def test_attr_int_facet_filter__empty_params(int_qf, compiler):
sq = int_qf.apply(SearchQuery(), {})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_int':
agg.Terms(Field('attr.int'), size=10_000)
}).to_dict(compiler=compiler))
qf_res = int_qf.process_result(
SearchResult(
{
'aggregations': {
'qf.attr_int': {
'buckets': [{
'key': 0x12_00000001,
'doc_count': 123,
}, {
'key': 0x144_0000dead,
'doc_count': 99
}, {
'key': 0x12_f0000000,
'doc_count': 1
}]
}
}
},
aggregations=sq.get_context().aggregations))
def test_exists(self):
self.client.search_exists.return_value = {"exists" : True}
self.assertEqual(
SearchQuery(index=self.index, doc_cls=self.index.car).exists(refresh=True),
True
)
self.client.search_exists.assert_called_with(
index='test',
doc_type='car',
body=None,
refresh=True
)
self.client.search_exists.return_value = {"exists" : False}
self.assertEqual(
SearchQuery(index=self.index)
.filter(self.index.car.status == 1)
.function_score({'boost_factor': 3})
.exists(),
False
)
self.client.search_exists.assert_called_with(
index='test',
doc_type='car',
body={
"query": {
"filtered": {
"filter": {
"term": {"status": 1}
}
}
}
}
)
def test_attr_range_facet_filter__single_selected_filter(range_qf, compiler):
sq = range_qf.apply(SearchQuery(), {'a8__gte': 2.71})
assert_search_query(
sq,
SearchQuery().aggs({
'qf.attr_range.filter':
agg.Filter(Range('attr.float', gte=0x8_402d70a4, lte=0x8_7f800000),
aggs={
'qf.attr_range':
agg.Terms(script=Script(
'doc[params.field].value >>> 32',
lang='painless',
params={
'field': 'attr.float',
}),
size=100)
}),
'qf.attr_range:8':
agg.Filter(Range('attr.float', gte=0x8_00000000,
lte=0x8_ffffffff), )
}).post_filter(Range('attr.float', gte=0x8_402d70a4,
lte=0x8_7f800000)), compiler)
qf_res = range_qf.process_results(
SearchResult(
{
'aggregations': {
'qf.attr_range.filter': {
'doc_count': 32,
'qf.attr_range': {
'buckets': [{
'key': 8,
'doc_count': 32
}, {
'key': 439,
'doc_count': 18
}]
}
},
'qf.attr_range:8': {
'doc_count': 84
}
}
},
aggregations=sq.get_context().aggregations))
assert qf_res.attr_range.name == 'attr_range'
assert qf_res.attr_range.alias == 'a'
f = qf_res.attr_range.get_facet(8)
assert f.attr_id == 8
assert f.count == 84
assert f.selected is True
f = qf_res.attr_range.get_facet(439)
assert f.attr_id == 439
assert f.count == 18
assert f.selected is False
def test_attr_int_facet_filter__include_values(int_qf_with_values, compiler):
sq = int_qf_with_values.apply(SearchQuery(), {'a18': '58084'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_int.filter':
agg.Filter(
Term('attr.int', 0x12_0000e2e4),
aggs={'qf.attr_int': agg.Terms(Field('attr.int'), size=10_000)}),
'qf.attr_int:18':
agg.Terms(Field('attr.int'),
size=100,
include=[0x12_0000e2e4, 0x12_0000e7e5])
}).post_filter(Term('attr.int', 0x12_0000e2e4)).to_dict(compiler=compiler))
def test_attr_int_simple_filter_no_alias(compiler):
qf = QueryFilter()
qf.add_filter(AttrIntSimpleFilter('attr', Field('attr.int')))
sq = qf.apply(SearchQuery(), {})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a18': '224'})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'attr18': '1234'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Term('attr.int', 0x12_000004d2)).to_dict(compiler=compiler))
def test_count(self):
self.client.count.return_value = {
"count" : 1024,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
}
}
self.assertEqual(
SearchQuery(index=self.index, doc_cls=self.index.car)
.count(),
1024
)
self.client.count.assert_called_with(
index='test',
doc_type='car',
body=None,
)
self.client.count.return_value = {
"count" : 2,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
}
}
self.assertEqual(
SearchQuery(index=self.index)
.filter(self.index.car.status == 1)
