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Searching

Overview

This chapter covers how to search with ElasticUtils.

All about S: S

What is S?

:pyelasticutils.S helps you define an Elasticsearch search.

searcher = S()

This creates an untyped :pyelasticutils.S using the defaults:

  • uses an :pyelasticsearch.client.Elasticsearch instance configured to connect to localhost -- call :pyelasticutils.S.es to specify connection parameters
  • searches across all indexes -- call :pyelasticutils.S.indexes to specify indexes
  • searches across all doctypes -- call :pyelasticutils.S.doctypes to specify doctypes

S is chainable

:pyelasticutils.S has methods that return a new S instance with the additional specified criteria. In this way S is chainable and you can reuse S objects for your searches.

For example:

s1 = S()

s2 = s1.query(content__text='tabs')

s3 = s2.filter(awesome=True)

s4 = s2.filter(awesome=False)

s1, s2, and s3 are all different S objects. s1 is a match all.

s2 has a query.

s3 has everything in s2 with a awesome=True filter.

s4 has everything in s2 with a awesome=False filter.

S can be typed and untyped

When you create an :pyelasticutils.S with no type, it's called an untyped S. By default, search results for a untyped S are returned in the form of a sequence of :pyelasticutils.DefaultMappingType instances. You can explicitly state that you want a sequence of dicts or lists, too. See queries-shapes for more details on how to return results in various formats.

You can also construct a typed S which is an S with a :pyelasticutils.MappingType subclass. By default, search results for a typed S are returned in the form of a sequence of instances of that type. See mapping-type-chapter for more about MappingTypes.

S can be sliced to return the results you want

By default Elasticsearch gives you the first 10 results.

If you want something different than that, :pyelasticutils.S supports slicing allowing you to get back the specific results you're looking for.

For example:

some_s = S()

results = list(some_s)         # returns first 10 results (default)
results = list(some_s[:10])    # returns first 10 results
results = list(some_s[10:20])  # returns results 10 through 19

The slicing is chainable, too:

some_s = S()[:10]
first_ten_pitchers = some_s.filter(position='pitcher')

Note

The slicing happens on the Elasticsearch side---it doesn't pull all the results back and then slice them in Python. Ew.

Note

Unlike slicing other things in Python, if you choose a start, but no end, then you get 10 results starting with the start.

In other words, this:

some_s = S()[10:]

does not give you all the results from index 10 onwards. Instead it gives you results 10 through 19.

If you want "all the results from index 10 onwards", then you could do something like this:

SOME_LARGE_NUMBER = 1000000
some_s = S()[10:SOME_LARGE_NUMBER]

If you know you have fewer results than SOME_LARGE_NUMBER or you could do this which will kick off two Elasticsearch queries:

some_s = S()[10:some_s.count()]

Note that doing open-ended queries like this has the same ramifications as calling :pyelasticutils.S.everything. Refer to that documentation for the fearsome details.

S is lazy

The search won't execute until you do one of the following:

  1. use the :pyelasticutils.S in an iterable context
  2. call :pylen on a :pyelasticutils.S
  3. call the :pyelasticutils.S.execute, :pyelasticutils.S.everything, :pyelasticutils.S.count, :pyelasticutils.S.suggestions or :pyelasticutils.S.facet_counts methods

Once you execute the search, then it will cache the results and further executions of that :pyelasticutils.S won't result in another roundtrip to your Elasticsearch cluster.

S results can be returned in many shapes

An untyped S (e.g. S()) will return instances of :pyelasticutils.DefaultMappingType by default.

A typed S (e.g. S(FooMappingType)), will return instances of that type (e.g. type FooMappingType) by default.

:pyelasticutils.S.values_list gives you a list of tuples. See documentation for more details.

:pyelasticutils.S.values_dict gives you a list of dicts. See documentation for more details.

If you use :pyelasticutils.S.execute, you get back a :pyelasticutils.SearchResults instance which has additional useful bits including the raw response from Elasticsearch. See documentation for details.

Where to search

Specifying connection parameters: es

:pyelasticutils.S will generate an :pyelasticsearch.client.Elasticsearch object that connects to localhost by default. That's usually not what you want. You can use the :pyelasticutils.S.es method to specify the arguments used to create the elasticsearch-py Elasticsearch object.

Examples:

q = S().es(urls=['localhost'])
q = S().es(urls=['localhost:9200'], timeout=10)

See :pyelasticutils.get_es for the list of arguments you can pass in.

