def update_from_dict(self, d: Dict) -> "Search":
"""
Apply options from a serialized body to the current instance. Modifies
the object in-place. Used mostly by ``from_dict``.
"""
d = d.copy()
if "query" in d:
self._query = Query(d.pop("query"))
if "post_filter" in d:
self._post_filter = Query(d.pop("post_filter"))
aggs = d.pop("aggs", d.pop("aggregations", {}))
if aggs:
self._aggs = Aggs(aggs)
if "sort" in d:
self._sort = d.pop("sort")
if "_source" in d:
self._source = d.pop("_source")
if "highlight" in d:
high = d.pop("highlight").copy()
self._highlight = high.pop("fields")
self._highlight_opts = high
if "suggest" in d:
self._suggest = d.pop("suggest")
if "text" in self._suggest:
text = self._suggest.pop("text")
for s in self._suggest.values():
s.setdefault("text", text)
if "script_fields" in d:
self._script_fields = d.pop("script_fields")
self._params.update(d)
return self
def test_parse_as_tabular(self):
# with single agg at root
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mapping=MAPPING)
index_names, index_values = Aggregations(
data=sample.ES_AGG_RESPONSE,
aggs=my_agg,
index=None,
client=None,
query=None,
).serialize_as_tabular(row_as_tuple=True)
self.assertEqual(index_names,
["classification_type", "global_metrics.field.name"])
self.assertEqual(
index_values,
[
(
("multilabel", "ispracticecompatible"),
{
"avg_f1_micro": 0.72,
"avg_nb_classes": 18.71,
"doc_count": 128
},
),
(
("multilabel", "gpc"),
{
"avg_f1_micro": 0.95,
"avg_nb_classes": 183.21,
"doc_count": 119
},
),
(
("multilabel", "preservationmethods"),
{
"avg_f1_micro": 0.8,
"avg_nb_classes": 9.97,
"doc_count": 76
},
),
(
("multiclass", "kind"),
{
"avg_f1_micro": 0.89,
"avg_nb_classes": 206.5,
"doc_count": 370
},
),
(
("multiclass", "gpc"),
{
"avg_f1_micro": 0.93,
"avg_nb_classes": 211.12,
"doc_count": 198
},
),
],
)
def get_wrapper_declared_agg():
return (Aggs(mapping=MAPPING).groupby(
["classification_type", "global_metrics.field.name"]).aggs([
Avg("avg_nb_classes", field="global_metrics.dataset.nb_classes"),
Avg(
"avg_f1_micro",
field="global_metrics.performance.test.micro.f1_score",
),
]))
def test_parse_as_tabular_multiple_roots(self):
# with multiple aggs at root
my_agg = Aggs({
"classification_type": {
"terms": {
"field": "classification_type"
}
},
"avg_f1_score": {
"avg": {
"field": "global_metrics.performance.test.micro.f1_score"
}
},
})
raw_response = {
"classification_type": {
"doc_count_error_upper_bound":
0,
"sum_other_doc_count":
0,
"buckets": [
{
"key": "multiclass",
"doc_count": 439
},
{
"key": "multilabel",
"doc_count": 433
},
],
},
"avg_f1_score": {
"value": 0.815
},
}
index_names, index_values = Aggregations(
data=raw_response,
aggs=my_agg,
index=None,
client=None,
query=None,
).serialize_as_tabular(row_as_tuple=True, expand_sep=" || ")
self.assertEqual(index_names, [])
self.assertEqual(
index_values,
[(
(),
{
"avg_f1_score": 0.815,
"classification_type || multiclass": 439,
"classification_type || multilabel": 433,
},
)],
)
def test_parse_as_dataframe(self):
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mapping=MAPPING)
df = Aggregations(
data=sample.ES_AGG_RESPONSE,
aggs=my_agg,
index=None,
client=None,
query=None,
).serialize_as_dataframe()
self.assertIsInstance(df, pd.DataFrame)
self.assertEqual(set(df.index.names),
{"classification_type", "global_metrics.field.name"})
self.assertEqual(set(df.columns),
{"avg_f1_micro", "avg_nb_classes", "doc_count"})
self.assertEqual(
df.index.to_list(),
[
("multilabel", "ispracticecompatible"),
("multilabel", "gpc"),
("multilabel", "preservationmethods"),
("multiclass", "kind"),
("multiclass", "gpc"),
],
)
self.assertEqual(
df.to_dict(orient="rows"),
[
{
"avg_f1_micro": 0.72,
"avg_nb_classes": 18.71,
"doc_count": 128
},
{
"avg_f1_micro": 0.95,
"avg_nb_classes": 183.21,
"doc_count": 119
},
{
"avg_f1_micro": 0.8,
"avg_nb_classes": 9.97,
"doc_count": 76
},
{
"avg_f1_micro": 0.89,
"avg_nb_classes": 206.5,
"doc_count": 370
},
{
"avg_f1_micro": 0.93,
"avg_nb_classes": 211.12,
"doc_count": 198
},
],
)
def test_normalize_buckets(self):
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mapping=MAPPING)
response = Aggregations(
data=sample.ES_AGG_RESPONSE,
aggs=my_agg,
index=None,
client=None,
query=None,
).serialize_as_normalized()
self.assertEqual(ordered(response),
ordered(sample.EXPECTED_NORMALIZED_RESPONSE))
def test_parse_as_tree(self, *_):
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mapping=MAPPING)
response = Aggregations(
data=sample.ES_AGG_RESPONSE,
aggs=my_agg,
index=None,
client=None,
query=None,
).serialize_as_tree()
self.assertIsInstance(response, AggsResponseTree)
self.assertEqual(response.__str__(),
sample.EXPECTED_RESPONSE_TREE_REPR)
def __init__(self, using=None, index=None, mapping=None):
"""
Search request to elasticsearch.