.function_score({'boost_factor': 3})
.count(),
2
)
self.client.count.assert_called_with(
index='test',
doc_type='car',
body={
"query": {
"filtered": {
"filter": {
"term": {"status": 1}
}
}
}
}
)
def test_attr_int_facet_filter__single_selected_value(int_qf, compiler):
params = {'a18': '58084'}
sq = int_qf.apply(SearchQuery(), params)
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_int.filter':
agg.Filter(Term('attr.int', 0x12_0000e2e4),
aggs={
'qf.attr_int': agg.Terms(Field('attr.int'),
size=10_000),
}),
'qf.attr_int:18':
agg.Terms(Field('attr.int'), size=100),
}).post_filter(Term('attr.int', 0x12_0000e2e4)).to_dict(compiler=compiler))
qf_res = int_qf.process_result(
SearchResult(
{
'aggregations': {
'qf.attr_int.filter': {
'doc_count': 201,
'qf.attr_int': {
'buckets': [{
'key': 0x144_0000dead,
'doc_count': 123,
}, {
'key': 0x12_0000e2e4,
'doc_count': 119
}, {
'key': 0x144_0000beef,
'doc_count': 1
}]
}
},
'qf.attr_int:18': {
'buckets': [
{
'key': 0x12_0000e2e4,
'doc_count': 99
},
{
'key': 0x12_0000e7e5,
'doc_count': 88
},
]
}
}
},
aggregations=sq.get_context().aggregations))
def test_multi_search_with_error(self):
self.client.msearch = MagicMock(
return_value={
u'responses': [{
u'_shards': {
u'failed': 0,
u'successful': 64,
u'total': 64
},
u'hits': {
u'hits': [],
u'max_score': 0.0,
u'total': 27802974
},
u'timed_out': False,
u'took': 59
}, {
u'error':
u'SearchPhaseExecutionException[Failed to execute phase [query], all shards failed;'
}]
})
ProductDoc = self.index.product
sq1 = SearchQuery(doc_cls=ProductDoc, search_type='count', routing=123)
sq2 = (SearchQuery(
index=self.cluster['us'],
doc_cls=ProductDoc).filter(ProductDoc.status == 0).limit(1))
self.assertRaises(MultiSearchError,
lambda: self.cluster.multi_search([sq1, sq2]))
results = self.cluster.multi_search([sq1, sq2], raise_on_error=False)
self.assertIs(results[0], sq1.result)
self.assertIs(results[1], sq2.result)
self.assertEqual(results[0].total, 27802974)
self.assertEqual(results[0].took, 59)
self.assertEqual(results[0].timed_out, False)
self.assertEqual(results[0].max_score, 0.0)
self.assertEqual(len(results[0].hits), 0)
self.assertRaisesRegexp(DelayedElasticsearchException,
r'^SearchPhaseExecutionException',
lambda: results[1].total)
self.assertRaisesRegexp(DelayedElasticsearchException,
r'^SearchPhaseExecutionException',
lambda: results[1].hits)
self.assertRaises(AttributeError, lambda: results[1].unknown_attr)
def test_multi_search_with_error(self):
self.client.msearch = MagicMock(
return_value={
u'responses': [
{
u'_shards': {
u'failed': 0,
u'successful': 64,
u'total': 64
},
u'hits': {
u'hits': [],
u'max_score': 0.0,
u'total': 27802974
},
u'timed_out': False,
u'took': 59
},
{
u'error': u'SearchPhaseExecutionException[Failed to execute phase [query], all shards failed;'
}
]
}
)
ProductDoc = self.index.product
sq1 = SearchQuery(doc_cls=ProductDoc, search_type='count', routing=123)
sq2 = (
SearchQuery(index=self.cluster['us'], doc_cls=ProductDoc)
.filter(ProductDoc.status == 0)
.limit(1)
)
self.assertRaises(MultiSearchError, lambda: self.cluster.multi_search([sq1, sq2]))
results = self.cluster.multi_search([sq1, sq2], raise_on_error=False)
self.assertIs(results[0], sq1.get_result())
self.assertIs(results[1], sq2.get_result())
self.assertEqual(results[0].total, 27802974)
self.assertEqual(results[0].took, 59)
self.assertEqual(results[0].timed_out, False)
self.assertEqual(results[0].max_score, 0.0)
self.assertEqual(len(results[0].hits), 0)
self.assertRaisesRegexp(DelayedElasticsearchException, r'^SearchPhaseExecutionException', lambda: results[1].total)
self.assertRaisesRegexp(DelayedElasticsearchException, r'^SearchPhaseExecutionException', lambda: results[1].hits)
self.assertRaises(AttributeError, lambda: results[1].unknown_attr)
