mirror of
https://gitee.com/milvus-io/milvus.git
synced 2024-12-03 20:39:36 +08:00
733 lines
37 KiB
Python
733 lines
37 KiB
Python
import os
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import logging
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import pdb
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import string
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import time
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import random
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import json
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import csv
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from multiprocessing import Process
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import numpy as np
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import concurrent.futures
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from queue import Queue
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import locust_user
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from milvus import DataType
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from client import MilvusClient
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from runner import Runner
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import utils
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import parser
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DELETE_INTERVAL_TIME = 5
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INSERT_INTERVAL = 50000
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logger = logging.getLogger("milvus_benchmark.local_runner")
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class LocalRunner(Runner):
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"""run local mode"""
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def __init__(self, host, port):
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super(LocalRunner, self).__init__()
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self.host = host
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self.port = port
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def run(self, run_type, collection):
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logger.debug(run_type)
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logger.debug(collection)
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collection_name = collection["collection_name"] if "collection_name" in collection else None
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milvus_instance = MilvusClient(collection_name=collection_name, host=self.host, port=self.port)
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logger.info(milvus_instance.show_collections())
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# TODO:
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# self.env_value = milvus_instance.get_server_config()
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# ugly implemention
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# self.env_value = utils.convert_nested(self.env_value)
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# self.env_value.pop("logs")
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# self.env_value.pop("network")
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# logger.info(self.env_value)
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if run_type in ["insert_performance", "insert_flush_performance"]:
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
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ni_per = collection["ni_per"]
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build_index = collection["build_index"]
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if milvus_instance.exists_collection():
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milvus_instance.drop()
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time.sleep(10)
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vector_type = self.get_vector_type(data_type)
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other_fields = collection["other_fields"] if "other_fields" in collection else None
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milvus_instance.create_collection(dimension, data_type=vector_type, other_fields=other_fields)
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if build_index is True:
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index_type = collection["index_type"]
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index_param = collection["index_param"]
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index_field_name = utils.get_default_field_name(vector_type)
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milvus_instance.create_index(index_field_name, index_type, metric_type, index_param=index_param)
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res = self.do_insert(milvus_instance, collection_name, data_type, dimension, collection_size, ni_per)
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milvus_instance.flush()
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logger.debug("Table row counts: %d" % milvus_instance.count())
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if build_index is True:
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logger.debug("Start build index for last file")
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milvus_instance.create_index(index_field_name, index_type, metric_type, index_param=index_param)
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elif run_type == "delete_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
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ni_per = collection["ni_per"]
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auto_flush = collection["auto_flush"] if "auto_flush" in collection else True
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if not milvus_instance.exists_collection():
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logger.error(milvus_instance.show_collections())
