: "ancestor")
        out_Animal_ParentOf @recurse(depth: 1) {
            name @output(out_name: "self_or_descendant")
        }
    }
}

In this case, when the recursion starts at depth = 0, the Animal within the recursion scope will be the same Animal at the root vertex field, and therefore, in the depth = 0 step of the recursion, the value of the self_or_descendant field will be equal to the value of the ancestor field.

Constraints and Rules

“The types must work out” – when applied within a scope of type A, to a vertex field of type B, at least one of the following must be true:
- A is a GraphQL union;
- B is a GraphQL interface, and A is a type that implements that interface;
- A and B are the same type.
@recurse can only be applied to vertex fields other than the root vertex field of a query.
Cannot be used within a scope marked @optional or @fold.
The depth parameter of the recursion must always have a value greater than or equal to 1. Using depth = 1 produces the current vertex and its neighboring vertices along the specified edge.
Type coercions and @filter directives within a scope marked @recurse do not limit the recursion depth. Conceptually, recursion to the specified depth happens first, and then type coercions and @filter directives eliminate some of the locations reached by the recursion.
As demonstrated by the examples above, the recursion always starts at depth 0, so the recursion scope always includes the vertex at the scope that encloses the vertex field marked @recurse.

@output_source

See the Completeness of returned results section for a description of the directive and examples.

Constraints and Rules

May exist at most once in any given GraphQL query.
Can exist only on a vertex field, and only on the last vertex field used in the query.
Cannot be used within a scope marked @optional or @fold.

Supported filtering operations

Comparison operators

Supported comparison operators:

Equal to: =
Not equal to: !=
Greater than: >
Less than: <
Greater than or equal to: >=
Less than or equal to: <=

Example Use

Equal to (`=`):

{
    Species {
        name @filter(op_name: "=", value: ["$species_name"])
        uuid @output(out_name: "species_uuid")
    }
}

This returns one row for every Species whose name is equal to the value of the $species_name parameter. Each row contains the uuid of the Species in a column named species_uuid.

Greater than or equal to (`>=`):

{
    Animal {
        name @output(out_name: "name")
        birthday @output(out_name: "birthday")
                 @filter(op_name: ">=", value: ["$point_in_time"])
    }
}

This returns one row for every Animal vertex that was born after or on a $point_in_time. Each row contains the animal’s name and birthday in columns named name and birthday, respectively.

Constraints and Rules

All comparison operators must be on a property field.

name_or_alias

Allows you to filter on vertices which contain the exact string $wanted_name_or_alias in their name or alias fields.

Example Use

{
    Animal @filter(op_name: "name_or_alias", value: ["$wanted_name_or_alias"]) {
        name @output(out_name: "name")
    }
}

This returns one row for every Animal vertex whose name and/or alias is equal to $wanted_name_or_alias. Each row contains the animal’s name in a column named name.

The value provided for $wanted_name_or_alias must be the full name and/or alias of the Animal. Substrings will not be matched.

Constraints and Rules

Must be on a vertex field that has name and alias properties.

between

Example Use

{
    Animal {
        name @output(out_name: "name")
        birthday @filter(op_name: "between", value: ["$lower", "$upper"])
                 @output(out_name: "birthday")
    }
}

This returns:

One row for every Animal vertex whose birthday is in between $lower and $upper dates (inclusive). Each row contains the animal’s name in a column named name.

Constraints and Rules

Must be on a property field.
The lower and upper bounds represent an inclusive interval, which means that the output may contain values that match them exactly.

in_collection

Example Use

{
    Animal {
        name @output(out_name: "animal_name")
        color @output(out_name: "color")
              @filter(op_name: "in_collection", value: ["$colors"])
    }
}

This returns one row for every Animal vertex which has a color contained in a list of colors. Each row contains the Animal’s name and color in columns named animal_name and color, respectively.

Constraints and Rules

Must be on a property field that is not of list type.

not_in_collection

Example Use

{
    Animal {
        name @output(out_name: "animal_name")
        color @output(out_name: "color")
              @filter(op_name: "not_in_collection", value: ["$colors"])
    }
}

This returns one row for every Animal vertex which has a color not contained in a list of colors. Each row contains the Animal’s name and color in columns named animal_name and color, respectively.

Constraints and Rules

Must be on a property field that is not of list type.

has_substring

Example Use

{
    Animal {
        name @filter(op_name: "has_substring", value: ["$substring"])
             @output(out_name: "animal_name")
    }
}

This returns one row for every Animal vertex whose name contains the value supplied for the $substring parameter. Each row contains the matching Animal’s name in a column named animal_name.

Constraints and Rules

Must be on a property field of string type.

starts_with

Example Use

{
    Animal {
        name @filter(op_name: "starts_with", value: ["$prefix"])
             @output(out_name: "animal_name")
    }
}

This returns one row for every Animal vertex whose name starts with the value supplied for the $prefix parameter. Each row contains the matching Animal’s name in a column named animal_name.

Constraints and Rules

Must be on a property field of string type.

ends_with

Example Use

{
    Animal {
        name @filter(op_name: "ends_with", value: ["$suffix"])
             @output(out_name: "animal_name")
    }
}

This returns one row for every Animal vertex whose name ends with the value supplied for the $suffix parameter. Each row contains the matching Animal’s name in a column named animal_name.

Constraints and Rules

Must be on a property field of string type.

contains

Example Use

{
    Animal {
        alias @filter(op_name: "contains", value: ["$wanted"])
        name @output(out_name: "animal_name")
    }
}

This returns one row for every Animal vertex whose list of aliases contains the value supplied for the $wanted parameter. Each row contains the matching Animal’s name in a column named animal_name.

Constraints and Rules

Must be on a property field of list type.

not_contains

Example Use

{
    Animal {
        alias @filter(op_name: "not_contains", value: ["$wanted"])
        name @output(out_name: "animal_name")
    }
}

This returns one row for every Animal vertex whose list of aliases does not contain the value supplied for the $wanted parameter. Each row contains the matching Animal’s name in a column named animal_name.

Constraints and Rules

Must be on a property field of list type.

intersects

Example Use

{
    Animal {
        alias @filter(op_name: "intersects", value: ["$wanted"])
        name @output(out_name: "animal_name")
    }
}

This returns one row for every Animal vertex whose list of aliases has a non-empty intersection with the list of values supplied for the $wanted parameter. Each row contains the matching Animal’s name in a column named animal_name.