def test_attr_int_facet_filter__existing_post_filter(int_qf, compiler):
sq = int_qf.apply(SearchQuery().post_filter(Range('price', lt=100)), {})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_int.filter':
agg.Filter(
Range('price', lt=100),
aggs={'qf.attr_int': agg.Terms(Field('attr.int'), size=10_000)})
}).post_filter(Range('price', lt=100)).to_dict(compiler=compiler))
sq = int_qf.apply(
SearchQuery().post_filter(Range('price', lt=100), meta={'price':
True}), {})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_int.filter':
agg.Filter(
Range('price', lt=100),
aggs={'qf.attr_int': agg.Terms(Field('attr.int'), size=10_000)})
}).post_filter(Range('price', lt=100)).to_dict(compiler=compiler))
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 test_suggest(self):
sq = SearchQuery()
sq = sq.suggest(text="Complete",
in_title={'term': {'size': 3, 'field': 'title'}})
self.assert_expression(
sq,
{
'suggest': {
'text': 'Complete',
'in_title': {
'term': {
'size': 3,
'field': 'title',
}
}
}
}
)
sq = sq.suggest(in_body={'completion': {'field': 'body'}})
self.assert_expression(
sq,
{
'suggest': {
'text': 'Complete',
'in_title': {
'term': {
'size': 3,
'field': 'title',
}
},
'in_body': {
'completion': {
'field': 'body',
}
},
}
}
)
sq = sq.suggest(None)
self.assert_expression(sq, {})
def test_suggest(self):
sq = SearchQuery()
sq = sq.suggest(text="Complete",
in_title={'term': {'size': 3, 'field': 'title'}})
self.assert_expression(
sq,
{
'suggest': {
'text': 'Complete',
'in_title': {
'term': {
'size': 3,
'field': 'title',
}
}
}
}
)
sq = sq.suggest(in_body={'completion': {'field': 'body'}})
self.assert_expression(
sq,
{
'suggest': {
'text': 'Complete',
'in_title': {
'term': {
'size': 3,
'field': 'title',
}
},
'in_body': {
'completion': {
'field': 'body',
}
},
}
}
)
sq = sq.suggest(None)
self.assert_expression(sq, {})
def test_attr_bool_facet_filter__empty_params(bool_qf, compiler):
sq = bool_qf.apply(SearchQuery(), {})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_bool':
agg.Terms(Field('attr.bool'), size=100)
}).to_dict(compiler=compiler))
qf_res = bool_qf.process_result(
SearchResult(
{
'aggregations': {
'qf.attr_bool': {
'buckets': [{
'key': 0b11,
'doc_count': 123,
}, {
'key': 0b10,
'doc_count': 99
}, {
'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 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 False
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 False
facet = qf_res.attr_bool.get_facet(2)
assert len(facet.all_values) == 1
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 False
def run(options):
"""Run benchmark."""
prof = cProfile.Profile()
cov = coverage.Coverage()
times = OrderedDict.fromkeys(['data_load', 'json_loads', 'searchResult'])
start = time.monotonic() * 1000
raw_data = options.input.read()
times['data_load'] = time.monotonic() * 1000 - start
start = time.monotonic() * 1000
raw_results = json.loads(raw_data)
times['json_loads'] = time.monotonic() * 1000 - start
query = SearchQuery(MatchAll(),
doc_cls=SimpleDocument)
if 'aggregations' in raw_results:
query = query.aggs(terms=Terms(SimpleDocument.integer_0))
gc.disable()
if options.profile:
cov.start()
prof.enable()
SearchResult(
raw_results,
query._aggregations,
doc_cls=query._get_doc_cls(),
instance_mapper=query._instance_mapper)
times['searchResult'] = time.monotonic() * 1000 - start
if options.profile:
prof.disable()
cov.stop()
gc.enable()
for key, duration in times.items():
print("Took {} {:10.3f}ms".format(key, duration))
if options.profile:
prof.print_stats('cumulative')
cov.report()
cov.html_report()
def test_attr_range_facet_filter__empty(range_qf, compiler):
sq = range_qf.apply(SearchQuery(), {})
assert_search_query(
sq,
SearchQuery().aggs({
