mirror of https://github.com/milvus-io/milvus.git
[test]Update bulk insert test (#21891)
Signed-off-by: zhuwenxing <wenxing.zhu@zilliz.com>pull/21811/head
zhuwenxing
GitHub
parent
02cfcbbd2f
commit
3b4e5ac297
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@ -140,7 +140,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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)
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self.collection_wrap.load()
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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log.info(
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f"query seg info: {self.utility_wrap.get_query_segment_info(c_name)[0]}"
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)
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@ -220,7 +220,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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)
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self.collection_wrap.load()
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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log.info(
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f"query seg info: {self.utility_wrap.get_query_segment_info(c_name)[0]}"
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)
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@ -228,7 +228,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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topk = 2
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search_data = cf.gen_vectors(nq, dim)
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search_params = ct.default_search_params
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time.sleep(5)
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time.sleep(20)
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res, _ = self.collection_wrap.search(
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search_data,
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df.vec_field,
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@ -310,11 +310,12 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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assert m_partition.num_entities == entities
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assert self.collection_wrap.num_entities == entities
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log.debug(state)
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time.sleep(20)
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res, _ = self.utility_wrap.index_building_progress(c_name)
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exp_res = {"total_rows": entities, "indexed_rows": entities}
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assert res == exp_res
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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log.info(
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f"query seg info: {self.utility_wrap.get_query_segment_info(c_name)[0]}"
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)
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@ -403,7 +404,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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assert self.collection_wrap.num_entities == entities
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# verify search and query
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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search_data = cf.gen_binary_vectors(1, dim)[1]
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search_params = {"metric_type": "JACCARD", "params": {"nprobe": 10}}
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res, _ = self.collection_wrap.search(
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@ -423,7 +424,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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@pytest.mark.parametrize("is_row_based", [True])
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@pytest.mark.parametrize("auto_id", [True, False])
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@pytest.mark.parametrize(
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"fields_num_in_file", ["equal", "more", "less"]
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"fields_num_in_file", ["more", "less", "equal"]
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) # "equal", "more", "less"
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@pytest.mark.parametrize("dim", [16])
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@pytest.mark.parametrize("entities", [500])
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@ -491,7 +492,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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assert not success
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if is_row_based:
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if fields_num_in_file == "less":
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failed_reason = f"field '{additional_field}' missed at the row 0"
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failed_reason = f"value of field '{additional_field}' is missed"
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else:
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failed_reason = f"field '{df.float_field}' is not defined in collection schema"
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else:
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@ -506,12 +507,15 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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log.info(f" collection entities: {num_entities}")
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assert num_entities == entities
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# verify no index
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# verify index status
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res, _ = self.collection_wrap.has_index()
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assert res is True
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res, _ = self.utility_wrap.index_building_progress(c_name)
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exp_res = {'total_rows': entities, 'indexed_rows': entities}
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assert res == exp_res
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# verify search and query
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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nq = 3
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topk = 10
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search_data = cf.gen_vectors(nq, dim)
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@ -608,13 +612,14 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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num_entities = self.collection_wrap.num_entities
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log.info(f"collection entities: {num_entities}")
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assert num_entities == bulk_insert_row + direct_insert_row