Specifying indexes to search: indexes

An untyped S will search all indexes by default.

A typed S will search the index returned by the :pyelasticutils.MappingType.get_index method.

If that's not what you want, use the :pyelasticutils.S.indexes method.

For example, this searches all indexes:

q = S()

This searches just "someindex":

q = S().indexes('someindex')

This searches "thisindex" and "thatindex":

q = S().indexes('thisindex', 'thatindex')

This searches whatever FooMappingType.get_index() returns:

q = S(FooMappingType)

Specifying doctypes to search: doctypes

An untyped S will search all doctypes by default.

A typed S will search the doctype returned by the :pyelasticutils.MappingType.get_mapping_type_name method.

If that's not what you want, then you should use the :pyelasticutils.S.doctypes method.

For example, this searches all doctypes:

q = S()

This searches just the "sometype" doctype:

q = S().doctypes('sometype')

This searches "thistype" and "thattype":

q = S().doctypes('thistype', 'thattype')

By default, S does a Match All

By default, :pyelasticutils.S with no filters or queries specified will do a match_all query in Elasticsearch.

Queries: query

Queries are specified using the :pyelasticutils.S.query method. See those docs for API details.

ElasticUtils uses this syntax for specifying queries:

fieldname__fieldaction=value

  1. fieldname: the field the query applies to
  2. fieldaction: the kind of query it is
  3. value: the value to query for

The fieldname and fieldaction are separated by __ (that's two underscores).

For example:

q = S().query(title__match='taco trucks')

will do an Elasticsearch match query on the title field for "taco trucks".

There are many different field actions to choose from:

field action Elasticsearch query type
(no action specified) Term query
term Term query
terms Terms query
in Terms query
text Text query (DEPRECATED)
match Match query1
prefix Prefix query2
gt, gte, lt, lte Range query
range Range query3
fuzzy Fuzzy query
wildcard Wildcard query
text_phrase Text phrase query (DEPRECATED)
match_phrase Match phrase query4
query_string Querystring query5
http://www.elasticsearch.org/guide/reference/query-dsl/

Elasticsearch docs for query dsl

http://www.elasticsearch.org/guide/reference/query-dsl/term-query.html

Elasticsearch docs on term queries

http://www.elasticsearch.org/guide/reference/query-dsl/terms-query.html

Elasticsearch docs on terms queries

http://www.elasticsearch.org/guide/reference/query-dsl/text-query.html

Elasticsearch docs on text and text_phrase queries

http://www.elasticsearch.org/guide/reference/query-dsl/match-query.html

Elasticsearch docs on match and match_phrase queries

http://www.elasticsearch.org/guide/reference/query-dsl/prefix-query.html

Elasticsearch docs on prefix queries

http://www.elasticsearch.org/guide/reference/query-dsl/range-query.html

Elasticsearch docs on range queries

http://www.elasticsearch.org/guide/reference/query-dsl/fuzzy-query.html

Elasticsearch docs on fuzzy queries

http://www.elasticsearch.org/guide/reference/query-dsl/wildcard-query.html

Elasticsearch docs on wildcard queries

http://www.elasticsearch.org/guide/reference/query-dsl/query-string-query.html

Elasticsearch docs on query_string queries

Advanced queries: Q and query_raw

calling .query() multiple times

Calling :pyelasticutils.S.query multiple times will combine all the queries together.

should, must and must_not

By default all queries must match a document in order for the document to show up in the search results.

You can alter this behavior by flagging your queries with should, must, and must_not flags.

should

A query added with should=True affects the score for a result, but it won't prevent the document from being in the result set.

Example:

qs = S().query(title__text='castle',
               summary__text='castle',
               should=True)

If the document matches either the title__text or the summary__text then it's included in the results set. It doesn't have to match both.

must

This is the default, so if you don't specify, then it's a must.

A query added with must=True must match in order for the document to be in the result set.

Example:

qs = S().query(title__text='castle',
               summary__text='castle')

qs = S().query(title__text='castle',
               summary__text='castle',
               must=True)

These two are equivalent. The document must match both the title__text and summary__text queries in order to be included in the result set. If it doesn't match one of them, then it's not included.

must_not

A query added with must_not=True must NOT match in order for the document to be in the result set.

Example:

qs = (S().query(title__text='castle')
         .query(author='castle', must_not=True))

For a document to be included in the result set, it must match the title__text query and must NOT match the author query. I.e. The title must have "castle", but the document can't have been written by someone with "castle" in their name.

