def test_attr_range_facet_filter__existing_post_filter(range_qf, compiler):
sq = range_qf.apply(SearchQuery().post_filter(Field('status').term(0)), {})
assert_search_query(
sq,
SearchQuery().aggs({
'qf.attr_range.filter':
agg.Filter(Term('status', 0),
aggs={
'qf.attr_range':
agg.Terms(script=Script(
'doc[params.field].value >>> 32',
lang='painless',
params={
'field': 'attr.float',
}),
size=100),
}),
}).post_filter(Term('status', 0)), compiler)
def _get_filter_expression(self, attr_id: int,
values) -> t.Optional[Expression]:
w = [
merge_attr_value_int(attr_id, v)
for v in self._parse_values(values, 'exact')
]
if not w:
return None
if len(w) == 1:
return Term(self.field, w[0])
return Terms(self.field, w)
def test_attr_bool_simple_filter(compiler):
qf = QueryFilter()
qf.add_filter(
AttrBoolSimpleFilter('attr_bool', Field('attr.bool'), alias='a'))
sq = qf.apply(SearchQuery(), {})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a1': 'true'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Term('attr.bool', 0x3)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a1': 'True'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Term('attr.bool', 0x3)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a1': [True]})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Term('attr.bool', 0x3)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a1': 'False'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Term('attr.bool', 0x2)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a1': ['true', 'false']})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Terms('attr.bool', [0x3, 0x2])).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a1': ['true', 'false'], 'a2': 'false'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Terms('attr.bool',
[0x3, 0x2])).filter(Term('attr.bool',
0x4)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a2147483648': '1'})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a1': 'TRUE'})
assert sq.to_dict(compiler=compiler) == {}
def test_search_query_compile(self):
f = DynamicDocument.fields
sq = SearchQuery()
self.assert_expression(sq, {})
self.assertEqual(collect_doc_classes(sq), set())
sq = SearchQuery(Term(f.user, 'kimchy')).limit(10).offset(0)
self.assert_expression(
sq,
{
"from": 0,
"size": 10,
"query": {
"term": {"user": "******"}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = SearchQuery(Term(f.user, 'kimchy')).filter(f.age >= 16)
self.assert_expression(
sq,
{
"query": {
"filtered": {
"query": {
"term": {"user": "******"}
},
"filter": {
"range": {
"age": {"gte": 16}
}
}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = SearchQuery(Term(f.user, 'kimchy'), _compiler=QueryCompiled20).filter(f.age >= 16)
self.assert_expression(
sq,
{
"query": {
"bool": {
"must": {
"term": {"user": "******"}
},
"filter": {
"range": {
"age": {"gte": 16}
}
}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery(Term(f.user, 'kimchy'))
.query(f.user != 'kimchy')
)
self.assert_expression(
sq,
{
"query": {
"bool": {
"must_not": [
{
"term": {"user": "******"}
}
]
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery(Term(f.user, 'kimchy'))
.query(None)
)
self.assert_expression(sq, {})
self.assertEqual(collect_doc_classes(sq), set())
sq = (
SearchQuery(Term(f.user, 'kimchy'))
.filter(f.age >= 16)
.filter(f.lang == 'English')
)
self.assert_expression(
sq,
{
"query": {
"filtered": {
"query": {
"term": {"user": "******"}
},
"filter": {
"bool": {
"must": [
{
"range": {
"age": {"gte": 16}
}
},
{
"term": {
"lang": "English"
}
}
]
}
}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.order_by(
f.opinion_rating.desc(missing='_last'),
f.opinion_count.desc(),
f.id
)
)
self.assert_expression(
sq,
{
"sort": [
{
"opinion_rating": {
"order": "desc",
"missing": "_last"
}
},
{
"opinion_count": "desc"
},
"id"
]
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.order_by(
f.opinion_rating.desc(missing='_last'),
f.opinion_count.desc(),
f.id
)
.order_by(None)
.order_by(None)
)
self.assert_expression(sq, {})
self.assertEqual(collect_doc_classes(sq), set())
sq = SearchQuery().source(f.name, f.company)
self.assert_expression(
sq,
{
"_source": ["name", "company"]
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = SearchQuery().source(exclude=[f.name, f.company])
self.assert_expression(
sq,
{
"_source": {
"exclude": ["name", "company"]
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.source(
include=[f.obj1.wildcard('*'), f.obj2.wildcard('*')],
# FIXME: f.wildcard('*')
exclude=DynamicDocument.wildcard('*').description
)
)
self.assert_expression(
sq,
{
"_source": {
"include": ["obj1.*", "obj2.*"],
"exclude": "*.description"
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.source(None)
.source(f.name, f.company)
.source(None)
)
self.assert_expression(sq, {})
self.assertEqual(collect_doc_classes(sq), set())
sq = (
SearchQuery()
.source(f.name, f.company)
.source(False)
)
self.assert_expression(
sq,
{
"_source": False
}
)
self.assertEqual(collect_doc_classes(sq), set())
sq = (
SearchQuery()
.source(True)
)
self.assert_expression(
sq,
{
"_source": True
}
)
self.assertEqual(collect_doc_classes(sq), set())
sq = SearchQuery().fields(f.name, f.company)
self.assert_expression(
sq,
{
"fields": ["name", "company"]
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.fields(True)
)
self.assert_expression(
sq,
{
"fields": '*'
}
)
self.assertEqual(collect_doc_classes(sq), set())
sq = (
SearchQuery()
.fields(None)
.fields(f.name, f.company)
.fields(None)
)
self.assert_expression(sq, {})
self.assertEqual(collect_doc_classes(sq), set())
sq = (
SearchQuery()
