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Faceted Search

The library comes with a simple abstraction aimed at helping you develop faceted navigation for your data.

Note

This API is experimental and will be subject to change. Any feedback is welcome.

Configuration

You can provide several configuration options (as class attributes) when declaring a FacetedSearch subclass:

index

the name of the index (as string) to search through, defaults to '_all'.

doc_types

list of Document subclasses or strings to be used, defaults to ['_all'].

fields

list of fields on the document type to search through. The list will be passes to MultiMatch query so can contain boost values ('title^5'), defaults to ['*'].

facets

dictionary of facets to display/filter on. The key is the name displayed and values should be instances of any Facet subclass, for example: {'tags': TermsFacet(field='tags')}

sort

tuple or list of fields on which the results should be sorted. The format of the individual fields are to be the same as those passed to ~elasticsearch_dsl.Search.sort.

Facets

There are several different facets available:

TermsFacet

provides an option to split documents into groups based on a value of a field, for example TermsFacet(field='category')

DateHistogramFacet

split documents into time intervals, example: DateHistogramFacet(field="published_date", interval="day")

HistogramFacet

similar to DateHistogramFacet but for numerical values: HistogramFacet(field="rating", interval=2)

RangeFacet

allows you to define your own ranges for a numerical fields: RangeFacet(field="comment_count", ranges=[("few", (None, 2)), ("lots", (2, None))])

NestedFacet

is just a simple facet that wraps another to provide access to nested documents: NestedFacet('variants', TermsFacet(field='variants.color'))

By default facet results will only calculate document count, if you wish for a different metric you can pass in any single value metric aggregation as the metric kwarg (TermsFacet(field='tags', metric=A('max', field=timestamp))). When specifying metric the results will be, by default, sorted in descending order by that metric. To change it to ascending specify metric_sort="asc" and to just sort by document count use metric_sort=False.

Advanced

If you require any custom behavior or modifications simply override one or more of the methods responsible for the class' functions:

search(self)

is responsible for constructing the Search object used. Override this if you want to customize the search object (for example by adding a global filter for published articles only).

query(self, search)

adds the query postion of the search (if search input specified), by default using MultiField query. Override this if you want to modify the query type used.

highlight(self, search)

defines the highlighting on the Search object and returns a new one. Default behavior is to highlight on all fields specified for search.

Usage

The custom subclass can be instantiated empty to provide an empty search (matching everything) or with query and filters.

query

is used to pass in the text of the query to be performed. If None is passed in (default) a MatchAll query will be used. For example 'python web'

filters

is a dictionary containing all the facet filters that you wish to apply. Use the name of the facet (from .facets attribute) as the key and one of the possible values as value. For example {'tags': 'python'}.

Response

the response returned from the FacetedSearch object (by calling .execute()) is a subclass of the standard Response class that adds a property called facets which contains a dictionary with lists of buckets -each represented by a tuple of key, document count and a flag indicating whether this value has been filtered on.

Example

from datetime import date

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')
    }

    def search(self):
        # override methods to add custom pieces
        s = super().search()
        return s.filter('range', publish_from={'lte': 'now/h'})

bs = BlogSearch('python web', {'publishing_frequency': date(2015, 6)})
response = bs.execute()

# access hits and other attributes as usual
total = response.hits.total
print('total hits', total.relation, total.value)
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)