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# verify no index
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# verify index status
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time.sleep(20)
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res, _ = self.utility_wrap.index_building_progress(c_name)
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exp_res = {'total_rows': num_entities, 'indexed_rows': num_entities}
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assert res == exp_res
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# verify search and query
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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nq = 3
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topk = 10
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search_data = cf.gen_vectors(nq, dim=dim)
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@ -702,7 +707,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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assert res == exp_res
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# verify search and query
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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nq = 3
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topk = 10
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search_data = cf.gen_vectors(nq, 16)
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@ -811,7 +816,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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assert res is True
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# verify search and query
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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search_data = cf.gen_vectors(1, dim)
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search_params = ct.default_search_params
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res, _ = self.collection_wrap.search(
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@ -829,118 +834,14 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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results, _ = self.collection_wrap.query(expr=expr)
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assert len(results) == len(ids)
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@pytest.mark.tags(CaseLabel.L3)
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@pytest.mark.parametrize("is_row_based", [pytest.param(True, marks=pytest.mark.xfail(reason="issue: https://github.com/milvus-io/milvus/issues/19499"))]) # True, False
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@pytest.mark.parametrize("auto_id", [True, False]) # True, False
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@pytest.mark.parametrize("dim", [16]) # 16
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@pytest.mark.parametrize("entities", [100]) # 3000
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@pytest.mark.parametrize("file_nums", [32]) # 10
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@pytest.mark.parametrize("multi_folder", [True, False]) # True, False
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def test_float_vector_from_multi_files(
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self, is_row_based, auto_id, dim, entities, file_nums, multi_folder
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):
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"""
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collection: auto_id
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collection schema: [pk, float_vector,
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float_scalar, int_scalar, string_scalar, bool_scalar]
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Steps:
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1. create collection
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2. build index and load collection
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3. import data from multiple files
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4. verify the data entities
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5. verify index status
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6. verify search successfully
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7. verify query successfully
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"""
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files = prepare_bulk_insert_json_files(
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minio_endpoint=self.minio_endpoint,
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bucket_name=self.bucket_name,
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is_row_based=is_row_based,
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rows=entities,
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dim=dim,
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auto_id=auto_id,
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data_fields=default_multi_fields,
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file_nums=file_nums,
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multi_folder=multi_folder,
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force=True,
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)
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self._connect()
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c_name = cf.gen_unique_str("bulk_insert")
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fields = [
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cf.gen_int64_field(name=df.pk_field, is_primary=True),
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cf.gen_float_vec_field(name=df.vec_field, dim=dim),
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cf.gen_int32_field(name=df.int_field),
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cf.gen_string_field(name=df.string_field),
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cf.gen_bool_field(name=df.bool_field),
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cf.gen_float_field(name=df.float_field)
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]
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schema = cf.gen_collection_schema(fields=fields, auto_id=auto_id)
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self.collection_wrap.init_collection(c_name, schema=schema)
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# build index
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index_params = ct.default_index
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self.collection_wrap.create_index(
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field_name=df.vec_field, index_params=index_params
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)
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# load collection
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self.collection_wrap.load()
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# import data
