dsl_tutorials.md

mapped_pages
https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/_tutorials.html

Tutorials [_tutorials]

Search [_search]

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

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

response = client.search(
    index="my-index",
    body={
      "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 DSL module:

from elasticsearch import Elasticsearch
from elasticsearch.dsl import Search, query, aggs

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

s = Search(using=client, index="my-index") \
    .query(query.Match("title", "python"))   \
    .filter(query.Term("category", "search")) \
    .exclude(query.Match("description", "beta"))

s.aggs.bucket('per_tag', aggs.Terms(field="tags")) \
    .metric('max_lines', aggs.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 DSL module took care of:

• creating appropriate Query objects from classes
• 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 [_persistence]

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
connections.create_connection(hosts="https://localhost:9200")

class Article(Document):
    title: str = mapped_field(Text(analyzer='snowball', fields={'raw': Keyword()}))
    body: str = mapped_field(Text(analyzer='snowball'))
    tags: str = mapped_field(Keyword())
    published_from: datetime
    lines: int

    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
Article.init()

# 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.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 Python type hints and additional mapping configuration when necessary
• 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.

Pre-built Faceted Search [_pre_built_faceted_search]

If you have your Documents defined you can very easily create a faceted search class to simplify searching and filtering.

from elasticsearch.dsl import FacetedSearch, TermsFacet, DateHistogramFacet

class BlogSearch(FacetedSearch):
    doc_types = [Article, ]
    # fields that should be searched
    fields = ['tags', 'title', 'body']

    facets = {
        # use bucket aggregations to define facets
        'tags': TermsFacet(field='tags'),
        'publishing_frequency': DateHistogramFacet(field='published_from', interval='month')
    }

# empty search
bs = BlogSearch()
response = bs.execute()

for hit in response:
    print(hit.meta.score, hit.title)

for (tag, count, selected) in response.facets.tags:
    print(tag, ' (SELECTED):' if selected else ':', count)

for (month, count, selected) in response.facets.publishing_frequency:
    print(month.strftime('%B %Y'), ' (SELECTED):' if selected else ':', count)

You can find more details in the faceted_search chapter.

Update By Query [_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(
    index="my-index",
    body={
      "query": {
        "bool": {
          "must": [{"match": {"tag": "python"}}],
          "must_not": [{"match": {"description": "beta"}}]
        }
      },
      "script"={
        "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
from elasticsearch.dsl.query import Match

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 the standard client [_migration_from_the_standard_client]

You don’t have to port your entire application to get the benefits of the DSL module, 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(query.Term("tags", "python"))

# Convert back to dict to plug back into existing code
body = s.to_dict()