:arg using: `Elasticsearch` instance to use
:arg index: limit the search to index
:arg mapping: mapping used for query validation
All the parameters supplied (or omitted) at creation type can be later
overridden by methods (`using`, `index` and `mapping` respectively).
"""
self._sort = []
self._source = None
self._highlight = {}
self._highlight_opts = {}
self._suggest = {}
self._script_fields = {}
mapping = Mapping(mapping)
self._mapping = mapping
self._aggs = Aggs(mapping=mapping)
self._query = Query(mapping=mapping)
self._post_filter = Query(mapping=mapping)
super(Search, self).__init__(using=using, index=index)
def test_grouping_agg(self):
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mappings=MAPPINGS)
agg_response = Aggregations(data=sample.ES_AGG_RESPONSE,
_search=Search().aggs(my_agg))
# none provided
self.assertIsNone(agg_response._grouping_agg()[0])
# fake provided
with self.assertRaises(KeyError):
agg_response._grouping_agg("yolo")
# not bucket provided
with self.assertRaises(ValueError):
agg_response._grouping_agg("avg_f1_micro")
# real provided
self.assertEqual(
agg_response._grouping_agg("global_metrics.field.name")[0],
"global_metrics.field.name",
)
def test_response_tree(self, uuid_mock):
uuid_mock.side_effect = range(1000)
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mapping=MAPPING)
response_tree = AggsResponseTree(aggs=my_agg, index=None).parse(
sample.ES_AGG_RESPONSE)
self.assertEqual(response_tree.__str__(),
sample.EXPECTED_RESPONSE_TREE_REPR)
self.assertEqual(len(response_tree.list()), 18)
multilabel_gpc_bucket = next(
(b for b in response_tree.list()
if b.level == "global_metrics.field.name" and b.key == "gpc"))
# bucket properties will give parents levels and keys
self.assertEqual(
response_tree.bucket_properties(multilabel_gpc_bucket),
OrderedDict([
("global_metrics.field.name", "gpc"),
("classification_type", "multilabel"),
]),
)
def __init__(
self,
using: Optional[Elasticsearch] = None,
index: Optional[Union[str, Tuple[str], List[str]]] = None,
mappings: Optional[Union[MappingsDict, Mappings]] = None,
nested_autocorrect: bool = False,
repr_auto_execute: bool = False,
document_class: DocumentMeta = None,
) -> None:
"""
Search request to elasticsearch.
:arg using: `Elasticsearch` instance to use
:arg index: limit the search to index
:arg mappings: mappings used for query validation
:arg nested_autocorrect: in case of missing nested clause, will insert it automatically
:arg repr_auto_execute: execute query and display results as dataframe, requires client to be provided
All the parameters supplied (or omitted) at creation type can be later
overridden by methods (`using`, `index` and `mappings` respectively).
"""
self._sort: List[Union[str, Dict[str, Any]]] = []
self._source: Any = None
self._highlight: Dict[str, Any] = {}
self._highlight_opts: Dict[str, Any] = {}
self._suggest: Dict[str, Any] = {}
self._script_fields: Dict[str, Any] = {}
mappings = _mappings(mappings)
self._mappings: Optional[Mappings] = mappings
self._aggs: Aggs = Aggs(mappings=mappings,
nested_autocorrect=nested_autocorrect)
self._query: Query = Query(mappings=mappings,
nested_autocorrect=nested_autocorrect)
self._post_filter: Query = Query(mappings=mappings,
nested_autocorrect=nested_autocorrect)
self._repr_auto_execute: bool = repr_auto_execute
self._document_class: Optional[DocumentMeta] = document_class
super(Search, self).__init__(using=using, index=index)
def test_parse_as_dataframe(self):
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mappings=MAPPINGS)
df = Aggregations(data=sample.ES_AGG_RESPONSE,
_search=Search().aggs(my_agg)).to_dataframe(
grouped_by="global_metrics.field.name")
self.assertIsInstance(df, pd.DataFrame)
self.assertEqual(set(df.index.names),
{"classification_type", "global_metrics.field.name"})
self.assertEqual(set(df.columns),
{"avg_f1_micro", "avg_nb_classes", "doc_count"})
self.assertEqual(
df.to_dict(orient="index"),
{
("multiclass", "gpc"): {
"avg_f1_micro": 0.93,
"avg_nb_classes": 211.12,
"doc_count": 198,
},
("multiclass", "kind"): {
"avg_f1_micro": 0.89,
"avg_nb_classes": 206.5,
"doc_count": 370,
},
("multilabel", "ispracticecompatible"): {
"avg_f1_micro": 0.72,
"avg_nb_classes": 18.71,
"doc_count": 128,
},
("multilabel", "preservationmethods"): {
"avg_f1_micro": 0.8,
"avg_nb_classes": 9.97,
"doc_count": 76,
},
},
)
def test_client_bound_response(self, uuid_mock):
uuid_mock.side_effect = range(1000)
client_mock = Mock(spec=["search"])
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mapping=MAPPING)
response_tree = AggsResponseTree(aggs=my_agg, index=None).parse(
sample.ES_AGG_RESPONSE)
response = IResponse(
client=client_mock,
tree=response_tree,
index_name="some_index",
depth=1,
query={"term": {
"some_field": 1
}},
)
# ensure that navigation to attributes works with autocompletion (dir is used in ipython)
self.assertIn("classification_type_multiclass", dir(response))
self.assertIn("classification_type_multilabel", dir(response))
multilabel = response.classification_type_multilabel
self.assertIsInstance(multilabel, IResponse)
self.assertIs(multilabel._initial_tree, response._tree)
self.assertIn("global_metrics_field_name_gpc", dir(multilabel))