The Q class

You can manipulate query units with the :pyelasticutils.Q class. For example, you can incrementally build your query:

q = Q()

if search_authors:
    q += Q(author_name=search_text, should=True)

if search_keywords:
    q += Q(keyword=search_text, should=True)

q += Q(title__text=search_text, summary__text=search_text,
       should=True)

The + Python operator will combine two Q instances together and return a new instance.

You can then use one or more Q classes in a query call:

if search_authors:
    q += Q(author_name=search_text, should=True)

if search_keywords:
    q += Q(keyword=search_text, should=True)

q += Q(title__text=search_text, summary__text=search_text,
       should=True)

s = S().query(q)

query_raw

:pyelasticutils.S.query_raw lets you explicitly define the query portion of an Elasticsearch search.

For example:

q = S().query_raw({'match': {'title': 'example'}})

This will override all .query() calls you've made in your :pyelasticutils.S before and after the .query_raw call.

This is helpful if ElasticUtils is missing functionality you need.

adding new query actions

You can subclass :pyelasticutils.S and add handling for additional query actions. This is helpful in two circumstances:

  1. ElasticUtils doesn't have support for that query type
  2. ElasticUtils doesn't support that query type in a way you need---for example, ElasticUtils uses different argument values

See :pyelasticutils.S for more details on how to do this.

Filters: filter

Filters are specified using the :pyelasticutils.S.filter method. See those docs for API details.

q = S().filter(language='korean')

will do a search and only return results where the language is Korean.

:pyelasticutils.S.filter uses the same syntax for specifying fields, actions and values as :pyelasticutils.S.query.

field action elasticsearch filter
in Terms filter
gt, gte, lt, lte Range filter
range Range filter6
prefix, startswith Prefix filter
(no action) Term filter

You can also filter on fields that have None as a value or have no value:

q = S().filter(language=None)

This uses the Elasticsearch Missing filter.

Note

In order to filter on fields that have None as a value, you have to tell Elasticsearch that the field can have null values. To do this, you have to add null_value: True to the mapping for that field.

http://www.elasticsearch.org/guide/reference/mapping/core-types.html

Advanced filters: F and filter_raw

and vs. or

Calling filter multiple times is equivalent to an "and"ing of the filters.

For example:

q = (S().filter(style='korean')
        .filter(price='FREE'))

will do a query for style 'korean' AND price 'FREE'. Anything that has a style other than 'korean' or a price other than 'FREE' is removed from the result set.

You can do the same thing by putting both filters in the same :pyelasticutils.S.filter call.

For example:

q = S().filter(style='korean', price='FREE')

The F class

Suppose you want either Korean or Mexican food. For that, you need an "or". You can do something like this:

q = S().filter(or_={'style': 'korean', 'style':'mexican'})

But, wow---that's icky looking and not particularly helpful!

So, we've also got an :pyelasticutils.F class that makes this sort of thing easier.

You can do the previous example with F like this:

q = S().filter(F(style='korean') | F(style='mexican'))

will get you all the search results that are either "korean" or "mexican" style.

What if you want Mexican food, but only if it's FREE, otherwise you want Korean?:

q = S().filter(F(style='mexican', price='FREE') | F(style='korean'))

F supports & (and), | (or) and ~ (not) operations.

Additionally, you can create an empty F and build it incrementally:

qs = S()
f = F()
if some_crazy_thing:
    f &= F(price='FREE')
if some_other_crazy_thing:
    f |= F(style='mexican')

qs = qs.filter(f)

If neither some_crazy_thing or some_other_crazy_thing are True, then F will be empty. That's ok because empty filters are ignored.

filter_raw

:pyelasticutils.S.filter_raw lets you explicitly define the filter portion of an Elasticsearch search.

For example:

qs = S().filter_raw({'term': {'title': 'foo'}})

This will override all .filter() calls you've made in your :pyelasticutils.S before and after the .filter_raw call.

This is helpful if ElasticUtils is missing functionality you need.

adding new filteractions

You can subclass :pyelasticutils.S and add handling for additional filter actions. This is helpful in two circumstances:

  1. ElasticUtils doesn't have support for that filter type
  2. ElasticUtils doesn't support that filter type in a way you need---for example, ElasticUtils uses different argument values

See :pyelasticutils.S for more details on how to do this.