Constraints and Rules

Must be on a property field of list type.

has_edge_degree

Example Use

{
    Animal {
        name @output(out_name: "animal_name")

        out_Animal_ParentOf @filter(op_name: "has_edge_degree", value: ["$child_count"]) @optional {
            uuid
        }
    }
}

This returns one row for every Animal vertex that has exactly $child_count children (i.e. where the out_Animal_ParentOf edge appears exactly $child_count times). Each row contains the matching Animal’s name, in a column named animal_name.

The uuid field within the out_Animal_ParentOf vertex field is added simply to satisfy the GraphQL syntax rule that requires at least one field to exist within any {}. Since this field is not marked with any directive, it has no effect on the query.

N.B.: Please note the @optional directive on the vertex field being filtered above. If in your use case you expect to set $child_count to 0, you must also mark that vertex field @optional. Recall that absence of @optional implies that at least one such edge must exist. If the has_edge_degree filter is used with a parameter set to 0, that requires the edge to not exist. Therefore, if the @optional is not present in this situation, no valid result sets can be produced, and the resulting query will return no results.

Constraints and Rules

Must be on a vertex field that is not the root vertex of the query.
Tagged values are not supported as parameters for this filter.
If the runtime parameter for this operator can be 0, it is strongly recommended to also apply @optional to the vertex field being filtered (see N.B. above for details).

is_null

Example Use

{
    Animal {
        name @output(out_name: "animal_name")
        color @filter(op_name: "is_null", value: [])
    }
}

This returns one row for every Animal that does not have a color defined.

Constraints and Rules

Must be applied on a property field.
value must be empty.

is_not_null

Example Use

{
    Animal {
        name @output(out_name: "animal_name")
        color @filter(op_name: "is_not_null", value: [])
    }
}

This returns one row for every Animal that has a color defined.

Constraints and Rules

Must be applied on a property field.
value must be empty.

Type coercions

Type coercions are operations that create a new scope whose type is different than the type of the enclosing scope of the coercion – they coerce the enclosing scope into a different type. Type coercions are represented with GraphQL inline fragments.

Example Use

{
    Species {
        name @output(out_name: "species_name")
        out_Species_Eats {
            ... on Food {
                name @output(out_name: "food_name")
            }
        }
    }
}

Here, the out_Species_Eats vertex field is of the Union__Food__FoodOrSpecies__Species union type. To proceed with the query, the user must choose which of the types in the Union__Food__FoodOrSpecies__Species union to use. In this example, ... on Food indicates that the Food type was chosen, and any vertices at that scope that are not of type Food are filtered out and discarded.

{
    Species {
        name @output(out_name: "species_name")
        out_Entity_Related {
            ... on Species {
                name @output(out_name: "entity_name")
            }
        }
    }
}

In this query, the out_Entity_Related is of Entity type. However, the query only wants to return results where the related entity is a Species, which ... on Species ensures is the case.

Constraints and Rules

Must be the only selection in scope. No field may exist in the same scope as a type coercion. No scope may contain more than one type coercion.

Meta fields

__typename

The compiler supports the standard GraphQL meta field __typename, which returns the runtime type of the scope where the field is found. Assuming the GraphQL schema matches the database’s schema, the runtime type will always be a subtype of (or exactly equal to) the static type of the scope determined by the GraphQL type system. Below, we provide an example query in which the runtime type is a subtype of the static type, but is not equal to it.

The __typename field is treated as a property field of type String, and supports all directives that can be applied to any other property field.

Example Use

{
    Entity {
        __typename @output(out_name: "entity_type")
        name @output(out_name: "entity_name")
    }
}

This query returns one row for each Entity vertex. The scope in which __typename appears is of static type Entity. However, Animal is a type of Entity, as are Species, Food, and others. Vertices of all subtypes of Entity will therefore be returned, and the entity_type column that outputs the __typename field will show their runtime type: Animal, Species, Food, etc.

_x_count

The _x_count meta field is a non-standard meta field defined by the GraphQL compiler that makes it possible to interact with the number of elements in a scope marked @fold. By applying directives like @output and @filter to this meta field, queries can output the number of elements captured in the @fold and filter down results to select only those with the desired fold sizes.

We use the _x_ prefix to signify that this is an extension meta field introduced by the compiler, and not part of the canonical set of GraphQL meta fields defined by the GraphQL specification. We do not use the GraphQL standard double-underscore (__) prefix for meta fields, since all names with that prefix are explicitly reserved and prohibited from being used in directives, fields, or any other artifacts.

Adding the `_x_count` meta field to your schema

Since the _x_count meta field is not currently part of the GraphQL standard, it has to be explicitly added to all interfaces and types in your schema. There are two ways to do this.

The preferred way to do this is to use the EXTENDED_META_FIELD_DEFINITIONS constant as a starting point for building your interfaces’ and types’ field descriptions:

from graphql import GraphQLInt, GraphQLField, GraphQLObjectType, GraphQLString
from graphql_compiler import EXTENDED_META_FIELD_DEFINITIONS

fields = EXTENDED_META_FIELD_DEFINITIONS.copy()
fields.update({
    'foo': GraphQLField(GraphQLString),
    'bar': GraphQLField(GraphQLInt),
    # etc.
})
graphql_type = GraphQLObjectType('MyType', fields)
# etc.

If you are not able to programmatically define the schema, and instead simply have a pre-made GraphQL schema object that you are able to mutate, the alternative approach is via the insert_meta_fields_into_existing_schema() helper function defined by the compiler:

# assuming that existing_schema is your GraphQL schema object
insert_meta_fields_into_existing_schema(existing_schema)
# existing_schema was mutated in-place and all custom meta-fields were added

Example Use

{
    Animal {
        name @output(out_name: "name")
        out_Animal_ParentOf @fold {
            _x_count @output(out_name: "number_of_children")
            name @output(out_name: "child_names")
        }
    }
}

This query returns one row for each Animal vertex. Each row contains its name, and the number and names of its children. While the output type of the child_names selection is a list of strings, the output type of the number_of_children selection is an integer.