gpc = multilabel.global_metrics_field_name_gpc
self.assertIsInstance(gpc, IResponse)
self.assertIs(gpc._initial_tree, response._tree)
# test filter query used to list documents belonging to bucket
self.assertTrue(
equal_queries(
gpc.get_bucket_filter(),
{
"bool": {
"must": [
{
"term": {
"global_metrics.field.name": {
"value": "gpc"
}
}
},
{
"term": {
"classification_type": {
"value": "multilabel"
}
}
},
{
"term": {
"some_field": {
"value": 1
}
}
},
]
}
},
))
def test_parse_as_tabular(self):
# with single agg at root
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mappings=MAPPINGS)
index_names, index_values = Aggregations(
data=sample.ES_AGG_RESPONSE,
_search=Search().aggs(my_agg)).to_tabular(
index_orient=True, grouped_by="global_metrics.field.name")
self.assertEqual(index_names,
["classification_type", "global_metrics.field.name"])
self.assertEqual(
index_values,
{
("multilabel", "ispracticecompatible"): {
"avg_f1_micro": 0.72,
"avg_nb_classes": 18.71,
"doc_count": 128,
},
("multilabel", "preservationmethods"): {
"avg_f1_micro": 0.8,
"avg_nb_classes": 9.97,
"doc_count": 76,
},
("multiclass", "kind"): {
"avg_f1_micro": 0.89,
"avg_nb_classes": 206.5,
"doc_count": 370,
},
("multiclass", "gpc"): {
"avg_f1_micro": 0.93,
"avg_nb_classes": 211.12,
"doc_count": 198,
},
},
)
# index_orient = False
index_names, index_values = Aggregations(
data=sample.ES_AGG_RESPONSE,
_search=Search().aggs(my_agg)).to_tabular(
index_orient=False, grouped_by="global_metrics.field.name")
self.assertEqual(index_names,
["classification_type", "global_metrics.field.name"])
self.assertEqual(
index_values,
[
{
"avg_f1_micro": 0.72,
"avg_nb_classes": 18.71,
"classification_type": "multilabel",
"doc_count": 128,
"global_metrics.field.name": "ispracticecompatible",
},
{
"avg_f1_micro": 0.8,
"avg_nb_classes": 9.97,
"classification_type": "multilabel",
"doc_count": 76,
"global_metrics.field.name": "preservationmethods",
},
{
"avg_f1_micro": 0.89,
"avg_nb_classes": 206.5,
"classification_type": "multiclass",
"doc_count": 370,
"global_metrics.field.name": "kind",
},
{
"avg_f1_micro": 0.93,
"avg_nb_classes": 211.12,
"classification_type": "multiclass",
"doc_count": 198,
"global_metrics.field.name": "gpc",
},
],
)
def test_parse_as_tabular_multiple_roots(self):
# with multiple aggs at root
my_agg = Aggs({
"classification_type": {
"terms": {
"field": "classification_type"
}
},
"avg_f1_score": {
"avg": {
"field": "global_metrics.performance.test.micro.f1_score"
}
},
})
raw_response = {
"classification_type": {
"doc_count_error_upper_bound":
0,
"sum_other_doc_count":
0,
"buckets": [
{
"key": "multiclass",
"doc_count": 439
},
{
"key": "multilabel",
"doc_count": 433
},
],
},
"avg_f1_score": {
"value": 0.815
},
}
index_names, index_values = Aggregations(
data=raw_response,
_search=Search().aggs(my_agg)).to_tabular(index_orient=True,
expand_sep=" || ")
self.assertEqual(index_names, [])
self.assertEqual(
index_values,
{
(): {
"avg_f1_score": 0.815,
"classification_type || multiclass": 439,
"classification_type || multilabel": 433,
}
},
)
# with specified grouped_by
index_names, index_values = Aggregations(
data=raw_response, _search=Search().aggs(my_agg)).to_tabular(
grouped_by="classification_type")
self.assertEqual(index_names, ["classification_type"])
self.assertEqual(
index_values,
{
("multiclass", ): {
"doc_count": 439
},
("multilabel", ): {
"doc_count": 433
}
},
)
class Search(Request):
def __init__(self, using=None, index=None, mapping=None):
"""
Search request to elasticsearch.
:arg using: `Elasticsearch` instance to use
:arg index: limit the search to index
:arg mapping: mapping used for query validation
All the parameters supplied (or omitted) at creation type can be later
overridden by methods (`using`, `index` and `mapping` respectively).
"""
self._sort = []
self._source = None
self._highlight = {}
self._highlight_opts = {}
self._suggest = {}
self._script_fields = {}
mapping = Mapping(mapping)
self._mapping = mapping
self._aggs = Aggs(mapping=mapping)
self._query = Query(mapping=mapping)
self._post_filter = Query(mapping=mapping)
super(Search, self).__init__(using=using, index=index)
def query(self, *args, **kwargs):
s = self._clone()
s._query = s._query.query(*args, **kwargs)
return s
query.__doc__ = Query.query.__doc__
def filter(self, *args, **kwargs):
s = self._clone()
s._query = s._query.filter(*args, **kwargs)
return s
filter.__doc__ = Query.filter.__doc__
def must_not(self, *args, **kwargs):
s = self._clone()
s._query = s._query.must_not(*args, **kwargs)
return s
must_not.__doc__ = Query.must_not.__doc__
def should(self, *args, **kwargs):
s = self._clone()
s._query = s._query.should(*args, **kwargs)
return s
should.__doc__ = Query.should.__doc__
def must(self, *args, **kwargs):
s = self._clone()
s._query = s._query.must(*args, **kwargs)
return s
must.__doc__ = Query.must.__doc__
def aggs(self, *args, **kwargs):
s = self._clone()
s._aggs = s._aggs.aggs(*args, **kwargs)
return s
aggs.__doc__ = Aggs.aggs.__doc__
def groupby(self, *args, **kwargs):
s = self._clone()
s._aggs = s._aggs.groupby(*args, **kwargs)
return s
groupby.__doc__ = Aggs.groupby.__doc__
def __iter__(self):
"""
Iterate over the hits.