.fields(f.name, f.company)
.fields(False)
)
self.assert_expression(
sq,
{
"fields": []
}
)
self.assertEqual(collect_doc_classes(sq), set())
self.assert_expression(
SearchQuery()
.function_score({'random_score': {"seed": 1234}}),
{
"query": {
"function_score": {
"functions": [
{
"random_score": {"seed": 1234}
}
],
}
}
}
)
sq = (
SearchQuery(MultiMatch('Iphone 6', fields=[f.name, f.description]))
.filter(f.status == 0)
.function_score(None)
.function_score({'_score': {"seed": 1234}})
.function_score(None)
.function_score({'field_value_factor': {'field': f.popularity,
'factor': 1.2,
'modifier': 'sqrt'}},
boost_mode='sum')
.function_score({'boost_factor': 3,
'filter': f.region == 12})
)
self.assert_expression(
sq,
{
"query": {
"filtered": {
"query": {
"function_score": {
"query": {
"multi_match": {
"query": "Iphone 6",
"fields": ["name", "description"]
}
},
"functions": [
{
"field_value_factor": {
"field": "popularity",
"factor": 1.2,
"modifier": "sqrt"
}
},
{
"filter": {
"term": {"region": 12}
},
"boost_factor": 3
}
],
"boost_mode": "sum"
}
},
"filter": {
"term": {"status": 0}
}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.filter(f.status == 0)
.boost_score(
{'filter': f.discount_percent >= 10, 'weight': 1000},
{'filter': f.discount_percent >= 50, 'weight': 2000},
{'filter': f.presence == 'available', 'weight': 10000},
)
)
self.assert_expression(
sq,
{
"query": {
"filtered": {
"query": {
"function_score": {
"functions": [
{
"filter": {"range": {"discount_percent": {"gte": 10}}},
"weight": 1000
},
{
"filter": {"range": {"discount_percent": {"gte": 50}}},
"weight": 2000
},
{
"filter": {"term": {"presence": "available"}},
"weight": 10000
},
],
"score_mode": "sum",
"boost_mode": "sum"
}
},
"filter": {
"term": {"status": 0}
}
}
}
}
)
sq = (
SearchQuery(f.name.match('test'))
.filter(f.status == 0)
.function_score(
{'field_value_factor': {'field': f.popularity}},
)
.boost_score(
{'filter': f.discount_percent >= 10, 'weight': 100},
)
.boost_score(None)
.boost_score(
{'filter': f.discount_percent >= 10, 'weight': 1000},
{'filter': f.discount_percent >= 50, 'weight': 2000},
score_mode='max',
)
)
self.assert_expression(
sq,
{
"query": {
"filtered": {
"query": {
"function_score": {
"query": {
"function_score": {
"query": {
"match": {
"name": "test"
}
},
"functions": [
{
"field_value_factor": {
"field": "popularity"
}
}
]
}
},
"functions": [
{
"filter": {"range": {"discount_percent": {"gte": 10}}},
"weight": 1000
},
{
"filter": {"range": {"discount_percent": {"gte": 50}}},
"weight": 2000
},
],
"score_mode": "max",
"boost_mode": "sum"
}
},
"filter": {
"term": {"status": 0}
}
}
}
}
)
sq = (
SearchQuery()
.rescore(
QueryRescorer(
self.index.t.field1.match('the quick brown', type='phrase', slop=2)
)
)
.rescore(None)
.rescore(
QueryRescorer(
self.index.t.field1.match('the quick brown fox', type='phrase', slop=2),
query_weight=0.7,
rescore_query_weight=1.2
),
window_size=100,
)
.rescore(
QueryRescorer(
FunctionScore(script_score={'script': "log10(doc['numeric'].value + 2)"}),
score_mode='multiply'
),
window_size=10,
)
)
self.assert_expression(
sq,
{
"rescore": [
{
"window_size": 100,
"query": {
"rescore_query": {
"match": {
"field1": {
"query": "the quick brown fox",
"type": "phrase",
"slop": 2
}
}
},
"query_weight": 0.7,
"rescore_query_weight": 1.2
}
},
{
"window_size": 10,
"query": {
"score_mode": "multiply",
"rescore_query": {
"function_score": {
"script_score": {
"script": "log10(doc['numeric'].value + 2)"
}
}
}
}
}
]
}
)
self.assertEqual(collect_doc_classes(sq), {self.index.t})
sq = SearchQuery().post_filter(self.index.shirt.color == 'red')
self.assert_expression(
sq,
{
"post_filter": {
"term": {"color": "red"}
}
}
)
self.assertEqual(collect_doc_classes(sq), {self.index.shirt})
sq = (
SearchQuery()
.filter(self.index.shirt.brand == 'gucci')
.post_filter(self.index.shirt.color == 'red')
.post_filter(self.index.shirt.model == 't-shirt')
)
self.assert_expression(
sq,
{
"query": {
"filtered": {
"filter": {
"term": {"brand": "gucci"}
}
}
},
"post_filter": {
"bool": {
"must": [
{"term": {"color": "red"}},
{"term": {"model": "t-shirt"}}
]
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {self.index.shirt})
def test_aggs(self):
f = DynamicDocument.fields
a = agg.AggExpression()
self.assertRaises(NotImplementedError, a.build_agg_result, {})
a = agg.Avg(f.price)
self.assert_expression(
a,
{
"avg": {"field": "price"}
}
)
res = a.build_agg_result({
'value': 75.3
})
self.assertAlmostEqual(res.value, 75.3)
res = a.build_agg_result({
'value': None
})
self.assertIs(res.value, None)
aa = a.clone()
self.assertIsNot(a, aa)
self.assertEqual(a.__visit_name__, aa.__visit_name__)
self.assertEqual(a.params, aa.params)
a = agg.Min(f.price)
self.assert_expression(
a,
{
"min": {"field": "price"}
}
)
res = a.build_agg_result({
'value': 38
})
self.assertAlmostEqual(res.value, 38)
res = a.build_agg_result({
'value': 1297167619690,
'value_as_string': '2011-02-08T12:20:19.690Z'
})
self.assertAlmostEqual(res.value, 1297167619690)
a = agg.Max(f.price)
self.assert_expression(
a,
{
"max": {"field": "price"}
}
)
res = a.build_agg_result({
'value': 45693.5
})
self.assertAlmostEqual(res.value, 45693.5)
a = agg.Stats(f.grade)
self.assert_expression(
a,
{
"stats": {"field": "grade"}
}
)
a = a.build_agg_result(
{
"count": 6,
"min": 60,
"max": 98,
"avg": 78.5,
"sum": 471
}
)
self.assertEqual(a.count, 6)
self.assertEqual(a.min, 60)
self.assertEqual(a.max, 98)
self.assertAlmostEqual(a.avg, 78.5)
self.assertEqual(a.sum, 471)
a = agg.ExtendedStats(f.grade)
self.assert_expression(
a,
{
"extended_stats": {"field": "grade"}
}
)