{
    Animal {
        name @output(out_name: "name")
        out_Animal_ParentOf @fold {
            _x_count @filter(op_name: ">=", value: ["$min_children"])
                    @output(out_name: "number_of_children")
            name @filter(op_name: "has_substring", value: ["$substr"])
                 @output(out_name: "child_names")
        }
    }
}

Here, we’ve modified the above query to add two more filtering constraints to the returned rows:

child Animal vertices must contain the value of $substr as a substring in their name, and
Animal vertices must have at least $min_children children that satisfy the above filter.

Importantly, any filtering on _x_count is applied after any other filters and type coercions that are present in the @fold in question. This order of operations matters a lot: selecting Animal vertices with 3+ children, then filtering the children based on their names is not the same as filtering the children first, and then selecting Animal vertices that have 3+ children that matched the earlier filter.

Constraints and Rules

The _x_count field is only allowed to appear within a vertex field marked @fold.
Filtering on _x_count is always applied after any other filters and type coercions present in that @fold.
Filtering or outputting the value of the _x_count field must always be done at the innermost scope of the @fold. It is invalid to expand vertex fields within a @fold after filtering or outputting the value of the _x_count meta field.

How is filtering on `_x_count` different from `@filter` with `has_edge_degree`?

The has_edge_degree filter allows filtering based on the number of edges of a particular type. There are situations in which filtering with has_edge_degree and filtering using = on _x_count produce equivalent queries. Here is one such pair of queries:

{
    Species {
        name @output(out_name: "name")
        in_Animal_OfSpecies @filter(op_name: "has_edge_degree", value: ["$num_animals"]) {
            uuid
        }
    }
}

and

{
    Species {
        name @output(out_name: "name")
        in_Animal_OfSpecies @fold {
            _x_count @filter(op_name: "=", value: ["$num_animals"])
        }
    }
}

In both of these queries, we ask for the names of the Species vertices that have precisely $num_animals members. However, we have expressed this question in two different ways: once as a property of the Species vertex (“the degree of the in_Animal_OfSpecies is $num_animals”), and once as a property of the list of Animal vertices produced by the @fold (“the number of elements in the @fold is $num_animals”).

When we add additional filtering within the Animal vertices of the in_Animal_OfSpecies vertex field, this distinction becomes very important. Compare the following two queries:

{
    Species {
        name @output(out_name: "name")
        in_Animal_OfSpecies @filter(op_name: "has_edge_degree", value: ["$num_animals"]) {
            out_Animal_LivesIn {
                name @filter(op_name: "=", value: ["$location"])
            }
        }
    }
}

versus

{
    Species {
        name @output(out_name: "name")
        in_Animal_OfSpecies @fold {
            out_Animal_LivesIn {
                _x_count @filter(op_name: "=", value: ["$num_animals"])
                name @filter(op_name: "=", value: ["$location"])
            }
        }
    }
}

In the first, for the purposes of the has_edge_degree filtering, the location where the animals live is irrelevant: the has_edge_degree only makes sure that the Species vertex has the correct number of edges of type in_Animal_OfSpecies, and that’s it. In contrast, the second query ensures that only Species vertices that have $num_animals animals that live in the selected location are returned – the location matters since the @filter on the _x_count field applies to the number of elements in the @fold scope.

The GraphQL schema

This section assumes that the reader is familiar with the way schemas work in the reference implementation of GraphQL.

The GraphQL schema used with the compiler must contain the custom directives and custom Date and DateTime scalar types defined by the compiler:

directive @recurse(depth: Int!) on FIELD

directive @filter(value: [String!]!, op_name: String!) on FIELD | INLINE_FRAGMENT

directive @tag(tag_name: String!) on FIELD

directive @output(out_name: String!) on FIELD

directive @output_source on FIELD

directive @optional on FIELD

directive @fold on FIELD

scalar DateTime

scalar Date

If constructing the schema programmatically, one can simply import the the Python object representations of the custom directives and the custom types:

from graphql_compiler import DIRECTIVES  # the list of custom directives
from graphql_compiler import GraphQLDate, GraphQLDateTime  # the custom types

Since the GraphQL and OrientDB type systems have different rules, there is no one-size-fits-all solution to writing the GraphQL schema for a given database schema. However, the following rules of thumb are useful to keep in mind:

Generally, represent OrientDB abstract classes as GraphQL interfaces. In GraphQL’s type system, GraphQL interfaces cannot inherit from other GraphQL interfaces.
Generally, represent OrientDB non-abstract classes as GraphQL types, listing the GraphQL interfaces that they implement. In GraphQL’s type system, GraphQL types cannot inherit from other GraphQL types.
Inheritance relationships between two OrientDB non-abstract classes, or between two OrientDB abstract classes, introduce some difficulties in GraphQL. When modelling your data in OrientDB, it’s best to avoid such inheritance if possible.
If it is impossible to avoid having two non-abstract OrientDB classes A and B such that B inherits from A, you have two options:
- You may choose to represent the A OrientDB class as a GraphQL interface, which the GraphQL type corresponding to B can implement. In this case, the GraphQL schema preserves the inheritance relationship between A and B, but sacrifices the representation of any inheritance relationships A may have with any OrientDB superclasses.
- You may choose to represent both A and B as GraphQL types. The tradeoff in this case is exactly the opposite from the previous case: the GraphQL schema sacrifices the inheritance relationship between A and B, but preserves the inheritance relationships of A with its superclasses. In this case, it is recommended to create a GraphQL union type A | B, and to use that GraphQL union type for any vertex fields that in OrientDB would be of type A.
If it is impossible to avoid having two abstract OrientDB classes A and B such that B inherits from A, you similarly have two options:
- You may choose to represent B as a GraphQL type that can implement the GraphQL interface corresponding to A. This makes the GraphQL schema preserve the inheritance relationship between A and B, but sacrifices the ability for other GraphQL types to inherit from B.
- You may choose to represent both A and B as GraphQL interfaces, sacrificing the schema’s representation of the inheritance between A and B, but allowing GraphQL types to inherit from both A and B. If necessary, you can then create a GraphQL union type A | B and use it for any vertex fields that in OrientDB would be of type A.
It is legal to fully omit classes and fields that are not representable in GraphQL. The compiler currently does not support OrientDB’s EmbeddedMap type nor embedded non-primitive typed fields, so such fields can simply be omitted in the GraphQL representation of their classes. Alternatively, the entire OrientDB class and all edges that may point to it may be omitted entirely from the GraphQL schema.