"""
return iter(self.execute())
def __getitem__(self, n):
"""
Support slicing the `Search` instance for pagination.
Slicing equates to the from/size parameters. E.g.::
s = Search().query(...)[0:25]
is equivalent to::
s = Search().query(...).params(from=0, size=25)
"""
s = self._clone()
if isinstance(n, slice):
# If negative slicing, abort.
if n.start and n.start < 0 or n.stop and n.stop < 0:
raise ValueError("Search does not support negative slicing.")
# Elasticsearch won't get all results so we default to size: 10 if
# stop not given.
s._params["from"] = n.start or 0
s._params["size"] = n.stop - (n.start
or 0) if n.stop is not None else 10
return s
else: # This is an index lookup, equivalent to slicing by [n:n+1].
# If negative index, abort.
if n < 0:
raise ValueError("Search does not support negative indexing.")
s._params["from"] = n
s._params["size"] = 1
return s
def size(self, size):
"""Equivalent to::
s = Search().params(size=size)
"""
s = self._clone()
s._params["size"] = size
return s
@classmethod
def from_dict(cls, d):
"""
Construct a new `Search` instance from a raw dict containing the search
body. Useful when migrating from raw dictionaries.
Example::
s = Search.from_dict({
"query": {
"bool": {
"must": [...]
}
},
"aggs": {...}
})
s = s.filter('term', published=True)
"""
s = cls()
s.update_from_dict(d)
return s
def _clone(self):
"""
Return a clone of the current search request. Performs a shallow copy
of all the underlying objects. Used internally by most state modifying
APIs.
"""
s = self.__class__(using=self._using,
index=self._index,
mapping=self._mapping)
s._params = self._params.copy()
s._sort = self._sort[:]
s._source = copy.copy(
self._source) if self._source is not None else None
s._highlight = self._highlight.copy()
s._highlight_opts = self._highlight_opts.copy()
s._suggest = self._suggest.copy()
s._script_fields = self._script_fields.copy()
s._aggs = self._aggs.clone()
s._query = self._query.clone()
s._post_filter = self._post_filter.clone()
s._mapping = self._mapping.clone()
return s
def update_from_dict(self, d):
"""
Apply options from a serialized body to the current instance. Modifies
the object in-place. Used mostly by ``from_dict``.
"""
d = d.copy()
if "query" in d:
self._query = Query(d.pop("query"))
if "post_filter" in d:
self._post_filter = Query(d.pop("post_filter"))
aggs = d.pop("aggs", d.pop("aggregations", {}))
if aggs:
self._aggs = Aggs(aggs)
if "sort" in d:
self._sort = d.pop("sort")
if "_source" in d:
self._source = d.pop("_source")
if "highlight" in d:
high = d.pop("highlight").copy()
self._highlight = high.pop("fields")
self._highlight_opts = high
if "suggest" in d:
self._suggest = d.pop("suggest")
if "text" in self._suggest:
text = self._suggest.pop("text")
for s in self._suggest.values():
s.setdefault("text", text)
if "script_fields" in d:
self._script_fields = d.pop("script_fields")
self._params.update(d)
return self
def script_fields(self, **kwargs):
"""
Define script fields to be calculated on hits. See
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-script-fields.html
for more details.
Example::
s = Search()
s = s.script_fields(times_two="doc['field'].value * 2")
s = s.script_fields(
times_three={
'script': {
'inline': "doc['field'].value * params.n",
'params': {'n': 3}
}
}
)
"""
s = self._clone()
for name in kwargs:
if isinstance(kwargs[name], string_types):
kwargs[name] = {"script": kwargs[name]}
s._script_fields.update(kwargs)
return s
def source(self, fields=None, **kwargs):
"""
Selectively control how the _source field is returned.
:arg fields: wildcard string, array of wildcards, or dictionary of includes and excludes
If ``fields`` is None, the entire document will be returned for
each hit. If fields is a dictionary with keys of 'includes' and/or
'excludes' the fields will be either included or excluded appropriately.
Calling this multiple times with the same named parameter will override the
previous values with the new ones.
Example::
s = Search()
s = s.source(includes=['obj1.*'], excludes=["*.description"])
s = Search()
s = s.source(includes=['obj1.*']).source(excludes=["*.description"])
"""
s = self._clone()
if fields and kwargs:
raise ValueError(
"You cannot specify fields and kwargs at the same time.")
if fields is not None:
s._source = fields
return s
if kwargs and not isinstance(s._source, dict):
s._source = {}
for key, value in kwargs.items():
if value is None:
try:
del s._source[key]
except KeyError:
pass
else:
s._source[key] = value
return s
def sort(self, *keys):
"""
Add sorting information to the search request. If called without
arguments it will remove all sort requirements. Otherwise it will
replace them. Acceptable arguments are::
'some.field'
'-some.other.field'
{'different.field': {'any': 'dict'}}
so for example::
s = Search().sort(
'category',
'-title',
{"price" : {"order" : "asc", "mode" : "avg"}}
)
will sort by ``category``, ``title`` (in descending order) and
``price`` in ascending order using the ``avg`` mode.