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logger.error("Table: %s not found" % collection_name)
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return
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length = milvus_instance.count()
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ids = [i for i in range(length)]
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loops = int(length / ni_per)
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if auto_flush is False:
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milvus_instance.set_config("storage", "auto_flush_interval", BIG_FLUSH_INTERVAL)
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for i in range(loops):
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delete_ids = ids[i*ni_per: i*ni_per+ni_per]
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logger.debug("Delete %d - %d" % (delete_ids[0], delete_ids[-1]))
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milvus_instance.delete(delete_ids)
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logger.debug("Table row counts: %d" % milvus_instance.count())
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logger.debug("Table row counts: %d" % milvus_instance.count())
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milvus_instance.flush()
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logger.debug("Table row counts: %d" % milvus_instance.count())
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elif run_type == "build_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
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index_type = collection["index_type"]
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index_param = collection["index_param"]
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if not milvus_instance.exists_collection():
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logger.error("Table name: %s not existed" % collection_name)
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return
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vector_type = self.get_vector_type(data_type)
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index_field_name = utils.get_default_field_name(vector_type)
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# drop index
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logger.debug("Drop index")
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milvus_instance.drop_index(index_field_name)
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start_mem_usage = milvus_instance.get_mem_info()["memory_used"]
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start_time = time.time()
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milvus_instance.create_index(index_field_name, index_type, metric_type, index_param=index_param)
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end_time = time.time()
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logger.debug("Table row counts: %d" % milvus_instance.count())
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end_mem_usage = milvus_instance.get_mem_info()["memory_used"]
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logger.debug("Diff memory: %s, current memory usage: %s, build time: %s" % ((end_mem_usage - start_mem_usage), end_mem_usage, round(end_time - start_time, 1)))
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elif run_type == "search_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
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run_count = collection["run_count"]
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top_ks = collection["top_ks"]
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nqs = collection["nqs"]
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search_params = collection["search_params"]
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filter_query = []
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filters = collection["filters"] if "filters" in collection else []
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# pdb.set_trace()
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# ranges = collection["range"] if "range" in collection else None
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# terms = collection["term"] if "term" in collection else None
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# if ranges:
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# filter_query.append(eval(ranges))
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# if terms:
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# filter_query.append(eval(terms))
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vector_type = self.get_vector_type(data_type)
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vec_field_name = utils.get_default_field_name(vector_type)
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# for debugging
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# time.sleep(3600)
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if not milvus_instance.exists_collection():
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logger.error("Table name: %s not existed" % collection_name)
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return
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vector_type = self.get_vector_type(data_type)
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vec_field_name = utils.get_default_field_name(vector_type)
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logger.info(milvus_instance.count())
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result = milvus_instance.describe_index()
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logger.info(result)
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milvus_instance.preload_collection()
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mem_usage = milvus_instance.get_mem_info()["memory_used"]
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logger.info(mem_usage)
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for search_param in search_params:
