mirror of https://github.com/milvus-io/milvus.git
Add some cases of search params (#19137)
Signed-off-by: “nico” <cheng.yuan@zilliz.com> Signed-off-by: “nico” <cheng.yuan@zilliz.com>pull/18949/head
NicoYuan1986
GitHub
parent
4de1bfe5bc
commit
5b077ae980
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@ -1244,6 +1244,65 @@ class TestCollectionSearch(TestcaseBase):
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"limit": nb_old + nb_new,
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"_async": _async})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_different_data_distribution_without_index(self, dim, auto_id, _async):
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"""
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target: test search different data distribution without index
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method: 1. connect milvus
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2. create a collection
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3. insert data
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4. Load and search
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expected: Search successfully
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"""
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# 1. connect, create collection and insert data
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self._connect()
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collection_w = self.init_collection_general(prefix, False, dim=dim)[0]
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dataframe = cf.gen_default_dataframe_data(dim=dim, start=-1500)
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collection_w.insert(dataframe)
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# 2. load and search
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collection_w.load()
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit,
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_async=_async,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"limit": default_limit,
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"_async": _async})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_different_data_distribution_with_index(self, dim, auto_id, _async):
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"""
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target: test search different data distribution with index
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method: 1. connect milvus
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2. create a collection
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3. insert data
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4. create an index
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5. Load and search
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expected: Search successfully
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"""
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# 1. connect, create collection and insert data
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self._connect()
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collection_w = self.init_collection_general(prefix, False, dim=dim)[0]
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dataframe = cf.gen_default_dataframe_data(dim=dim, start=-1500)
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collection_w.insert(dataframe)
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# 2. create index
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index_param = {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 100}}
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collection_w.create_index("float_vector", index_param)
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# 3. load and search
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collection_w.load()
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit,
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_async=_async,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"limit": default_limit,
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"_async": _async})
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.skip(reason="debug")
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def test_search_max_dim(self, auto_id, _async):
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@ -1293,6 +1352,119 @@ class TestCollectionSearch(TestcaseBase):
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"limit": nq,
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"_async": _async})
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@pytest.mark.xfail(reason="issue #19129")
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@pytest.mark.tags(CaseLabel.L2)
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def test_search_max_nq(self, auto_id, dim, _async):
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"""
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target: test search with max nq
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method: connect milvus, create collection, insert, load and search with max nq
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expected: search successfully with max nq
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"""
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self._connect()
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nq = 17000
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collection_w, _, _, insert_ids = self.init_collection_general(prefix, True,
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auto_id=auto_id,
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dim=dim)[0:4]
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collection_w.load()
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log.info("test_search_max_nq: searching collection %s" % collection_w.name)
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vectors = [[random.random() for _ in range(dim)] for _ in range(nq)]
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collection_w.search(vectors[:nq], default_search_field,
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default_search_params, default_limit,
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default_search_exp, _async=_async,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": nq,
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"ids": insert_ids,
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"limit": default_limit,
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"_async": _async})
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@pytest.mark.xfail(reason="issue #19130")
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@pytest.mark.tags(CaseLabel.L2)
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@pytest.mark.parametrize("shards_num", [1, 10, 128, 256])
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def test_search_with_non_default_shard_nums(self, auto_id, shards_num, _async):
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"""
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target: test search with non_default shards_num
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method: connect milvus, create collection with several shard numbers , insert, load and search
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expected: search successfully with the non_default shards_num
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"""
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self._connect()
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name = cf.gen_unique_str(prefix)
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collection_w = self.init_collection_wrap(name=name, shards_num=shards_num)
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dataframe = cf.gen_default_dataframe_data()
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collection_w.insert(dataframe)
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collection_w.load()
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vectors = [[random.random() for _ in range(default_dim)] for _ in range(default_nq)]
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collection_w.search(vectors[:default_nq], default_search_field,
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default_search_params, default_limit,
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default_search_exp, _async=_async,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"limit": default_limit,
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"_async": _async})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("M", [4, 64])
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@pytest.mark.parametrize("efConstruction", [8, 512])
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def test_search_HNSW_index_with_max_ef(self, M, efConstruction, auto_id, _async):
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"""
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target: test search HNSW index with max ef
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method: connect milvus, create collection , insert, create index, load and search
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expected: search successfully
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"""
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dim = M * 4
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self._connect()
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collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True,
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partition_num=1,
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auto_id=auto_id,
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dim=dim, is_index=True)[0:5]
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HNSW_index_params = {"M": M, "efConstruction": efConstruction}
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HNSW_index = {"index_type": "HNSW", "params": HNSW_index_params, "metric_type": "L2"}
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collection_w.create_index("float_vector", HNSW_index)
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collection_w.load()
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search_param = {"metric_type": "L2", "params": {"ef": 32768}}
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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collection_w.search(vectors[:default_nq], default_search_field,
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search_param, default_limit,
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default_search_exp, _async=_async,
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travel_timestamp=time_stamp,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"ids": insert_ids,
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"limit": default_limit,
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"_async": _async})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("M", [4, 64])
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@pytest.mark.parametrize("efConstruction", [8, 512])
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@pytest.mark.parametrize("limit", [1, 10, 3000])
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def test_search_HNSW_index_with_min_ef(self, M, efConstruction, limit, auto_id, _async):
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"""
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target: test search HNSW index with min ef
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method: connect milvus, create collection , insert, create index, load and search
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expected: search successfully
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"""
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dim = M * 4
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ef = limit
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self._connect()
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collection_w, _, _, insert_ids, time_stamp = self.init_collection_general(prefix, True,
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partition_num=1,
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auto_id=auto_id,
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dim=dim, is_index=True)[0:5]
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HNSW_index_params = {"M": M, "efConstruction": efConstruction}
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HNSW_index = {"index_type": "HNSW", "params": HNSW_index_params, "metric_type": "L2"}
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collection_w.create_index("float_vector", HNSW_index)
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collection_w.load()
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search_param = {"metric_type": "L2", "params": {"ef": ef}}
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vectors = [[random.random() for _ in range(dim)] for _ in range(default_nq)]
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collection_w.search(vectors[:default_nq], default_search_field,
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search_param, limit,
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default_search_exp, _async=_async,
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travel_timestamp=time_stamp,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": default_nq,
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"ids": insert_ids,
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"limit": limit,
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"_async": _async})
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@pytest.mark.tags(CaseLabel.L1)
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@pytest.mark.parametrize("index, params",
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zip(ct.all_index_types[:6],
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