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A simple Python interface for AppSync resolvers and Gremlin traversals.

Project description

AppSync - Gremlin

Overview

Through the AppSync-Gremlin, developers can write powerful queries in GraphQL without having to worry too much about the underlying database query language in AWS Neptune. The AppSync-Gremlin provides lambda function code that converts query operation types (from GraphQL) to a gremlin traversal.

Definitions

  • Property field: A field corresponding to a property of a vertex in the AWS Neptune graph database. In the below example, the name field is a property field.

    {
      User {
          name
          following {
              name
          }
      }
    }
    

    Query 1 : (Vertex Field Example)

  • Vertex field: A field corresponding to a vertex in the AWS Neptune graph database. In the above example, location is a vertex field.

  • Vertex list fields: A field corresponding to a list of vertices in the AWS Neptune graph database. In the above example, following is a vertex list field.

  • Result set: An assignment of vertices in the graph to fields 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. 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 (anything in an output scope).

  • Scope: The part of a query between any pair of parentheses or curly braces. We often refer to the parts between parentheses as the input scope and the parts between curly braces as the output scope or payload scope. For example, consider the query

    {
      User (
          input: {
              name: {
                  eq: "John"
              }
          }
      ) {
          name
          following {
              name
          }
      }
    }
    

    Query 2 : (Scope Example)

Filtering Operations and Pagination

Filtering

We define a filtering standard on the following scalar fields:

  • ID: For ID filtering we define the following input for filtering:
    input IDFilterInput {
      ne: ID
      eq: ID
    
      in: [ID!]
      not_in: [ID!]
    }
    
  • String: For String filtering we define the following input for filtering
    input StringFilterInput {
      ne: String
      eq: String
    
      in: [String!]
      not_in: [String!]
    
      contains: String
      not_contains: String
    
      begins_with: String
      not_begins_with: String
    
      ends_with: String
      not_ends_with: String
    }
    
  • Int: For Integer filtering we define the following input for filtering
    input IntFilterInput {
      ne: Int
      eq: Int
      le: Int
      lt: Int
      ge: Int
      gt: Int
    
      in: [Int!]
      not_in: [int!]
    }
    
  • Float: For Float filtering we define the following input for filtering
    input FloatFilterInput {
      ne: Float
      eq: Float
      le: Float
      lt: Float
      ge: Float
      gt: Float
    
      in: [Float!]
      not_in: [Float!]
    }
    
  • Boolean: For Boolean filtering we define the following input for filtering
    input BooleanFilterInput {
      eq: Boolean
      ne: Boolean
    }
    
  • DateTime: For DateTime we first have to define our own DateTime input definition. To avoid confusion and to prevent the use of different DateTime formats in this interface, we have defined the following DateTimeInput to expose the individual date components (such as day, month, year, etc) as well as a formatted field which is the ISO 8601 string representation of the DateTime value:
    input DateTimeInput {
      year: Int
      month: Int
      day: Int
      hour: Int
      minute: Int
      second: Int
      formatted: DateTime #custom datetime scalar
    }
    
    Using this input definition, we can then create the following input for filtering:
    input DateTimeFilterInput {
      eq: DateTimeInput
      ne: DateTimeInput
    
      in: [DateTimeInput!]
      not_in: [DateTimeInput!]
    
      le: DateTimeInput
      lt: DateTimeInput
      ge: DateTimeInput
      gt: DateTimeInput
    }
    

and a filtering standard on enum types:

enum ENUM_FIELD_TYPE {
    E_1
    E_2
    .
    .
    .
    E_n
}

input EnumFilterInput {
    eq: ENUM_FIELD_TYPE
    ne: ENUM_FIELD_TYPE
    in: [ENUM_FIELD_TYPE!]
    not_in: [ENUM_FIELD_TYPE!]
}

Note that these standards must be manually implemented in the original GraphQL schema. In future we may devise some method of augmenting a GraphQL schema so we don't have to manually implement them.

Each of these scalar filters has a corresponding filter in the AppSync-Gremlin library. For example, the StringFilterInput has the scalar filter string_filter, etc.

Implementing Filters

The AppSync-Gremlin library also allows custom vertex, relationship and scalar filters to be implemented.