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logger.info("Search param: %s" % json.dumps(search_param))
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filter_param = []
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if not filters:
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filters.append(None)
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for filter in filters:
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if isinstance(filter, dict) and "range" in filter:
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filter_query.append(eval(filter["range"]))
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filter_param.append(filter["range"])
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if isinstance(filter, dict) and "term" in filter:
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filter_query.append(eval(filter["term"]))
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filter_param.append(filter["term"])
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logger.info("filter param: %s" % json.dumps(filter_param))
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res = self.do_query(milvus_instance, collection_name, vec_field_name, top_ks, nqs, run_count, search_param, filter_query)
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headers = ["Nq/Top-k"]
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headers.extend([str(top_k) for top_k in top_ks])
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logger.info("Search param: %s" % json.dumps(search_param))
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utils.print_table(headers, nqs, res)
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mem_usage = milvus_instance.get_mem_info()["memory_used"]
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logger.info(mem_usage)
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elif run_type == "locust_search_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
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ni_per = collection["ni_per"]
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build_index = collection["build_index"]
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vector_type = self.get_vector_type(data_type)
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index_field_name = utils.get_default_field_name(vector_type)
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# if build_index is True:
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# index_type = collection["index_type"]
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# index_param = collection["index_param"]
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# # TODO: debug
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# if milvus_instance.exists_collection():
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# milvus_instance.drop()
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# time.sleep(10)
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# other_fields = collection["other_fields"] if "other_fields" in collection else None
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# milvus_instance.create_collection(dimension, data_type=vector_type, other_fields=other_fields)
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# milvus_instance.create_index(index_field_name, index_type, metric_type, index_param=index_param)
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# res = self.do_insert(milvus_instance, collection_name, data_type, dimension, collection_size, ni_per)
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# milvus_instance.flush()
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# logger.debug("Table row counts: %d" % milvus_instance.count())
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# if build_index is True:
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# logger.debug("Start build index for last file")
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# milvus_instance.create_index(index_field_name, index_type, metric_type, index_param=index_param)
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real_metric_type = utils.metric_type_trans(metric_type)
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### spawn locust requests
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task = collection["task"]
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connection_type = "single"
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connection_num = task["connection_num"]
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if connection_num > 1:
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connection_type = "multi"
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clients_num = task["clients_num"]
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hatch_rate = task["hatch_rate"]
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during_time = utils.timestr_to_int(task["during_time"])
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task_types = task["types"]
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# """
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# task:
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# connection_num: 1
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# clients_num: 100
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# hatch_rate: 2
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# during_time: 5m
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# types:
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# -
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# type: query
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# weight: 1
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# params:
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# top_k: 10
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# nq: 1
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# # filters:
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# # -