'qf.attr_range':
agg.Terms(script=Script('doc[params.field].value >>> 32',
lang='painless',
params={
'field': 'attr.float',
}),
size=100),
}), compiler)
qf_res = range_qf.process_results(
SearchResult(
{
'aggregations': {
'qf.attr_range': {
'buckets': [{
'key': '8',
'doc_count': 84
}, {
'key': '439',
'doc_count': 28
}]
}
}
},
aggregations=sq.get_context().aggregations))
assert qf_res.attr_range.name == 'attr_range'
assert qf_res.attr_range.alias == 'a'
f = qf_res.attr_range.get_facet(8)
assert f.attr_id == 8
assert f.count == 84
assert f.selected is False
f = qf_res.attr_range.get_facet(439)
assert f.attr_id == 439
assert f.count == 28
assert f.selected is False
def test_exists(self):
self.client.search.return_value = {
"hits": {"total": 1, "max_score": 1.0, "hits": []}
}
self.assertEqual(
SearchQuery(index=self.index, doc_cls=self.index['car']).exists(),
True
)
self.client.search.assert_called_with(
index='test',
doc_type='car',
body={'size': 0},
terminate_after=1,
)
self.client.search.return_value = {
"hits": {"total": 0, "max_score": 0.0, "hits": []}
}
self.assertEqual(
SearchQuery(index=self.index)
.filter(self.index['car'].status == 1)
.function_score({'boost_factor': 3})
.exists(),
False
)
self.client.search.assert_called_with(
index='test',
doc_type='car',
body={
"size": 0,
"query": {
"bool": {
"filter": {
"term": {"status": 1}
}
}
}
},
terminate_after=1,
)
def run(options):
"""Run benchmark."""
prof = cProfile.Profile()
cov = coverage.Coverage()
times = OrderedDict.fromkeys(['data_load', 'json_loads', 'searchResult'])
start = time.monotonic() * 1000
raw_data = options.input.read()
times['data_load'] = time.monotonic() * 1000 - start
start = time.monotonic() * 1000
raw_results = json.loads(raw_data)
times['json_loads'] = time.monotonic() * 1000 - start
query = SearchQuery(MatchAll(), doc_cls=SimpleDocument)
if 'aggregations' in raw_results:
query = query.aggs(terms=Terms(SimpleDocument.integer_0))
gc.disable()
if options.profile:
cov.start()
prof.enable()
SearchResult(raw_results,
query._aggregations,
doc_cls=query._get_doc_cls(),
instance_mapper=query._instance_mapper)
times['searchResult'] = time.monotonic() * 1000 - start
if options.profile:
prof.disable()
cov.stop()
gc.enable()
for key, duration in times.items():
print("Took {} {:10.3f}ms".format(key, duration))
if options.profile:
prof.print_stats('cumulative')
cov.report()
cov.html_report()
def test_search_params(self):
sq = SearchQuery()
self.assertEqual(
sq._search_params,
{}
)
sq = SearchQuery(search_type='count')
self.assertEqual(
sq._search_params,
{
'search_type': 'count'
}
)
sq = sq.with_search_type(None)
self.assertEqual(
sq._search_params,
{}
)
sq = sq.with_search_params({'search_type': 'count', 'query_cache': True}, unknown_param='none')
self.assertEqual(
sq._search_params,
{
'search_type': 'count',
'query_cache': True,
'unknown_param': 'none',
}
)
sq = sq.with_routing(1234)
self.assertEqual(
sq._search_params,
{
'routing': 1234,
'search_type': 'count',
'query_cache': True,
'unknown_param': 'none',
}
)
def test_scroll(es_index, cars):
with pytest.warns(UserWarning, match='Cannot determine document class'):
search_res = es_index.search(
SearchQuery(),
scroll='1m',
)
assert search_res.total == 2
assert len(search_res.hits) == 2
assert search_res.scroll_id is not None
scroll_res = es_index.scroll(search_res.scroll_id, scroll='1m')
assert scroll_res.total == 2
assert len(scroll_res.hits) == 0
clear_scroll_res = es_index.clear_scroll(scroll_res.scroll_id)
assert clear_scroll_res.succeeded is True
def test_search_params(self):
sq = SearchQuery()
self.assertEqual(
sq._search_params,
{}
)
sq = SearchQuery(search_type='count')
self.assertEqual(
sq._search_params,
{
'search_type': 'count'
}
)
