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 > 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.

Update By Query

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()