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https://gitee.com/milvus-io/milvus.git
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928 lines
46 KiB
Python
928 lines
46 KiB
Python
import os
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import logging
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import pdb
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import time
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import re
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import random
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import traceback
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import json
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import csv
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import threading
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from multiprocessing import Process
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import numpy as np
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from milvus import DataType
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from yaml import full_load, dump
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import concurrent.futures
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import locust_user
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from client import MilvusClient
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import parser
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from runner import Runner
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from milvus_metrics.api import report
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from milvus_metrics.models import Env, Hardware, Server, Metric
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import helm_utils
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import utils
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logger = logging.getLogger("milvus_benchmark.k8s_runner")
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namespace = "milvus"
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default_port = 19530
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DELETE_INTERVAL_TIME = 5
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# INSERT_INTERVAL = 100000
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INSERT_INTERVAL = 50000
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BIG_FLUSH_INTERVAL = 3600
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DEFAULT_FLUSH_INTERVAL = 1
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timestamp = int(time.time())
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default_path = "/var/lib/milvus"
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class K8sRunner(Runner):
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"""run docker mode"""
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def __init__(self):
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super(K8sRunner, self).__init__()
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self.service_name = utils.get_unique_name()
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self.host = None
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self.port = default_port
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self.hostname = None
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self.env_value = None
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self.hardware = None
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self.deploy_mode = None
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def init_env(self, milvus_config, server_config, server_host, deploy_mode, image_type, image_tag):
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logger.debug("Tests run on server host:")
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logger.debug(server_host)
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self.hostname = server_host
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self.deploy_mode = deploy_mode
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if self.hostname:
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try:
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cpus = helm_utils.get_host_cpus(self.hostname)
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except Exception as e:
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logger.error(str(e))
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cpus = 64
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logger.debug(type(cpus))
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if server_config:
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if "cpus" in server_config.keys():
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cpus = min(server_config["cpus"], int(cpus))
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else:
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server_config.update({"cpus": cpus})
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else:
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server_config = {"cpus": cpus}
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self.hardware = Hardware(name=self.hostname, cpus=cpus)
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# update values
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helm_path = os.path.join(os.getcwd(), "../milvus-helm/charts/milvus")
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values_file_path = helm_path + "/values.yaml"
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if not os.path.exists(values_file_path):
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raise Exception("File %s not existed" % values_file_path)
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if milvus_config:
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helm_utils.update_values(values_file_path, deploy_mode, server_host, milvus_config, server_config)
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try:
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logger.debug("Start install server")
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self.host = helm_utils.helm_install_server(helm_path, deploy_mode, image_tag, image_type, self.service_name,
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namespace)
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except Exception as e:
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logger.error("Helm install server failed: %s" % (str(e)))
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logger.error(traceback.format_exc())
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logger.error(self.hostname)
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self.clean_up()
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return False
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logger.debug(server_config)
