mirror of https://github.com/milvus-io/milvus.git
test: Update search iterator result check for dup ids (#40190)
Related issue: #40161 1. fix the search iterator result assertion for dup ids 2. use normalized vectors for iterator tests --------- Signed-off-by: yanliang567 <yanliang.qiao@zilliz.com>pull/40224/head
yanliang567
GitHub
parent
32c00dbc1b
commit
84f7e2d895
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@ -458,8 +458,8 @@ class ResponseChecker:
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if check_items.get("limit"):
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if "range_filter" not in check_items and "radius" not in check_items:
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assert len(pk_list) / check_items["limit"] >= 0.9
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assert len(pk_list) == len(set(pk_list))
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log.info("check: total %d results" % len(pk_list))
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log.debug(f"check: total {len(pk_list)} results, set len: {len(set(pk_list))}")
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assert len(pk_list) == len(set(pk_list)) != 0
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return True
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@ -39,7 +39,6 @@ class TestMilvusClientSearchIteratorInValid(TestMilvusClientV2Base):
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yield request.param
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@pytest.mark.tags(CaseLabel.L1)
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# @pytest.mark.skip("https://github.com/milvus-io/milvus/issues/39045")
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def test_milvus_client_search_iterator_using_mul_db(self, search_params):
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"""
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target: test search iterator(high level api) case about mul db
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@ -57,8 +56,7 @@ class TestMilvusClientSearchIteratorInValid(TestMilvusClientV2Base):
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collections = self.list_collections(client)[0]
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assert collection_name in collections
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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@ -71,7 +69,7 @@ class TestMilvusClientSearchIteratorInValid(TestMilvusClientV2Base):
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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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# 5. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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search_params = {"params": search_params}
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error_msg = "alias or database may have been changed"
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self.search_iterator(client, collection_name, vectors_to_search, batch_size, search_params=search_params,
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@ -82,7 +80,6 @@ class TestMilvusClientSearchIteratorInValid(TestMilvusClientV2Base):
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self.drop_collection(client, collection_name)
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@pytest.mark.tags(CaseLabel.L1)
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# @pytest.mark.skip("https://github.com/milvus-io/milvus/issues/39087")
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def test_milvus_client_search_iterator_alias_different_col(self, search_params):
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"""
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target: test search iterator(high level api) case about alias
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@ -104,15 +101,14 @@ class TestMilvusClientSearchIteratorInValid(TestMilvusClientV2Base):
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collections = self.list_collections(client)[0]
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assert collection_name_new in collections
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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self.insert(client, collection_name_new, rows)
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self.flush(client, collection_name_new)
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# 3. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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search_params = {"params": search_params}
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error_msg = "alias or database may have been changed"
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self.search_iterator(client, alias, vectors_to_search, batch_size, search_params=search_params,
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@ -168,13 +164,12 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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"dim": default_dim,
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"consistency_level": 0})
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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# 3. search iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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check_items = {"batch_size": batch_size, "limit": default_nb, "metric_type": default_metric_type}
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if "radius" in search_params:
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check_items["radius"] = search_params["radius"]
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@ -211,14 +206,13 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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index_params.add_index(default_vector_field_name, metric_type=default_metric_type)
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self.create_collection(client, collection_name, dimension=dim, schema=schema, index_params=index_params)
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [
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{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
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{default_primary_key_field_name: str(i), default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_string_field_name: str(i), "nullable_field": None, "array_field": None} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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# 3. search iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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check_items = {"batch_size": batch_size, "limit": default_nb, "metric_type": default_metric_type}
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if "radius" in search_params:
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check_items["radius"] = search_params["radius"]
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@ -254,14 +248,13 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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new_name = collection_name + "new"