Execution model

Since the GraphQL compiler can target multiple different query languages, each with its own behaviors and limitations, the execution model must also be defined as a function of the compilation target language. While we strive to minimize the differences between compilation targets, some differences are unavoidable.

The compiler abides by the following principles:

When the database is queried with a compiled query string, its response must always be in the form of a list of results.
The precise format of each such result is defined by each compilation target separately.
- gremlin, MATCH and SQL return data in a tabular format, where each result is a row of the table, and fields marked for output are columns.
- However, future compilation targets may have a different format. For example, each result may appear in the nested tree format used by the standard GraphQL specification.
Each such result must satisfy all directives and types in its corresponding GraphQL query.
The returned list of results is not guaranteed to be complete!
- In other words, there may have been additional result sets that satisfy all directives and types in the corresponding GraphQL query, but were not returned by the database.
- However, compilation target implementations are encouraged to return complete results if at all practical. The MATCH compilation target is guaranteed to produce complete results.

Completeness of returned results

To explain the completeness of returned results in more detail, assume the database contains the following example graph:

a  ---->_ x
|____   /|
    _|_/
   / |____
  /      \/
b  ----> y

Let a, b, x, y be the values of the name property field of four vertices. Let the vertices named a and b be of type S, and let x and y be of type T. Let vertex a be connected to both x and y via directed edges of type E. Similarly, let vertex b also be connected to both x and y via directed edges of type E.

Consider the GraphQL query:

{
    S {
        name @output(out_name: "s_name")
        out_E {
            name @output(out_name: "t_name")
        }
    }
}

Between the data in the database and the query’s structure, it is clear that combining any of a or b with any of x or y would produce a valid result. Therefore, the complete result list, shown here in JSON format, would be:

[
    {"s_name": "a", "t_name": "x"},
    {"s_name": "a", "t_name": "y"},
    {"s_name": "b", "t_name": "x"},
    {"s_name": "b", "t_name": "y"},
]

This is precisely what the MATCH compilation target is guaranteed to produce. The remainder of this section is only applicable to the gremlin compilation target. If using MATCH, all of the queries listed in the remainder of this section will produce the same, complete result list.

Since the gremlin compilation target does not guarantee a complete result list, querying the database using a query string generated by the gremlin compilation target will produce only a partial result list resembling the following:

[
    {"s_name": "a", "t_name": "x"},
    {"s_name": "b", "t_name": "x"},
]

Due to limitations in the underlying query language, gremlin will by default produce at most one result for each of the starting locations in the query. The above Gr aphQL query started at the type S, so each s_name in the returned result list is therefore distinct. Furthermore, there is no guarantee (and no way to know ahead of time) whether x or y will be returned as the t_name value in each result, as they are both valid results.

Users may apply the @output_source directive on the last scope of the query to alter this behavior:

{
    S {
        name @output(out_name: "s_name")
        out_E @output_source {
            name @output(out_name: "t_name")
        }
    }
}

Rather than producing at most one result for each S, the query will now produce at most one result for each distinct value that can be found at out_E, where the directive is applied:

[
    {"s_name": "a", "t_name": "x"},
    {"s_name": "a", "t_name": "y"},
]

Conceptually, applying the @output_source directive makes it as if the query were written in the opposite order:

{
    T {
        name @output(out_name: "t_name")
        in_E {
            name @output(out_name: "s_name")
        }
    }
}

SQL

Relational databases are supported by compiling to SQLAlchemy core as an intermediate language, and then relying on SQLAlchemy’s compilation of the dialect-specific SQL query. The compiler does not return a string for SQL compilation, but instead a SQLAlchemy Query object that can be executed through a SQLAlchemy engine.

Our SQL backend supports basic traversals, filters, tags and outputs, but there are still some pieces in development:

Directives: @fold
Filter operators: has_edge_degree
Dialect-specific features, like Postgres array types, and use of filter operators specific to them: contains, intersects, name_or_alias
Meta fields: __typename, _x_count

End-to-End SQL Example

To query a SQL backend simply reflect the needed schema data from the database using SQLAlchemy, compile the GraphQL query to a SQLAlchemy Query, and execute the query against the engine as in the example below:

from graphql_compiler import get_sqlalchemy_schema_info_from_specified_metadata, graphql_to_sql
from sqlalchemy import MetaData, create_engine

engine = create_engine('<connection string>')

# Reflect the default database schema. Each table must have a primary key.
# See "Including tables without explicitly enforced primary keys" otherwise.
metadata = MetaData(bind=engine)
metadata.reflect()

# Wrap the schema information into a SQLAlchemySchemaInfo object.
sql_schema_info = get_sqlalchemy_schema_info_from_specified_metadata(
    metadata.tables, {}, engine.dialect)

# Write GraphQL query.
graphql_query = '''
{
    Animal {
        name @output(out_name: "animal_name")
    }
}
'''
parameters = {}

# Compile and execute query.
compilation_result = graphql_to_sql(sql_schema_info, graphql_query, parameters)
query_results = [dict(row) for row in engine.execute(compilation_result.query)]

Advanced Features

SQL Edges

Edges can be specified in SQL through the direct_edges parameter as illustrated below. SQL edges gets rendered as out_edgeName and in_edgeName in the source and destination GraphQL objects respectively and edge traversals get compiled to SQL joins between the source and destination tables using the specified columns. We use the term direct_edges below since the compiler may support other types of SQL edges in the future such as edges that are backed by SQL association tables.

from graphql_compiler import get_sqlalchemy_schema_info_from_specified_metadata, graphql_to_sql
from graphql_compiler.schema_generation.sqlalchemy.edge_descriptors import DirectEdgeDescriptor
from sqlalchemy import MetaData, create_engine

# Set engine and reflect database metadata. (See example above for more details).
engine = create_engine('<connection string>')
metadata = MetaData(bind=engine)
metadata.reflect()

# Specify SQL edges.
direct_edges = {
    'Animal_LivesIn': DirectEdgeDescriptor(
        from_vertex='Animal',  # Name of the source GraphQL object as specified.
        from_column='location',  # Name of the column of the underlying source table to join on.
        to_vertex='Location',  # Name of the destination GraphQL object as specified.
        to_column='uuid',   # Name of the column of the underlying destination table to join on.
     )
}