a = a.build_agg_result(
{
"count": 6,
"min": 72,
"max": 117.6,
"avg": 94.2,
"sum": 565.2,
"sum_of_squares": 54551.51999999999,
"variance": 218.2799999999976,
"std_deviation": 14.774302013969987
}
)
self.assertEqual(a.count, 6)
self.assertEqual(a.min, 72)
self.assertAlmostEqual(a.max, 117.6)
self.assertAlmostEqual(a.avg, 94.2)
self.assertAlmostEqual(a.sum, 565.2)
self.assertAlmostEqual(a.sum_of_squares, 54551.51999999999)
self.assertAlmostEqual(a.variance, 218.2799999999976)
self.assertAlmostEqual(a.std_deviation, 14.774302013969987)
percentiles_agg = agg.Percentiles(f.load_time, percents=[95, 99, 99.9])
self.assert_expression(
percentiles_agg,
{
"percentiles": {
"field": "load_time",
"percents": [95, 99, 99.9]
}
}
)
a = percentiles_agg.build_agg_result(
{
"values": {
"95.0": 60,
"99.0": 150,
"99.9": 153,
}
}
)
self.assertEqual(
a.values,
[(95.0, 60), (99.0, 150), (99.9, 153)],
)
self.assertEqual(a.get_value(95), 60)
self.assertEqual(a.get_value(95.0), 60)
self.assertEqual(a.get_value(99), 150)
self.assertEqual(a.get_value(99.0), 150)
self.assertEqual(a.get_value(99.9), 153)
a = percentiles_agg.build_agg_result(
{
"values": {
"95.0": 60,
"95.0_as_string": "60",
"99.0": 150,
"99.0_as_string": "150",
"99.9": 153,
"99.9_as_string": "153",
}
}
)
self.assertEqual(
a.values,
[(95.0, 60), (99.0, 150), (99.9, 153)],
)
self.assertEqual(a.get_value(95), 60)
self.assertEqual(a.get_value(95.0), 60)
self.assertEqual(a.get_value(99), 150)
self.assertEqual(a.get_value(99.0), 150)
self.assertEqual(a.get_value(99.9), 153)
percentiles_agg = agg.Percentiles(f.load_time, percents=[50])
self.assert_expression(
percentiles_agg,
{
"percentiles": {
"field": "load_time",
"percents": [50]
}
}
)
a = percentiles_agg.build_agg_result(
{
"values": {
"50.0": "NaN",
}
}
)
self.assertEqual(
len(a.values),
1
)
self.assertAlmostEqual(
a.values[0][0],
50.0
)
self.assertTrue(
math.isnan(a.values[0][1])
)
self.assertTrue(math.isnan(a.get_value(50)))
self.assertTrue(math.isnan(a.get_value(50.0)))
ranks_agg = agg.PercentileRanks(f.load_time, values=[14.8, 30])
self.assert_expression(
ranks_agg,
{
"percentile_ranks": {
"field": "load_time",
"values": [14.8, 30.0]
}
}
)
a = ranks_agg.build_agg_result(
{
"values": {
"14.8": 12.32,
"30": 100,
}
}
)
self.assertEqual(
a.values,
[(14.8, 12.32), (30.0, 100)],
)
self.assertEqual(
a.values,
[(14.8, 12.32), (30.0, 100)],
)
self.assertAlmostEqual(a.get_percent(14.8), 12.32)
self.assertAlmostEqual(a.get_percent(13.7 + 1.1), 12.32)
self.assertAlmostEqual(a.get_percent(30), 100.0)
self.assertAlmostEqual(a.get_percent(30.0), 100.0)
a = ranks_agg.build_agg_result(
{
"values": {
"14.8": 12.32,
"14.8_as_string": "12.32",
"30": 100,
"30_as_string": "100",
}
}
)
self.assertEqual(
a.values,
[(14.8, 12.32), (30.0, 100)],
)
self.assertEqual(
a.values,
[(14.8, 12.32), (30.0, 100)],
)
self.assertAlmostEqual(a.get_percent(14.8), 12.32)
self.assertAlmostEqual(a.get_percent(13.7 + 1.1), 12.32)
self.assertAlmostEqual(a.get_percent(30), 100.0)
self.assertAlmostEqual(a.get_percent(30.0), 100.0)
a = agg.Cardinality(f.author, precision_threshold=100)
self.assert_expression(
a,
{
"cardinality": {
"field": "author",
"precision_threshold": 100
}
}
)
a = a.build_agg_result(
{
"value": 184
}
)
self.assertEqual(a.value, 184)
a = agg.Global()
self.assert_expression(a, {"global": {}})
a = a.build_agg_result(
{"doc_count": 185}
)
self.assertEqual(a.doc_count, 185)
a = agg.Filter(f.company == 1)
self.assert_expression(a, {"filter": {"term": {"company": 1}}})
a2 = a.clone()
self.assertIsNot(a, a2)
self.assert_expression(a2, {"filter": {"term": {"company": 1}}})
a = a.build_agg_result(
{"doc_count": 148}
)
self.assertEqual(a.doc_count, 148)
a = agg.Terms(f.status)
self.assert_expression(
a,
{
"terms": {"field": "status"}
}
)
a1 = a.clone()
self.assertIsNot(a, a1)
a = a.build_agg_result(
{
'buckets': [
{'doc_count': 7353499, 'key': 0},
{'doc_count': 2267139, 'key': 1},
{'doc_count': 1036951, 'key': 4},
{'doc_count': 438384, 'key': 2},
{'doc_count': 9594, 'key': 3},
{'doc_count': 46, 'key': 5}
]
}
)
self.assertEqual(len(a.buckets), 6)
self.assertEqual(list(iter(a)), a.buckets)
self.assertEqual(a.buckets[0].key, 0)
self.assertEqual(a.buckets[0].doc_count, 7353499)
self.assertEqual(repr(a.buckets[0]), '<Bucket key=0 doc_count=7353499>')
self.assertIs(a.buckets[0], a.get_bucket(0))
self.assertEqual(a.buckets[1].key, 1)
self.assertEqual(a.buckets[1].doc_count, 2267139)
self.assertIs(a.buckets[1], a.get_bucket(1))
self.assertEqual(repr(a.buckets[1]), '<Bucket key=1 doc_count=2267139>')
self.assertEqual(a.buckets[2].key, 4)
self.assertEqual(a.buckets[2].doc_count, 1036951)
self.assertIs(a.buckets[2], a.get_bucket(4))
self.assertEqual(repr(a.buckets[2]), '<Bucket key=4 doc_count=1036951>')
self.assertEqual(a.buckets[3].key, 2)
self.assertEqual(a.buckets[3].doc_count, 438384)
self.assertIs(a.buckets[3], a.get_bucket(2))
self.assertEqual(repr(a.buckets[3]), '<Bucket key=2 doc_count=438384>')
self.assertEqual(a.buckets[4].key, 3)
self.assertEqual(a.buckets[4].doc_count, 9594)
self.assertIs(a.buckets[4], a.get_bucket(3))
self.assertEqual(repr(a.buckets[4]), '<Bucket key=3 doc_count=9594>')
self.assertEqual(a.buckets[5].key, 5)
self.assertEqual(a.buckets[5].doc_count, 46)
self.assertIs(a.buckets[5], a.get_bucket(5))
self.assertEqual(repr(a.buckets[5]), '<Bucket key=5 doc_count=46>')
a = agg.Terms(f.is_visible, type=Boolean)
self.assert_expression(
a,
{
"terms": {"field": "is_visible"}
}
)
a = a.build_agg_result(
{
'buckets': [
{'doc_count': 7, 'key': 'T'},
{'doc_count': 2, 'key': 'F'},
]
}
)
self.assertEqual(len(a.buckets), 2)