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self.rename_collection(client, old_name, new_name)
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, new_name, rows)
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self.flush(client, new_name)
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# assert self.num_entities(client, collection_name)[0] == default_nb
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# 3. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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check_items = {"batch_size": batch_size, "limit": default_nb, "metric_type": default_metric_type}
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if "radius" in search_params:
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check_items["radius"] = search_params["radius"]
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@ -288,17 +281,16 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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collections = self.list_collections(client)[0]
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assert collection_name in collections
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{
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default_primary_key_field_name: i,
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default_vector_field_name: list(rng.random((1, default_dim))[0]),
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default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0,
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default_int32_array_field_name: [i, i + 1, i + 2],
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default_string_array_field_name: [str(i), str(i + 1), str(i + 2)]
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} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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# 3. search iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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check_items = {"batch_size": batch_size, "limit": default_nb, "metric_type": default_metric_type}
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if "radius" in search_params:
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check_items["radius"] = search_params["radius"]
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@ -321,14 +313,13 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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# 1. create collection
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self.create_collection(client, collection_name, default_dim, id_type="string", max_length=ct.default_length)
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [
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{default_primary_key_field_name: str(i), default_vector_field_name: list(rng.random((1, default_dim))[0]),
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{default_primary_key_field_name: str(i), default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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self.insert(client, collection_name, rows)
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self.flush(client, collection_name)
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# 3. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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check_items = {"batch_size": batch_size, "limit": default_nb, "metric_type": default_metric_type}
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if "radius" in search_params:
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check_items["radius"] = search_params["radius"]
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@ -353,15 +344,14 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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self.create_collection(client, collection_name, default_dim, metric_type=metric_type, auto_id=auto_id,
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consistency_level="Strong")
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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if auto_id:
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for row in rows:
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row.pop(default_primary_key_field_name)
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self.insert(client, collection_name, rows)
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# 3. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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limit = default_limit if default_limit < default_batch_size else default_batch_size
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check_items = {"batch_size": default_batch_size, "limit": limit, "metric_type": metric_type}
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if "radius" in search_params:
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@ -390,15 +380,14 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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self.create_collection(client, collection_name, default_dim, metric_type=metric_type, auto_id=auto_id,
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consistency_level="Strong")
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# 2. insert
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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if auto_id:
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for row in rows:
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row.pop(default_primary_key_field_name)
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self.insert(client, collection_name, rows)
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# 3. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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limit = default_limit if default_limit < default_batch_size else default_batch_size
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check_items = {"batch_size": default_batch_size, "limit": limit, "metric_type": metric_type}
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if "radius" in search_params:
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@ -427,15 +416,14 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. insert
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default_nb = 1000
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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pks = self.insert(client, collection_name, rows)[0]
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# 3. delete
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delete_num = 3
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self.delete(client, collection_name, ids=[i for i in range(delete_num)])
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# 4. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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insert_ids = [i for i in range(default_nb)]
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for insert_id in range(delete_num):
