import time import copy import logging from milvus_benchmark import parser from milvus_benchmark.runners import utils from milvus_benchmark.runners.base import BaseRunner logger = logging.getLogger("milvus_benchmark.runners.build") class BuildRunner(BaseRunner): """run build""" name = "build_performance" def __init__(self, env, metric): super(BuildRunner, self).__init__(env, metric) def extract_cases(self, collection): collection_name = collection["collection_name"] if "collection_name" in collection else None (data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name) ni_per = collection["ni_per"] vector_type = utils.get_vector_type(data_type) other_fields = collection["other_fields"] if "other_fields" in collection else None collection_info = { "dimension": dimension, "metric_type": metric_type, "dataset_name": collection_name, "collection_size": collection_size, "other_fields": other_fields, "ni_per": ni_per } index_field_name = utils.get_default_field_name(vector_type) index_type = collection["index_type"] index_param = collection["index_param"] index_info = { "index_type": index_type, "index_param": index_param } flush = True if "flush" in collection and collection["flush"] == "no": flush = False self.init_metric(self.name, collection_info, index_info, search_info=None) case_metric = copy.deepcopy(self.metric) case_metric.set_case_metric_type() case_metrics = list() case_params = list() case_metrics.append(case_metric) case_param = { "collection_name": collection_name, "data_type": data_type, "dimension": dimension, "collection_size": collection_size, "ni_per": ni_per, "metric_type": metric_type, "vector_type": vector_type, "other_fields": other_fields, "flush_after_insert": flush, "index_field_name": index_field_name, "index_type": index_type, "index_param": index_param, } case_params.append(case_param) return case_params, case_metrics def prepare(self, **case_param): collection_name = case_param["collection_name"] self.milvus.set_collection(collection_name) if not self.milvus.exists_collection(): logger.info("collection not exist") logger.debug({"collection count": self.milvus.count()}) def run_case(self, case_metric, **case_param): index_field_name = case_param["index_field_name"] start_time = time.time() self.milvus.create_index(index_field_name, case_param["index_type"], case_param["metric_type"], index_param=case_param["index_param"]) build_time = round(time.time() - start_time, 2) tmp_result = {"build_time": build_time} return tmp_result class InsertBuildRunner(BuildRunner): """run insert and build""" name = "insert_build_performance" def __init__(self, env, metric): super(InsertBuildRunner, self).__init__(env, metric) def prepare(self, **case_param): collection_name = case_param["collection_name"] dimension = case_param["dimension"] vector_type = case_param["vector_type"] other_fields = case_param["other_fields"] self.milvus.set_collection(collection_name) if self.milvus.exists_collection(): logger.debug("Start drop collection") self.milvus.drop() time.sleep(utils.DELETE_INTERVAL_TIME) self.milvus.create_collection(dimension, data_type=vector_type, other_fields=other_fields) self.insert(self.milvus, collection_name, case_param["data_type"], dimension, case_param["collection_size"], case_param["ni_per"]) start_time = time.time() self.milvus.flush() flush_time = round(time.time() - start_time, 2) logger.debug({"collection count": self.milvus.count()}) logger.debug({"flush_time": flush_time})