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# # range:
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# # int64:
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# # LT: 0
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# # GT: 1000000
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# search_param:
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# nprobe: 16
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# """
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run_params = {"tasks": {}, "clients_num": clients_num, "spawn_rate": hatch_rate, "during_time": during_time}
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for task_type in task_types:
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run_params["tasks"].update({task_type["type"]: task_type["weight"] if "weight" in task_type else 1})
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#. collect stats
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locust_stats = locust_user.locust_executor(self.host, self.port, collection_name, connection_type=connection_type, run_params=run_params)
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logger.info(locust_stats)
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elif run_type == "search_ids_stability":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
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search_params = collection["search_params"]
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during_time = collection["during_time"]
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ids_length = collection["ids_length"]
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ids = collection["ids"]
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logger.info(milvus_instance.count())
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index_info = milvus_instance.describe_index()
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logger.info(index_info)
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g_top_k = int(collection["top_ks"].split("-")[1])
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l_top_k = int(collection["top_ks"].split("-")[0])
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g_id = int(ids.split("-")[1])
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l_id = int(ids.split("-")[0])
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g_id_length = int(ids_length.split("-")[1])
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l_id_length = int(ids_length.split("-")[0])
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milvus_instance.preload_collection()
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start_mem_usage = milvus_instance.get_mem_info()["memory_used"]
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logger.debug(start_mem_usage)
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start_time = time.time()
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while time.time() < start_time + during_time * 60:
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search_param = {}
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top_k = random.randint(l_top_k, g_top_k)
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ids_num = random.randint(l_id_length, g_id_length)
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l_ids = random.randint(l_id, g_id-ids_num)
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# ids_param = [random.randint(l_id_length, g_id_length) for _ in range(ids_num)]
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ids_param = [id for id in range(l_ids, l_ids+ids_num)]
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for k, v in search_params.items():
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search_param[k] = random.randint(int(v.split("-")[0]), int(v.split("-")[1]))
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logger.debug("Query top-k: %d, ids_num: %d, param: %s" % (top_k, ids_num, json.dumps(search_param)))
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result = milvus_instance.query_ids(top_k, ids_param, search_param=search_param)
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end_mem_usage = milvus_instance.get_mem_info()["memory_used"]
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metrics = {
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"during_time": during_time,
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"start_mem_usage": start_mem_usage,
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"end_mem_usage": end_mem_usage,
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"diff_mem": end_mem_usage - start_mem_usage,
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}
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logger.info(metrics)
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elif run_type == "search_performance_concurrents":
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data_type, dimension, metric_type = parser.parse_ann_collection_name(collection_name)
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hdf5_source_file = collection["source_file"]
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use_single_connection = collection["use_single_connection"]
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concurrents = collection["concurrents"]
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top_ks = collection["top_ks"]
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nqs = collection["nqs"]
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search_params = self.generate_combinations(collection["search_params"])
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if not milvus_instance.exists_collection():
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logger.error("Table name: %s not existed" % collection_name)
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return
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logger.info(milvus_instance.count())
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result = milvus_instance.describe_index()