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# for debugging
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if not self.host:
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logger.error("Helm install server failed")
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self.clean_up()
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return False
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return True
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def clean_up(self):
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logger.debug("Start clean up: %s" % self.service_name)
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helm_utils.helm_del_server(self.service_name, namespace)
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def report_wrapper(self, milvus_instance, env_value, hostname, collection_info, index_info, search_params,
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run_params=None, server_config=None):
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metric = Metric()
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metric.set_run_id(timestamp)
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metric.env = Env(env_value)
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metric.env.OMP_NUM_THREADS = 0
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metric.hardware = self.hardware
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# TODO: removed
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# server_version = milvus_instance.get_server_version()
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# server_mode = milvus_instance.get_server_mode()
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# commit = milvus_instance.get_server_commit()
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server_version = "0.12.0"
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server_mode = self.deploy_mode
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metric.server = Server(version=server_version, mode=server_mode, build_commit=None)
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metric.collection = collection_info
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metric.index = index_info
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metric.search = search_params
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metric.run_params = run_params
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return metric
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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)
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# TODO: removed
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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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self.env_value = collection
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if run_type == "insert_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
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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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index_info = {}
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search_params = {}
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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,
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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_info = {
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"index_type": index_type,
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"index_param": index_param
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}
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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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logger.debug(milvus_instance.describe_index())
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res = self.do_insert(milvus_instance, collection_name, data_type, dimension, collection_size, ni_per)
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flush_time = 0.0
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if "flush" in collection and collection["flush"] == "no":
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logger.debug("No manual flush")
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else:
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start_time = time.time()
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milvus_instance.flush()
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flush_time = time.time() - start_time
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logger.debug(milvus_instance.count())
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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,
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"other_fields": other_fields,
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"ni_per": ni_per
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}
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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info,
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search_params)
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total_time = res["total_time"]
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build_time = 0
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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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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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build_time = time.time() - start_time
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total_time = total_time + build_time
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metric.metrics = {
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"type": run_type,
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"value": {
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"total_time": total_time,
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"qps": res["qps"],
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"ni_time": res["ni_time"],
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"flush_time": flush_time,
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"build_time": build_time
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}
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}
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report(metric)
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elif run_type == "build_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
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collection_name)
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index_type = collection["index_type"]
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index_param = collection["index_param"]
