Graphene Django 查询 Elasticsearch

Question

我的数据存储在 elasticsearch 中，而且非常大。所以我想使用 graphql 来正确查询它。我正在使用 django 石墨烯包来查询它。对于数据库模型，它工作正常。我的 elasticsearch json 架构

https://pastebin.com/EQBnnCBU 下面是我的类型定义和查询代码

https://pastebin.com/fsr9V1Rf

问题是我无法理解如何为弹性 json 架构编写查询架构。

只需要初步帮助或任何可以帮助我的解释

我已经在这里检查了答案django-graphene without model 但没有帮助

我当前的 ElasticType 架构

class ElasticType(graphene.ObjectType):
    id = graphene.ID()
    index = graphene.String()
    found = graphene.String()
    properties = graphene.String()

Answer 1

你可以试试graphene-elastic。

如示例所述，您需要声明要过滤哪些字段，它会自动使它们可用于使用指定的查找过滤器进行过滤。

一些代码部分（与您的项目无关，只是为了说明它是如何工作的，从项目文档中复制而来）。

示例文档定义

import datetime
from elasticsearch_dsl import (
    Boolean,
    Date,
    Document,
    Keyword,
    Nested,
    Text,
    Integer,
)


class Post(Document):

    title = Text(
        fields={'raw': Keyword()}
    )
    content = Text()
    created_at = Date()
    published = Boolean()
    category = Text(
        fields={'raw': Keyword()}
    )
    tags = Text(
        analyzer='snowball',
        fields={'raw': Keyword(multi=True)},
        multi=True
    )
    num_views = Integer()

    class Index:
        name = 'blog_post'
        settings = {
            'number_of_shards': 1,
            'number_of_replicas': 1,
            'blocks': {'read_only_allow_delete': None},
        }

    def add_tag(self, name):
        self.tags.append(name)

    def save(self, ** kwargs):
        self.created_at = datetime.datetime.now()
        return super().save(** kwargs)

示例架构定义

import graphene
from graphene_elastic import (
    ElasticsearchObjectType,
    ElasticsearchConnectionField,
)
from graphene_elastic.filter_backends import (
    FilteringFilterBackend,
    SearchFilterBackend,
)
from graphene_elastic.constants import (
    LOOKUP_FILTER_PREFIX,
    LOOKUP_FILTER_TERM,
    LOOKUP_FILTER_TERMS,
    LOOKUP_FILTER_WILDCARD,
    LOOKUP_QUERY_EXCLUDE,
    LOOKUP_QUERY_IN,
)

# Object type definition
class Post(ElasticsearchObjectType):

    class Meta(object):
        document = PostDocument
        interfaces = (Node,)
        filter_backends = [
            FilteringFilterBackend,
            SearchFilterBackend,
        ]
        filter_fields = {
            'title': {
                'field': 'title.raw',
                'lookups': [
                    LOOKUP_FILTER_TERM,
                    LOOKUP_FILTER_TERMS,
                    LOOKUP_FILTER_PREFIX,
                    LOOKUP_FILTER_WILDCARD,
                    LOOKUP_QUERY_IN,
                    LOOKUP_QUERY_EXCLUDE,
                ],
                'default_lookup': LOOKUP_FILTER_TERM,
            },
            'category': 'category.raw',
            'tags': 'tags.raw',
            'num_views': 'num_views',
        }
        search_fields = {
            'title': {'boost': 4},
            'content': {'boost': 2},
            'category': None,
        }


# Query definition
class Query(graphene.ObjectType):
    all_post_documents = ElasticsearchConnectionField(Post)

# Schema definition
schema = graphene.Schema(query=Query)

示例查询

query PostsQuery {
  allPostDocuments(filter:{
        category:{terms:["Elastic", "Python"]}
    }) {
    edges {
      node {
        id
        title
        category
        content
        createdAt
        comments
      }
    }
  }
}

或者：

{
  allPostDocuments(filter:{
        category:{term:"Python"},
        numViews:{gt:"700"}
    }) {
    edges {
      node {
        category
        title
        comments
        numViews
      }
    }
  }
}

研究文档以获取更多信息。