Query-time field boosting: boost

ElasticUtils allows you to specify query-time field boosts with :pyelasticutils.S.boost.

These boosts take effect at the time the query is executing. After the query has executed, then the boost is applied and that becomes the final score for the query.

This is a useful way to weight queries for some fields over others.

See :pyelasticutils.S.boost for more details.

Note

Boosts are ignored if you use query_raw.

Ordering: order_by

ElasticUtils :pyelasticutils.S.order_by lets you change the order of the search results.

See :pyelasticutils.S.order_by for more details.

http://www.elasticsearch.org/guide/reference/api/search/sort.html

Elasticsearch docs on sort parameter in the Search API

Demoting: demote

You can demote documents that match query criteria:

q = (S().query(title='trucks')
        .demote(0.5, description__text='gross'))

This does a query for trucks, but demotes any that have "gross" in the description with a fraction boost of 0.5.

Note

You can only call :pyelasticutils.S.demote once. Calling it again overwrites previous calls.

This is implemented using the boosting query in Elasticsearch. Anything you specify with :pyelasticutils.S.query goes into the positive section. The negative query and negative boost portions are specified as the first and second arguments to :pyelasticutils.S.demote.

Note

Order doesn't matter. So:

q = (S().query(title='trucks')
        .demote(0.5, description__text='gross'))

does the same thing as:

q = (S().demote(0.5, description__text='gross')
        .query(title='trucks'))
http://www.elasticsearch.org/guide/reference/query-dsl/boosting-query.html

Elasticsearch docs on boosting query (which are as clear as mud)

Highlighting: highlight

ElasticUtils can highlight excerpts for search results.

See :pyelasticutils.S.highlight for more details.

Suggestions: suggest

Spelling suggestions can be asked for by using the :pyelasticutils.S.suggest method, and then retrieved in :pyelasticutils.S.suggestions:

q = S().query(text='Aice').suggest('mysuggest', 'Alice', field='text')
print q.suggestions()['mysuggest'][0]['options']

Note

Spelling suggestions require Elasticsearch 0.90 or later.

Facets

Basic facets: facet

q = (S().query(title='taco trucks')
        .facet('style', 'location'))

will do a query for "taco trucks" and return terms facets for the style and location fields.

Note that the fieldname you provide in the :pyelasticutils.S.facet call becomes the facet name as well.

The facet counts are available through :pyelasticutils.S.facet_counts. For example:

q = (S().query(title='taco trucks')
        .facet('style', 'location'))
counts = q.facet_counts()

Also, you can get them with the facets attribute of the search results:

q = (S().query(title='taco trucks')
        .facet('style', 'location'))

results = q.execute()
counts = results.facets

You can also restrict the number of terms returned per facet by passing a size keyword argument to :pyelasticutils.S.facet:

q = S().query(title='taco trucks')
        .facet('style', 'location', size=5)

Facet Results

The execution methods :pyelasticutils.S.facet_counts and :pyelasticutils.S.execute will return a dictionary containing the named parameter and a :pyelasticutils.FacetResult object.

For example:

>>> facet_counts = S().facet('primary_country_id').facet_counts()
>>> facet_counts
{u'primary_country_id': <elasticutils.FacetResult at 0x45f12d0>}

The FacetResult object contains all of the information returned in the facet stanza.

In the above case, we faceted on primary_country_id as a terms facet. To see the facet results simply iterate over the FacetResult object:

>>> for facet_result in facet_counts['primary_country_id']:
...     print facet_result
...
{u'count': 187293, u'term': 41}
{u'count': 24177, u'term': 9}
{u'count': 17200, u'term': 50}
{u'count': 13015, u'term': 15}
{u'count': 10296, u'term': 30}
{u'count': 8824, u'term': 32}
{u'count': 7703, u'term': 6}
{u'count': 7502, u'term': 23}
{u'count': 5614, u'term': 2}
{u'count': 5214, u'term': 33}

And to get the "other", "missing" and "total" information from the facetresult:

>>> facet_counts['primary_country_id'].missing
3475

>>> facet_counts['primary_country_id'].other
25273

>>> facet_counts['primary_country_id'].total
312111

FacetResult is backwords compatible with older versions of ElasticUtils, so you shouldn't need to change anything when upgrading:

>>> some_s = S().facet_raw(primary_country_id={'statistical':{"field":"primary_country_id"}})
>>> facet_counts = some_s.facet_counts()
>>> facet_counts['primary_country_id'].max == facet_counts['primary_country_id']['max']
True

Facets and scope (filters and global)

What happens if your search includes filters?