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logger.info(result)
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milvus_instance.preload_collection()
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dataset = utils.get_dataset(hdf5_source_file)
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for concurrent_num in concurrents:
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top_k = top_ks[0]
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for nq in nqs:
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mem_usage = milvus_instance.get_mem_info()["memory_used"]
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logger.info(mem_usage)
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query_vectors = self.normalize(metric_type, np.array(dataset["test"][:nq]))
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logger.debug(search_params)
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for search_param in search_params:
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logger.info("Search param: %s" % json.dumps(search_param))
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total_time = 0.0
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if use_single_connection is True:
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connections = [MilvusClient(collection_name=collection_name, host=self.host, port=self.port)]
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with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_num) as executor:
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future_results = {executor.submit(
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self.do_query_qps, connections[0], query_vectors, top_k, search_param=search_param) : index for index in range(concurrent_num)}
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else:
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connections = [MilvusClient(collection_name=collection_name, host=self.hos, port=self.port) for i in range(concurrent_num)]
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with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_num) as executor:
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future_results = {executor.submit(
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self.do_query_qps, connections[index], query_vectors, top_k, search_param=search_param) : index for index in range(concurrent_num)}
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for future in concurrent.futures.as_completed(future_results):
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total_time = total_time + future.result()
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qps_value = total_time / concurrent_num
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logger.debug("QPS value: %f, total_time: %f, request_nums: %f" % (qps_value, total_time, concurrent_num))
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mem_usage = milvus_instance.get_mem_info()["memory_used"]
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logger.info(mem_usage)
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elif run_type == "ann_accuracy":
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hdf5_source_file = collection["source_file"]
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collection_name = collection["collection_name"]
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index_types = collection["index_types"]
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index_params = collection["index_params"]
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top_ks = collection["top_ks"]
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nqs = collection["nqs"]
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search_params = collection["search_params"]
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# mapping to search param list
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search_params = self.generate_combinations(search_params)
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# mapping to index param list
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index_params = self.generate_combinations(index_params)
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data_type, dimension, metric_type = parser.parse_ann_collection_name(collection_name)
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dataset = utils.get_dataset(hdf5_source_file)
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true_ids = np.array(dataset["neighbors"])
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vector_type = self.get_vector_type_from_metric(metric_type)
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vec_field_name = utils.get_default_field_name(vector_type)
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real_metric_type = utils.metric_type_trans(metric_type)
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# re-create collection
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if milvus_instance.exists_collection(collection_name):
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milvus_instance.drop()
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time.sleep(DELETE_INTERVAL_TIME)
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milvus_instance.create_collection(dimension, data_type=vector_type)
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insert_vectors = self.normalize(metric_type, np.array(dataset["train"]))
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if len(insert_vectors) != dataset["train"].shape[0]:
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raise Exception("Row count of insert vectors: %d is not equal to dataset size: %d" % (len(insert_vectors), dataset["train"].shape[0]))
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logger.debug("The row count of entities to be inserted: %d" % len(insert_vectors))
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# insert batch once
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# milvus_instance.insert(insert_vectors)
