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.


pip install elasticsearch-dsl


Please see the examples directory to see some complex examples using elasticsearch-dsl.


The library is compatible with all Elasticsearch versions since 2.x but you have to use a matching major version:

For Elasticsearch 8.0 and later, use the major version 8 (8.x.y) of the library.

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 8.x

# Elasticsearch 7.x

# Elasticsearch 6.x

# Elasticsearch 5.x

# Elasticsearch 2.x

The development is happening on main, older branches only get bugfix releases

Search Example

Let’s have a typical search request written directly as a dict:

from elasticsearch import Elasticsearch
client = Elasticsearch("https://localhost:9200")

response = client.search(
      "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 elasticsearch import Elasticsearch
from elasticsearch_dsl import Search

client = Elasticsearch("https://localhost:9200")

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:

from datetime import datetime
from elasticsearch_dsl import Document, Date, Integer, Keyword, Text, connections

# Define a default Elasticsearch client

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

# 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 = 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.

Update By Query Example

Let’s resume the simple example of articles on a blog, and let’s assume that each article has a number of likes. For this example, imagine we want to increment the number of likes by 1 for all articles that match a certain tag and do not match a certain description. Writing this as a dict, we would have the following code:

from elasticsearch import Elasticsearch
client = Elasticsearch()

response = client.update_by_query(
      "query": {
        "bool": {
          "must": [{"match": {"tag": "python"}}],
          "must_not": [{"match": {"description": "beta"}}]
        "source": "ctx._source.likes++",
        "lang": "painless"

Using the DSL, we can now express this query as such:

from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search, UpdateByQuery

client = Elasticsearch()
ubq = UpdateByQuery(using=client, index="my-index") \
      .query("match", title="python")   \
      .exclude("match", description="beta") \
      .script(source="ctx._source.likes++", lang="painless")

response = ubq.execute()

As you can see, the Update By Query object provides many of the savings offered by the Search object, and additionally allows one to update the results of the search based on a script assigned in the same manner.

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()


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

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