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t0 = time.time()
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err_msg = "row-based import, only allow one JSON file each time"
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task_id, _ = self.utility_wrap.do_bulk_insert(
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collection_name=c_name, files=files,
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check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": err_msg},
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)
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# logging.info(f"bulk insert task ids:{task_id}")
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# success, states = self.utility_wrap.wait_for_bulk_insert_tasks_completed(
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# task_ids=[task_id], timeout=90
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# )
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# tt = time.time() - t0
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# log.info(f"bulk insert state:{success} in {tt}")
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# if not is_row_based:
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# assert not success
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# failed_reason = "is duplicated" # "the field xxx is duplicated"
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# for state in states.values():
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# assert state.state_name in ["Failed", "Failed and cleaned"]
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# assert failed_reason in state.infos.get("failed_reason", "")
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# else:
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# assert success
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# num_entities = self.collection_wrap.num_entities
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# log.info(f" collection entities: {num_entities}")
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# assert num_entities == entities * file_nums
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#
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# # verify index built
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# res, _ = self.utility_wrap.index_building_progress(c_name)
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# exp_res = {'total_rows': entities * file_nums, 'indexed_rows': entities * file_nums}
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# assert res == exp_res
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#
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# # verify search and query
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# log.info(f"wait for load finished and be ready for search")
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# time.sleep(5)
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# nq = 5
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# topk = 1
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# search_data = cf.gen_vectors(nq, dim)
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# search_params = ct.default_search_params
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# res, _ = self.collection_wrap.search(
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# search_data,
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# df.vec_field,
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# param=search_params,
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# limit=topk,
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# check_task=CheckTasks.check_search_results,
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# check_items={"nq": nq, "limit": topk},
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# )
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# for hits in res:
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# ids = hits.ids
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# results, _ = self.collection_wrap.query(expr=f"{df.pk_field} in {ids}")
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# assert len(results) == len(ids)
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@pytest.mark.tags(CaseLabel.L3)
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@pytest.mark.parametrize("is_row_based", [True])
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@pytest.mark.parametrize("is_row_based", [False])
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@pytest.mark.parametrize("auto_id", [True, False])
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@pytest.mark.parametrize("multi_fields", [True, False])
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@pytest.mark.parametrize("dim", [15])
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@pytest.mark.parametrize("entities", [200])
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@pytest.mark.skip(reason="stop support for numpy files")
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# @pytest.mark.skip(reason="stop support for numpy files")
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def test_float_vector_from_numpy_file(
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self, is_row_based, auto_id, multi_fields, dim, entities
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):
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@ -1051,7 +952,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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)
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self.collection_wrap.load()
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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log.info(
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f"query seg info: {self.utility_wrap.get_query_segment_info(c_name)[0]}"
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)
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@ -1129,7 +1030,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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)
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self.collection_wrap.load()
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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# the pk value was automatically convert to int from float
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res, _ = self.collection_wrap.query(
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expr=f"{df.pk_field} in [3]", output_fields=[df.pk_field]
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@ -1194,7 +1095,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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)
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self.collection_wrap.load()
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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search_data = cf.gen_vectors(1, dim)
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search_params = ct.default_search_params
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res, _ = self.collection_wrap.search(