# Wrap the schema information into a SQLAlchemySchemaInfo object.
sql_schema_info = get_sqlalchemy_schema_info_from_specified_metadata(
    metadata.tables, direct_edges, engine.dialect)

# Write GraphQL query with edge traversal.
graphql_query = '''
{
    Animal {
        name @output(out_name: "animal_name")
        out_Animal_LivesIn {
            name @output(out_name: "location_name")
        }
    }
}
'''

# Compile query. Note that the edge traversal gets compiled to a SQL join.
compilation_result = graphql_to_sql(sql_schema_info, graphql_query, {})

Including tables without explicitly enforced primary keys

The compiler requires that each SQLAlchemy Table object in the SQLALchemySchemaInfo has a primary key. However, the primary key in the Table need not be the primary key in the underlying table. It may simply be a non-null and unique identifier of each row. To override the primary key of SQLAlchemy Table objects reflected from a database please follow the instructions in this link.

Including tables from multiple schemas

SQLAlchemy and SQL database management systems support the concept of multiple schemas. One can include Table objects from multiple schemas in the same SQLAlchemySchemaInfo. However, when doing so, one cannot simply use table names as GraphQL object names because two tables in different schemas can have the same the name. A solution that is not quite guaranteed to work, but will likely work in practice is to prepend the schema name as follows:

vertex_name_to_table = {}
for table in metadata.values():
    # The schema field may be None if the database name is specified in the connection string
    # and the table is in the default schema, (e.g. 'dbo' for mssql and 'public' for postgres).
    if table.schema:
        vertex_name = 'dbo' + table.name
    else:
        # If the database name is not specified in the connection string, then
        # the schema field is of the form <databaseName>.<schemaName>.
        # Since dots are not allowed in GraphQL type names we must remove them here.
        vertex_name = table.schema.replace('.', '') + table.name

    if vertex_name in vertex_name_to_table:
        raise AssertionError('Found two tables with conflicting GraphQL object names.')

    vertex_name_to_table[vertex_name] = table

Including manually defined `Table` objects

The Table objects in the SQLAlchemySchemaInfo do not need to be reflected from the database. They also can be manually specified as in this link. However, if specifying Table objects manually, please make sure to include a primary key for each table and to use only SQL types allowed for the dialect specified in the SQLAlchemySchemaInfo.

Macro system

The macro system allows users to reshape how they perceive their data, without requiring changes to the underlying database structures themselves.

In many real-life situations, the database schema does not fit the user’s mental model of the data. There are many causes of this, the most common one being database normalization. The representation of the data that is convenient for storage within a database is rarely the representation that makes for easy querying. As a result, users’ queries frequently include complex and repetitive query structures that work around the database’s chosen data model.

The compiler’s macro system empowers users reshaping their data’s structure to fit their mental model, minimizing query complexity and repetitiveness without requiring changes to the shape of the data in the underlying data systems. The compiler achieves this by allowing users to define macros – type-safe rules for programmatic query rewriting that transform user-provided queries on the desired data model into queries on the actual data model in the underlying data systems.

When macros are defined, the compiler loads them into a macro registry – a data structure that tracks all currently available macros, the resulting GraphQL schema (accounting for macros), and any additional metadata needed by the compiler. The compiler then leverages this registry to expand queries that rely on macros, rewriting them into equivalent queries that do not contain any macros and therefore reflect the actual underlying data model.

This makes macros somewhat similar to SQL’s idea of non-materialized views, though there are some key differences:

SQL views require database access and special permissions; databases are completely oblivious to the use of macros since by the time the database gets the query, all macro uses have been already expanded.
Macros can be stored and expanded client-side, so different users that query the same system may define their own personal macros which are not shared with other users or the server that executes the users’ GraphQL queries. This is generally not achievable with SQL.
Since macro expansion does not interact in any way with the underlying data system, it works seamlessly with all databases and even on schemas stitched together from multiple databases. In contrast, not all databases support SQL-like VIEW functionality.

Currently, the compiler supports one type of macro: macro edges, which allow the creation of “virtual” edges computed from existing ones. More types of macros are coming in the future.

Macro registry

The macro registry is where the definitions of all currently defined macros are stored, together with the resulting GraphQL schema they form, as well as any associated metadata that the compiler’s macro system may need in order to expand any macros encountered in a query.

To create a macro registry object for a given GraphQL schema, use the create_macro_registry function:

from graphql_compiler.macros import create_macro_registry

macro_registry = create_macro_registry(your_graphql_schema_object)

To retrieve the GraphQL schema object with all its macro-based additions, use the get_schema_with_macros function:

from graphql_compiler.macros import get_schema_with_macros

graphql_schema = get_schema_with_macros(macro_registry)

Schema for defining macros

Macro definitions rely on additional directives that are not normally defined in the schema the GraphQL compiler uses for querying. We intentionally do not include these directives in the schema used for querying, since defining macros and writing queries are different modes of use of the compiler, and we believe that controlling which sets of directives are available in which mode will minimize the potential for user confusion.

The get_schema_for_macro_definition() function is able to transform a querying schema into one that is suitable for defining macros. Getting such a schema may be useful, for example, when setting up a GraphQL editor (such as GraphiQL) to create and edit macros.

Macro edges

Macro edges allow users to define new edges that become part of the GraphQL schema, using existing edges as building blocks. They allow users to define shorthand for common querying operations, encapsulating uses of existing query functionality (e.g., tags, filters, recursion, type coercions, etc.) into a virtual edge with a user-specified name that exists only on a specific GraphQL type (and all its subtypes). Both macro edge definitions and their uses are fully type-checked, ensuring the soundness of both the macro definition and any queries that use it.

Overview and use of macro edges

Let us explain the idea of macro edges through a simple example.

Consider the following query, which returns the list of grandchildren of a given animal:

{
    Animal {
        name @filter(op_name: "=", value: ["$animal_name"])
        out_Animal_ParentOf {
            out_Animal_ParentOf {
                name @output(out_name: "grandchild_name")
            }
        }
    }
}

If operations on animals’ grandchildren are common in our use case, we may wish that an edge like out_Animal_GrandparentOf had existed and saved us some repetitive typing.