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loops = len(insert_vectors) // INSERT_INTERVAL + 1
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for i in range(loops):
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start = i*INSERT_INTERVAL
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end = min((i+1)*INSERT_INTERVAL, len(insert_vectors))
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if start < end:
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tmp_vectors = insert_vectors[start:end]
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ids = [i for i in range(start, end)]
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if not isinstance(tmp_vectors, list):
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entities = milvus_instance.generate_entities(tmp_vectors.tolist(), ids)
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res_ids = milvus_instance.insert(entities, ids=ids)
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else:
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entities = milvus_instance.generate_entities(tmp_vectors, ids)
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res_ids = milvus_instance.insert(entities, ids=ids)
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assert res_ids == ids
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milvus_instance.flush()
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res_count = milvus_instance.count()
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logger.info("Table: %s, row count: %d" % (collection_name, res_count))
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if res_count != len(insert_vectors):
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raise Exception("Table row count is not equal to insert vectors")
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for index_type in index_types:
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for index_param in index_params:
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logger.debug("Building index with param: %s, metric_type: %s" % (json.dumps(index_param), metric_type))
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milvus_instance.create_index(vec_field_name, index_type, metric_type, index_param=index_param)
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logger.info("Start preload collection: %s" % collection_name)
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milvus_instance.preload_collection()
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for search_param in search_params:
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for nq in nqs:
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query_vectors = self.normalize(metric_type, np.array(dataset["test"][:nq]))
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if not isinstance(query_vectors, list):
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query_vectors = query_vectors.tolist()
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for top_k in top_ks:
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logger.debug("Search nq: %d, top-k: %d, search_param: %s, metric_type: %s" % (nq, top_k, json.dumps(search_param), metric_type))
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vector_query = {"vector": {vec_field_name: {
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"topk": top_k,
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"query": query_vectors,
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"metric_type": real_metric_type,
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"params": search_param}
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}}
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result = milvus_instance.query(vector_query)
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result_ids = milvus_instance.get_ids(result)
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# pdb.set_trace()
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acc_value = self.get_recall_value(true_ids[:nq, :top_k].tolist(), result_ids)
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logger.info("Query ann_accuracy: %s" % acc_value)
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elif run_type == "accuracy":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
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search_params = collection["search_params"]
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# mapping to search param list
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search_params = self.generate_combinations(search_params)
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top_ks = collection["top_ks"]
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nqs = collection["nqs"]
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collection_info = {
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"dimension": dimension,
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"metric_type": metric_type,
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|
"dataset_name": collection_name
|
|
}
|
|
if not milvus_instance.exists_collection():
|
|
logger.error("Table name: %s not existed" % collection_name)
|
|
return
|
|
logger.info(milvus_instance.count())
|
|
index_info = milvus_instance.describe_index()
|
|
logger.info(index_info)
|
|
milvus_instance.preload_collection()
|
|
true_ids_all = self.get_groundtruth_ids(collection_size)
|
|
vector_type = self.get_vector_type(data_type)
|
|
vec_field_name = utils.get_default_field_name(vector_type)
|
|
for search_param in search_params:
|
|
headers = ["Nq/Top-k"]
|
|
res = []
|
|
for nq in nqs:
|
|
tmp_res = []
|
|
for top_k in top_ks:
|
|
search_param_group = {
|
|
"nq": nq,
|
|
"topk": top_k,
|
|
"search_param": search_param,
|
|
"metric_type": metric_type
|
|
}
|
|
logger.info("Query params: %s" % json.dumps(search_param_group))
|
|
result_ids = self.do_query_ids(milvus_instance, collection_name, vec_field_name, top_k, nq, search_param=search_param)
|
|
mem_used = milvus_instance.get_mem_info()["memory_used"]
|
|