The API returns a copy of the Search object and can thus be chained.
"""
s = self._clone()
s._sort = []
for k in keys:
if isinstance(k, string_types) and k.startswith("-"):
if k[1:] == "_score":
raise ValueError("Sorting by `-_score` is not allowed.")
k = {k[1:]: {"order": "desc"}}
s._sort.append(k)
return s
def highlight_options(self, **kwargs):
"""
Update the global highlighting options used for this request. For
example::
s = Search()
s = s.highlight_options(order='score')
"""
s = self._clone()
s._highlight_opts.update(kwargs)
return s
def highlight(self, *fields, **kwargs):
"""
Request highlighting of some fields. All keyword arguments passed in will be
used as parameters for all the fields in the ``fields`` parameter. Example::
Search().highlight('title', 'body', fragment_size=50)
will produce the equivalent of::
{
"highlight": {
"fields": {
"body": {"fragment_size": 50},
"title": {"fragment_size": 50}
}
}
}
If you want to have different options for different fields you can call ``highlight`` twice::
Search().highlight('title', fragment_size=50).highlight('body', fragment_size=100)
which will produce::
{
"highlight": {
"fields": {
"body": {"fragment_size": 100},
"title": {"fragment_size": 50}
}
}
}
"""
s = self._clone()
for f in fields:
s._highlight[f] = kwargs
return s
def suggest(self, name, text, **kwargs):
"""
Add a suggestions request to the search.
:arg name: name of the suggestion
:arg text: text to suggest on
All keyword arguments will be added to the suggestions body. For example::
s = Search()
s = s.suggest('suggestion-1', 'Elasticsearch', term={'field': 'body'})
"""
s = self._clone()
s._suggest[name] = {"text": text}
s._suggest[name].update(kwargs)
return s
def to_dict(self, count=False, **kwargs):
"""
Serialize the search into the dictionary that will be sent over as the
request's body.
:arg count: a flag to specify if we are interested in a body for count -
no aggregations, no pagination bounds etc.
All additional keyword arguments will be included into the dictionary.
"""
d = {}
if self._query:
d["query"] = self._query.to_dict()
# count request doesn't care for sorting and other things
if not count:
if self._post_filter:
d["post_filter"] = self._post_filter.to_dict()
if self._aggs:
d["aggs"] = self._aggs.to_dict()
if self._sort:
d["sort"] = self._sort
d.update(self._params)
if self._source not in (None, {}):
d["_source"] = self._source
if self._highlight:
d["highlight"] = {"fields": self._highlight}
d["highlight"].update(self._highlight_opts)
if self._suggest:
d["suggest"] = self._suggest
if self._script_fields:
d["script_fields"] = self._script_fields
d.update(kwargs)
return d
def count(self):
"""
Return the number of hits matching the query and filters. Note that
only the actual number is returned.
"""
es = get_connection(self._using)
d = self.to_dict(count=True)
return es.count(index=self._index, body=d)["count"]
def execute(self):
"""
Execute the search and return an instance of ``Response`` wrapping all
the data.
"""
es = get_connection(self._using)
return Response(es.search(index=self._index, body=self.to_dict()),
search=self)
def scan(self):
"""
Turn the search into a scan search and return a generator that will
iterate over all the documents matching the query.
Use ``params`` method to specify any additional arguments you with to
pass to the underlying ``scan`` helper from ``elasticsearch-py`` -
https://elasticsearch-py.readthedocs.io/en/master/helpers.html#elasticsearch.helpers.scan
"""
es = get_connection(self._using)
for hit in scan(es, query=self.to_dict(), index=self._index):
yield hit
def delete(self):
"""
delete() executes the query by delegating to delete_by_query()
"""
es = get_connection(self._using)
return es.delete_by_query(index=self._index, body=self.to_dict())
def __repr__(self):
return json.dumps(self.to_dict(), indent=2)
class Search(DSLMixin, Request):
_type_name = "search"
def __init__(
self,
using: Optional[Elasticsearch] = None,
index: Optional[Union[str, Tuple[str], List[str]]] = None,
mappings: Optional[Union[MappingsDict, Mappings]] = None,
nested_autocorrect: bool = False,
repr_auto_execute: bool = False,
document_class: DocumentMeta = None,
) -> None:
"""
Search request to elasticsearch.
:arg using: `Elasticsearch` instance to use
:arg index: limit the search to index
:arg mappings: mappings used for query validation
:arg nested_autocorrect: in case of missing nested clause, will insert it automatically
:arg repr_auto_execute: execute query and display results as dataframe, requires client to be provided
All the parameters supplied (or omitted) at creation type can be later
overridden by methods (`using`, `index` and `mappings` respectively).
