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Project Description

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.


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 or requirements.txt is:

# Elasticsearch 2.x

# Elasticsearch 1.x

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:

from elasticsearch1 import Elasticsearch
client = Elasticsearch()

response =
      "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:

from elasticsearch1 import Elasticsearch
from elasticsearch1_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:

from datetime import datetime
from elasticsearch1_dsl import DocType, String, Date, Integer
from elasticsearch1_dsl.connections import connections

# Define a default Elasticsearch client

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 > self.published_from

# create the mappings in elasticsearch

# create and save and article
article = Article(meta={'id': 42}, title='Hello world!', tags=['test'])
article.body = ''' looong text '''
article.published_from =

article = Article.get(id=42)

# Display 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:

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 is available at


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

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.

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Release History


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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
elasticsearch1_dsl-0.0.12-py2.py3-none-any.whl (39.8 kB) Copy SHA256 Checksum SHA256 2.7 Wheel Aug 1, 2016
elasticsearch1-dsl-0.0.12.tar.gz (29.4 kB) Copy SHA256 Checksum SHA256 Source Aug 1, 2016

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