acc_value = self.get_recall_value(true_ids_all[:nq, :top_k].tolist(), result_ids)
|
|
logger.info("Query accuracy: %s" % acc_value)
|
|
tmp_res.append(acc_value)
|
|
logger.info("Memory usage: %s" % mem_used)
|
|
res.append(tmp_res)
|
|
headers.extend([str(top_k) for top_k in top_ks])
|
|
logger.info("Search param: %s" % json.dumps(search_param))
|
|
utils.print_table(headers, nqs, res)
|
|
|
|
elif run_type == "stability":
|
|
(data_type, collection_size, dimension, metric_type) = parser.collection_parser(collection_name)
|
|
during_time = collection["during_time"]
|
|
operations = collection["operations"]
|
|
if not milvus_instance.exists_collection():
|
|
logger.error(milvus_instance.show_collections())
|
|
raise Exception("Table name: %s not existed" % collection_name)
|
|
milvus_instance.preload_collection()
|
|
start_mem_usage = milvus_instance.get_mem_info()["memory_used"]
|
|
start_row_count = milvus_instance.count()
|
|
logger.info(start_row_count)
|
|
vector_type = self.get_vector_type(data_type)
|
|
vec_field_name = utils.get_default_field_name(vector_type)
|
|
real_metric_type = utils.metric_type_trans(metric_type)
|
|
query_vectors = [[random.random() for _ in range(dimension)] for _ in range(10000)]
|
|
if "insert" in operations:
|
|
insert_xb = operations["insert"]["xb"]
|
|
if "delete" in operations:
|
|
delete_xb = operations["delete"]["xb"]
|
|
if "query" in operations:
|
|
g_top_k = int(operations["query"]["top_ks"].split("-")[1])
|
|
l_top_k = int(operations["query"]["top_ks"].split("-")[0])
|
|
g_nq = int(operations["query"]["nqs"].split("-")[1])
|
|
l_nq = int(operations["query"]["nqs"].split("-")[0])
|
|
search_params = operations["query"]["search_params"]
|
|
i = 0
|
|
start_time = time.time()
|
|
while time.time() < start_time + during_time * 60:
|
|
i = i + 1
|
|
q = self.gen_executors(operations)
|
|
for name in q:
|
|
try:
|
|
if name == "insert":
|
|
insert_ids = random.sample(list(range(collection_size)), insert_xb)
|
|
insert_vectors = [[random.random() for _ in range(dimension)] for _ in range(insert_xb)]
|
|
entities = milvus_instance.generate_entities(insert_vectors, insert_ids)
|
|
milvus_instance.insert(entities, ids=insert_ids)
|
|
elif name == "delete":
|
|
delete_ids = random.sample(list(range(collection_size)), delete_xb)
|
|
milvus_instance.delete(delete_ids)
|
|
elif name == "query":
|
|
top_k = random.randint(l_top_k, g_top_k)
|
|
nq = random.randint(l_nq, g_nq)
|
|
search_param = {}
|
|
for k, v in search_params.items():
|
|
search_param[k] = random.randint(int(v.split("-")[0]), int(v.split("-")[1]))
|
|
logger.debug("Query nq: %d, top-k: %d, param: %s" % (nq, top_k, json.dumps(search_param)))
|
|
vector_query = {"vector": {vec_field_name: {
|
|
"topk": top_k,
|
|
"query": query_vectors[:nq],
|
|
"metric_type": real_metric_type,
|
|
"params": search_param}
|
|
}}
|
|
result = milvus_instance.query(vector_query)
|
|
elif name in ["flush", "compact"]:
|
|
func = getattr(milvus_instance, name)
|
|
func()
|
|
logger.debug(milvus_instance.count())
|
|
except Exception as e:
|
|
logger.error(name)
|
|
logger.error(str(e))
|
|
raise
|
|
logger.debug("Loop time: %d" % i)
|
|
end_mem_usage = milvus_instance.get_mem_info()["memory_used"]
|
|
end_row_count = milvus_instance.count()
|
|
metrics = {
|
|
"during_time": during_time,
|
|
"start_mem_usage": start_mem_usage,
|
|
"end_mem_usage": end_mem_usage,
|
|
"diff_mem": end_mem_usage - start_mem_usage,
|
|
"row_count_increments": end_row_count - start_row_count
|
|
}
|
|
logger.info(metrics)
|
|
|
|
elif run_type == "loop_stability":
|
|
# init data
|
|
milvus_instance.clean_db()
|
|
pull_interval = collection["pull_interval"]
|
|
collection_num = collection["collection_num"]
|
|
concurrent = collection["concurrent"] if "concurrent" in collection else False
|
|
concurrent_num = collection_num
|
|
dimension = collection["dimension"] if "dimension" in collection else 128
|
|
insert_xb = collection["insert_xb"] if "insert_xb" in collection else 100000
|
|
index_types = collection["index_types"] if "index_types" in collection else ['ivf_sq8']
|
|
index_param = {"nlist": 256}
|
|
collection_names = []
|
|
milvus_instances_map = {}
|
|
insert_vectors = [[random.random() for _ in range(dimension)] for _ in range(insert_xb)]
|
|
ids = [i for i in range(insert_xb)]
|
|
# initialize and prepare
|
|
for i in range(collection_num):
|
|
name = utils.get_unique_name(prefix="collection_%d_" % i)
|
|
collection_names.append(name)
|
|
metric_type = random.choice(["l2", "ip"])
|
|
# default float_vector
|
|
milvus_instance = MilvusClient(collection_name=name, host=self.host)
|
|
milvus_instance.create_collection(dimension, other_fields=None)
|
|
index_type = random.choice(index_types)
|
|
field_name = utils.get_default_field_name()
|
|
milvus_instance.create_index(field_name, index_type, metric_type, index_param=index_param)
|
|
logger.info(milvus_instance.describe_index())
|
|
insert_vectors = utils.normalize(metric_type, insert_vectors)
|
|
entities = milvus_instance.generate_entities(insert_vectors, ids)
|
|
res_ids = milvus_instance.insert(entities, ids=ids)
|
|
milvus_instance.flush()
|
|
milvus_instances_map.update({name: milvus_instance})
|
|
logger.info(milvus_instance.describe_index())
|
|
|
|
# loop time unit: min -> s
|
|
pull_interval_seconds = pull_interval * 60
|
|
tasks = ["insert_rand", "delete_rand", "query_rand", "flush", "compact"]
|
|
i = 1
|
|
while True:
|
|
logger.info("Loop time: %d" % i)
|
|
start_time = time.time()
|
|
while time.time() - start_time < pull_interval_seconds:
|
|
if concurrent:
|
|
threads = []
|
|
for name in collection_names:
|
|
task_name = random.choice(tasks)
|
|
task_run = getattr(milvus_instances_map[name], task_name)
|
|
t = threading.Thread(target=task_run, args=())
|
|
threads.append(t)
|
|
t.start()
|
|
for t in threads:
|
|
t.join()
|
|
# with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_num) as executor:
|
|
# future_results = {executor.submit(getattr(milvus_instances_map[mp[j][0]], mp[j][1])): j for j in range(concurrent_num)}
|
|