sq = sq.with_search_type(None)
self.assertEqual(
sq._search_params,
{}
)
sq = sq.with_search_params({'search_type': 'count', 'query_cache': True}, unknown_param='none')
self.assertEqual(
sq._search_params,
{
'search_type': 'count',
'query_cache': True,
'unknown_param': 'none',
}
)
sq = sq.with_routing(1234)
self.assertEqual(
sq._search_params,
{
'routing': 1234,
'search_type': 'count',
'query_cache': True,
'unknown_param': 'none',
}
)
def test_aggregations(self):
f = DynamicDocument.fields
sq = SearchQuery().aggregations(min_price=agg.Min(f.price))
self.assert_expression(
sq,
{
"aggregations": {
"min_price": {
"min": {"field": "price"}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = SearchQuery().aggregations(genders=agg.Terms(f.gender))
self.assert_expression(
sq,
{
"aggregations": {
"genders": {
"terms": {"field": "gender"}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.aggregations(
type=agg.Terms(f.type, aggs={'min_price': agg.Min(f.price)})
)
)
self.assert_expression(
sq,
{
"aggregations": {
"type": {
"terms": {"field": "type"},
"aggregations": {
"min_price": {
"min": {"field": "price"}
}
}
}
}
},
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.aggregations(
top_tags=(
agg.Terms(
f.tags,
size=3,
aggs={
'top_tag_hits': agg.TopHits(
sort=f.last_activity_date.desc(),
size=1,
_source=Params(include=[f.title]))
}
)
)
)
)
self.assert_expression(
sq,
{
"aggregations": {
"top_tags": {
"terms": {
"field": "tags",
"size": 3
},
"aggregations": {
"top_tag_hits": {
"top_hits": {
"sort": {
"last_activity_date": "desc"
},
"_source": {
"include": ["title"]
},
"size" : 1
}
}
}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
sq = (
SearchQuery()
.aggregations({
'top_sites': agg.Terms(
f.domain,
order=Sort('top_hit', 'desc'),
aggs={
'top_tags_hits': agg.TopHits(),
'top_hit': agg.Max(script='_doc.score'),
}
)
})
)
self.assert_expression(
sq,
{
"aggregations": {
"top_sites": {
"terms": {
"field": "domain",
"order": {
"top_hit": "desc"
}
},
"aggregations": {
"top_tags_hits": {
"top_hits": {}
},
"top_hit" : {
"max": {
"script": "_doc.score"
}
}
}
}
}
}
)
self.assertEqual(collect_doc_classes(sq), {DynamicDocument})
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_highlight(self):
sq = SearchQuery()
sq = sq.highlight(fields={'content': {}})
self.assertEqual(collect_doc_classes(sq), set())
self.assert_expression(
sq,
{
"highlight": {
"fields": {
"content": {}
}
}
}
)
sq = SearchQuery()
sq = sq.highlight(
fields=[self.index.test.content],
pre_tags=['[em]'],
post_tags=['[/em]']
)
self.assertEqual(collect_doc_classes(sq), {self.index.test})
self.assert_expression(
sq,
{
"highlight": {
"fields": [
{
"content": {}
}
],
"pre_tags": ["[em]"],
"post_tags": ["[/em]"]
}
}
)
sq = SearchQuery()
sq = sq.highlight(
fields=[
self.index.test.content.highlight(
matched_fields=[self.index.test.content, self.index.test.content.plain],
type='fvh',
)
]
)
self.assertEqual(collect_doc_classes(sq), {self.index.test})
self.assert_expression(
sq,
{
"highlight": {
"fields": [
{
"content": {
"matched_fields": ["content", "content.plain"],
"type": "fvh"
}
}
]
}
}
)
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))
def test_attr_bool_facet_filter__unknown_param(bool_qf, compiler):
sq = bool_qf.apply(SearchQuery(), {'b18': 'true'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_bool':
agg.Terms(Field('attr.bool'), size=100)
}).to_dict(compiler=compiler))
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_multi_search(self):
self.client.msearch = MagicMock(
return_value={
u'responses': [{
u'_shards': {
u'failed': 0,
u'successful': 64,
u'total': 64
},
u'hits': {
u'hits': [],
u'max_score': 0.0,
u'total': 27802974
},
u'timed_out': False,
u'took': 59
}, {
u'_shards': {
u'failed': 0,