One of our options is to materialize such an edge in the underlying database itself. However, this causes denormalization of the database – there are now two places where an animal’s grandchildren are written down – requiring additional storage space, and introducing potential for user confusion and data inconsistency between the two representations.

Another option is to introduce a non-materialized view within the database that makes it appear that such an edge exists, and query this view via the GraphQL compiler. While this avoids some of the drawbacks of the previous approach, not all databases support non-materialized views. Also, querying users are not always able to add views to the database, and may require additional permissions on the database system.

Macro edges give us the opportunity to define a new out_Animal_GrandparentOf edge without involving the underlying database systems at all. We simply state that such an edge is constructed by composing two out_Animal_ParentOf edges together:

from graphql_compiler.macros import register_macro_edge

macro_edge_definition = '''{
    Animal @macro_edge_definition(name: "out_Animal_GrandparentOf") {
        out_Animal_ParentOf {
            out_Animal_ParentOf @macro_edge_target {
                uuid
            }
        }
    }
}'''
macro_edge_args = {}

register_macro_edge(your_macro_registry_object, macro_edge_definition, macro_edge_args)

Let’s dig into the GraphQL macro edge definition one step at a time:

We know that the new macro edge is being defined on the Animal GraphQL type, since that is the type where the definition begins.
The @macro_edge_definition directive specifies the name of the new macro edge.
The newly-defined out_Animal_GrandparentOf edge connects Animal vertices to the vertices reachable after exactly two traversals along out_Animal_ParentOf edges; this is what the @macro_edge_target directive signifies.
As the out_Animal_ParentOf field containing the @macro_edge_target directive is of type [Animal] (we know this from our schema), the compiler will automatically infer that the out_Animal_GrandparentOf macro edge also points to vertices of type Animal.
The uuid within the inner out_Animal_ParentOf scope is a “pro-forma” field – it is there simply to satisfy the GraphQL parser, since per the GraphQL specification, each pair of curly braces must reference at least one field. The named field has no meaning in this definition, and the user may choose to use any field that exists within that pair of curly braces. The preferred convention for pro-forma fields is to use whichever field represents the primary key of the given type in the underlying database.
This macro edge does not take arguments, so we set the macro_edge_args value to an empty dictionary. We will cover macro edges with arguments later.

Having defined this macro edge, we are now able to rewrite our original query into a simpler yet equivalent form:

{
    Animal {
        name @filter(op_name: "=", value: ["$animal_name"])
        out_Animal_GrandparentOf {
            name @output(out_name: "grandchild_name")
        }
    }
}

We can now observe the process of macro expansion in action:

from graphql_compiler.macros import perform_macro_expansion

query = '''{
    Animal {
        name @filter(op_name: "=", value: ["$animal_name"])
        out_Animal_GrandparentOf {
            name @output(out_name: "grandchild_name")
        }
    }
}'''
args = {
    'animal_name': 'Hedwig',
}

new_query, new_args = perform_macro_expansion(your_macro_registry_object, query, args)

print(new_query)
# Prints out the following query:
# {
#     Animal {
#         name @filter(op_name: "=", value: ["$animal_name"])
#         out_Animal_ParentOf {
#             out_Animal_ParentOf {
#                 name @output(out_name: "grandchild_name")
#             }
#         }
#     }
# }

print(new_args)
# Prints out the following arguments:
# {'animal_name': 'Hedwig'}

Advanced macro edges use cases

When defining macro edges, one may freely use other compiler query functionality, such as @recurse, @filter, @tag, and so on. Here is a more complex macro edge definition that relies on such more advanced features to define an edge that connects Animal vertices to their siblings who are both older and have a higher net worth:

from graphql_compiler.macros import register_macro_edge

macro_edge_definition = '''
{
    Animal @macro_edge_definition(name: "out_Animal_RicherOlderSiblings") {
        net_worth @tag(tag_name: "self_net_worth")
        out_Animal_BornAt {
            event_date @tag(tag_name: "self_birthday")
        }
        in_Animal_ParentOf {
            out_Animal_ParentOf @macro_edge_target {
                net_worth @filter(op_name: ">", value: ["%self_net_worth"])
                out_Animal_BornAt {
                    event_date @filter(op_name: "<", value: ["%self_birthday"])
                }
            }
        }
    }
}'''
macro_edge_args = {}

register_macro_edge(your_macro_registry_object, macro_edge_definition, macro_edge_args)

Similarly, macro edge definitions are also able to use runtime parameters in their @filter directives, by simply including the runtime parameters needed by the macro edge in the call to register_macro_edge(). The following example defines a macro edge connecting Animal vertices to their grandchildren that go by the name of “Nate”.

macro_edge_definition = '''
{
    Animal @macro_edge_definition(name: "out_Animal_GrandchildrenCalledNate") {
        out_Animal_ParentOf {
            out_Animal_ParentOf @filter(op_name: "name_or_alias", value: ["$nate_name"])
                                @macro_edge_target {
                uuid
            }
        }
    }
}'''
macro_edge_args = {
    'nate_name': 'Nate',
}

register_macro_edge(your_macro_registry_object, macro_edge_definition, macro_edge_args)

When a GraphQL query uses this macro edge, the perform_macro_expansion() function will automatically ensure that the macro edge’s arguments become part of the expanded query’s arguments:

query = '''{
    Animal {
        name @output(out_name: "animal_name")
        out_Animal_GrandchildrenCalledNate {
            uuid @output(out_name: "grandchild_id")
        }
    }
}'''
args = {}
expanded_query, new_args = perform_macro_expansion(your_macro_registry_object, query, args)

print(expanded_query)
# Prints out the following query:
# {
#     Animal {
#         name @output(out_name: "animal_name")
#         out_Animal_ParentOf {
#             out_Animal_ParentOf @filter(op_name: "name_or_alias", value: ["$nate_name"]) {
#                 uuid @output(out_name: "grandchild_id")
#             }
#         }
#     }
# }

print(new_args)
# Prints out the following arguments:
# {'nate_name': 'Nate'}

Constraints and rules for macro edge definitions

Macro edge definitions cannot use other macros as part of their definition.
A macro definition contains exactly one @macro_edge_definition and one @macro_edge_target directive. These directives can only be used within macro edge definitions.
The @macro_edge_target cannot be at or within a scope marked @fold or @optional.
The scope marked @macro_edge_target cannot immediately contain a type coercion. Instead, place the @macro_edge_target directive at the type coercion itself instead of on its enclosing scope.
Macros edge definitions cannot contain uses of @output or @output_source.

