Elasticsearch (DSL)/ OpenSearch (DSL) integration for Graphene.
- Graphene 2.x. Support for Graphene 1.x is not intended.
- Python 3.6, 3.7, 3.8, 3.9 and 3.10. Support for Python 2 is not intended.
- Elasticsearch 6.x, 7.x. Support for Elasticsearch 5.x is not intended.
- OpenSearch 1.x, 2.x.
- Implemented
ElasticsearchConnectionFieldandElasticsearchObjectTypeare the core classes to work withgraphene. - Pluggable backends for searching, filtering, ordering, etc. Don't like existing ones? Override, extend or write your own.
- Search backend.
- Filter backend.
- Ordering backend.
- Pagination.
- Highlighting backend.
- Source filter backend.
- Faceted search backend (including global aggregations).
- Post filter backend.
- Score filter backend.
- Query string backend.
- Simple query string backend.
See the Road-map for what's yet planned to implemented.
Do you need a similar tool for Django REST Framework? Check django-elasticsearch-dsl-drf.
Check the live demo app (FastAPI + Graphene 2 + Elasticsearch 7) hosted on Heroku and bonsai.io.
Documentation is available on Read the Docs.
Install latest stable version from PyPI:
pip install graphene-elasticOr latest development version from GitHub:
pip install https://github.com/barseghyanartur/graphene-elastic/archive/master.zipNote
Staring from version 0.8, the elasticsearch and elasticsearch-dsl packages are no longer installed by default. You must either install them explicitly in your requirements or install as optional dependencies as follows: pip install graphene-elastic[elasticsearch]. Alternatively, you can use opensearch-py and opensearch-dsl. You would then need to install the opensearch-py and opensearch-dsl packages explicitly in your requirements or install them as optional dependencies as follows: pip install graphene-elastic[opensearch].
Note
In the examples, we use elasticsearch_dsl package for schema definition. You can however use opensearch_dsl or if you want to achieve portability between Elasticsearch and OpenSearch, use anysearch package. Read more here.
pip install -r requirements.txtThe following command will create indexes for User and Post documents and populate them with sample data:
./scripts/populate_elasticsearch_data.shsearch_index/documents/post.py
See examples/search_index/documents/post.py for full example.
import datetime
from elasticsearch_dsl import (
Boolean,
Date,
Document,
InnerDoc,
Keyword,
Nested,
Text,
Integer,
)
class Comment(InnerDoc):
author = Text(fields={'raw': Keyword()})
content = Text(analyzer='snowball')
created_at = Date()
def age(self):
return datetime.datetime.now() - self.created_at
class Post(Document):
title = Text(
fields={'raw': Keyword()}
)
content = Text()
created_at = Date()
published = Boolean()
category = Text(
fields={'raw': Keyword()}
)
comments = Nested(Comment)
tags = Text(
analyzer=html_strip,
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},
}Run the sample Flask app:
./scripts/run_flask.shOpen Flask graphiql client
http://127.0.0.1:8001/graphqlRun the sample Django app:
./scripts/run_django.sh runserverOpen Django graphiql client
http://127.0.0.1:8000/graphqlConnectionField is the most flexible and feature rich solution you have. It uses filter backends which you can tie to your needs the way you want in a declarative manner.
Sample schema definition
import graphene
from graphene_elastic import (
ElasticsearchObjectType,
ElasticsearchConnectionField,
)
from graphene_elastic.filter_backends import (
FilteringFilterBackend,
SearchFilterBackend,
HighlightFilterBackend,
OrderingFilterBackend,
DefaultOrderingFilterBackend,
)
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,
HighlightFilterBackend,
OrderingFilterBackend,
DefaultOrderingFilterBackend,
]