self.assertEqual(a.buckets[0].key, True)
self.assertEqual(a.buckets[0].doc_count, 7)
self.assertIs(a.buckets[0], a.get_bucket(True))
self.assertEqual(a.buckets[1].key, False)
self.assertEqual(a.buckets[1].doc_count, 2)
self.assertIs(a.buckets[1], a.get_bucket(False))
a = agg.Terms(f.category, type=List(Integer))
self.assert_expression(
a,
{
"terms": {"field": "category"}
}
)
a = a.build_agg_result(
{
'buckets': [
{'doc_count': 792, 'key': 28},
{'doc_count': 185, 'key': 3},
]
}
)
self.assertEqual(len(a.buckets), 2)
self.assertEqual(a.buckets[0].key, 28)
self.assertEqual(a.buckets[0].doc_count, 792)
self.assertIs(a.buckets[0], a.get_bucket(28))
self.assertEqual(a.buckets[1].key, 3)
self.assertEqual(a.buckets[1].doc_count, 185)
self.assertIs(a.buckets[1], a.get_bucket(3))
class ProductDocument(Document):
is_visible = Field(Boolean)
a = agg.Terms(ProductDocument.is_visible)
self.assert_expression(
a,
{
"terms": {"field": "is_visible"}
}
)
a = a.build_agg_result(
{
'buckets': [
{'doc_count': 7, 'key': 'T'},
{'doc_count': 2, 'key': 'F'},
]
}
)
self.assertEqual(len(a.buckets), 2)
self.assertEqual(a.buckets[0].key, True)
self.assertEqual(a.buckets[0].doc_count, 7)
self.assertIs(a.buckets[0], a.get_bucket(True))
self.assertEqual(a.buckets[1].key, False)
self.assertEqual(a.buckets[1].doc_count, 2)
self.assertIs(a.buckets[1], a.get_bucket(False))
a = agg.SignificantTerms(f.crime_type)
self.assert_expression(
a,
{
"significant_terms": {"field": "crime_type"}
}
)
a = a.build_agg_result(
{
"doc_count": 47347,
"buckets" : [
{
"key": "Bicycle theft",
"doc_count": 3640,
"score": 0.371,
"bg_count": 66799,
},
{
"key": "Mobile phone theft",
"doc_count": 27617,
"score": 0.0599,
"bg_count": 53182,
}
]
}
)
self.assertEqual(len(a.buckets), 2)
self.assertEqual(a.buckets[0].key, 'Bicycle theft')
self.assertEqual(a.buckets[0].doc_count, 3640)
self.assertAlmostEqual(a.buckets[0].score, 0.371)
self.assertEqual(a.buckets[0].bg_count, 66799)
self.assertIs(a.buckets[0], a.get_bucket('Bicycle theft'))
self.assertEqual(a.buckets[1].key, 'Mobile phone theft')
self.assertEqual(a.buckets[1].doc_count, 27617)
self.assertAlmostEqual(a.buckets[1].score, 0.0599)
self.assertEqual(a.buckets[1].bg_count, 53182)
self.assertIs(a.buckets[1], a.get_bucket('Mobile phone theft'))
a = agg.Range(
f.price,
ranges=[{'to': 200}, {'from': 200, 'to': 1000}, {'from': 1000}],
type=Integer,
)
self.assert_expression(
a,
{
"range": {
"field": "price",
"ranges": [
{"to": 200},
{"from": 200, "to": 1000},
{"from": 1000}
]
}
}
)
a1 = a.clone()
self.assertIsNot(a1, a)
a = a.build_agg_result(
{
"buckets": [
{
"to": 200,
"doc_count": 12
},
{
"from": 200,
"to": 1000,
"doc_count": 197
},
{
"from": 1000,
"doc_count": 8
}
]
}
)
self.assertEqual(len(a.buckets), 3)
self.assertEqual(a.buckets[0].doc_count, 12)
self.assertEqual(a.buckets[1].doc_count, 197)
self.assertEqual(a.buckets[2].doc_count, 8)
a = agg.Filters([Term(f.body, 'error'), Term(f.body, 'warning')])
self.assert_expression(
a,
{
"filters": {
"filters": [
{"term": {"body": "error"}},
{"term": {"body": "warning"}}
]
}
}
)
a = a.build_agg_result(
{
"buckets": [
{
"doc_count" : 34
},
{
"doc_count" : 439
},
]
}
)
self.assertEqual(len(a.buckets), 2)
self.assertIs(a.buckets[0].key, None)
self.assertEqual(a.buckets[0].doc_count, 34)
self.assertIs(a.buckets[1].key, None)
self.assertEqual(a.buckets[1].doc_count, 439)
self.assertIs(a.get_bucket(None), None)
a = agg.Filters(Params(errors=Term(f.body, 'error'), warnings=Term(f.body, 'warning')))
self.assert_expression(
a,
{
"filters": {
"filters": {
"errors": {"term": {"body": "error"}},
"warnings": {"term": {"body": "warning"}}
}
}
}
)
a = a.build_agg_result(
{
"buckets": {
"errors": {
"doc_count" : 34
},
"warnings": {
"doc_count" : 439
},
}
}
)
self.assertEqual(len(a.buckets), 2)
self.assertIs(a.buckets[0].key, 'errors')
self.assertEqual(a.buckets[0].doc_count, 34)
self.assertIs(a.buckets[0], a.get_bucket('errors'))
self.assertIs(a.buckets[1].key, 'warnings')
self.assertEqual(a.buckets[1].doc_count, 439)
self.assertIs(a.buckets[1], a.get_bucket('warnings'))
a = agg.Nested(f.resellers, aggs={'min_price': agg.Min(f.resellers.price)})
self.assert_expression(
a,
{
"nested": {"path": "resellers"},
"aggregations": {
"min_price": {"min": {"field": "resellers.price"}}
}
}
)
a = a.build_agg_result(
{
"min_price": {
"value" : 350
}
}
)
self.assertEqual(a.get_aggregation('min_price').value, 350)
a = agg.Nested(
f.resellers,
aggs={"resellers": agg.Terms(
f.resellers.id,
aggs={"reverse": agg.ReverseNested(
f.resellers,
aggs={"reseller_volume": agg.Sum(f.price)}
)})})
self.assert_expression(
a,
{
"nested": {"path": "resellers"},
"aggregations": {
"resellers": {
"terms": {"field": "resellers.id"},
"aggregations": {
"reverse": {
"reverse_nested": {"path": "resellers"},
"aggregations": {
"reseller_volume": {
"sum": {"field": "price"}
}
}
}
}
}
}
}
)
a = a.build_agg_result(
{
"doc_count": 100,
"resellers": {
"buckets": [
{
"key": 1122,
"doc_count": 48,
"reverse": {
"reseller_volume": {"value": 500100}
}
},
{
"key": 2233,
"doc_count": 52,
"reverse": {
"reseller_volume": {"value": 100500}
}
}
]
}
}
)
self.assertEqual(
a
.get_aggregation("resellers")
.buckets[1]
.get_aggregation("reverse")
.get_aggregation("reseller_volume")
.value,
100500
)
a = agg.Sampler(shard_size=1000, aggs={'avg_price': agg.Avg(f.price)})
self.assert_expression(
a,
{
"sampler": {"shard_size": 1000},
"aggregations": {
"avg_price": {"avg": {"field": "price"}}
}
}
)
a = a.build_agg_result(
{
"doc_count": 1000,
"avg_price": {
"value" : 750
}
}
)
self.assertEqual(a.doc_count, 1000)