u'successful': 16,
u'total': 16
},
u'hits': {
u'hits': [{
u'_id': u'56565215',
u'_index': u'ca',
u'_type': u'product',
u'_score': 1.0,
u'_source': {
u'name': u'Gold usb cable',
u'price': {
u'local': 92.421,
u'unit': 5.67
},
u'status': 0
},
}],
u'max_score':
1.0,
u'total':
272
},
u'timed_out': False,
u'took': 53
}]
})
ProductDoc = self.index.product
sq1 = SearchQuery(doc_cls=ProductDoc, search_type='count', routing=123)
sq2 = (SearchQuery(
index=self.cluster['us'],
doc_cls=ProductDoc).filter(ProductDoc.status == 0).limit(1))
results = self.cluster.multi_search([sq1, sq2])
self.client.msearch.assert_called_with(body=[{
'type': 'product',
'search_type': 'count',
'routing': 123
}, {}, {
'index': 'us',
'type': 'product'
}, {
'query': {
'bool': {
'filter': {
'term': {
'status': 0
}
}
}
},
'size': 1
}])
self.assertIs(results[0], sq1.result)
self.assertIs(results[1], sq2.result)
self.assertEqual(results[0].total, 27802974)
self.assertEqual(len(results[0].hits), 0)
self.assertEqual(results[1].total, 272)
self.assertEqual(len(results[1].hits), 1)
doc = results[1].hits[0]
self.assertEqual(doc._id, '56565215')
self.assertEqual(doc._index, 'ca')
self.assertEqual(doc._type, 'product')
self.assertAlmostEqual(doc._score, 1.0)
self.assertEqual(doc.name, 'Gold usb cable')
self.assertAlmostEqual(doc.price.local, 92.421)
self.assertAlmostEqual(doc.price.unit, 5.67)
self.assertEqual(doc.status, 0)
def test_highlight(self):
sq = SearchQuery()
sq = sq.highlight(fields={'content': {}})
self.assertEqual(collect_doc_classes(sq), set())
self.assert_expression(
sq,
{
"highlight": {
"fields": {
"content": {}
}
}
}
)
sq = SearchQuery()
sq = sq.highlight(
fields=[self.index.test.content],
pre_tags=['[em]'],
post_tags=['[/em]']
)
self.assertEqual(collect_doc_classes(sq), {self.index.test})
self.assert_expression(
sq,
{
"highlight": {
"fields": [
{
"content": {}
}
],
"pre_tags": ["[em]"],
"post_tags": ["[/em]"]
}
}
)
sq = SearchQuery()
sq = sq.highlight(
fields=[
self.index.test.content.highlight(
matched_fields=[self.index.test.content, self.index.test.content.plain],
type='fvh',
)
]
)
self.assertEqual(collect_doc_classes(sq), {self.index.test})
self.assert_expression(
sq,
{
"highlight": {
"fields": [
{
"content": {
"matched_fields": ["content", "content.plain"],
"type": "fvh"
}
}
]
}
}
)
def _apply_filter_expression(self, search_query: SearchQuery,
expr: Expression,
attr_id: int) -> SearchQuery:
return search_query.filter(expr)
def test_attr_int_simple_filter(compiler):
qf = QueryFilter()
qf.add_filter(AttrIntSimpleFilter('attr_int', Field('attr.int'),
alias='a'))
sq = qf.apply(SearchQuery(), {})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'b18': '224'})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a18': '1234'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Term('attr.int', 0x12_000004d2)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a18': ['1234', '5678']})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Terms('attr.int',
[0x12_000004d2, 0x12_0000162e])).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a18': ['1234', '5678'], 'a324': '90'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Terms('attr.int', [0x12_000004d2, 0x12_0000162e])).filter(
Term('attr.int', 0x144_0000005a)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a18': '0x1234'})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a18-19': '1234'})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a2147483648': '1'})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a1': '2147483648'})
assert sq.to_dict(compiler=compiler) == {}
def test_attr_range_facet_filter__multiple_selected_filters(
range_qf, compiler):
sq = range_qf.apply(SearchQuery(), {'a8__gte': 2.71, 'a99__lte': 3.14})