# For `FilteringFilterBackend` backend
filter_fields = {
# The dictionary key (in this case `title`) is the name of
# the corresponding GraphQL query argument. The dictionary
# value could be simple or complex structure (in this case
# complex). The `field` key points to the `title.raw`, which
# is the field name in the Elasticsearch document
# (`PostDocument`). Since `lookups` key is provided, number
# of lookups is limited to the given set, while term is the
# default lookup (as specified in `default_lookup`).
'title': {
'field': 'title.raw',
# Available lookups
'lookups': [
LOOKUP_FILTER_TERM,
LOOKUP_FILTER_TERMS,
LOOKUP_FILTER_PREFIX,
LOOKUP_FILTER_WILDCARD,
LOOKUP_QUERY_IN,
LOOKUP_QUERY_EXCLUDE,
],
# Default lookup
'default_lookup': LOOKUP_FILTER_TERM,
},
# The dictionary key (in this case `category`) is the name of
# the corresponding GraphQL query argument. Since no lookups
# or default_lookup is provided, defaults are used (all lookups
# available, term is the default lookup). The dictionary value
# (in this case `category.raw`) is the field name in the
# Elasticsearch document (`PostDocument`).
'category': 'category.raw',
# The dictionary key (in this case `tags`) is the name of
# the corresponding GraphQL query argument. Since no lookups
# or default_lookup is provided, defaults are used (all lookups
# available, term is the default lookup). The dictionary value
# (in this case `tags.raw`) is the field name in the
# Elasticsearch document (`PostDocument`).
'tags': 'tags.raw',
# The dictionary key (in this case `num_views`) is the name of
# the corresponding GraphQL query argument. Since no lookups
# or default_lookup is provided, defaults are used (all lookups
# available, term is the default lookup). The dictionary value
# (in this case `num_views`) is the field name in the
# Elasticsearch document (`PostDocument`).
'num_views': 'num_views',
}
# For `SearchFilterBackend` backend
search_fields = {
'title': {'boost': 4},
'content': {'boost': 2},
'category': None,
}
# For `OrderingFilterBackend` backend
ordering_fields = {
# The dictionary key (in this case `tags`) is the name of
# the corresponding GraphQL query argument. The dictionary
# value (in this case `tags.raw`) is the field name in the
# Elasticsearch document (`PostDocument`).
'title': 'title.raw',
# The dictionary key (in this case `created_at`) is the name of
# the corresponding GraphQL query argument. The dictionary
# value (in this case `created_at`) is the field name in the
# Elasticsearch document (`PostDocument`).
'created_at': 'created_at',
# The dictionary key (in this case `num_views`) is the name of
# the corresponding GraphQL query argument. The dictionary
# value (in this case `num_views`) is the field name in the
# Elasticsearch document (`PostDocument`).
'num_views': 'num_views',
}
# For `DefaultOrderingFilterBackend` backend
ordering_defaults = (
'-num_views', # Field name in the Elasticsearch document
'title.raw', # Field name in the Elasticsearch document
)
# For `HighlightFilterBackend` backend
highlight_fields = {
'title': {
'enabled': True,
'options': {
'pre_tags': ["<b>"],
'post_tags': ["</b>"],
}
},
'content': {
'options': {
'fragment_size': 50,
'number_of_fragments': 3
}
},
'category': {},
}
# Query definition
class Query(graphene.ObjectType):
all_post_documents = ElasticsearchConnectionField(Post)
# Schema definition
schema = graphene.Schema(query=Query)Since we didn't specify any lookups on category, by default all lookups are available and the default lookup would be term. Note, that in the {value:"Elastic"} part, the value stands for default lookup, whatever it has been set to.
query PostsQuery {
allPostDocuments(filter:{category:{value:"Elastic"}}) {
edges {
node {
id
title
category
content
createdAt
comments
}
}
}
}But, we could use another lookup (in example below - terms). Note, that in the {terms:["Elastic", "Python"]} part, the terms is the lookup name.
query PostsQuery {
allPostDocuments(
filter:{category:{terms:["Elastic", "Python"]}}
) {
edges {
node {
id
title
category
content
createdAt
comments
}
}
}
}Or apply a gt (range) query in addition to filtering:
{
allPostDocuments(filter:{
category:{term:"Python"},
numViews:{gt:"700"}
}) {
edges {
node {
category
title
comments
numViews
}
}
}
}The following lookups are available:
containsends_with(orendsWithfor camelCase)excludeexistsgtgteinis_null(orisNullfor camelCase)ltlteprefixrangestarts_with(orstartsWithfor camelCase)termtermswildcard
See dedicated documentation on filter lookups for more information.
Search in all fields:
query {
allPostDocuments(
search:{query:"Release Box"}
) {
edges {
node {
category
title
content
}
}
}
}Search in specific fields:
query {
allPostDocuments(
search:{
title:{value:"Release", boost:2},
content:{value:"Box"}
}
) {
edges {
node {
category
title
content
}
}
}
}Possible choices are ASC and DESC.
query {
allPostDocuments(
filter:{category:{term:"Photography"}},
ordering:{title:ASC}
) {
edges {
node {
category
title
content
numViews
tags
}
}
}
}The first, last, before and after arguments are supported. By default number of results is limited to 100.
query {
allPostDocuments(first:12) {
pageInfo {
startCursor
endCursor
hasNextPage
hasPreviousPage
}
edges {
cursor
node {
category
title
content
numViews
}
}
}
}Simply, list the fields you want to highlight. This works only in combination with search.
query {
allPostDocuments(
search:{content:{value:"alice"}, title:{value:"alice"}},
highlight:[category, content]
) {
edges {
node {
title
content
highlight
}
cursor
}
}
}Road-map and development plans.
This package is designed after django-elasticsearch-dsl-drf and is intended to offer similar functionality.
Lots of features are planned to be released in the upcoming Beta releases:
- Suggester backend.
- Nested backend.
- Geo-spatial backend.
- Filter lookup
geo_bounding_box(orgeoBoundingBoxfor camelCase). - Filter lookup
geo_distance(orgeoDistancefor camelCase). - Filter lookup
geo_polygon(orgeoPolygonfor camelCase). - More-like-this backend.
Stay tuned or reach out if you want to help.
Project is covered with tests.
make docker-testBy defaults tests are executed against the Elasticsearch 7.x.
Run Elasticsearch 7.x with Docker
docker-compose up elasticsearchInstall test requirements
pip install -r requirements/test.txtTo test with all supported Python versions type:
toxTo test against specific environment, type:
tox -e py38-elastic7To test just your working environment type:
./runtests.pyTo run a single test module in your working environment type:
./runtests.py src/graphene_elastic/tests/test_filter_backend.pyTo run a single test class in a given test module in your working environment type:
./runtests.py src/graphene_elastic/tests/test_filter_backend.py::FilterBackendElasticTestCaseFor development purposes, you could use the flask app (easy to debug). Standard pdb works (import pdb; pdb.set_trace()). If ipdb does not work well for you, use ptpdb.
Keep the following hierarchy.
=====
title
=====
header
======
sub-header
----------
sub-sub-header
~~~~~~~~~~~~~~
sub-sub-sub-header
^^^^^^^^^^^^^^^^^^
sub-sub-sub-sub-header
++++++++++++++++++++++
sub-sub-sub-sub-sub-header
**************************GPL-2.0-only OR LGPL-2.1-or-later
For any security issues contact me at the e-mail given in the Author section. For overall issues, go to GitHub.
Artur Barseghyan <[email protected]>
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