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@ -1268,7 +1169,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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)
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self.collection_wrap.load()
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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# log.info(f"query seg info: {self.utility_wrap.get_query_segment_info(c_name)[0]}")
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search_data = cf.gen_vectors(1, dim)
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search_params = ct.default_search_params
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@ -1342,7 +1243,7 @@ class TestBulkInsert(TestcaseBaseBulkInsert):
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# verify search and query
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log.info(f"wait for load finished and be ready for search")
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time.sleep(5)
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time.sleep(20)
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search_data = cf.gen_vectors(1, dim)
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search_params = ct.default_search_params
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res, _ = self.collection_wrap.search(
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@ -1442,17 +1343,80 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
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task_ids=[task_id], timeout=90
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)
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assert not success
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failed_reason = "JSON parser: row count is 0"
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failed_reason = "row count is 0"
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for state in states.values():
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assert state.state_name in ["Failed", "Failed and cleaned"]
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assert failed_reason in state.infos.get("failed_reason", "")
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@pytest.mark.tags(CaseLabel.L3)
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@pytest.mark.parametrize("is_row_based", [True]) # True, False
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@pytest.mark.parametrize("auto_id", [True, False]) # True, False
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@pytest.mark.parametrize("dim", [16]) # 16
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@pytest.mark.parametrize("entities", [100]) # 3000
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@pytest.mark.parametrize("file_nums", [32]) # 10
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@pytest.mark.parametrize("multi_folder", [True, False]) # True, False
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def test_float_vector_from_multi_files(
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self, is_row_based, auto_id, dim, entities, file_nums, multi_folder
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):
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"""
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collection: auto_id
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collection schema: [pk, float_vector,
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float_scalar, int_scalar, string_scalar, bool_scalar]
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Steps:
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1. create collection
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2. build index and load collection
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3. import data from multiple files
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4. verify the data entities
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5. verify index status
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6. verify search successfully
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7. verify query successfully
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"""
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files = prepare_bulk_insert_json_files(
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minio_endpoint=self.minio_endpoint,
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bucket_name=self.bucket_name,
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is_row_based=is_row_based,
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rows=entities,
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dim=dim,
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auto_id=auto_id,
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data_fields=default_multi_fields,
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file_nums=file_nums,
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multi_folder=multi_folder,
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force=True,
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)
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self._connect()
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c_name = cf.gen_unique_str("bulk_insert")
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fields = [
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cf.gen_int64_field(name=df.pk_field, is_primary=True),
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cf.gen_float_vec_field(name=df.vec_field, dim=dim),
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cf.gen_int32_field(name=df.int_field),
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cf.gen_string_field(name=df.string_field),
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cf.gen_bool_field(name=df.bool_field),
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cf.gen_float_field(name=df.float_field)
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]
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schema = cf.gen_collection_schema(fields=fields, auto_id=auto_id)
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self.collection_wrap.init_collection(c_name, schema=schema)
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# build index
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index_params = ct.default_index
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self.collection_wrap.create_index(
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field_name=df.vec_field, index_params=index_params
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)
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# load collection
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self.collection_wrap.load()
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# import data
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t0 = time.time()
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err_msg = "row-based import, only allow one JSON file each time"
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task_id, _ = self.utility_wrap.do_bulk_insert(