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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
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}
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index_info = {
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"index_type": index_type,
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"index_param": index_param
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}
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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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search_params = {}
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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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start_time = time.time()
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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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# TODO: need to check
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milvus_instance.create_index(index_field_name, index_type, metric_type, index_param=index_param)
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logger.debug(milvus_instance.describe_index())
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logger.debug(milvus_instance.count())
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end_time = time.time()
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# end_mem_usage = milvus_instance.get_mem_info()["memory_used"]
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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info,
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search_params)
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metric.metrics = {
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"type": "build_performance",
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"value": {
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"build_time": round(end_time - start_time, 1),
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}
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}
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report(metric)
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elif run_type == "delete_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
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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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search_params = {}
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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
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}
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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 name: %s not existed" % collection_name)
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return
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length = milvus_instance.count()
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logger.info(length)
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index_info = milvus_instance.describe_index()
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logger.info(index_info)
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ids = [i for i in range(length)]
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loops = int(length / ni_per)
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milvus_instance.load_collection()
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# TODO: remove
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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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# 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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start_flush_time = time.time()
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milvus_instance.flush()
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end_flush_time = time.time()
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end_time = time.time()
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# end_mem_usage = milvus_instance.get_mem_info()["memory_used"]
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logger.debug("Table row counts: %d" % milvus_instance.count())
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# milvus_instance.set_config("storage", "auto_flush_interval", DEFAULT_FLUSH_INTERVAL)
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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info,
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search_params)
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delete_time = round(end_time - start_time, 1)
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metric.metrics = {
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"type": "delete_performance",
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"value": {
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"delete_time": delete_time,
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"qps": round(collection_size / delete_time, 1)
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}
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}
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if auto_flush is False:
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flush_time = round(end_flush_time - start_flush_time, 1)
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metric.metrics["value"].update({"flush_time": flush_time})
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report(metric)
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elif run_type == "get_ids_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
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collection_name)
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ids_length_per_segment = collection["ids_length_per_segment"]
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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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collection_info = {
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"dimension": dimension,
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"metric_type": metric_type,
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"dataset_name": collection_name
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}
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search_params = {}
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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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for ids_num in ids_length_per_segment:
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segment_num, get_ids = milvus_instance.get_rand_ids_each_segment(ids_num)