self.assertEqual(a.get_aggregation('avg_price').value, 750)
# complex aggregation with sub aggregations
a = agg.Global()
a = a.aggs({
'selling_type': agg.Terms(
f.selling_type,
aggs={
'price_avg': agg.Avg(f.price),
'price_min': agg.Min(f.price),
'price_max': agg.Max(f.price),
'price_hist': agg.Histogram(f.price, interval=50),
}
),
'price_avg': agg.Avg(f.price),
}
)
self.assert_expression(
a,
{
"global": {},
"aggregations": {
"selling_type": {
"terms": {"field": "selling_type"},
"aggregations": {
"price_avg": {"avg": {"field": "price"}},
"price_min": {"min": {"field": "price"}},
"price_max": {"max": {"field": "price"}},
"price_hist": {
"histogram": {
"field": "price",
"interval": 50,
'min_doc_count': 1
}
},
}
},
"price_avg": {"avg": {"field": "price"}}
}
}
)
a = a.build_agg_result(
{
'doc_count': 100,
'selling_type': {
'buckets': [
{
'key': 'retail',
'doc_count': 70,
'price_avg': {'value': 60.5},
'price_min': {'value': 1.1},
'price_max': {'value': 83.4},
'price_hist': {
'buckets': [
{'key': 50, 'doc_count': 60},
{'key': 100, 'doc_count': 7},
{'key': 150, 'doc_count': 3},
]
},
},
{
'key': 'wholesale',
'doc_count': 30,
'price_avg': {'value': 47.9},
'price_min': {'value': 20.1},
'price_max': {'value': 64.8},
'price_hist': {
'buckets': [
{'key': 0, 'doc_count': 17},
{'key': 50, 'doc_count': 5},
{'key': 100, 'doc_count': 6},
{'key': 150, 'doc_count': 2},
]
},
},
],
},
'price_avg': {'value': 56.3},
}
)
self.assertEqual(a.doc_count, 100)
type_agg = a.get_aggregation('selling_type')
self.assertEqual(len(type_agg.buckets), 2)
self.assertEqual(type_agg.buckets[0].key, 'retail')
self.assertEqual(type_agg.buckets[0].doc_count, 70)
self.assertIs(type_agg.buckets[0], type_agg.get_bucket('retail'))
self.assertAlmostEqual(type_agg.buckets[0].get_aggregation('price_avg').value, 60.5)
self.assertAlmostEqual(type_agg.buckets[0].get_aggregation('price_min').value, 1.1)
self.assertAlmostEqual(type_agg.buckets[0].get_aggregation('price_max').value, 83.4)
price_hist_agg = type_agg.buckets[0].get_aggregation('price_hist')
self.assertEqual(price_hist_agg.buckets[0].key, 50)
self.assertEqual(price_hist_agg.buckets[0].doc_count, 60)
self.assertIs(price_hist_agg.buckets[0], price_hist_agg.get_bucket(50))
self.assertEqual(price_hist_agg.buckets[1].key, 100)
self.assertEqual(price_hist_agg.buckets[1].doc_count, 7)
self.assertIs(price_hist_agg.buckets[1], price_hist_agg.get_bucket(100))
self.assertEqual(price_hist_agg.buckets[2].key, 150)
self.assertEqual(price_hist_agg.buckets[2].doc_count, 3)
self.assertIs(price_hist_agg.buckets[2], price_hist_agg.get_bucket(150))
self.assertEqual(len(price_hist_agg.buckets), 3)
self.assertEqual(type_agg.buckets[1].key, 'wholesale')
self.assertEqual(type_agg.buckets[1].doc_count, 30)
self.assertIs(type_agg.buckets[1], type_agg.get_bucket('wholesale'))
self.assertAlmostEqual(type_agg.buckets[1].get_aggregation('price_avg').value, 47.9)
self.assertAlmostEqual(type_agg.buckets[1].get_aggregation('price_min').value, 20.1)
self.assertAlmostEqual(type_agg.buckets[1].get_aggregation('price_max').value, 64.8)
price_hist_agg = type_agg.buckets[1].get_aggregation('price_hist')
self.assertEqual(len(price_hist_agg.buckets), 4)
self.assertEqual(price_hist_agg.buckets[0].key, 0)
self.assertEqual(price_hist_agg.buckets[0].doc_count, 17)
self.assertIs(price_hist_agg.buckets[0], price_hist_agg.get_bucket(0))
self.assertEqual(price_hist_agg.buckets[1].key, 50)
self.assertEqual(price_hist_agg.buckets[1].doc_count, 5)
self.assertIs(price_hist_agg.buckets[1], price_hist_agg.get_bucket(50))
self.assertEqual(price_hist_agg.buckets[2].key, 100)
self.assertEqual(price_hist_agg.buckets[2].doc_count, 6)
self.assertIs(price_hist_agg.buckets[2], price_hist_agg.get_bucket(100))
self.assertEqual(price_hist_agg.buckets[3].key, 150)
self.assertEqual(price_hist_agg.buckets[3].doc_count, 2)
self.assertIs(price_hist_agg.buckets[3], price_hist_agg.get_bucket(150))
self.assertEqual(a.get_aggregation('price_avg').value, 56.3)
class QuestionDocument(DynamicDocument):
pass
class PaperDocument(DynamicDocument):
pass
question_mapper = Mock(
return_value={
'602679': Mock(id=602679, type='question'),
'602678': Mock(id=602678, type='question'),
}
)
paper_mapper = Mock(return_value={'602672': Mock(id=602672, type='paper')})
top_hits_agg = agg.Terms(
f.tags,
size=3,
aggs={
'top_tags_hits': agg.TopHits(
size=1,
sort=f.last_activity_date.desc(),
_source={'include': f.title},
instance_mapper={
QuestionDocument: question_mapper, PaperDocument: paper_mapper
},
)
}
)
self.assert_expression(
top_hits_agg,
{
"terms": {
"field": "tags",
"size": 3
},
"aggregations": {
"top_tags_hits": {
"top_hits": {
"sort": {
"last_activity_date": "desc"
},
"_source": {
"include": "title"
},
"size" : 1
}
}
}
}
)
a = top_hits_agg.build_agg_result(
{
"buckets": [
{
"key": "windows-7",
"doc_count": 25365,
"top_tags_hits": {
"hits": {
"total": 25365,
"max_score": 1,
"hits": [
{
"_index": "stack",
"_type": "question",
"_id": "602679",
"_score": 1,
"_source": {
"title": "Windows port opening"
},
"sort": [
1370143231177
]
}
]
}
}
},
{
"key": "linux",
"doc_count": 18342,
"top_tags_hits": {
"hits": {
"total": 18342,
"max_score": 1,
"hits": [
{
"_index": "stack",
"_type": "paper",
"_id": "602672",
"_score": 1,
"_source": {
"title": "Ubuntu RFID Screensaver lock-unlock"
},
"sort": [
1370143379747
]
}
]
}
}
},
{
"key": "windows",
"doc_count": 18119,
"top_tags_hits": {
"hits": {
"total": 18119,
"max_score": 1,
"hits": [
{
"_index": "stack",
"_type": "question",
"_id": "602678",
"_score": 1,
"_source": {
"title": "If I change my computers date / time, what could be affected?"