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collection_name=c_name, files=files,
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check_task=CheckTasks.err_res, check_items={"err_code": 1, "err_msg": err_msg},
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)
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@pytest.mark.tags(CaseLabel.L3)
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@pytest.mark.parametrize("is_row_based", [True])
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@pytest.mark.parametrize("auto_id", [True, False])
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@pytest.mark.parametrize("dim", [8]) # 8
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@pytest.mark.parametrize("entities", [100]) # 100
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# @pytest.mark.xfail(reason="issue https://github.com/milvus-io/milvus/issues/19658")
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def test_wrong_file_type(self, is_row_based, auto_id, dim, entities):
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"""
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collection schema: [pk, float_vector]
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@ -1507,7 +1471,7 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
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log.info(f"bulk insert state:{success} in {tt}")
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assert not success
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failed_reason = f"the file '{files[0]}' has no corresponding field in collection"
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failed_reason2 = "unsupportted file type"
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failed_reason2 = "unsupported file type"
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for state in states.values():
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assert state.state_name in ["Failed", "Failed and cleaned"]
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assert failed_reason in state.infos.get("failed_reason", "") or \
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@ -1560,7 +1524,7 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
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assert not success
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if is_row_based:
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value = df.vec_field # if auto_id else df.pk_field
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failed_reason = f"JSON parser: invalid row-based JSON format, the key {value} is not found"
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failed_reason = f"invalid JSON format, the root key should be 'rows', but get {value}"
|
||||
else:
|
||||
failed_reason = "JSON parse: row count is 0"
|
||||
for state in states.values():
|
||||
|
|
@ -1779,7 +1743,7 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
|
|||
)
|
||||
log.info(f"bulk insert state:{success}")
|
||||
assert not success
|
||||
failed_reason = f"array size {dim} doesn't equal to vector dimension {wrong_dim} of field vectors"
|
||||
failed_reason = f"array size {dim} doesn't equal to vector dimension {wrong_dim} of field 'vectors'"
|
||||
for state in states.values():
|
||||
assert state.state_name in ["Failed", "Failed and cleaned"]
|
||||
assert failed_reason in state.infos.get("failed_reason", "")
|
||||
|
|
@ -1818,7 +1782,6 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
|
|||
@pytest.mark.parametrize("is_row_based", [True])
|
||||
@pytest.mark.parametrize("dim", [4])
|
||||
@pytest.mark.parametrize("entities", [200])
|
||||
@pytest.mark.xfail(reason="issue: https://github.com/milvus-io/milvus/issues/19553")
|
||||
def test_non_existing_partition(self, is_row_based, dim, entities):
|
||||
"""
|
||||
collection: create a collection
|
||||
|
|
@ -1845,7 +1808,7 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
|
|||
self.collection_wrap.init_collection(c_name, schema=schema)
|
||||
# import data into a non existing partition
|
||||
p_name = "non_existing"
|
||||
err_msg = f" partition {p_name} does not exist"
|
||||
err_msg = f"partition ID not found for partition name '{p_name}'"
|
||||
task_id, _ = self.utility_wrap.do_bulk_insert(
|
||||
collection_name=c_name,
|
||||
partition_name=p_name,
|
||||
|
|
@ -1853,6 +1816,7 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
|
|||
check_task=CheckTasks.err_res,
|
||||
check_items={"err_code": 11, "err_msg": err_msg},
|
||||
)
|
||||
print(task_id)
|
||||
|
||||
@pytest.mark.tags(CaseLabel.L3)
|
||||
@pytest.mark.parametrize("is_row_based", [True])
|
||||
|
|
@ -1901,7 +1865,7 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
|
|||
log.info(f"bulk insert state:{success}")
|
||||
assert not success
|
||||
failed_reason = (
|
||||
f"doesn't equal to vector dimension {dim} of field vectors"
|
||||
f"doesn't equal to vector dimension {dim} of field 'vectors'"
|
||||
)
|
||||
for state in states.values():
|
||||
assert state.state_name in ["Failed", "Failed and cleaned"]
|
||||
|
|
@ -2234,6 +2198,7 @@ class TestBulkInsertInvalidParams(TestcaseBaseBulkInsert):
|
|||
]
|
||||
schema = cf.gen_collection_schema(fields=fields, auto_id=auto_id)
|
||||
self.collection_wrap.init_collection(c_name, schema=schema)
|
||||
|
||||
# import data
|
||||
task_id, _ = self.utility_wrap.do_bulk_insert(
|
||||
collection_name=c_name, files=files
|
||||
|
|
@ -2522,7 +2487,7 @@ class TestBulkInsertAdvanced(TestcaseBaseBulkInsert):
|
|||
)
|
||||
self.collection_wrap.load()
|
||||
log.info(f"wait for load finished and be ready for search")
|
||||
time.sleep(5)
|
||||
time.sleep(20)
|
||||
loaded_segs = len(self.utility_wrap.get_query_segment_info(c_name)[0])
|
||||
log.info(f"query seg info: {loaded_segs} segs loaded.")
|
||||
search_data = cf.gen_vectors(1, dim)
|
||||
|
|
|
|||
|
|
@ -99,7 +99,9 @@ class ResponseChecker:
|
|||
assert len(error_dict) > 0
|
||||
if isinstance(res, Error):
|
||||
error_code = error_dict[ct.err_code]
|
||||
assert res.code == error_code or error_dict[ct.err_msg] in res.message
|
||||
assert res.code == error_code or error_dict[ct.err_msg] in res.message, f"expected error code: {error_code} \
|
||||
or error message: {error_dict[ct.err_msg]}, \
|
||||
actual error code: {res.code}, actual error message: {res.message}"
|
||||
else:
|
||||
log.error("[CheckFunc] Response of API is not an error: %s" % str(res))
|
||||
assert False
|
||||
|
|
@ -227,8 +229,8 @@ class ResponseChecker:
|
|||
log.error("search_results_check: limit(topK) searched (%d) "
|
||||
"is not equal with expected (%d)"
|
||||
% (len(hits), check_items["limit"]))
|
||||
assert len(hits) == check_items["limit"]
|
||||
assert len(hits.ids) == check_items["limit"]
|
||||
assert len(hits) == check_items["limit"], f"expect {check_items['limit']}, but got {len(hits)}"
|
||||
assert len(hits.ids) == check_items["limit"], f"expect {check_items['limit']}, but got {len(hits.ids)}"
|
||||
else:
|
||||
if check_items.get("ids", None) is not None:
|
||||
ids_match = pc.list_contain_check(list(hits.ids),
|
||||
|
|
|
|||
Loading…
Reference in New Issue