graphql-compiler
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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 can currently autogenerate a
GraphQL schema by introspecting an OrientDB database, (see End to End Example).
In the near future, we plan to add schema autogeneration from SQLAlchemy metadata as well.
For a more detailed overview and getting started guide, please see
our blog post.
Table of contents
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 as 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
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]
schema, type_equivalence_hints = get_graphql_schema_from_orientdb_schema_data(schema_data)
print(print_schema(schema))
graphql_query = '''
{
Animal {
name @output(out_name: "animal_name")
net_worth @filter(op_name: "=", value: ["$net_worth"])
}
}
'''
parameters = {
'net_worth': '100',
}
compilation_result = graphql_to_match(schema, graphql_query, parameters, type_equivalence_hints)
print(compilation_result.query)
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 lists starting
at the scope marked @fold
.
Example Use
{
Animal {
name @output(out_name: "animal_name")
out_Animal_ParentOf @fold {
name @output(out_name: "child_names")
}
}
}
Each returned row has two columns: animal_name
with the name of each Animal
in the graph,
and child_names
with a list of the names of all children of the Animal
named animal_name
.
If a given Animal
has no children, its child_names
list is empty.
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. - There must be at least one
@output
field within a @fold
scope. - 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
.- Use of type coercions or
@filter
at or within the vertex field marked @fold
is allowed.
Only data that satisfies the given type coercions and filters is returned by the @fold
. - If the compiler is able to prove that the 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 is a valid use of @fold
:
{
Animal {
out_Animal_ParentOf @fold {
in_Animal_ParentOf {
in_Animal_ParentOf {
out_Entity_Related {
... on Animal {
name @output(out_name: "final_name")
}
}
}
}
}
}
}
@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 $substring
parameter. Each row contains the matching Animal
's name
in a column named animal_name
.
Constraints and Rules
- Must be on a property field of string type.
contains
Example Use
{
Animal {
alias @filter(op_name: "contains", value: ["$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).
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: "food_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.
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 GraphQL 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
The following table outlines GraphQL compiler features, and their support (if any) by various
relational database flavors:
Feature/Dialect | Required Edges | @filter | @output | @recurse | @fold | @optional | @output_source |
---|
PostgreSQL | No | Limited, intersects, has_edge_degree, and name_or_alias filter unsupported | Limited, __typename output metafield unsupported | No | No | No | No |
SQLite | No | Limited, intersects, has_edge_degree, and name_or_alias filter unsupported | Limited, __typename output metafield unsupported | No | No | No | No |
Microsoft SQL Server | No | Limited, intersects, has_edge_degree, and name_or_alias filter unsupported | Limited, __typename output metafield unsupported | No | No | No | No |
MySQL | No | Limited, intersects, has_edge_degree, and name_or_alias filter unsupported | Limited, __typename output metafield unsupported | No | No | No | No |
MariaDB | No | Limited, intersects, has_edge_degree, and name_or_alias filter unsupported | Limited, __typename output metafield unsupported | No | No | No | No |
Configuring SQLAlchemy
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 string to query
the target database.
For the SQL backend, GraphQL types are assumed to have a SQL table of the same name, and with the
same properties. For example, a schema type
type Animal {
name: String
}
is expected to correspond to a SQLAlchemy table object of the same name, case insensitive. For this
schema type this could look like:
from sqlalchemy import MetaData, Table, Column, String
metadata = MetaData()
animal_table = Table(
'animal',
metadata,
Column('name', String(length=12)),
)
If a table of the schema type name does not exist, an exception will be raised at compile time. See
Configuring the SQL Database to Match the GraphQL Schema
for a possible option to resolve such naming discrepancies.
End-To-End SQL Example
An end-to-end example including relevant GraphQL schema and SQLAlchemy engine preparation follows.
This is intended to show the setup steps for the SQL backend of the GraphQL compiler, and
does not represent best practices for configuring and running SQLAlchemy in a production system.
from graphql import parse
from graphql.utils.build_ast_schema import build_ast_schema
from sqlalchemy import MetaData, Table, Column, String, create_engine
from graphql_compiler.compiler.ir_lowering_sql.metadata import SqlMetadata
from graphql_compiler import graphql_to_sql
schema_text = '''
schema {
query: RootSchemaQuery
}
# IMPORTANT NOTE: all compiler directives are expected here, but not shown to keep the example brief
directive @filter(op_name: String!, value: [String!]!) on FIELD | INLINE_FRAGMENT
# < more directives here, see the GraphQL schema section of this README for more details. >
directive @output(out_name: String!) on FIELD
type Animal {
name: String
}
'''
schema = build_ast_schema(parse(schema_text))
metadata = MetaData()
animal_table = Table(
'animal',
metadata,
Column('name', String(length=12)),
)
engine = create_engine('<connection string>')
sql_metadata = SqlMetadata(engine.dialect, metadata)
graphql_query = '''
{
Animal {
name @output(out_name: "animal_name")
@filter(op_name: "in_collection", value: ["$names"])
}
}
'''
parameters = {
'names': ['animal name 1', 'animal name 2'],
}
compilation_result = graphql_to_sql(schema, graphql_query, parameters, sql_metadata)
query = compilation_result.query
query_results = [dict(result_proxy) for result_proxy in engine.execute(query)]
Configuring the SQL Database to Match the GraphQL Schema
For simplicity, the SQL backend expects an exact match between SQLAlchemy Tables and GraphQL types,
and between SQLAlchemy Columns and GraphQL fields. What if the table name or column name in the
database doesn't conform to these rules? Eventually the plan is to make this aspect of the
SQL backend more configurable. In the near-term, a possible way to address this is by using
SQL views.
For example, suppose there is a table in the database called animal_table
and it has a column
called animal_name
. If the desired schema has type
type Animal {
name: String
}
Then this could be exposed via a view like:
CREATE VIEW animal AS
SELECT
animal_name AS name
FROM animal_table
At this point, the animal
view can be used in the SQLAlchemy Table for the purposes of compiling.
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
)
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]
index_records = client.command(ORIENTDB_INDEX_RECORDS_QUERY)
index_query_data = [record.oRecordData for record in index_records]
schema_graph = get_orientdb_schema_graph(schema_data, index_query_data)
print(schema_graph.get_subclass_set('Animal'))
print(schema_graph.get_vertex_schema_element_or_raise('Animal').out_connections)
print(schema_graph.get_edge_schema_element_or_raise('Animal_Eats').out_connections)
print(schema_graph.get_edge_schema_element_or_raise('Animal_Eats').base_out_connection)
print(schema_graph.get_unique_indexes_for_class('Animal'))
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.
Copyright 2017-present Kensho Technologies, LLC. The present date is determined by the timestamp
of the most recent commit in the repository.