assert_search_query(
sq,
SearchQuery().aggs({
'qf.attr_range.filter':
agg.Filter(
Bool.must(
Range('attr.float', gte=0x8_402d70a4, lte=0x8_7f800000),
Bool.should(
Range('attr.float',
gte=0x63_00000000,
lte=0x63_4048f5c3),
Range('attr.float',
gte=0x63_80000000,
lte=0x63_ff800000))),
aggs={
'qf.attr_range':
agg.Terms(script=Script('doc[params.field].value >>> 32',
lang='painless',
params={
'field': 'attr.float',
}),
size=100)
}),
'qf.attr_range:8':
agg.Filter(
Bool.must(
Bool.should(
Range('attr.float',
gte=0x63_00000000,
lte=0x63_4048f5c3),
Range('attr.float',
gte=0x63_80000000,
lte=0x63_ff800000)),
Range('attr.float', gte=0x8_00000000, lte=0x8_ffffffff))),
'qf.attr_range:99':
agg.Filter(
Bool.must(
Range('attr.float', gte=0x8_402d70a4, lte=0x8_7f800000),
Range('attr.float', gte=0x63_00000000,
lte=0x63_ffffffff))),
}).post_filter(Range('attr.float', gte=0x8_402d70a4,
lte=0x8_7f800000)).post_filter(
Bool.should(
Range('attr.float',
gte=0x63_00000000,
lte=0x63_4048f5c3),
Range('attr.float',
gte=0x63_80000000,
lte=0x63_ff800000))), compiler)
qf_res = range_qf.process_results(
SearchResult(
{
'aggregations': {
'qf.attr_range.filter': {
'doc_count': 32,
'qf.attr_range': {
'buckets': [{
'key': 8,
'doc_count': 32
}, {
'key': 99,
'doc_count': 18
}]
}
},
'qf.attr_range:8': {
'doc_count': 84
},
'qf.attr_range:99': {
'doc_count': 33
}
}
},
aggregations=sq.get_context().aggregations))
assert qf_res.attr_range.name == 'attr_range'
assert qf_res.attr_range.alias == 'a'
f = qf_res.attr_range.get_facet(8)
assert f.attr_id == 8
assert f.count == 84
assert f.selected is True
f = qf_res.attr_range.get_facet(99)
assert f.attr_id == 99
assert f.count == 33
assert f.selected is True
def test_multi_search(self):
self.client.msearch = MagicMock(
return_value={
u'responses': [
{
u'_shards': {
u'failed': 0,
u'successful': 64,
u'total': 64
},
u'hits': {
u'hits': [],
u'max_score': 0.0,
u'total': 27802974
},
u'timed_out': False,
u'took': 59
},
{
u'_shards': {
u'failed': 0,
u'successful': 16,
u'total': 16
},
u'hits': {
u'hits': [
{
u'_id': u'56565215',
u'_index': u'ca',
u'_type': u'product',
u'_score': 1.0,
u'_source': {
u'name': u'Gold usb cable',
u'price': {u'local': 92.421, u'unit': 5.67},
u'status': 0
},
}
],
u'max_score': 1.0,
u'total': 272
},
u'timed_out': False,
u'took': 53
}
]
}
)
ProductDoc = self.index.product
sq1 = SearchQuery(doc_cls=ProductDoc, search_type='count', routing=123)
sq2 = (
SearchQuery(index=self.cluster['us'], doc_cls=ProductDoc)
.filter(ProductDoc.status == 0)
.limit(1)
)
results = self.cluster.multi_search([sq1, sq2])
self.client.msearch.assert_called_with(
body=[
{'type': 'product', 'search_type': 'count', 'routing': 123},
{},
{'index': 'us', 'type': 'product'},
{'query': {'filtered': {'filter': {'term': {'status': 0}}}}, 'size': 1}
]
)
self.assertIs(results[0], sq1.get_result())
self.assertIs(results[1], sq2.get_result())
self.assertEqual(results[0].total, 27802974)
self.assertEqual(len(results[0].hits), 0)
self.assertEqual(results[1].total, 272)
self.assertEqual(len(results[1].hits), 1)
doc = results[1].hits[0]
self.assertEqual(doc._id, '56565215')
self.assertEqual(doc._index, 'ca')
self.assertEqual(doc._type, 'product')
self.assertAlmostEqual(doc._score, 1.0)
self.assertEqual(doc.name, 'Gold usb cable')
self.assertAlmostEqual(doc.price.local, 92.421)
self.assertAlmostEqual(doc.price.unit, 5.67)
self.assertEqual(doc.status, 0)
def test_attr_int_facet_filter__multiple_selected_values(int_qf, compiler):
sq = int_qf.apply(SearchQuery(), {
'a18': '58084',
'a324': ['57005', '48879']
})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_int.filter':
agg.Filter(Bool.must(
Term('attr.int', 0x12_0000e2e4),
Terms('attr.int', [0x144_0000dead, 0x144_0000beef]),
),
aggs={