Here's an example:

q = (S().query(title='taco trucks')
        .filter(style='korean')
        .facet('style', 'location'))

The "style" and "location" facets here ONLY apply to the results of the query and are not affected at all by the filters.

If you want your filters to apply to your facets as well, pass in the filtered flag.

For example:

q = (S().query(title='taco trucks')
        .filter(style='korean')
        .facet('style', 'location', filtered=True))

What if you want the filters to apply just to one of the facets and not the other? You need to add them incrementally.

For example:

q = (S().query(title='taco trucks')
        .filter(style='korean')
        .facet('style', filtered=True)
        .facet('location'))

What if you want the facets to apply to the entire corpus and not just the results from the query? Use the global_ flag.

For example:

q = (S().query(title='taco trucks')
        .filter(style='korean')
        .facet('style', 'location', global_=True))

Note

The flag name is global_ with an underscore at the end. Why? Because global with no underscore is a Python keyword.

Facets... RAW!: facet_raw

Elasticsearch facets can do a lot of other things. Because of this, there exists :pyelasticutils.S.facet_raw which will do whatever you need it to. Specify key/value args by facet name.

You could do the first facet example with:

q = (S().query(title='taco trucks')
        .facet_raw(style={'terms': {'field': 'style'}}))

One of the things this lets you do is scripted facets.

For example:

q = (S().query(title='taco trucks')
        .facet_raw(styles={
            'field': 'style',
            'script': 'term == korean ? true : false'
        }))

Warning

If for some reason you have specified a facet with the same name using both :pyelasticutils.S.facet and :pyelasticutils.S.facet_raw, the facet_raw stuff will override the facet stuff.

Filter and query facets

You can also define arbitrary facets for queries and facets as documented in Elasticsearch's docs.

For example:

q = (S().query(title='taco trucks')
        .facet_raw(korean_or_mexican={
            'filter': {
                'or': [
                    {'term': {'style': 'korean'}},
                    {'term': {'style': 'mexican'}},
                ]
            }
        }))

Then access the custom facet via the name you passed into facet_raw:

counts = q.facet_counts()
korean_or_mexican_count = counts['korean_or_mexican']['count']

The same can be done with queries:

q = (S().query(title='taco trucks')
      .facet_raw(korean={
          'query': {
              'term': {'style': 'korean'},
          }
      }))

Scores and explanations

Seeing the score

Wondering what the score for a document was? ElasticUtils puts that in the score attribute of the es_meta object of the search result. For example, let's search an index that holds knowledge base articles for ones with the word "crash" in them and print out the scores:

q = S().query(title__text='crash', content__text='crash')

for result in q:
    print result.es_meta.score

This works regardless of what form the search results are in.

Getting an explanation: explain

Wondering why one document shows up higher in the results than another that should have shown up higher? Wonder how that score was computed? You can set the search to pass the explain flag to Elasticsearch with :pyelasticutils.S.explain.

ElasticUtils puts the explanation in the explanation attribute of the es_meta object of the search result.

For example, let's do a query on a search corpus of knowledge base articles for articles with the word "crash" in them:

q = (S().query(title__text='crash', content__text='crash')
        .explain())

for result in q:
    print result.es_meta.explanation

This works regardless of what form the search results are in.

http://www.elasticsearch.org/guide/reference/api/search/explain.html

Elasticsearch docs on explain (which are pretty bereft of details).

  1. Elasticsearch 0.19.9 renamed text queries to match queries. If you're using Elasticsearch 0.19.9 or later, you should use match and match_phrase. If you're using a version prior to 0.19.9 use text and text_phrase.↩

  2. You can also use startswith, but that's deprecated.↩

  3. The range field action is a shortcut for defining both sides of the range at once. The range is inclusive on both sides and accepts a tuple with the lower value first and upper value second.↩

  4. Elasticsearch 0.19.9 renamed text queries to match queries. If you're using Elasticsearch 0.19.9 or later, you should use match and match_phrase. If you're using a version prior to 0.19.9 use text and text_phrase.↩

  5. When doing query_string queries, if the query text is malformed it'll raise a SearchPhaseExecutionException exception.↩

  6. The range field action is a shortcut for defining both sides of the range at once. The range is inclusive on both sides and accepts a tuple with the lower value first and upper value second.↩