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start_time = time.time()
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get_res = milvus_instance.get_entities(get_ids)
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total_time = time.time() - start_time
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avg_time = total_time / segment_num
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run_params = {"ids_num": ids_num}
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logger.info(
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"Segment num: %d, ids num per segment: %d, run_time: %f" % (segment_num, ids_num, total_time))
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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info,
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index_info, search_params, run_params=run_params)
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metric.metrics = {
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"type": run_type,
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"value": {
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"total_time": round(total_time, 1),
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"avg_time": round(avg_time, 1)
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}
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}
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report(metric)
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elif run_type == "search_performance":
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
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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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# filter_query = collection["filter"] if "filter" in collection else None
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filters = collection["filters"] if "filters" in collection else []
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filter_query = []
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search_params = collection["search_params"]
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fields = self.get_fields(milvus_instance, collection_name)
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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
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"fields": fields
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}
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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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index_info = milvus_instance.describe_index()
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logger.info(index_info)
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milvus_instance.load_collection()
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logger.info("Start warm up query")
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res = self.do_query(milvus_instance, collection_name, vec_field_name, [1], [1], 2,
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search_param=search_params[0], filter_query=filter_query)
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logger.info("End warm up query")
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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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if not filters:
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filters.append(None)
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for filter in filters:
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filter_param = []
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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,
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search_param, filter_query=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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for index_nq, nq in enumerate(nqs):
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for index_top_k, top_k in enumerate(top_ks):
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search_param_group = {
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"nq": nq,
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"topk": top_k,
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"search_param": search_param,
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"filter": filter_param
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}
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search_time = res[index_nq][index_top_k]
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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname,
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collection_info, index_info, search_param_group)
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metric.metrics = {
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"type": "search_performance",
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"value": {
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"search_time": search_time
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}
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}
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report(metric)
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elif run_type == "locust_insert_stress":
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pass
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elif run_type in ["locust_search_performance", "locust_insert_performance", "locust_mix_performance"]:
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(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
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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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index_info = {}
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search_params = {}
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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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milvus_instance.create_collection(dimension, data_type=vector_type, other_fields=None)
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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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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_info = {
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"index_type": index_type,
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"index_param": index_param
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}
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milvus_instance.create_index(index_field_name, index_type, metric_type, index_param=index_param)
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logger.debug(milvus_instance.describe_index())