},
"sort": [
1370142868283
]
}
]
}
}
}
]
},
doc_cls_map={'question': QuestionDocument, 'paper': PaperDocument},
mapper_registry={},
)
self.assertEqual(len(a.buckets), 3)
self.assertEqual(a.buckets[0].doc_count, 25365)
self.assertEqual(a.buckets[0].key, 'windows-7')
top_tags_agg = a.buckets[0].get_aggregation('top_tags_hits')
self.assertEqual(top_tags_agg.total, 25365)
self.assertEqual(top_tags_agg.max_score, 1)
self.assertEqual(len(top_tags_agg.hits), 1)
self.assertIsInstance(top_tags_agg.hits[0], QuestionDocument)
self.assertEqual(top_tags_agg.hits[0]._index, 'stack')
self.assertEqual(top_tags_agg.hits[0]._type, 'question')
self.assertEqual(top_tags_agg.hits[0]._score, 1)
self.assertEqual(top_tags_agg.hits[0]._id, '602679')
self.assertEqual(top_tags_agg.hits[0].title, 'Windows port opening')
self.assertEqual(top_tags_agg.hits[0].instance.id, 602679)
self.assertEqual(top_tags_agg.hits[0].instance.type, 'question')
self.assertEqual(a.buckets[1].doc_count, 18342)
self.assertEqual(a.buckets[1].key, 'linux')
top_tags_agg = a.buckets[1].get_aggregation('top_tags_hits')
self.assertEqual(top_tags_agg.total, 18342)
self.assertEqual(top_tags_agg.max_score, 1)
self.assertEqual(len(top_tags_agg.hits), 1)
self.assertIsInstance(top_tags_agg.hits[0], PaperDocument)
self.assertEqual(top_tags_agg.hits[0]._index, 'stack')
self.assertEqual(top_tags_agg.hits[0]._type, 'paper')
self.assertEqual(top_tags_agg.hits[0]._score, 1)
self.assertEqual(top_tags_agg.hits[0]._id, '602672')
self.assertEqual(top_tags_agg.hits[0].title, 'Ubuntu RFID Screensaver lock-unlock')
self.assertEqual(top_tags_agg.hits[0].instance.id, 602672)
self.assertEqual(top_tags_agg.hits[0].instance.type, 'paper')
self.assertEqual(a.buckets[2].doc_count, 18119)
self.assertEqual(a.buckets[2].key, 'windows')
top_tags_agg = a.buckets[2].get_aggregation('top_tags_hits')
self.assertEqual(top_tags_agg.total, 18119)
self.assertEqual(top_tags_agg.max_score, 1)
self.assertEqual(len(top_tags_agg.hits), 1)
self.assertIsInstance(top_tags_agg.hits[0], DynamicDocument)
self.assertEqual(top_tags_agg.hits[0]._index, 'stack')
self.assertEqual(top_tags_agg.hits[0]._type, 'question')
self.assertEqual(top_tags_agg.hits[0]._score, 1)
self.assertEqual(top_tags_agg.hits[0]._id, '602678')
self.assertEqual(top_tags_agg.hits[0].title, 'If I change my computers date / time, what could be affected?')
self.assertEqual(top_tags_agg.hits[0].instance.id, 602678)
self.assertEqual(top_tags_agg.hits[0].instance.type, 'question')
self.assertEqual(question_mapper.call_count, 1)
self.assertEqual(paper_mapper.call_count, 1)
def test_expression(self):
f = DynamicDocument.fields
e = Params({'foo': 'bar'})
self.assert_expression(
e,
{"foo": "bar"}
)
self.assertEqual(e['foo'], 'bar')
self.assertTrue('foo' in e)
self.assert_expression(
Match(f.message, 'this is a test'),
{
"match": {
"message": "this is a test",
}
}
)
self.assert_expression(
Match(
f.message, 'this is a test',
minimum_should_match='100%',
cutoff_frequency=0.001,
boost=2.1
),
{
"match": {
"message": {
"query": "this is a test",
"minimum_should_match": "100%",
"cutoff_frequency": 0.001,
"boost": 2.1,
}
}
}
)
self.assert_expression(
Term(f.user, 'kimchy'),
{
"term": {"user": "******"}
}
)
self.assert_expression(
Term(f.user, 'kimchy', boost=1.2),
{
"term": {"user": {"value": "kimchy", "boost": 1.2}}
}
)
self.assert_expression(
Term('user.login', 'kimchy'),
{
"term": {"user.login": "******"}
}
)
self.assert_expression(
Terms(f.status, [0]),
{
"terms": {
"status": [0]
}
}
)
self.assert_expression(
Terms(f.tags, ['blue', 'pill'], minimum_should_match=1),
{
"terms": {
"tags": ["blue", "pill"],
"minimum_should_match": 1
}
}
)
self.assert_expression(
Exists(f.tags),
{
"exists": {"field": "tags"}
}
)
self.assert_expression(
Missing(f.tags, _cache=True),
{
"missing": {
"field": "tags",
"_cache": True
}
}
)
self.assert_expression(
Bool(
must=Term(f.user, 'kimchy'),
filter=Term(f.tag, 'tech'),
must_not=Range(f.age, from_=10, to=20),
should=[Term(f.tag, 'wow'), Term(f.tag, 'elasticsearch', boost=2.1)],
minimum_should_match=1,
boost=1.0,
),
{
"bool": {
"must": {
"term": {"user": "******"}
},
"filter": {
"term": {"tag": "tech"}
},
"must_not": {
"range": {
"age": {"from": 10, "to": 20}
}
},
"should": [
{
"term": {"tag": "wow"}
},
{
"term": {"tag": {"value": "elasticsearch", "boost": 2.1}}
}
],
"minimum_should_match": 1,
"boost": 1.0
}
}
)
e = MultiMatch(
"Will Smith",
[self.index.star.title.boost(4), self.index.star.wildcard('*_name').boost(2)],
minimum_should_match='100%'
)
self.assert_expression(
e,
{
"multi_match": {
"query": "Will Smith",
"fields": ["title^4", "*_name^2"],
"minimum_should_match": "100%"
}
}
)
self.assertEqual(
e._collect_doc_classes(),
{self.index.star}
)
self.assert_expression(
Range(self.index.product.price, lte=100, boost=2.2, execution='index', _cache=False),
{
"range": {
"price": {"lte": 100, "boost": 2.2},
"execution": "index",
"_cache": False
}
}
)
self.assert_expression(
Boosting(
positive=Term(f.field1, 'value1'),
negative=Term(f.field2, 'value2'),
negative_boost=0.2
),
{
"boosting": {
"positive": {
"term": {
"field1": "value1"
}
},
"negative": {
"term": {
"field2": "value2"
}
},
