# elasticsearch-dsl-py **Repository Path**: rogerspy/elasticsearch-dsl-py ## Basic Information - **Project Name**: elasticsearch-dsl-py - **Description**: No description available - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-07-12 - **Last Updated**: 2021-07-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README Elasticsearch DSL ================= Elasticsearch DSL is a high-level library whose aim is to help with writing and running queries against Elasticsearch. It is built on top of the official low-level client (`elasticsearch-py `_). It provides a more convenient and idiomatic way to write and manipulate queries. It stays close to the Elasticsearch JSON DSL, mirroring its terminology and structure. It exposes the whole range of the DSL from Python either directly using defined classes or a queryset-like expressions. It also provides an optional wrapper for working with documents as Python objects: defining mappings, retrieving and saving documents, wrapping the document data in user-defined classes. To use the other Elasticsearch APIs (eg. cluster health) just use the underlying client. Installation ------------ :: pip install elasticsearch-dsl Examples -------- Please see the `examples `_ directory to see some complex examples using ``elasticsearch-dsl``. Compatibility ------------- The library is compatible with all Elasticsearch versions since ``2.x`` but you **have to use a matching major version**: For **Elasticsearch 7.0** and later, use the major version 7 (``7.x.y``) of the library. For **Elasticsearch 6.0** and later, use the major version 6 (``6.x.y``) of the library. For **Elasticsearch 5.0** and later, use the major version 5 (``5.x.y``) of the library. For **Elasticsearch 2.0** and later, use the major version 2 (``2.x.y``) of the library. The recommended way to set your requirements in your `setup.py` or `requirements.txt` is:: # Elasticsearch 7.x elasticsearch-dsl>=7.0.0,<8.0.0 # Elasticsearch 6.x elasticsearch-dsl>=6.0.0,<7.0.0 # Elasticsearch 5.x elasticsearch-dsl>=5.0.0,<6.0.0 # Elasticsearch 2.x elasticsearch-dsl>=2.0.0,<3.0.0 The development is happening on ``master``, older branches only get bugfix releases Search Example -------------- Let's have a typical search request written directly as a ``dict``: .. code:: python from elasticsearch import Elasticsearch client = Elasticsearch() response = client.search( index="my-index", body={ "query": { "bool": { "must": [{"match": {"title": "python"}}], "must_not": [{"match": {"description": "beta"}}], "filter": [{"term": {"category": "search"}}] } }, "aggs" : { "per_tag": { "terms": {"field": "tags"}, "aggs": { "max_lines": {"max": {"field": "lines"}} } } } } ) for hit in response['hits']['hits']: print(hit['_score'], hit['_source']['title']) for tag in response['aggregations']['per_tag']['buckets']: print(tag['key'], tag['max_lines']['value']) The problem with this approach is that it is very verbose, prone to syntax mistakes like incorrect nesting, hard to modify (eg. adding another filter) and definitely not fun to write. Let's rewrite the example using the Python DSL: .. code:: python from elasticsearch import Elasticsearch from elasticsearch_dsl import Search client = Elasticsearch() s = Search(using=client, index="my-index") \ .filter("term", category="search") \ .query("match", title="python") \ .exclude("match", description="beta") s.aggs.bucket('per_tag', 'terms', field='tags') \ .metric('max_lines', 'max', field='lines') response = s.execute() for hit in response: print(hit.meta.score, hit.title) for tag in response.aggregations.per_tag.buckets: print(tag.key, tag.max_lines.value) As you see, the library took care of: * creating appropriate ``Query`` objects by name (eq. "match") * composing queries into a compound ``bool`` query * putting the ``term`` query in a filter context of the ``bool`` query * providing a convenient access to response data * no curly or square brackets everywhere Persistence Example ------------------- Let's have a simple Python class representing an article in a blogging system: .. code:: python from datetime import datetime from elasticsearch_dsl import Document, Date, Integer, Keyword, Text, connections # Define a default Elasticsearch client connections.create_connection(hosts=['localhost']) class Article(Document): title = Text(analyzer='snowball', fields={'raw': Keyword()}) body = Text(analyzer='snowball') tags = Keyword() published_from = Date() lines = Integer() class Index: name = 'blog' settings = { "number_of_shards": 2, } def save(self, ** kwargs): self.lines = len(self.body.split()) return super(Article, self).save(** kwargs) def is_published(self): return datetime.now() > self.published_from # create the mappings in elasticsearch Article.init() # create and save and article article = Article(meta={'id': 42}, title='Hello world!', tags=['test']) article.body = ''' looong text ''' article.published_from = datetime.now() article.save() article = Article.get(id=42) print(article.is_published()) # Display cluster health print(connections.get_connection().cluster.health()) In this example you can see: * providing a default connection * defining fields with mapping configuration * setting index name * defining custom methods * overriding the built-in ``.save()`` method to hook into the persistence life cycle * retrieving and saving the object into Elasticsearch * accessing the underlying client for other APIs You can see more in the persistence chapter of the documentation. Migration from ``elasticsearch-py`` ----------------------------------- You don't have to port your entire application to get the benefits of the Python DSL, you can start gradually by creating a ``Search`` object from your existing ``dict``, modifying it using the API and serializing it back to a ``dict``: .. code:: python body = {...} # insert complicated query here # Convert to Search object s = Search.from_dict(body) # Add some filters, aggregations, queries, ... s.filter("term", tags="python") # Convert back to dict to plug back into existing code body = s.to_dict() Development ----------- Activate Virtual Environment (`virtualenvs `_): .. code:: bash $ virtualenv venv $ source venv/bin/activate To install all of the dependencies necessary for development, run: .. code:: bash $ pip install -e '.[develop]' To run all of the tests for ``elasticsearch-dsl-py``, run: .. code:: bash $ python setup.py test Alternatively, it is possible to use the ``run_tests.py`` script in ``test_elasticsearch_dsl``, which wraps `pytest `_, to run subsets of the test suite. Some examples can be seen below: .. code:: bash # Run all of the tests in `test_elasticsearch_dsl/test_analysis.py` $ ./run_tests.py test_analysis.py # Run only the `test_analyzer_serializes_as_name` test. $ ./run_tests.py test_analysis.py::test_analyzer_serializes_as_name ``pytest`` will skip tests from ``test_elasticsearch_dsl/test_integration`` unless there is an instance of Elasticsearch on which a connection can occur. By default, the test connection is attempted at ``localhost:9200``, based on the defaults specified in the ``elasticsearch-py`` `Connection `_ class. **Because running the integration tests will cause destructive changes to the Elasticsearch cluster, only run them when the associated cluster is empty.** As such, if the Elasticsearch instance at ``localhost:9200`` does not meet these requirements, it is possible to specify a different test Elasticsearch server through the ``TEST_ES_SERVER`` environment variable. .. code:: bash $ TEST_ES_SERVER=my-test-server:9201 ./run_tests Documentation ------------- Documentation is available at https://elasticsearch-dsl.readthedocs.io. Contribution Guide ------------------ Want to hack on Elasticsearch DSL? Awesome! We have `Contribution-Guide `_. License ------- Copyright 2013 Elasticsearch Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 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.