  • Scalar filters: We define a scalar filter as a filter that maps GraphQL fields to Gremlin predicates. Consider an id_filter. In GraphQL, the IDFilterInput has the following fields: eq, ne, in, not_in. The Gremlin predicates that correspond to these fields are eq, neq, within, without. So we construct the following dictionary to map the GraphQL fields to the Gremlin predicates:

    from gremlin_python.process.traversal import eq, neq, within, without
    
    def id_scalar_filter():
        return {
            "eq": eq,
            "ne": neq,
            "in": within,
            "not_in": without
        }
    

    By using this dictionary, we can construct a filter function that applies the predicates to some traversal g, such that the traverser is at some vertices v with property property_name, producing a filtered traversal with the general form ,

      `g' = g.has(property_name, p_1).has(property_name, p_2). ... .has(property_name, p_n)`,
    

    where p_1, p_2, ..., p_n are predicates that are applied to the property property_name at v.

    The @scalar_filter decorator provided by the AppSync-Gremlin library produces a function that takes a property_name and returns the function described above. So by applying this to the above id_filter gives us

    from gremlin_python.process.traversal import eq, neq, within, without
    from appsync_gremlin import scalar_filter
    
    @scalar_filter
    def id__filter():
        return {
            "eq": eq,
            "ne": neq,
            "in": within,
            "not_in": without
        }
    

    However, at this stage the filter cannot actually be used, since it requires a property_name. The AppSync-Gremlin library provides two different ways of applying a property_name to a filter. Either we can simply call id_scalar_filter with out desired property_name, or we can apply the @name decorator.

    For scalar filters, we advice that the former is used. Since this provides a better generalisation for your scalar filter. Since a vertex could implement it's own custom id with property name "id" or simply make use of the built in vertex id T.id.

  • Vertex filter: We define a vertex filter as a filter that maps GraphQL fields to scalar or relationship (see below) filters. Let us consider the following GraphQL User type:

    type User {
        id: ID!
        email: String!
    
        name: String
        about: String
    
        following: [User]!
        followed_by: [User]!
    }
    

    We have 4 scalar fields (id, email, name, about) and 2 vertex list fields (following, followed_by). In the Graph database, we define a User vertex with properties email, name, about (string) and a single relationship User -> FOLLOWS -> User. So in a similar fashion to a scalar filter, let us define some function that returns a dictionary mapping the GraphQL fields to filters:

    from gremlin_python.process.traversal import T  
    from appsync_gremlin import id_filter, string_filter
    
    def user_filter():
        return {
            "id": id_filter(T.id),
            "email": string_filter("email"),
            "name": string_filter("name"),
            "about": string_filter("about"),
            "following": ?,
            "followed_by": ?
        }
    

    By using this dictionary we can construct a filter function that applies the scalar and relationship filters to some supplied traversal g, such that the traverser is at some vertices v which matches the desired filter (in this case the travesers must all be at User vertices), producing a filtered traversal g' with the general form:

      g' = r_1(r_2( ... (r_n(s_1( ... s_m(g) ... ))) ... ))
    

    where r_1, r_2, ..., r_n are relationship filters and s_1, s_2, ..., s_m are scalar filters. Note that the order of application of scalar and relationship filters does not matter, however, we advice that scalar filters should be applied first as it is more efficient.

    The @vertex_filter decorator provided by the AppSync-Gremlin library produces a function that takes a vertex_label and returns the function described above. So by applying this to the above user_filter gives us

    from gremlin_python.process.traversal import T
    from appsync_gremlin import id_filter, string_filter, vertex_filter, name
    
    @name("User")
    @vertex_filter
    def user_filter():
        return {
            "id": id_filter(T.id),
            "email": string_filter("email"),
            "name": string_filter("name"),
            "about": string_filter("about"),
            "following": ?,
            "followed_by": ?
        }
    

    Similarly to the scalar_filter, we can apply the @name decorator to pass the vertex_label to the filter function. This label should match the label of the User vertex in the Graph database.

    With the vertex filter now completely implemented, we can now define our relationship filters (see below). In this case we have the relationship filters:

    from gremlin_python.process.traversal import T  
    from appsync_gremlin import id_filter, string_filter, vertex_filter, name, relationship_filter, RelationshipDirection
    
    @name("User")
    @vertex_filter
    def user_filter():
        return {
            "id": id_filter(T.id),
            "email": string_filter("email"),
            "name": string_filter("name"),
            "about": string_filter("about"),
            "following": relationship_filter(("FOLLOWS", RelationshipDirection.OUT), user_filter),
            "followed_by": relationship_filter(("FOLLOWS", RelationshipDirection.IN), user_filter)
        }
    
  • Relationship filter: We define a relationship R from some vertex u to v as R = (name, direction). We can filter u based on the whether a relationship R exists and whether v satisfies certain conditions.