Constraints and rules for macro edge usage

The @optional and @recurse directives cannot be used on macro edges.
During the process of macro edge expansion, any directives applied on the vertex field belonging to the macro edge are applied to the vertex field marked with @macro_edge_target in the macro edge’s definition.

In the future, we hope to add support for using @optional on macro edges. We have opened a GitHub issue to track this effort, and we welcome contributions!

Miscellaneous

Pretty-Printing GraphQL Queries

To pretty-print GraphQL queries, use the included pretty-printer:

python -m graphql_compiler.tool <input_file.graphql >output_file.graphql

It’s modeled after Python’s json.tool, reading from stdin and writing to stdout.

Expanding `@optional` vertex fields

Including an optional statement in GraphQL has no performance issues on its own, but if you continue expanding vertex fields within an optional scope, there may be significant performance implications.

Going forward, we will refer to two different kinds of @optional directives.

A “simple” optional is a vertex with an @optional directive that does not expand any vertex fields within it. For example:

{
    Animal {
        name @output(out_name: "name")
        in_Animal_ParentOf @optional {
            name @output(out_name: "parent_name")
        }
    }
}

OrientDB MATCH currently allows the last step in any traversal to be optional. Therefore, the equivalent MATCH traversal for the above GraphQL is as follows:

SELECT
Animal___1.name as `name`,
Animal__in_Animal_ParentOf___1.name as `parent_name`
FROM (
MATCH {
    class: Animal,
    as: Animal___1
}.in('Animal_ParentOf') {
    as: Animal__in_Animal_ParentOf___1
}
RETURN $matches
)

A “compound” optional is a vertex with an @optional directive which does expand vertex fields within it. For example:

{
    Animal {
        name @output(out_name: "name")
        in_Animal_ParentOf @optional {
            name @output(out_name: "parent_name")
            in_Animal_ParentOf {
                name @output(out_name: "grandparent_name")
            }
        }
    }
}

Currently, this cannot represented by a simple MATCH query. Specifically, the following is NOT a valid MATCH statement, because the optional traversal follows another edge:

-- NOT A VALID QUERY
SELECT
Animal___1.name as `name`,
Animal__in_Animal_ParentOf___1.name as `parent_name`
FROM (
MATCH {
    class: Animal,
    as: Animal___1
}.in('Animal_ParentOf') {
    optional: true,
    as: Animal__in_Animal_ParentOf___1
}.in('Animal_ParentOf') {
    as: Animal__in_Animal_ParentOf__in_Animal_ParentOf___1
}
RETURN $matches
)

Instead, we represent a compound optional by taking an union (UNIONALL) of two distinct MATCH queries. For instance, the GraphQL query above can be represented as follows:

SELECT EXPAND($final_match)
LET
    $match1 = (
        SELECT
            Animal___1.name AS `name`
        FROM (
            MATCH {
                class: Animal,
                as: Animal___1,
                where: (
                    (in_Animal_ParentOf IS null)
                    OR
                    (in_Animal_ParentOf.size() = 0)
                ),
            }
        )
    ),
    $match2 = (
        SELECT
            Animal___1.name AS `name`,
            Animal__in_Animal_ParentOf___1.name AS `parent_name`
        FROM (
            MATCH {
                class: Animal,
                as: Animal___1
            }.in('Animal_ParentOf') {
                as: Animal__in_Animal_ParentOf___1
            }.in('Animal_ParentOf') {
                as: Animal__in_Animal_ParentOf__in_Animal_ParentOf___1
            }
        )
    ),
    $final_match = UNIONALL($match1, $match2)

In the first case where the optional edge is not followed, we have to explicitly filter out all vertices where the edge could have been followed. This is to eliminate duplicates between the two MATCH selections.

The previous example is not exactly how we implement compound optionals (we also have SELECT statements within $match1 and $match2), but it illustrates the the general idea.

Performance Penalty

If we have many compound optionals in the given GraphQL, the above procedure results in the union of a large number of MATCH queries. Specifically, for n compound optionals, we generate 2n different MATCH queries. For each of the 2n subsets S of the n optional edges:

We remove the @optional restriction for each traversal in S.
For each traverse t in the complement of S, we entirely discard t along with all the vertices and directives within it, and we add a filter on the previous traverse to ensure that the edge corresponding to t does not exist.

Therefore, we get a performance penalty that grows exponentially with the number of compound optional edges. This is important to keep in mind when writing queries with many optional directives.

If some of those compound optionals contain @optional vertex fields of their own, the performance penalty grows since we have to account for all possible subsets of @optional statements that can be satisfied simultaneously.

Optional `type_equivalence_hints` parameter

This compilation parameter is a workaround for the limitations of the GraphQL and Gremlin type systems:

GraphQL does not allow type to inherit from another type, only to implement an interface.
Gremlin does not have first-class support for inheritance at all.

Assume the following GraphQL schema:

type Animal {
    name: String
}

type Cat {
    name: String
}

type Dog {
    name: String
}

union AnimalCatDog = Animal | Cat | Dog

type Foo {
    adjacent_animal: AnimalCatDog
}

An appropriate type_equivalence_hints value here would be { Animal: AnimalCatDog }. This lets the compiler know that the AnimalCatDog union type is implicitly equivalent to the Animal type, as there are no other types that inherit from Animal in the database schema. This allows the compiler to perform accurate type coercions in Gremlin, as well as optimize away type coercions across edges of union type if the coercion is coercing to the union’s equivalent type.

Setting type_equivalence_hints = { Animal: AnimalCatDog } during compilation would enable the use of a @fold on the adjacent_animal vertex field of Foo:

{
    Foo {
        adjacent_animal @fold {
            ... on Animal {
                name @output(out_name: "name")
            }
        }
    }
}

SchemaGraph

When building a GraphQL schema from the database metadata, we first build a SchemaGraph from the metadata and then, from the SchemaGraph, build the GraphQL schema. The SchemaGraph is also a representation of the underlying database schema, but it has three main advantages that make it a more powerful schema introspection tool:

It’s able to store and expose a schema’s index information. The interface for accessing index information is provisional though and might change in the near future.
Its classes are allowed to inherit from non-abstract classes.
It exposes many utility functions, such as get_subclass_set, that make it easier to explore the schema.