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if insert_id in insert_ids:
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@ -466,15 +454,14 @@ class TestMilvusClientSearchIteratorValid(TestMilvusClientV2Base):
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self.create_collection(client, collection_name, default_dim, consistency_level="Strong")
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# 2. insert
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default_nb = 1000
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rng = np.random.default_rng(seed=19530)
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(rng.random((1, default_dim))[0]),
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rows = [{default_primary_key_field_name: i, default_vector_field_name: list(cf.gen_vectors(1, default_dim)[0]),
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default_float_field_name: i * 1.0, default_string_field_name: str(i)} for i in range(default_nb)]
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pks = self.insert(client, collection_name, rows)[0]
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# 3. delete
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delete_num = 3
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self.delete(client, collection_name, filter=f"id < {delete_num}")
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# 4. search_iterator
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vectors_to_search = rng.random((1, default_dim))
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vectors_to_search = cf.gen_vectors(1, default_dim)
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insert_ids = [i for i in range(default_nb)]
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for insert_id in range(delete_num):
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if insert_id in insert_ids:
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@ -10387,31 +10387,51 @@ class TestSearchIterator(TestcaseBase):
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""" Test case of search iterator """
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@pytest.mark.tags(CaseLabel.L0)
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@pytest.mark.parametrize("metric_type", ct.float_metrics)
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@pytest.mark.parametrize("vector_data_type", ["FLOAT_VECTOR", "FLOAT16_VECTOR", "BFLOAT16_VECTOR"])
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def test_search_iterator_normal(self, vector_data_type):
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def test_range_search_iterator_default(self, metric_type, vector_data_type):
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"""
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target: test search iterator normal
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target: test iterator range search
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method: 1. search iterator
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2. check the result, expect pk
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2. check the result, expect pk not repeat and meet the range requirements
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expected: search successfully
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"""
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# 1. initialize with data
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dim = 128
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collection_w = self.init_collection_general(prefix, True, dim=dim, is_index=False,
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batch_size = 100
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collection_w = self.init_collection_general(prefix, True, dim=default_dim, is_index=False,
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vector_data_type=vector_data_type)[0]
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collection_w.create_index(field_name, {"metric_type": "L2"})
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collection_w.create_index(field_name, {"metric_type": metric_type})
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collection_w.load()
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search_vector = cf.gen_vectors(1, default_dim, vector_data_type)
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search_params = {"metric_type": metric_type}
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collection_w.search_iterator(search_vector, field_name, search_params, batch_size,
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check_task=CheckTasks.check_search_iterator,
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check_items={"metric_type": metric_type,
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"batch_size": batch_size})
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limit = 200
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res = collection_w.search(search_vector, field_name, param=search_params, limit=200,
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check_task=CheckTasks.check_search_results,
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check_items={"nq": 1, "limit": limit})[0]
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# 2. search iterator
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search_params = {"metric_type": "L2"}
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vectors = cf.gen_vectors_based_on_vector_type(1, dim, vector_data_type)
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batch_size = 200
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collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
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check_task=CheckTasks.check_search_iterator,
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check_items={"batch_size": batch_size})
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batch_size = 600
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collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
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check_task=CheckTasks.check_search_iterator,
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check_items={"batch_size": batch_size})
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if metric_type != "L2":
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radius = res[0][limit//2].distance - 0.1 # pick a radius to make sure there exists results
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range_filter = res[0][0].distance + 0.1
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search_params = {"metric_type": metric_type, "params": {"radius": radius, "range_filter": range_filter}}
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collection_w.search_iterator(search_vector, field_name, search_params, batch_size,
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check_task=CheckTasks.check_search_iterator,
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check_items={"metric_type": metric_type, "batch_size": batch_size,
