elasticutils.S helps you define an Elasticsearch search.
searcher = S()
This creates an untyped elasticutils.S using the defaults:
elasticutils.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.
When you create an elasticutils.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 elasticutils.DefaultMappingType instances. You can explicitly state that you want a sequence of dicts or lists, too. See S results can be returned in many 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 elasticutils.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 types and Indexables for more about MappingTypes.
By default Elasticsearch gives you the first 10 results.
If you want something different than that, elasticutils.S supports slicing allowing you to get back the specific results you’re looking for.
For example:
some_s = S()
results = some_s[:10] # returns first 10 results
results = 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.
See also
The search won’t execute until you do one of the following:
Once you execute the search, then it will cache the results and further executions of that elasticutils.S won’t result in another roundtrip to your Elasticsearch cluster.
An untyped S (e.g. S()) will return instances of elasticutils.DefaultMappingType by default.
A typed S (e.g. S(Foo)), will return instances of that type (e.g. type Foo) by default.
elasticutils.S.values_list() gives you a list of tuples. See documentation for more details.
elasticutils.S.values_dict() gives you a list of dicts. See documentation for more details.
If you use elasticutils.S.execute(), you get back a elasticutils.SearchResults instance which has additional useful bits including the raw response from Elasticsearch. See documentation for details.
elasticutils.S will generate an pyelasticsearch.client.ElasticSearch object that connects to http://localhost:9200 by default. That’s usually not what you want. You can use the elasticutils.S.es() method to specify the arguments used to create the pyelasticsearch ElasticSearch object.
Examples:
q = S().es(urls=['http://localhost:9200'])
q = S().es(urls=['http://localhost:9200'], timeout=10)
See elasticutils.get_es() for the list of arguments you can pass in.
An untyped S will search all indexes by default.
A typed S will search the index returned by the elasticutils.MappingType.get_index() method.
If that’s not what you want, use the elasticutils.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')
An untyped S will search all doctypes by default.
A typed S will search the doctype returned by the elasticutils.MappingType.get_mapping_type_name() method.
If that’s not what you want, then you should use the elasticutils.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, elasticutils.S with no filters or queries specified will do a match_all query in Elasticsearch.
See also
Queries are specified using the elasticutils.S.query() method. See those docs for API details.
ElasticUtils uses this syntax for specifying queries:
fieldname__fieldaction=value
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 |
| text | Text query |
| match | Match query [1] |
| prefix | Prefix query [2] |
| gt, gte, lt, lte | Range query |
| fuzzy | Fuzzy query |
| wildcard | Wildcard query |
| text_phrase | Text phrase query |
| match_phrase | Match phrase query [1] |
| query_string | Querystring query [3] |
| [1] | (1, 2) 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] | When doing query_string queries, if the query text is malformed it’ll raise a SearchPhaseExecutionException exception. |
See also
Calling elasticutils.S.query() multiple times will combine all the queries together.
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.
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.
You can manipulate query units with the elasticutils.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)
elasticutils.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 elasticutils.S before and after the .query_raw call.
This is helpful if ElasticUtils is missing functionality you need.
You can subclass elasticutils.S and add handling for additional query actions. This is helpful in two circumstances:
See elasticutils.S for more details on how to do this.
Filters are specified using the elasticutils.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.
elasticutils.S.filter() uses the same syntax for specifying fields, actions and values as elasticutils.S.query().
| field action | elasticsearch filter |
|---|---|
| in | Terms filter |
| gt, gte, lt, lte | Range filter |
| 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
See also
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 elasticutils.S.filter() call.
For example:
q = S().filter(style='korean', price='FREE')
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 elasticutils.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.
You can subclass elasticutils.S and add handling for additional filter actions. This is helpful in two circumstances:
See elasticutils.S for more details on how to do this.
ElasticUtils allows you to specify query-time field boosts with elasticutils.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 elasticutils.S.boost() for more details.
Note
Boosts are ignored if you use query_raw.
ElasticUtils elasticutils.S.order_by() lets you change the order of the search results.
See elasticutils.S.order_by() for more details.
See also
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 elasticutils.S.demote() once. Calling it again overwrites previous calls.
This is implemented using the boosting query in Elasticsearch. Anything you specify with elasticutils.S.query() goes into the positive section. The negative query and negative boost portions are specified as the first and second arguments to elasticutils.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'))
See also
ElasticUtils can highlight excerpts for search results.
See elasticutils.S.highlight() for more details.
See also
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 elasticutils.S.facet() call becomes the facet name as well.
The facet counts are available through elasticutils.S.facet_counts(). For example:
q = (S().query(title='taco trucks')
.facet('style', 'location'))
counts = q.facet_counts()
See also
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.
See also
Elasticsearch facets can do a lot of other things. Because of this, there exists elasticutils.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 elasticutils.S.facet() and elasticutils.S.facet_raw(), the facet_raw stuff will override the facet stuff.
See also
Wondering what the score for a document was? ElasticUtils puts that in the _score on 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._score
This works regardless of what form the search results are in.
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 elasticutils.S.explain().
This returns data that will be in every item in the search results list as _explanation.
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._explanation
This works regardless of what form the search results are in.
See also