See below for a mock example of how to build and use the SchemaGraph:

from graphql_compiler.schema_generation.orientdb.schema_graph_builder import (
    get_orientdb_schema_graph
)
from graphql_compiler.schema_generation.orientdb.utils import (
    ORIENTDB_INDEX_RECORDS_QUERY, ORIENTDB_SCHEMA_RECORDS_QUERY
)

# Get schema metadata from hypothetical Animals database.
client = your_function_that_returns_an_orientdb_client()
schema_records = client.command(ORIENTDB_SCHEMA_RECORDS_QUERY)
schema_data = [record.oRecordData for record in schema_records]

# Get index data.
index_records = client.command(ORIENTDB_INDEX_RECORDS_QUERY)
index_query_data = [record.oRecordData for record in index_records]

# Build SchemaGraph.
schema_graph = get_orientdb_schema_graph(schema_data, index_query_data)

# Get all the subclasses of a class.
print(schema_graph.get_subclass_set('Animal'))
# {'Animal', 'Dog'}

# Get all the outgoing edge classes of a vertex class.
print(schema_graph.get_vertex_schema_element_or_raise('Animal').out_connections)
# {'Animal_Eats', 'Animal_FedAt', 'Animal_LivesIn'}

# Get the vertex classes allowed as the destination vertex of an edge class.
print(schema_graph.get_edge_schema_element_or_raise('Animal_Eats').out_connections)
# {'Fruit', 'Food'}

# Get the superclass of all classes allowed as the destination vertex of an edge class.
print(schema_graph.get_edge_schema_element_or_raise('Animal_Eats').base_out_connection)
# Food

# Get the unique indexes defined on a class.
print(schema_graph.get_unique_indexes_for_class('Animal'))
# [IndexDefinition(name='uuid', 'base_classname'='Animal', fields={'uuid'}, unique=True, ordered=False, ignore_nulls=False)]

In the future, we plan to add SchemaGraph generation from SQLAlchemy metadata. We also plan to add a mechanism where one can query a SchemaGraph using GraphQL queries.

Cypher query parameters

RedisGraph doesn’t support query parameters, so we perform manual parameter interpolation in the graphql_to_redisgraph_cypher function. However, for Neo4j, we can use Neo4j’s client to do parameter interpolation on its own so that we don’t reinvent the wheel.

The function insert_arguments_into_query does so based on the query language, which isn’t fine-grained enough here– for Cypher backends, we only want to insert parameters if the backend is RedisGraph, but not if it’s Neo4j.

Instead, the correct approach for Neo4j Cypher is as follows, given a Neo4j Python client called neo4j_client:

compilation_result = compile_graphql_to_cypher(
    schema, graphql_query, type_equivalence_hints=type_equivalence_hints)
with neo4j_client.driver.session() as session:
    result = session.run(compilation_result.query, parameters)

Amending Parsed Custom Scalar Types

Information about the description, serialization and parsing of custom scalar type objects is lost when a GraphQL schema is parsed from a string. This causes issues when working with custom scalar type objects. In order to avoid these issues, one can use the code snippet below to amend the definitions of the custom scalar types used by the compiler.

from graphql_compiler.schema import CUSTOM_SCALAR_TYPES
from graphql_compiler.schema_generation.utils import amend_custom_scalar_types

amend_custom_scalar_types(your_schema, CUSTOM_SCALAR_TYPES)

FAQ

Q: Do you really use GraphQL, or do you just use GraphQL-like syntax?

A: We really use GraphQL. Any query that the compiler will accept is entirely valid GraphQL, and we actually use the Python port of the GraphQL core library for parsing and type checking. However, since the database queries produced by compiling GraphQL are subject to the limitations of the database system they run on, our execution model is somewhat different compared to the one described in the standard GraphQL specification. See the Execution model section for more details.

Q: Does this project come with a GraphQL server implementation?

A: No – there are many existing frameworks for running a web server. We simply built a tool that takes GraphQL query strings (and their parameters) and returns a query string you can use with your database. The compiler does not execute the query string against the database, nor does it deserialize the results. Therefore, it is agnostic to the choice of server framework and database client library used.

Q: Do you plan to support other databases / more GraphQL features in the future?

A: We’d love to, and we could really use your help! Please consider contributing to this project by opening issues, opening pull requests, or participating in discussions.

Q: I think I found a bug, what do I do?

A: Please check if an issue has already been created for the bug, and open a new one if not. Make sure to describe the bug in as much detail as possible, including any stack traces or error messages you may have seen, which database you’re using, and what query you compiled.

Q: I think I found a security vulnerability, what do I do?

A: Please reach out to us at graphql-compiler-maintainer@kensho.com so we can triage the issue and take appropriate action.

License

Licensed under the Apache 2.0 License. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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graphql-compiler 2.0.0b2

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Quick Overview

Table of contents

Features

End-to-End Example

Definitions

Directives

@optional

Example Use

Constraints and Rules

@output

Example Use

Constraints and Rules

@fold

Example Use

Constraints and Rules

Example

@tag

Example Use

Constraints and Rules

@filter

Passing Parameters

Constraints and Rules

@recurse

Example Use

Constraints and Rules

@output_source

Constraints and Rules

Supported filtering operations

Comparison operators

Example Use

Equal to (=):

Greater than or equal to (>=):

Constraints and Rules

name_or_alias

Example Use

Constraints and Rules

between

Example Use

Constraints and Rules

in_collection

Example Use

Constraints and Rules

not_in_collection

Example Use

Constraints and Rules

has_substring

Example Use

Constraints and Rules

starts_with

Example Use

Constraints and Rules

ends_with

Example Use

Constraints and Rules

contains

Example Use

Constraints and Rules

not_contains

Example Use

Constraints and Rules

intersects

Example Use

Constraints and Rules

has_edge_degree

Example Use

Constraints and Rules

is_null

Example Use

Constraints and Rules

is_not_null

Example Use

Equal to (`=`):

Greater than or equal to (`>=`):

Adding the `_x_count` meta field to your schema

How is filtering on `_x_count` different from `@filter` with `has_edge_degree`?

Including manually defined `Table` objects

Expanding `@optional` vertex fields

Optional `type_equivalence_hints` parameter