Metadata-Version: 2.1
Name: elasticsearch-dsl
Version: 7.0.0
Summary: Python client for Elasticsearch
Home-page: https://github.com/elasticsearch/elasticsearch-dsl-py
Author: Honza Král
Author-email: honza.kral@gmail.com
License: Apache License, Version 2.0
Description: 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 <https://github.com/elastic/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
        <https://github.com/elastic/elasticsearch-dsl-py/tree/master/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 <http://docs.python-guide.org/en/latest/dev/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
        <http://doc.pytest.org/en/latest/>`_, 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
        <https://github.com/elastic/elasticsearch-py/blob/master/elasticsearch
        /connection/base.py#L29>`_ 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 <https://github.com/elastic/elasticsearch-dsl-py/blob/master/CONTRIBUTING.rst>`_.
        
        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.
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
Provides-Extra: develop