"""
self._sort: List[Union[str, Dict[str, Any]]] = []
self._source: Any = None
self._highlight: Dict[str, Any] = {}
self._highlight_opts: Dict[str, Any] = {}
self._suggest: Dict[str, Any] = {}
self._script_fields: Dict[str, Any] = {}
mappings = _mappings(mappings)
self._mappings: Optional[Mappings] = mappings
self._aggs: Aggs = Aggs(mappings=mappings,
nested_autocorrect=nested_autocorrect)
self._query: Query = Query(mappings=mappings,
nested_autocorrect=nested_autocorrect)
self._post_filter: Query = Query(mappings=mappings,
nested_autocorrect=nested_autocorrect)
self._repr_auto_execute: bool = repr_auto_execute
self._document_class: Optional[DocumentMeta] = document_class
super(Search, self).__init__(using=using, index=index)
def query(self,
type_or_query: TypeOrQuery,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
compound_param: str = None,
**body: Any) -> "Search":
s = self._clone()
s._query = s._query.query(type_or_query,
insert_below=insert_below,
on=on,
mode=mode,
compound_param=compound_param,
**body)
return s
query.__doc__ = Query.query.__doc__
def bool(self,
must: Optional[SingleOrMultipleQueryClause] = None,
should: Optional[SingleOrMultipleQueryClause] = None,
must_not: Optional[SingleOrMultipleQueryClause] = None,
filter: Optional[SingleOrMultipleQueryClause] = None,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
**body: Any) -> "Search":
s = self._clone()
s._query = s._query.bool(must=must,
should=should,
filter=filter,
must_not=must_not,
insert_below=insert_below,
on=on,
mode=mode,
**body)
return s
bool.__doc__ = Query.bool.__doc__
def filter(self,
type_or_query: TypeOrQuery,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
bool_body: ClauseBody = None,
**body: Any) -> "Search":
s = self._clone()
s._query = s._query.filter(type_or_query,
insert_below=insert_below,
on=on,
mode=mode,
bool_body=bool_body,
**body)
return s
filter.__doc__ = Query.filter.__doc__
def must_not(self,
type_or_query: TypeOrQuery,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
bool_body: ClauseBody = None,
**body: Any) -> "Search":
s = self._clone()
s._query = s._query.must_not(type_or_query,
insert_below=insert_below,
on=on,
mode=mode,
bool_body=bool_body,
**body)
return s
must_not.__doc__ = Query.must_not.__doc__
def should(self,
type_or_query: TypeOrQuery,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
bool_body: ClauseBody = None,
**body: Any) -> "Search":
s = self._clone()
s._query = s._query.should(type_or_query,
insert_below=insert_below,
on=on,
mode=mode,
bool_body=bool_body,
**body)
return s
should.__doc__ = Query.should.__doc__
def must(self,
type_or_query: TypeOrQuery,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
bool_body: ClauseBody = None,
**body: Any) -> "Search":
s = self._clone()
s._query = s._query.must(type_or_query,
insert_below=insert_below,
on=on,
mode=mode,
bool_body=bool_body,
**body)
return s
must.__doc__ = Query.must.__doc__
def exclude(self,
type_or_query: TypeOrQuery,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
bool_body: ClauseBody = None,
**body: Any) -> "Search":
"""Must not wrapped in filter context."""
s = self._clone()
s._query = s._query.filter(
Bool(must_not=Query._q(type_or_query=type_or_query, **body)),
insert_below=insert_below,
on=on,
mode=mode,
bool_body=bool_body,
)
return s
def post_filter(self,
type_or_query: TypeOrQuery,
insert_below: Optional[QueryName] = None,
on: Optional[QueryName] = None,
mode: InsertionModes = ADD,
compound_param: str = None,
**body: Any) -> "Search":
s = self._clone()
s._post_filter = s._post_filter.query(type_or_query=type_or_query,
insert_below=insert_below,
on=on,
mode=mode,
compound_param=compound_param,
**body)
return s
def agg(self,
name: AggName,
type_or_agg: Optional[TypeOrAgg] = None,
insert_below: Optional[AggName] = None,
at_root: bool_ = False,
**body: Any) -> "Search":
s = self._clone()
s._aggs = s._aggs.agg(name,
type_or_agg=type_or_agg,
insert_below=insert_below,
at_root=at_root,
**body)
return s
agg.__doc__ = Aggs.agg.__doc__
def aggs(
self,
aggs: Union[AggsDictOrNode, "Aggs"],
insert_below: Optional[AggName] = None,
at_root: bool_ = False,
) -> "Search":
s = self._clone()
s._aggs = s._aggs.aggs(aggs,
insert_below=insert_below,
at_root=at_root)
return s
aggs.__doc__ = Aggs.aggs.__doc__
def groupby(self,
name: AggName,
type_or_agg: Optional[TypeOrAgg] = None,
insert_below: Optional[AggName] = None,
at_root: bool_ = False,
**body: Any) -> "Search":
s = self._clone()
s._aggs = s._aggs.groupby(name,
type_or_agg=type_or_agg,
insert_below=insert_below,
at_root=at_root,
**body)
return s
groupby.__doc__ = Aggs.groupby.__doc__
def __iter__(self) -> Iterator[Hit]:
"""
Iterate over the hits. Return iterable of ``pandagg.response.Hit``.
"""
return iter(self.execute())
def __getitem__(self, n: Union[slice, List, int]) -> "Search":
"""
Support slicing the `Search` instance for pagination.
Slicing equates to the from/size parameters. E.g.::
s = Search().query(...)[0:25]
is equivalent to::
s = Search().query(...).params(from=0, size=25)
"""
s = self._clone()
if isinstance(n, slice):
# If negative slicing, abort.
if n.start and n.start < 0 or n.stop and n.stop < 0:
raise ValueError("Search does not support negative slicing.")
# Elasticsearch won't get all results so we default to size: 10 if
# stop not given.
s._params["from"] = n.start or 0
s._params["size"] = n.stop - (n.start
or 0) if n.stop is not None else 10
return s
if isinstance(n, list):
return s.source(includes=n)
# This is an index lookup, equivalent to slicing by [n:n+1].
# If negative index, abort.
if n < 0:
raise ValueError("Search does not support negative indexing.")
s._params["from"] = n
s._params["size"] = 1
return s
def size(self, size: int) -> "Search":
"""
Equivalent to::
s = Search().params(size=size)
"""
s = self._clone()
s._params["size"] = size
return s
@classmethod
def from_dict(cls, d: Dict) -> "Search":
"""
Construct a new `Search` instance from a raw dict containing the search
body. Useful when migrating from raw dictionaries.
Example::
s = Search.from_dict({
"query": {
"bool": {
"must": [...]