"negative_boost": 0.2
}
}
)
self.assert_expression(
Common(
f.body, 'nelly the elephant not as a cartoon',
cutoff_frequency=0.001,
minimum_should_match=dict(low_freq=2, high_freq=3),
),
{
"common": {
"body": {
"query": "nelly the elephant not as a cartoon",
"cutoff_frequency": 0.001,
"minimum_should_match": {
"low_freq": 2,
"high_freq": 3
}
}
}
}
)
self.assert_expression(
ConstantScore(filter=Term(f.user, 'kimchy'), boost=1.2),
{
"constant_score": {
"filter": {
"term": { "user": "******"}
},
"boost": 1.2
}
}
)
self.assert_expression(
FunctionScore(
query=MatchAll(),
field_value_factor={
'field': f.popularity,
'factor': 1.2,
'modifier': 'sqrt',
}
),
{
"function_score": {
"query": {"match_all": {}},
"field_value_factor": {
"field": "popularity",
"factor": 1.2,
"modifier": "sqrt"
}
}
}
)
self.assert_expression(
DisMax([Term(f.age, 34), Term(f.age, 35)], boost=1.2, tie_breaker=0.7),
{
"dis_max": {
"tie_breaker": 0.7,
"boost": 1.2,
"queries": [
{
"term" : { "age" : 34 }
},
{
"term" : { "age" : 35 }
}
]
}
}
)
self.assert_expression(
Filtered(
filter=Range(f.created, gte='now - 1d / d'),
query=Match(f.tweet, 'full text search')
),
{
"filtered": {
"query": {
"match": { "tweet": "full text search" }
},
"filter": {
"range": { "created": { "gte": "now - 1d / d" }}
}
}
}
)
self.assert_expression(
Ids(['123456']),
{
"ids": {
"values": ["123456"]
}
}
)
self.assert_expression(
Ids(['1', '4', '100'], type="my_type"),
{
"ids": {
"type": "my_type",
"values": ["1", "4", "100"]
}
}
)
self.assert_expression(
Prefix(f.user, 'ki', boost=2.0),
{
"prefix": { "user": { "value": "ki", "boost": 2.0 } }
}
)
self.assert_expression(
MatchAll(),
{"match_all": {}}
)
self.assert_expression(
MatchAll(boost=1.2),
{
"match_all": { "boost" : 1.2 }
}
)
self.assert_expression(
Query(Match(f.title, 'this that thus')),
{
"query": {
"match": {
"title": "this that thus"
}
}
}
)
self.assert_expression(
Query(Match(f.title, 'this that thus'), _cache=True),
{
"fquery": {
"query": {
"match": {
"title": "this that thus"
}
},
"_cache": True
}
}
)
self.assertRaises(NotImplementedError, BooleanExpression)
self.assert_expression(
And(
Range(f.post_date, from_='2010-03-01', to='2010-04-01'),
Prefix(f.name.second, 'ba')
),
{
"and": [
{
"range": {
"post_date": {
"from": "2010-03-01",
"to": "2010-04-01"
}
}
},
{
"prefix" : { "name.second" : "ba" }
}
]
}
)
self.assert_expression(
And(
Range(f.post_date, from_='2010-03-01', to='2010-04-01'),
Prefix(f.name.second, 'ba'),
_cache=True
),
{
"and": {
"filters": [
{
"range": {
"post_date": {
"from": "2010-03-01",
"to": "2010-04-01"
}
}
},
{
"prefix" : { "name.second" : "ba" }
}
],
"_cache": True
}
}
)
self.assert_expression(
Or(Term(f.name.second, 'banon'), Term(f.name.nick, 'kimchy')),
{
"or": [
{
"term": {"name.second": "banon"}
},
{
"term": {"name.nick": "kimchy"}
}
]
}
)
self.assert_expression(
And(Or(Term(f.name.nick, 'kimchy'))),
{
"term": {"name.nick": "kimchy"}
}
)
self.assert_expression(
Not(
Range(f.post_date, from_='2010-03-01', to='2010-04-01'),
),
{
"not": {
"range": {
"post_date": {
"from": "2010-03-01",
"to": "2010-04-01"
}
}
}
}
)
self.assert_expression(
Not(
Range(f.post_date, from_='2010-03-01', to='2010-04-01'),
_cache=True,
),
{
"not": {
"filter": {
"range": {
"post_date": {
"from": "2010-03-01",
"to": "2010-04-01"
}
}
},
"_cache": True
}
}
)
self.assert_expression(
Sort(f.post_date),
"post_date"
)
self.assert_expression(
Sort(f.age, 'desc'),
{
"age": "desc"
}
)
self.assert_expression(
Sort(f.price, 'asc', mode='avg'),
{
"price": {
"order": "asc",
"mode": "avg"
}
}
)
self.assert_expression(
Sort(
f.offer.price.sort, 'asc', mode='avg',
nested_filter=Term(f.offer.color, 'blue')
),
{
"offer.price.sort": {
"order": "asc",
"mode": "avg",
"nested_filter": {
"term": {"offer.color": "blue"}
}
}
}
)
self.assert_expression(
SpanFirst(SpanTerm(f.user, 'kimchy'), end=3),
{
"span_first": {
"match": {
"span_term": {"user": "******"}
},
"end": 3
}
}
)
self.assert_expression(
SpanMulti(Prefix(f.user, 'ki', boost=1.08)),
{
"span_multi": {
"match": {
"prefix": {
"user": {"value": "ki", "boost": 1.08}
}
}
}
}
)
self.assert_expression(
SpanNear(
[SpanTerm(f.field, 'value1'),
SpanTerm(f.field, 'value2'),
SpanTerm(f.field, 'value3')],
slop=12,
in_order=False,
collect_payloads=False,
),
{
"span_near": {
"clauses": [
{"span_term": {"field": "value1"}},
{"span_term": {"field": "value2"}},
{"span_term": {"field": "value3"}}
],
"slop": 12,
"in_order": False,
"collect_payloads": False
}
}
)
self.assert_expression(
SpanNot(
SpanTerm(f.field1, 'hoya'),
SpanNear([SpanTerm(f.field1, 'la'), SpanTerm(f.field1, 'hoya')], slop=0, in_order=True),
),
{
"span_not": {
"include": {
"span_term": {"field1": "hoya"}
},
"exclude": {
"span_near": {
"clauses": [
{"span_term": {"field1": "la"}},
{"span_term": {"field1": "hoya"}}
],
"slop": 0,
"in_order": True
}
}
}
}
)
self.assert_expression(
SpanOr(
[
SpanTerm(f.field, 'value1'),
SpanTerm(f.field, 'value2'),
SpanTerm(f.field, 'value3')
],
boost=2,
),
{
"span_or": {
"clauses": [
{"span_term": {"field": "value1"}},
{"span_term": {"field": "value2"}},
{"span_term": {"field": "value3"}}
],
"boost": 2
}
}
)
self.assert_expression(
Limit(1000),
{
"limit": {
"value": 1000
}
}