'qf.attr_int': agg.Terms(Field('attr.int'),
size=10_000),
}),
'qf.attr_int.filter:18':
agg.Filter(Terms('attr.int', [0x144_0000dead, 0x144_0000beef]),
aggs={
'qf.attr_int:18': agg.Terms(Field('attr.int'),
size=100),
}),
'qf.attr_int.filter:324':
agg.Filter(Term('attr.int', 0x12_0000e2e4),
aggs={
'qf.attr_int:324': agg.Terms(Field('attr.int'),
size=100),
})
}).post_filter(Term('attr.int', 0x12_0000e2e4)).post_filter(
Terms('attr.int',
[0x144_0000dead, 0x1440000beef])).to_dict(compiler=compiler))
qf_res = int_qf.process_result(
SearchResult(
{
'aggregations': {
'qf.attr_int.filter': {
'doc_count': 404,
'qf.attr_int': {
'buckets': [{
'key': 0x144_0000dead,
'doc_count': 1
}, {
'key': 0x12_0000e2e4,
'doc_count': 1
}, {
'key': 0x144_0000beef,
'doc_count': 1
}]
}
},
'qf.attr_int.filter:18': {
'doc_count': 200,
'qf.attr_int:18': {
'buckets': [
{
'key': 0x12_0000e2e4,
'doc_count': 99
},
{
'key': 0x12_0000e7e5,
'doc_count': 88
},
]
}
},
'qf.attr_int.filter:324': {
'doc_count': 200,
'qf.attr_int:324': {
'buckets': [
{
'key': 0x144_0000dead,
'doc_count': 123
},
{
'key': 0x144_0000beef,
'doc_count': 1
},
]
}
},
}
},
aggregations=sq.get_context().aggregations))
def test_attr_int_facet_filter__unknown_param(int_qf, compiler):
sq = int_qf.apply(SearchQuery(), {'b18': '224'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().aggs({
'qf.attr_int':
agg.Terms(Field('attr.int'), size=10_000)
}).to_dict(compiler=compiler))
def test_attr_range_simple_filter(compiler):
qf = QueryFilter()
qf.add_filter(
AttrRangeSimpleFilter('attr_float', Field('attr.float'), alias='a'))
sq = qf.apply(SearchQuery(), {})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a8__gte': '3.14'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Range('attr.float', gte=0x8_4048f5c3,
lte=0x8_7f800000)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a8__gte': '-3.14'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Bool.should(Range('attr.float', gte=0x8_80000000, lte=0x8_c048f5c3),
Range('attr.float', gte=0x8_00000000,
lte=0x8_7f800000))).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a8__lte': '-2.71'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Range('attr.float', gte=0x8_c02d70a4,
lte=0x8_ff800000)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a8__gte': ['1', '2.71'], 'a8__lte': '3.14'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Range('attr.float', gte=0x8_402d70a4,
lte=0x8_4048f5c3)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a8__gte': '-3.14', 'a8__lte': '-2.71'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Range('attr.float', gte=0x8_c02d70a4,
lte=0x8_c048f5c3)).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a8__gte': '-3.14', 'a8__lte': '3.14'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Bool.should(Range('attr.float', gte=0x8_80000000, lte=0x8_c048f5c3),
Range('attr.float', gte=0x8_00000000,
lte=0x8_4048f5c3))).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a8__gte': '3.14', 'a8__lte': '-3.14'})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Bool.must(Range('attr.float', gte=0x8_4048f5c3, lte=0x8_7f800000),
Range('attr.float', gte=0x8_c048f5c3,
lte=0x8_ff800000))).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {
'a8__gte': '2.71',
'a8__lte': '3.14',
'a99__lte': '99'
})
assert sq.to_dict(compiler=compiler) == (SearchQuery().filter(
Range('attr.float', gte=0x8_402d70a4, lte=0x8_4048f5c3)).filter(
Bool.should(
Range('attr.float', gte=0x63_00000000, lte=0x63_42c60000),
Range('attr.float', gte=0x63_80000000,
lte=0x63_ff800000))).to_dict(compiler=compiler))
sq = qf.apply(SearchQuery(), {'a99.9__gte': '99.9'})
assert sq.to_dict(compiler=compiler) == {}
sq = qf.apply(SearchQuery(), {'a99__gte': '100ee2'})
assert sq.to_dict(compiler=compiler) == {}