}
},
"aggs": {...}
})
s = s.filter('term', published=True)
"""
s = cls()
s.update_from_dict(d)
return s
def _clone(self) -> "Search":
"""
Return a clone of the current search request. Performs a shallow copy
of all the underlying objects. Used internally by most state modifying
APIs.
"""
s = Search(using=self._using,
index=self._index,
mappings=self._mappings)
s._params = self._params.copy()
s._sort = self._sort[:]
s._source = copy.copy(
self._source) if self._source is not None else None
s._highlight = self._highlight.copy()
s._highlight_opts = self._highlight_opts.copy()
s._suggest = self._suggest.copy()
s._script_fields = self._script_fields.copy()
s._aggs = self._aggs.clone()
s._query = self._query.clone()
s._post_filter = self._post_filter.clone()
s._mappings = None if self._mappings is None else self._mappings.clone(
)
s._repr_auto_execute = self._repr_auto_execute
s._document_class = self._document_class
return s
def update_from_dict(self, d: Dict) -> "Search":
"""
Apply options from a serialized body to the current instance. Modifies
the object in-place. Used mostly by ``from_dict``.
"""
d = d.copy()
if "query" in d:
self._query = Query(d.pop("query"))
if "post_filter" in d:
self._post_filter = Query(d.pop("post_filter"))
aggs = d.pop("aggs", d.pop("aggregations", {}))
if aggs:
self._aggs = Aggs(aggs)
if "sort" in d:
self._sort = d.pop("sort")
if "_source" in d:
self._source = d.pop("_source")
if "highlight" in d:
high = d.pop("highlight").copy()
self._highlight = high.pop("fields")
self._highlight_opts = high
if "suggest" in d:
self._suggest = d.pop("suggest")
if "text" in self._suggest:
text = self._suggest.pop("text")
for s in self._suggest.values():
s.setdefault("text", text)
if "script_fields" in d:
self._script_fields = d.pop("script_fields")
self._params.update(d)
return self
def script_fields(self, **kwargs: Any) -> "Search":
"""
Define script fields to be calculated on hits. See
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-script-fields.html
for more details.
Example::
s = Search()
s = s.script_fields(times_two="doc['field'].value * 2")
s = s.script_fields(
times_three={
'script': {
'inline': "doc['field'].value * params.n",
'params': {'n': 3}
}
}
)
"""
s = self._clone()
for name in kwargs:
if isinstance(kwargs[name], str):
kwargs[name] = {"script": kwargs[name]}
s._script_fields.update(kwargs)
return s
def source(self,
fields: Union[str, List[str], Dict[str, Any]] = None,
**kwargs: Any) -> "Search":
"""
Selectively control how the _source field is returned.
:arg fields: wildcard string, array of wildcards, or dictionary of includes and excludes
If ``fields`` is None, the entire document will be returned for
each hit. If fields is a dictionary with keys of 'includes' and/or
'excludes' the fields will be either included or excluded appropriately.
Calling this multiple times with the same named parameter will override the
previous values with the new ones.
Example::
s = Search()
s = s.source(includes=['obj1.*'], excludes=["*.description"])
s = Search()
s = s.source(includes=['obj1.*']).source(excludes=["*.description"])
"""
s = self._clone()
if fields and kwargs:
raise ValueError(
"You cannot specify fields and kwargs at the same time.")
if fields is not None:
s._source = fields
return s
if kwargs and not isinstance(s._source, dict):
s._source = {}
for key, value in kwargs.items():
if value is None:
try:
del s._source[key]
except KeyError:
pass
else:
s._source[key] = value
return s
def sort(self, *keys: Union[str, Dict[str, Any]]) -> "Search":
"""
Add sorting information to the search request. If called without
arguments it will remove all sort requirements. Otherwise it will
replace them. Acceptable arguments are::
'some.field'
'-some.other.field'
{'different.field': {'any': 'dict'}}
so for example::
s = Search().sort(
'category',
'-title',
{"price" : {"order" : "asc", "mode" : "avg"}}
)
will sort by ``category``, ``title`` (in descending order) and
``price`` in ascending order using the ``avg`` mode.
The API returns a copy of the Search object and can thus be chained.
"""
s = self._clone()
s._sort = []
for k in keys:
if isinstance(k, str) and k.startswith("-"):
if k[1:] == "_score":
raise ValueError("Sorting by `-_score` is not allowed.")
k = {k[1:]: {"order": "desc"}}
s._sort.append(k)
return s
def highlight_options(self, **kwargs: Any) -> "Search":
"""
Update the global highlighting options used for this request. For
example::
s = Search()
s = s.highlight_options(order='score')
"""
s = self._clone()
s._highlight_opts.update(kwargs)
return s
def highlight(self, *fields: str, **kwargs: Any) -> "Search":
"""
Request highlighting of some fields. All keyword arguments passed in will be
used as parameters for all the fields in the ``fields`` parameter. Example::
Search().highlight('title', 'body', fragment_size=50)
will produce the equivalent of::
{
"highlight": {
"fields": {
"body": {"fragment_size": 50},
"title": {"fragment_size": 50}
}
}
}
If you want to have different options for different fields you can call ``highlight`` twice::
Search().highlight('title', fragment_size=50).highlight('body', fragment_size=100)
which will produce::
{
"highlight": {
"fields": {
"body": {"fragment_size": 100},
"title": {"fragment_size": 50}
}
}
}
"""
s = self._clone()
for f in fields:
s._highlight[f] = kwargs
return s
def suggest(self, name: str, text: str, **kwargs: Any) -> "Search":
"""
Add a suggestions request to the search.