)
e = Nested(
self.index.movie.stars,
Match(self.index.movie.stars.full_name, 'Will Smith'),
score_mode='max',
)
self.assert_expression(
e,
{
"nested": {
"path": "stars",
"query": {
"match": {
"stars.full_name": "Will Smith"
}
},
"score_mode": "max"
}
}
)
self.assertEqual(
e._collect_doc_classes(),
{self.index.movie}
)
e = HasParent(
self.index.blog.tag == 'something',
parent_type=self.index.blog,
score_mode='score',
)
self.assert_expression(
e,
{
"has_parent": {
"parent_type": "blog",
"query": {
"term": {
"tag": "something"
}
},
"score_mode": "score"
}
}
)
self.assertEqual(
e._collect_doc_classes(),
set()
)
e = HasParent(
self.index.blog.tag == 'something',
score_mode='score',
)
self.assert_expression(
e,
{
"has_parent": {
"parent_type": "blog",
"query": {
"term": {
"tag": "something"
}
},
"score_mode": "score"
}
}
)
self.assertEqual(
e._collect_doc_classes(),
set()
)
e = HasChild(
self.index.blog_tag.tag == 'something',
type=self.index.blog_tag,
score_mode='sum',
)
self.assert_expression(
e,
{
"has_child": {
"type": "blog_tag",
"query": {
"term": {
"tag": "something"
}
},
"score_mode": "sum"
}
}
)
self.assertEqual(
e._collect_doc_classes(),
set()
)
e = HasChild(
self.index.blog_tag.tag == 'something',
score_mode='sum',
)
self.assert_expression(
e,
{
"has_child": {
"type": "blog_tag",
"query": {
"term": {
"tag": "something"
}
},
"score_mode": "sum"
}
}
)
self.assertEqual(
e._collect_doc_classes(),
set()
)
def test_attr_bool_facet_filter__multiple_selected_values(bool_qf, compiler):
sq = bool_qf.apply(SearchQuery(), {'a1': ['true', 'false'], 'a2': 'true'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_bool.filter':
agg.Filter(
Bool.must(
Terms('attr.bool', [0b11, 0b10]),
Term('attr.bool', 0b101),
),
aggs={'qf.attr_bool': agg.Terms(Field('attr.bool'), size=100)}),
'qf.attr_bool.filter:1':
agg.Filter(Term('attr.bool', 0b101),
aggs={
'qf.attr_bool:1':
agg.Terms(Field('attr.bool'),
size=2,
include=[0b10, 0b11])
}),
'qf.attr_bool.filter:2':
agg.Filter(Terms('attr.bool', [0b11, 0b10]),
aggs={
'qf.attr_bool:2':
agg.Terms(Field('attr.bool'),
size=2,
include=[0b100, 0b101])
}),
}).post_filter(
Bool.must(
Terms('attr.bool', [0b11, 0b10]),
Term('attr.bool', 0b101),
)).to_dict(compiler=compiler))
qf_res = bool_qf.process_result(
SearchResult(
{
'aggregations': {
'qf.attr_bool.filter': {
'doc_count': 200,
'qf.attr_bool': {
'buckets': [
{
'key': 0b11,
'doc_count': 123,
},
{
'key': 0b101,
'doc_count': 1
},
]
}
},
'qf.attr_bool.filter:1': {
'doc_count': 163,
'qf.attr_bool:1': {
'buckets': [
{
'key': 0b11,
'doc_count': 123,
},
{
'key': 0b10,
'doc_count': 99
},
]
}
},
'qf.attr_bool.filter:2': {
'doc_count': 144,
'qf.attr_bool:2': {
'buckets': [
{
'key': 0b101,
'doc_count': 1
},
]
}
},
}
},
aggregations=sq.get_context().aggregations))
assert len(qf_res.attr_bool.facets) == 2
facet = qf_res.attr_bool.get_facet(1)
assert len(facet.all_values) == 2
assert len(facet.selected_values) == 2
assert len(facet.values) == 0
assert facet.all_values[0] is facet.selected_values[0]
assert facet.all_values[1] is facet.selected_values[1]
assert facet.all_values[0].value is True
assert facet.all_values[0].count == 123
assert facet.all_values[0].count_text == '123'
assert facet.all_values[0].selected is True
assert facet.all_values[1].value is False
assert facet.all_values[1].count == 99
assert facet.all_values[1].count_text == '99'
assert facet.all_values[1].selected is True
facet = qf_res.attr_bool.get_facet(2)
assert len(facet.all_values) == 1
assert len(facet.selected_values) == 1
assert len(facet.values) == 0
assert facet.all_values[0] is facet.selected_values[0]
assert facet.all_values[0].value is True
assert facet.all_values[0].count == 1
assert facet.all_values[0].count_text == '1'
assert facet.all_values[0].selected is True
def test_combined_facet_filters(qf, compiler):
sq = qf.apply(SearchQuery(), {
'a1': 'true',
'a18': '58084',
'a324': '57005',
'a8__gte': '2.71',
})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_bool.filter':
agg.Filter(Bool.must(
Term('attr.bool', 0b11),
Term('attr.int', 0x12_0000e2e4),
Term('attr.int', 0x144_0000dead),
),
aggs={
'qf.attr_bool': agg.Terms(Field('attr.bool'), size=100),
}),
'qf.attr_bool.filter:1':
agg.Filter(Bool.must(
Term('attr.int', 0x12_0000e2e4),
Term('attr.int', 0x144_0000dead),
),
aggs={
'qf.attr_bool:1':
agg.Terms(
Field('attr.bool'),
size=2,
include=[0b10, 0b11],
),
}),
'qf.attr_int.filter':
agg.Filter(Bool.must(
Term('attr.bool', 0b11),
Term('attr.int', 0x12_0000e2e4),
Term('attr.int', 0x144_0000dead),
),
aggs={
'qf.attr_int': agg.Terms(Field('attr.int'),
size=10_000),
}),
'qf.attr_int.filter:18':
agg.Filter(Bool.must(
Term('attr.bool', 0b11),
Term('attr.int', 0x144_0000dead),
),
aggs={
'qf.attr_int:18': agg.Terms(Field('attr.int'),
size=100),
}),
'qf.attr_int.filter:324':
agg.Filter(Bool.must(
Term('attr.bool', 0b11),
Term('attr.int', 0x12_0000e2e4),
),
aggs={
'qf.attr_int:324': agg.Terms(Field('attr.int'),
size=100),
})
}).post_filter(Term('attr.bool', 0b11)).post_filter(
Term('attr.int', 0x12_0000e2e4)).post_filter(
Term('attr.int', 0x144_0000dead)).filter(
Range('attr.float', gte=0x8_402d70a4,
lte=0x8_7f800000)).to_dict(compiler=compiler))