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if run_type in ["locust_search_performance", "locust_mix_performance"]:
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res = self.do_insert(milvus_instance, collection_name, data_type, dimension, collection_size, ni_per)
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if "flush" in collection and collection["flush"] == "no":
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logger.debug("No manual flush")
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else:
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milvus_instance.flush()
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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, _async=True,
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index_param=index_param)
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logger.debug(milvus_instance.describe_index())
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logger.debug("Table row counts: %d" % milvus_instance.count())
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milvus_instance.load_collection()
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logger.info("Start warm up query")
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for i in range(2):
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res = self.do_query(milvus_instance, collection_name, vec_field_name, [1], [1], 2,
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search_param={"nprobe": 16})
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logger.info("End warm up query")
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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"]
|
|
if connection_num > 1:
|
|
connection_type = "multi"
|
|
clients_num = task["clients_num"]
|
|
hatch_rate = task["hatch_rate"]
|
|
during_time = utils.timestr_to_int(task["during_time"])
|
|
task_types = task["types"]
|
|
run_params = {"tasks": {}, "clients_num": clients_num, "spawn_rate": hatch_rate, "during_time": during_time}
|
|
for task_type in task_types:
|
|
run_params["tasks"].update({task_type["type"]: task_type["weight"] if "weight" in task_type else 1})
|
|
|
|
# . collect stats
|
|
locust_stats = locust_user.locust_executor(self.host, self.port, collection_name,
|
|
connection_type=connection_type, run_params=run_params)
|
|
logger.info(locust_stats)
|
|
collection_info = {
|
|
"dimension": dimension,
|
|
"metric_type": metric_type,
|
|
"dataset_name": collection_name
|
|
}
|
|
metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info,
|
|
search_params)
|
|
metric.metrics = {
|
|
"type": run_type,
|
|
"value": locust_stats}
|
|
report(metric)
|
|
|
|
elif run_type == "search_ids_stability":
|
|
(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
|
|
collection_name)
|
|
search_params = collection["search_params"]
|
|
during_time = collection["during_time"]
|
|
ids_length = collection["ids_length"]
|
|
ids = collection["ids"]
|
|
collection_info = {
|
|
"dimension": dimension,
|
|
"metric_type": metric_type,
|
|
"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)
|
|
g_top_k = int(collection["top_ks"].split("-")[1])
|
|
l_top_k = int(collection["top_ks"].split("-")[0])
|
|
g_id = int(ids.split("-")[1])
|
|
l_id = int(ids.split("-")[0])
|
|
g_id_length = int(ids_length.split("-")[1])
|
|
l_id_length = int(ids_length.split("-")[0])
|
|
|
|
milvus_instance.load_collection()
|
|
# start_mem_usage = milvus_instance.get_mem_info()["memory_used"]
|
|
# logger.debug(start_mem_usage)
|
|
start_time = time.time()
|
|
while time.time() < start_time + during_time * 60:
|
|
search_param = {}
|
|
top_k = random.randint(l_top_k, g_top_k)
|
|
ids_num = random.randint(l_id_length, g_id_length)
|
|
ids_param = [random.randint(l_id_length, g_id_length) for _ in range(ids_num)]
|
|
for k, v in search_params.items():
|
|
search_param[k] = random.randint(int(v.split("-")[0]), int(v.split("-")[1]))
|
|
logger.debug("Query top-k: %d, ids_num: %d, param: %s" % (top_k, ids_num, json.dumps(search_param)))
|
|
result = milvus_instance.query_ids(top_k, ids_param, search_param=search_param)
|
|
# end_mem_usage = milvus_instance.get_mem_info()["memory_used"]
|
|
metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info,
|
|
{})
|
|
metric.metrics = {
|
|
"type": "search_ids_stability",
|
|
"value": {
|
|
"during_time": during_time,
|
|
}
|
|
}
|
|
report(metric)
|
|
|
|
# for sift/deep datasets
|
|
# TODO: enable
|
|
elif run_type == "accuracy":
|
|
(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
|
|
collection_name)
|
|
search_params = collection["search_params"]
|
|
# mapping to search param list
|
|
search_params = self.generate_combinations(search_params)
|
|
|
|
top_ks = collection["top_ks"]
|
|
nqs = collection["nqs"]
|
|
collection_info = {
|
|
"dimension": dimension,
|
|
"metric_type": metric_type,
|
|
"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.load_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:
|
|
for top_k in top_ks:
|
|
tmp_res = []
|
|
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)
|
|
metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info,
|
|
index_info, search_param_group)
|
|
metric.metrics = {
|
|
"type": "accuracy",
|
|
"value": {
|
|
"acc": acc_value
|
|
}
|
|
}
|
|
report(metric)
|
|
# 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 == "ann_accuracy":
|
|
hdf5_source_file = collection["source_file"]
|
|
collection_name = collection["collection_name"]
|
|
index_types = collection["index_types"]
|
|
index_params = collection["index_params"]
|
|
top_ks = collection["top_ks"]
|
|
nqs = collection["nqs"]
|
|
search_params = collection["search_params"]
|
|
# mapping to search param list
|
|
search_params = self.generate_combinations(search_params)
|
|
# mapping to index param list
|
|
index_params = self.generate_combinations(index_params)
|
|
|
|
data_type, dimension, metric_type = parser.parse_ann_collection_name(collection_name)
|
|
collection_info = {
|
|
"dimension": dimension,
|
|
"metric_type": metric_type,
|
|
"dataset_name": collection_name
|
|
}
|
|
dataset = utils.get_dataset(hdf5_source_file)
|
|
if milvus_instance.exists_collection(collection_name):
|
|
logger.info("Re-create collection: %s" % collection_name)
|
|
milvus_instance.drop()
|
|
time.sleep(DELETE_INTERVAL_TIME)
|
|
true_ids = np.array(dataset["neighbors"])
|
|
vector_type = self.get_vector_type_from_metric(metric_type)
|
|
vec_field_name = utils.get_default_field_name(vector_type)
|
|
real_metric_type = utils.metric_type_trans(metric_type)
|
|
|
|
# re-create collection
|
|
if milvus_instance.exists_collection(collection_name):
|
|
milvus_instance.drop()
|
|
time.sleep(DELETE_INTERVAL_TIME)
|
|
milvus_instance.create_collection(dimension, data_type=vector_type)
|
|
insert_vectors = self.normalize(metric_type, np.array(dataset["train"]))
|
|