    Consider a Gremlin traversal g, such that the traverser is at some vertices u. We can produce a filtered traversal g' based on whether the vertices selected by g have the relationship R and the vertices v (the other vertex in R) satisfy a vertex filter v_f. This filtered traversal has the general form:

      g' = g.where(v_f(direction(name))).
    

    The AppSync-Gremlin library produces a function relationship_filter that takes a relationship, a tuple consisting of a edge label (name) and a edge direction (direction), and a vertex filter v_f for the other vertex in the relationship.

    (See above for example).

Pagination

We also implement a pagination standard. Note that pagination can only be applied to vertex list fields.

For simplicity, we've decided to implement an offset based pagination, as it allows us to make us of the Gremlin traversal step .range(first, offset). The stanardised pagination input is defined as follows:

input PaginationInput {
  page: Int!
  per_page: Int!
}

We then use page and per_page to compute first and offset using the function get_range, shown below.

from typing import Tuple

def get_range(page: int, per_page: int) -> Tuple[int, int]:
    """
    Returns the Gremlin range from page options in the format:
        (first, last)

    :param page: (Integer)
    :param per_page: (Integer)
    :return: (Integer, Integer)
    """

    return (page - 1) * per_page, page * per_page

Once the traversal has been submitted and the result set has been return, we format the response into a pagination response object. The GraphQL type for this response object for some GraphQL type Type is

type Type {
    .
    .
    .
}

type TypePage {
    data: [Type]!
    page: Int!
    per_page: Int!
    total: Int!
}

where total is the total number of pages available.

Error Handling and Request / Response Mapping Template

The AppSync-Gremlin library provides automatic error handling for AppSync. The library does this via the user of the AppSyncException. The AppSyncException requires 3 arguments when instantiated: error_type, error_message and error_data for type string, string and dictionary respectively.

For example, consider the mutation resolver that creates a User vertex. Naturally we want to ensure that a user doesn't have a duplicate vertex, therefore we must add some form of validation within the resolver code which raises an AppSyncException with the relevant error information if the validation fails.

from gremlin_python.process.graph_traversal import GraphTraversal
from appsync_gremlin import ResolverInput, AppSyncException, mutation_resolver

@mutation_resolver
def create_user(traversal: GraphTraversal, resolver_input: ResolverInput) -> GraphTraversal:

    username = resolver_input.arguments.get("username")
    user = traversal.V().hasLabel("User").has("username", username)

    if user.hasNext():
        raise AppSyncException(
            error_type="BAD_REQUEST",
            error_message="A user with username {} is already stored in the AWS Neptune database.".format(username),
            error_data={
                "username": username
            }
        )

    .
    .
    .

In order to ensure our AppSyncException work's with AppSync, we've had to define a request / response template mapping standard. For all resolvers, we must have the request template mapping:

{
  "version" : "2018-05-29",
  "operation": "(Invoke|BatchInvoke)",
  "payload": {
    "type_name": String!,
    "field_name": String!,
    "arguments": $util.toJson($context.args),
    "identity": $util.toJson($context.identity),
    "source": $util.toJson($context.source)
  }
}

and the response mapping template:

#if ($context.result && $context.result.error)
    $utils.error($context.result.error.error_message, $context.result.error.error_type, $context.result.error.data)
#else
    $utils.toJson($context.result.data)
#end

Usage

The AppSync-Gremlin library currently provides 4 different resolver types in the form of function decorators:

  • vertex_list_field_resolver
  • vertex_field_resolver
  • calculated_field_resolver
  • mutation_resolver

The first 3 resolvers are Query based resolvers, that is to say they are designed to be used for resolving GraphQL queries.

Each resolver has the same function type signature:

resolver : (GraphTraversal, ResolverInput) -> GraphTraversal

Note the custom ResolverInput object. A ResolverInput object simply stores the data passed from the Apache VTL request mapping template described in the section above. Hence the ResolverInput object has the following properties:

  • type_name: (String)
  • field_name: (String)
  • arguments: (Dictionary)
  • identity : (Dictionary | None)
  • source: (Dictionary | None)

Hence these properties can be referenced in the resolvers to build the Gremlin traversals.

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