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"radius": radius,
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"range_filter": range_filter})
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else:
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radius = res[0][limit//2].distance + 0.1
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range_filter = res[0][0].distance - 0.1
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search_params = {"metric_type": metric_type, "params": {"radius": radius, "range_filter": range_filter}}
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collection_w.search_iterator(search_vector, field_name, search_params, batch_size,
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check_task=CheckTasks.check_search_iterator,
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check_items={"metric_type": metric_type, "batch_size": batch_size,
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"radius": radius,
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"range_filter": range_filter})
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@pytest.mark.tags(CaseLabel.L1)
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def test_search_iterator_binary(self):
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@ -10455,113 +10475,6 @@ class TestSearchIterator(TestcaseBase):
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expr=expression, check_task=CheckTasks.check_search_iterator,
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check_items={})
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@pytest.mark.tags(CaseLabel.L2)
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def test_range_search_iterator_L2(self):
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"""
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target: test iterator range search
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method: 1. search iterator
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2. check the result, expect pk not repeat and meet the expr requirements
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expected: search successfully
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"""
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# 1. initialize with data
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batch_size = 100
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collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
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collection_w.create_index(field_name, {"metric_type": "L2"})
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collection_w.load()
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# 2. search iterator
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search_params = {"metric_type": "L2", "params": {"radius": 35.0, "range_filter": 34.0}}
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collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
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check_task=CheckTasks.check_search_iterator,
|
||||
check_items={"metric_type": "L2",
|
||||
"radius": 35.0,
|
||||
"range_filter": 34.0})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
def test_range_search_iterator_IP(self):
|
||||
"""
|
||||
target: test iterator range search
|
||||
method: 1. search iterator
|
||||
2. check the result, expect pk not repeat and meet the expr requirements
|
||||
expected: search successfully
|
||||
"""
|
||||
# 1. initialize with data
|
||||
batch_size = 100
|
||||
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
||||
collection_w.create_index(field_name, {"metric_type": "IP"})
|
||||
collection_w.load()
|
||||
# 2. search iterator
|
||||
search_params = {"metric_type": "IP", "params": {"radius": 0, "range_filter": 45}}
|
||||
collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
|
||||
check_task=CheckTasks.check_search_iterator,
|
||||
check_items={"metric_type": "IP",
|
||||
"radius": 0,
|
||||
"range_filter": 45})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L1)
|
||||
def test_range_search_iterator_COSINE(self):
|
||||
"""
|
||||
target: test iterator range search
|
||||
method: 1. search iterator
|
||||
2. check the result, expect pk not repeat and meet the expr requirements
|
||||
expected: search successfully
|
||||
"""
|
||||
# 1. initialize with data
|
||||
batch_size = 100
|
||||
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
||||
collection_w.create_index(field_name, {"metric_type": "COSINE"})
|
||||
collection_w.load()
|
||||
# 2. search iterator
|
||||
search_params = {"metric_type": "COSINE", "params": {"radius": 0.8, "range_filter": 1}}
|
||||
collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
|
||||
check_task=CheckTasks.check_search_iterator,
|
||||
check_items={"metric_type": "COSINE",
|
||||
"radius": 0.8,
|
||||
"range_filter": 1})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
def test_range_search_iterator_only_radius(self):
|
||||
"""
|
||||
target: test search iterator normal
|
||||
method: 1. search iterator
|
||||
2. check the result, expect pk not repeat and meet the expr requirements
|
||||
expected: search successfully
|
||||
"""
|
||||
# 1. initialize with data
|
||||
batch_size = 100
|
||||
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
||||
collection_w.create_index(field_name, {"metric_type": "L2"})
|
||||
collection_w.load()
|
||||
# 2. search iterator
|
||||
search_params = {"metric_type": "L2", "params": {"radius": 35.0}}
|
||||
collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
|
||||
check_task=CheckTasks.check_search_iterator,
|
||||
check_items={"metric_type": "L2",
|
||||
"radius": 35.0})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.skip("issue #25145")
|
||||
@pytest.mark.parametrize("index", ct.all_index_types[:7])
|
||||
@pytest.mark.parametrize("metrics", ct.float_metrics)
|
||||
def test_search_iterator_after_different_index_metrics(self, index, metrics):
|
||||
"""
|
||||
target: test search iterator using different index
|
||||
method: 1. search iterator
|
||||
2. check the result, expect pk not repeat and meet the expr requirements
|
||||
expected: search successfully
|
||||
"""
|
||||
# 1. initialize with data
|
||||
batch_size = 100
|
||||
collection_w = self.init_collection_general(prefix, True, is_index=False)[0]
|
||||
params = cf.get_index_params_params(index)
|
||||
default_index = {"index_type": index, "params": params, "metric_type": metrics}
|
||||
collection_w.create_index(field_name, default_index)
|
||||
collection_w.load()
|
||||
# 2. search iterator
|
||||
search_params = {"metric_type": metrics}
|
||||
collection_w.search_iterator(vectors[:1], field_name, search_params, batch_size,
|
||||
check_task=CheckTasks.check_search_iterator,
|
||||
check_items={})
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L2)
|
||||
@pytest.mark.parametrize("batch_size", [10, 100, 777, 1000])
|
||||
def test_search_iterator_with_different_limit(self, batch_size):
|
||||
|
|
|
|||
Loading…
Reference in New Issue