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.
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
Queryobjects by name (eq. "match")- composing queries into a compound
boolquery- creating a
filteredquery since.filter()was used- providing a convenient access to response data
- no curly or square brackets everywhere
Let's have a simple Python class representing an article in a blogging system:
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(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.
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:
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.