if len(insert_vectors) != dataset["train"].shape[0]:
|
|
raise Exception("Row count of insert vectors: %d is not equal to dataset size: %d" % (
|
|
len(insert_vectors), dataset["train"].shape[0]))
|
|
logger.debug("The row count of entities to be inserted: %d" % len(insert_vectors))
|
|
# Insert batch once
|
|
# milvus_instance.insert(insert_vectors)
|
|
loops = len(insert_vectors) // INSERT_INTERVAL + 1
|
|
for i in range(loops):
|
|
start = i * INSERT_INTERVAL
|
|
end = min((i + 1) * INSERT_INTERVAL, len(insert_vectors))
|
|
if start < end:
|
|
tmp_vectors = insert_vectors[start:end]
|
|
ids = [i for i in range(start, end)]
|
|
if not isinstance(tmp_vectors, list):
|
|
entities = milvus_instance.generate_entities(tmp_vectors.tolist(), ids)
|
|
res_ids = milvus_instance.insert(entities, ids=ids)
|
|
else:
|
|
entities = milvus_instance.generate_entities(tmp_vectors, ids)
|
|
res_ids = milvus_instance.insert(entities, ids=ids)
|
|
assert res_ids == ids
|
|
milvus_instance.flush()
|
|
res_count = milvus_instance.count()
|
|
logger.info("Table: %s, row count: %d" % (collection_name, res_count))
|
|
if res_count != len(insert_vectors):
|
|
raise Exception("Table row count is not equal to insert vectors")
|
|
for index_type in index_types:
|
|
for index_param in index_params:
|
|
logger.debug("Building index with param: %s" % json.dumps(index_param))
|
|
if milvus_instance.get_config("cluster.enable") == "true":
|
|
milvus_instance.create_index(vec_field_name, index_type, metric_type, _async=True,
|
|
index_param=index_param)
|
|
else:
|
|
milvus_instance.create_index(vec_field_name, index_type, metric_type,
|
|
index_param=index_param)
|
|
logger.info(milvus_instance.describe_index())
|
|
logger.info("Start load collection: %s" % collection_name)
|
|
milvus_instance.load_collection()
|
|
logger.info("End load collection: %s" % collection_name)
|
|
index_info = {
|
|
"index_type": index_type,
|
|
"index_param": index_param
|
|
}
|
|
logger.debug(index_info)
|
|
warm_up = True
|
|
for search_param in search_params:
|
|
for nq in nqs:
|
|
query_vectors = self.normalize(metric_type, np.array(dataset["test"][:nq]))
|
|
if not isinstance(query_vectors, list):
|
|
query_vectors = query_vectors.tolist()
|
|
for top_k in top_ks:
|
|
search_param_group = {
|
|
"nq": len(query_vectors),
|
|
"topk": top_k,
|
|
"search_param": search_param,
|
|
"metric_type": metric_type
|
|
}
|
|
logger.debug(search_param_group)
|
|
vector_query = {"vector": {vec_field_name: {
|
|
"topk": top_k,
|
|
"query": query_vectors,
|
|
"metric_type": real_metric_type,
|
|
"params": search_param}
|
|
}}
|
|
for i in range(2):
|
|
result = milvus_instance.query(vector_query)
|
|
warm_up = False
|
|
logger.info("End warm up")
|
|
result = milvus_instance.query(vector_query)
|
|
result_ids = milvus_instance.get_ids(result)
|
|
acc_value = self.get_recall_value(true_ids[:nq, :top_k].tolist(), result_ids)
|
|
logger.info("Query ann_accuracy: %s" % acc_value)
|
|
metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname,
|
|
collection_info, index_info, search_param_group)
|
|
metric.metrics = {
|
|
"type": "ann_accuracy",
|
|
"value": {
|
|
"acc": acc_value
|
|
}
|
|
}
|
|
report(metric)
|
|
|
|
elif run_type == "search_stability":
|
|
(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
|
|
collection_name)
|
|
search_params = collection["search_params"]
|
|
during_time = collection["during_time"]
|
|
collection_info = {
|
|
"dimension": dimension,
|
|
"metric_type": metric_type,
|
|
"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)
|
|
g_top_k = int(collection["top_ks"].split("-")[1])
|
|
g_nq = int(collection["nqs"].split("-")[1])
|
|
l_top_k = int(collection["top_ks"].split("-")[0])
|
|
l_nq = int(collection["nqs"].split("-")[0])
|
|
milvus_instance.load_collection()
|
|
# start_mem_usage = milvus_instance.get_mem_info()["memory_used"]
|
|
# logger.debug(start_mem_usage)
|
|
start_row_count = milvus_instance.count()
|
|
logger.debug(milvus_instance.describe_index())
|
|
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)
|
|
start_time = time.time()
|
|
while time.time() < start_time + during_time * 60:
|
|
search_param = {}
|
|
top_k = random.randint(l_top_k, g_top_k)
|
|
nq = random.randint(l_nq, g_nq)
|
|
for k, v in search_params.items():
|
|
search_param[k] = random.randint(int(v.split("-")[0]), int(v.split("-")[1]))
|
|
query_vectors = [[random.random() for _ in range(dimension)] for _ in range(nq)]
|
|
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}
|
|
}}
|
|
milvus_instance.query(vector_query)
|
|
# end_mem_usage = milvus_instance.get_mem_info()["memory_used"]
|
|
metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info,
|
|
{})
|
|
metric.metrics = {
|
|
"type": "search_stability",
|
|
"value": {
|
|
"during_time": during_time,
|
|
}
|
|
}
|
|
report(metric)
|
|
|
|
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", "query_rand", "flush"]
|
|
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()
|
|
|
|
logger.debug("Restart server")
|
|
helm_utils.restart_server(self.service_name, namespace)
|
|
# new connection
|
|
# for name in collection_names:
|
|
# milvus_instance = MilvusClient(collection_name=name, host=self.host)
|
|
# milvus_instances_map.update({name: milvus_instance})
|
|
time.sleep(30)
|
|
i = i + 1
|
|
|
|
elif run_type == "stability":
|
|
(data_type, collection_size, dimension, metric_type) = parser.collection_parser(
|
|
collection_name)
|
|
during_time = collection["during_time"]
|
|
operations = collection["operations"]
|
|
collection_info = {
|
|
"dimension": dimension,
|
|
"metric_type": metric_type,
|
|
"dataset_name": collection_name
|
|
}
|
|
if not milvus_instance.exists_collection():
|
|
logger.error(milvus_instance.show_collections())
|
|
raise Exception("Table name: %s not existed" % collection_name)
|
|
logger.info(milvus_instance.count())
|
|
index_info = milvus_instance.describe_index()
|
|
logger.info(index_info)
|
|
# 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()
|
|
metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info,
|
|
{})
|
|
metric.metrics = {
|
|
"type": "stability",
|
|
"value": {
|
|
"during_time": during_time,
|
|
"row_count_increments": end_row_count - start_row_count
|
|
}
|
|
}
|
|
report(metric)
|
|
|
|
elif run_type == "debug":
|
|
time.sleep(7200)
|
|
default_insert_vectors = [[random.random() for _ in range(128)] for _ in range(500000)]
|
|
interval = 50000
|
|
for loop in range(1, 7):
|
|
insert_xb = loop * interval
|
|
insert_vectors = default_insert_vectors[:insert_xb]
|
|
insert_ids = [i for i in range(insert_xb)]
|
|
entities = milvus_instance.generate_entities(insert_vectors, insert_ids)
|
|
for j in range(5):
|
|
milvus_instance.insert(entities, ids=insert_ids)
|
|
time.sleep(10)
|
|
|
|
else:
|
|
raise Exception("Run type not defined")
|
|
logger.debug("All test finished")
|