Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ks-avinash/elasticsearch-dsl-py

High level Python client for Elasticsearch
https://github.com/ks-avinash/elasticsearch-dsl-py

Last synced: 3 months ago
JSON representation

High level Python client for Elasticsearch

Awesome Lists containing this project

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.



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": {

            "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



  * 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 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.