Metadata-Version: 2.0 Name: elasticsearch-dsl Version: 2.0.0 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 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 :: 2.6 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.2 Classifier: Programming Language :: Python :: 3.3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: Implementation :: CPython Classifier: Programming Language :: Python :: Implementation :: PyPy Requires-Dist: six Requires-Dist: python-dateutil Requires-Dist: elasticsearch (>=2.0.0,<3.0.0) 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. Compatibility ------------- The library is compatible with all Elasticsearch versions since ``1.x`` but you **have to use a matching major version**: For **Elasticsearch 2.0** and later, use the major version 2 (``2.x.y``) of the library. For **Elasticsearch 1.0** and later, use the major version 0 (``0.x.y``) of the library. The recommended way to set your requirements in your `setup.py` or `requirements.txt` is:: # Elasticsearch 2.x elasticsearch-dsl>=2.0.0,<3.0.0 # Elasticsearch 1.x elasticsearch-dsl<2.0.0 The development is happening on ``master`` and ``1.x`` branches, respectively. 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": { "filtered": { "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, Q client = Elasticsearch() s = Search(using=client, index="my-index") \ .filter("term", category="search") \ .query("match", title="python") \ .query(~Q("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 * creating a ``filtered`` query since ``.filter()`` was used * 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 DocType, String, Date, Integer from elasticsearch_dsl.connections import connections # Define a default Elasticsearch client connections.create_connection(hosts=['localhost']) class Article(DocType): title = String(analyzer='snowball', fields={'raw': String(index='not_analyzed')}) body = String(analyzer='snowball') tags = String(index='not_analyzed') published_from = Date() lines = Integer() class Meta: index = 'blog' 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() Documentation ------------- Documentation is available at https://elasticsearch-dsl.readthedocs.org. 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.