# for future in concurrent.futures.as_completed(future_results):
|
|
# future.result()
|
|
else:
|
|
tmp_collection_name = random.choice(collection_names)
|
|
task_name = random.choice(tasks)
|
|
logger.info(tmp_collection_name)
|
|
logger.info(task_name)
|
|
task_run = getattr(milvus_instances_map[tmp_collection_name], task_name)
|
|
task_run()
|
|
# new connection
|
|
# for name in collection_names:
|
|
# milvus_instance = MilvusClient(collection_name=name, host=self.host)
|
|
# milvus_instances_map.update({name: milvus_instance})
|
|
i = i + 1
|
|
|
|
elif run_type == "locust_mix_performance":
|
|
(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
|
|
collection_name)
|
|
ni_per = collection["ni_per"]
|
|
build_index = collection["build_index"]
|
|
vector_type = self.get_vector_type(data_type)
|
|
index_field_name = utils.get_default_field_name(vector_type)
|
|
# drop exists collection
|
|
if milvus_instance.exists_collection():
|
|
milvus_instance.drop()
|
|
time.sleep(10)
|
|
# create collection
|
|
other_fields = collection["other_fields"] if "other_fields" in collection else None
|
|
milvus_instance.create_collection(dimension, data_type=DataType.FLOAT_VECTOR, collection_name=collection_name, other_fields=other_fields)
|
|
logger.info(milvus_instance.get_info())
|
|
# insert entities
|
|
insert_vectors = [[random.random() for _ in range(dimension)] for _ in range(ni_per)]
|
|
insert_ids = random.sample(list(range(collection_size)), ni_per)
|
|
insert_vectors = utils.normalize(metric_type, insert_vectors)
|
|
entities = milvus_instance.generate_entities(insert_vectors, insert_ids, collection_name)
|
|
milvus_instance.insert(entities, ids=insert_ids)
|
|
# flush
|
|
milvus_instance.flush()
|
|
logger.info(milvus_instance.get_stats())
|
|
logger.debug("Table row counts: %d" % milvus_instance.count())
|
|
# create index
|
|
if build_index is True:
|
|
index_type = collection["index_type"]
|
|
index_param = collection["index_param"]
|
|
logger.debug("Start build index for last file")
|
|
milvus_instance.create_index(index_field_name, index_type, metric_type, index_param)
|
|
logger.debug(milvus_instance.describe_index())
|
|
# locust
|
|
task = collection["tasks"]
|
|
task_file = utils.get_unique_name()
|
|
task_file_script = task_file + '.py'
|
|
task_file_csv = task_file + '_stats.csv'
|
|
task_types = task["types"]
|
|
connection_type = "single"
|
|
connection_num = task["connection_num"]
|
|
if connection_num > 1:
|
|
connection_type = "multi"
|
|
clients_num = task["clients_num"]
|
|
hatch_rate = task["hatch_rate"]
|
|
during_time = task["during_time"]
|
|
def_strs = ""
|
|
# define def str
|
|
for task_type in task_types:
|
|
type = task_type["type"]
|
|
weight = task_type["weight"]
|
|
if type == "flush":
|
|
def_str = """
|
|
@task(%d)
|
|
def flush(self):
|
|
client = get_client(collection_name)
|
|
client.flush(collection_name=collection_name)
|
|
""" % weight
|
|
if type == "compact":
|
|
def_str = """
|
|
@task(%d)
|
|
def compact(self):
|
|
client = get_client(collection_name)
|
|
client.compact(collection_name)
|
|
""" % weight
|
|
if type == "query":
|
|
def_str = """
|
|
@task(%d)
|
|
def query(self):
|
|
client = get_client(collection_name)
|
|
params = %s
|
|
X = [[random.random() for i in range(dim)] for i in range(params["nq"])]
|
|
vector_query = {"vector": {"%s": {
|
|
"topk": params["top_k"],
|
|
"query": X,
|
|
"metric_type": "%s",
|
|
"params": params["search_param"]}}}
|
|
client.query(vector_query, filter_query=params["filters"], collection_name=collection_name)
|
|
""" % (weight, task_type["params"], index_field_name, utils.metric_type_trans(metric_type))
|
|
if type == "insert":
|
|
def_str = """
|
|
@task(%d)
|
|
def insert(self):
|
|
client = get_client(collection_name)
|
|
params = %s
|
|
insert_ids = random.sample(list(range(100000)), params["nb"])
|
|
insert_vectors = [[random.random() for _ in range(dim)] for _ in range(params["nb"])]
|
|
insert_vectors = utils.normalize("l2", insert_vectors)
|
|
entities = generate_entities(insert_vectors, insert_ids)
|
|
client.insert(entities,ids=insert_ids, collection_name=collection_name)
|
|
""" % (weight, task_type["params"])
|
|
if type == "delete":
|
|
def_str = """
|
|
@task(%d)
|
|
def delete(self):
|
|
client = get_client(collection_name)
|
|
ids = [random.randint(1, 1000000) for i in range(1)]
|
|
client.delete(ids, collection_name)
|
|
""" % weight
|
|
def_strs += def_str
|
|
print(def_strs)
|
|
# define locust code str
|
|
code_str = """
|
|
import random
|
|
import json
|
|
from locust import User, task, between
|
|
from locust_task import MilvusTask
|
|
from client import MilvusClient
|
|
import utils
|
|
|
|
host = '%s'
|
|
port = %s
|
|
collection_name = '%s'
|
|
dim = %s
|
|
connection_type = '%s'
|
|
m = MilvusClient(host=host, port=port)
|
|
|
|
|
|
def get_client(collection_name):
|
|
if connection_type == 'single':
|
|
return MilvusTask(m=m)
|
|
elif connection_type == 'multi':
|
|
return MilvusTask(connection_type='multi', host=host, port=port, collection_name=collection_name)
|
|
|
|
|
|
def generate_entities(vectors, ids):
|
|
return m.generate_entities(vectors, ids, collection_name)
|
|
|
|
|
|
class MixTask(User):
|
|
wait_time = between(0.001, 0.002)
|
|
%s
|
|
""" % (self.host, self.port, collection_name, dimension, connection_type, def_strs)
|
|
with open(task_file_script, "w+") as fd:
|
|
fd.write(code_str)
|
|
locust_cmd = "locust -f %s --headless --csv=%s -u %d -r %d -t %s" % (
|
|
task_file_script,
|
|
task_file,
|
|
clients_num,
|
|
hatch_rate,
|
|
during_time)
|
|
logger.info(locust_cmd)
|
|
try:
|
|
res = os.system(locust_cmd)
|
|
except Exception as e:
|
|
logger.error(str(e))
|
|
return
|
|
|
|
# . retrieve and collect test statistics
|
|
metric = None
|
|
with open(task_file_csv, newline='') as fd:
|
|
dr = csv.DictReader(fd)
|
|
for row in dr:
|
|
if row["Name"] != "Aggregated":
|
|
continue
|
|
metric = row
|
|
logger.info(metric)
|
|
|
|
else:
|
|
raise Exception("Run type not defined")
|
|
logger.debug("All test finished")
|