:arg name: name of the suggestion
:arg text: text to suggest on
All keyword arguments will be added to the suggestions body. For example::
s = Search()
s = s.suggest('suggestion-1', 'Elasticsearch', term={'field': 'body'})
"""
s = self._clone()
s._suggest[name] = {"text": text}
s._suggest[name].update(kwargs)
return s
def to_dict(self, count: bool_ = False, **kwargs: Any) -> SearchDict:
"""
Serialize the search into the dictionary that will be sent over as the
request's body.
:arg count: a flag to specify if we are interested in a body for count -
no aggregations, no pagination bounds etc.
All additional keyword arguments will be included into the dictionary.
"""
d: SearchDict = {}
if self._query:
dq = self._query.to_dict()
if dq:
d["query"] = dq
# count request doesn't care for sorting and other things
if not count:
if self._post_filter:
pfd = self._post_filter.to_dict()
if pfd:
d["post_filter"] = pfd
if self._aggs:
d["aggs"] = self._aggs.to_dict()
if self._sort:
d["sort"] = self._sort
# query params are not typed in search dict
d.update(self._params) # type: ignore
if self._source not in (None, {}):
d["_source"] = self._source
if self._highlight:
highlights: Dict[str, Any] = {"fields": self._highlight}
highlights.update(self._highlight_opts)
d["highlight"] = highlights
if self._suggest:
d["suggest"] = self._suggest
if self._script_fields:
d["script_fields"] = self._script_fields
# TODO: check if those kwargs are really useful
d.update(kwargs) # type: ignore
return d
def count(self) -> int:
"""
Return the number of hits matching the query and filters. Note that
only the actual number is returned.
"""
es = self._get_connection()
d = self.to_dict(count=True)
return es.count(index=self._index, body=d)["count"]
def execute(self) -> SearchResponse:
"""
Execute the search and return an instance of ``Response`` wrapping all
the data.
"""
es = self._get_connection()
raw_data = es.search(index=self._index, body=self.to_dict())
return SearchResponse(data=raw_data, _search=self) # type: ignore
def scan_composite_agg(self, size: int) -> Iterator[BucketDict]:
"""Iterate over the whole aggregation composed buckets, yields buckets."""
s: Search = self._clone().size(0)
s._aggs = s._aggs.as_composite(size=size)
a_name, _ = s._aggs.get_composition_supporting_agg()
r: SearchResponse = s.execute()
buckets: List[BucketDict] = r.aggregations.data[
a_name][ # type: ignore
"buckets"]
after_key: AfterKey = r.aggregations.data[a_name][
"after_key"] # type: ignore
init: bool = True
while init or len(buckets) == size:
init = False
s._aggs = s._aggs.as_composite(size=size, after=after_key)
r = s.execute()
agg_clause_response = r.aggregations.data[a_name]
buckets = agg_clause_response["buckets"] # type: ignore
for bucket in buckets:
yield bucket
if "after_key" in agg_clause_response:
after_key = agg_clause_response["after_key"] # type: ignore
else:
break
def scan_composite_agg_at_once(self, size: int) -> Aggregations:
"""Iterate over the whole aggregation composed buckets (converting Aggs into composite agg if possible), and
return all buckets at once in a Aggregations instance.
"""
all_buckets = list(self.scan_composite_agg(size=size))
s: Search = self._clone().size(0)
s._aggs = s._aggs.as_composite(size=size)
agg_name: AggName
agg_name, _ = s._aggs.get_composition_supporting_agg() # type: ignore
# artificially merge all buckets as if they were returned in a single query
return Aggregations(_search=s,
data={agg_name: {
"buckets": all_buckets
}})
def scan(self) -> Iterator[Hit]:
"""
Turn the search into a scan search and return a generator that will
iterate over all the documents matching the query.
Use ``params`` method to specify any additional arguments you with to
pass to the underlying ``scan`` helper from ``elasticsearch-py`` -
https://elasticsearch-py.readthedocs.io/en/master/helpers.html#elasticsearch.helpers.scan
"""
es = self._get_connection()
for hit in scan(es, query=self.to_dict(), index=self._index):
yield Hit(hit, _document_class=self._document_class)
def delete(self) -> DeleteByQueryResponse:
"""
delete() executes the query by delegating to delete_by_query()
"""
es = self._get_connection()
return es.delete_by_query(index=self._index,
body=self.to_dict()) # type: ignore
def __eq__(self, other: Any) -> bool_:
return (isinstance(other, Search) and other._index == self._index
and other.to_dict() == self.to_dict())
def _auto_execution_df_result(self) -> pd.DataFrame:
try:
import pandas as pd # noqa
except ImportError:
raise ImportError("repr_auto_execute requires pandas dependency")
if self._aggs.to_dict():
# hits are not necessary to display aggregation results
r = self.size(0).execute()
return r.aggregations.to_dataframe()
r = self.execute()
return r.hits.to_dataframe()
def __repr__(self) -> str:
# inspired by https://github.com/elastic/eland/blob/master/eland/dataframe.py#L471 idea to execute search at
# __repr__ to have more interactive experience
if not self._repr_auto_execute:
return json.dumps(self.to_dict(), indent=2)
return self._auto_execution_df_result().__repr__()
def _repr_html_(self) -> Optional[str]:
if not self._repr_auto_execute:
return None
return self._auto_execution_df_result()._repr_html_()
def test_normalize_buckets(self):
my_agg = Aggs(sample.EXPECTED_AGG_QUERY, mappings=MAPPINGS)
response = Aggregations(data=sample.ES_AGG_RESPONSE,
_search=Search().aggs(my_agg)).to_normalized()
self.assertEqual(ordered(response),
ordered(sample.EXPECTED_NORMALIZED_RESPONSE))
