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.

Search Example

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

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:

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:

from datetime import datetime
from elasticsearch import Elasticsearch
from elasticsearch_dsl import DocType, String, Date, Integer, connections

# Define a default Elasticsearch client
connections.add_connection(Elasticsearch())

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().save(** kwargs)

    def is_published(self):
        return datetime.now() < self.published_from

 article = Article(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 Elasticsearch client

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

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()

License

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.

Metadata

Release files for elasticsearch-dsl 0.0.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for elasticsearch-dsl 0.0.3
File	Size	Uploaded
elasticsearch-dsl-0.0.3.tar.gz	19.0 kB	Jan 23, 2015	Details

Built distribution (wheel)

Table of built distributions (wheels) for elasticsearch-dsl 0.0.3
File	Interpreter	ABI	Platform	Reset
elasticsearch_dsl-0.0.3-py2.py3-none-any.whl 27.3 kB Jan 23, 2015	Python 2, Python 3	none	any	Details

Total release size: 46.3 kB

Release files / elasticsearch-dsl-0.0.3.tar.gz

Download URL	elasticsearch-dsl-0.0.3.tar.gz
Size	19.0 kB
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Release files / elasticsearch_dsl-0.0.3-py2.py3-none-any.whl

Download URL	elasticsearch_dsl-0.0.3-py2.py3-none-any.whl
Size	27.3 kB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`2d46fb1e79bd23d5ea463303c6f5b45356e3dcbc87cde6aee578a83b29d82bc0`
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