mirror of https://github.com/milvus-io/milvus.git
591 lines
28 KiB
Python
591 lines
28 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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from multiprocessing import Process
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import numpy as np
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from yaml import full_load, dump
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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 utils
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logger = logging.getLogger("milvus_benchmark.k8s_runner")
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namespace = "milvus"
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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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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.name = utils.get_unique_name()
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self.host = None
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self.ip = None
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self.hostname = None
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self.env_value = None
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def init_env(self, server_config, server_host, image_type, image_tag):
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self.hostname = server_host
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# update values
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helm_path = os.path.join(os.getcwd(), "../milvus-helm")
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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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utils.update_values(values_file_path, server_host, server_config)
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try:
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logger.debug("Start install server")
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self.host, self.ip = utils.helm_install_server(helm_path, image_tag, image_type, self.name, 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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self.clean_up()
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return False
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# for debugging
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# self.host = "192.168.1.101"
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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(self.name)
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utils.helm_del_server(self.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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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 = Hardware(name=hostname)
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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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metric.server = Server(version=server_version, mode=server_mode, build_commit=commit)
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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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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"]
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milvus_instance = MilvusClient(collection_name=collection_name, ip=self.ip)
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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.pop("logs")
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if run_type == "insert_performance":
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(data_type, collection_size, index_file_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.delete()
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time.sleep(10)
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index_info = {}
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search_params = {}
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milvus_instance.create_collection(collection_name, dimension, index_file_size, metric_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_type, 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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logger.info(res)
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milvus_instance.flush()
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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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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info, search_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": res["total_time"],
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"qps": res["qps"],
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"ni_time": res["ni_time"]
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}
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}
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report(metric)
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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_type, index_param)
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logger.debug(milvus_instance.describe_index())
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if run_type == "insert_flush_performance":
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(data_type, collection_size, index_file_size, dimension, metric_type) = parser.collection_parser(collection_name)
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ni_per = collection["ni_per"]
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if milvus_instance.exists_collection():
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milvus_instance.delete()
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time.sleep(10)
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index_info = {}
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search_params = {}
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milvus_instance.create_collection(collection_name, dimension, index_file_size, metric_type)
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res = self.do_insert(milvus_instance, collection_name, data_type, dimension, collection_size, ni_per)
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logger.info(res)
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logger.debug(milvus_instance.count())
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start_time = time.time()
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milvus_instance.flush()
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end_time = time.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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}
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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info, search_params)
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metric.metrics = {
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"type": run_type,
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"value": {
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"flush_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 == "build_performance":
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(data_type, collection_size, index_file_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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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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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()
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start_mem_usage = milvus_instance.get_mem_info()["memory_used"]
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milvus_instance.create_index(index_type, 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, 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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"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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}
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report(metric)
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elif run_type == "delete_performance":
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(data_type, collection_size, index_file_size, dimension, metric_type) = parser.collection_parser(collection_name)
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ni_per = collection["ni_per"]
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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("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.preload_collection()
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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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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_vectors(delete_ids)
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milvus_instance.flush()
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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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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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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info, search_params)
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metric.metrics = {
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"type": "delete_performance",
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"value": {
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"delete_time": round(end_time - start_time, 1),
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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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}
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report(metric)
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elif run_type == "search_performance":
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(data_type, collection_size, index_file_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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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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# fro 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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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.preload_collection()
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logger.info("Start warm up query")
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res = self.do_query(milvus_instance, collection_name, [1], [1], 2, search_param=search_params[0])
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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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res = self.do_query(milvus_instance, collection_name, top_ks, nqs, run_count, search_param)
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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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}
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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, 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 == "search_ids_stability":
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(data_type, collection_size, index_file_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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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("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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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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ids_param = [random.randint(l_id_length, g_id_length) for _ in range(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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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info, {})
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metric.metrics = {
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"type": "search_ids_stability",
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"value": {
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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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}
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report(metric)
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# for sift/deep datasets
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# TODO: enable
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elif run_type == "accuracy":
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(data_type, collection_size, index_file_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
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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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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.preload_collection()
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true_ids_all = self.get_groundtruth_ids(collection_size)
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for search_param in search_params:
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for top_k in top_ks:
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for nq in nqs:
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total = 0
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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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}
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logger.info("Query params: %s" % json.dumps(search_param_group))
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result_ids, result_distances = self.do_query_ids(milvus_instance, collection_name, top_k, nq, search_param=search_param)
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acc_value = self.get_recall_value(true_ids_all[:nq, :top_k].tolist(), result_ids)
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logger.info("Query accuracy: %s" % acc_value)
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metric = self.report_wrapper(milvus_instance, self.env_value, self.hostname, collection_info, index_info, search_param_group)
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metric.metrics = {
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"type": "accuracy",
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"value": {
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"acc": acc_value
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}
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}
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report(metric)
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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_file_sizes = collection["index_file_sizes"]
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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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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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dataset = utils.get_dataset(hdf5_source_file)
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if milvus_instance.exists_collection(collection_name):
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logger.info("Re-create collection: %s" % collection_name)
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milvus_instance.delete()
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time.sleep(DELETE_INTERVAL_TIME)
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true_ids = np.array(dataset["neighbors"])
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for index_file_size in index_file_sizes:
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milvus_instance.create_collection(collection_name, dimension, index_file_size, metric_type)
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logger.info(milvus_instance.describe())
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insert_vectors = self.normalize(metric_type, np.array(dataset["train"]))
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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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tmp_vectors = insert_vectors[start:end]
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if start < end:
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if not isinstance(tmp_vectors, list):
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milvus_instance.insert(tmp_vectors.tolist(), ids=[i for i in range(start, end)])
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else:
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milvus_instance.insert(tmp_vectors, ids=[i for i in range(start, end)])
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milvus_instance.flush()
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logger.info("Table: %s, row count: %s" % (collection_name, milvus_instance.count()))
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if milvus_instance.count() != len(insert_vectors):
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logger.error("Table row count is not equal to insert vectors")
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return
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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" % json.dumps(index_param))
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milvus_instance.create_index(index_type, index_param=index_param)
|
|
logger.info(milvus_instance.describe_index())
|
|
logger.info("Start preload collection: %s" % collection_name)
|
|
milvus_instance.preload_collection()
|
|
index_info = {
|
|
"index_type": index_type,
|
|
"index_param": index_param
|
|
}
|
|
logger.debug(index_info)
|
|
for search_param in search_params:
|
|
for nq in nqs:
|
|
query_vectors = self.normalize(metric_type, np.array(dataset["test"][:nq]))
|
|
for top_k in top_ks:
|
|
search_param_group = {
|
|
"nq": len(query_vectors),
|
|
"topk": top_k,
|
|
"search_param": search_param
|
|
}
|
|
logger.debug(search_param_group)
|
|
if not isinstance(query_vectors, list):
|
|
result = milvus_instance.query(query_vectors.tolist(), top_k, search_param=search_param)
|
|
else:
|
|
result = milvus_instance.query(query_vectors, top_k, search_param=search_param)
|
|
result_ids = result.id_array
|
|
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, index_file_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.preload_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)
|
|
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)))
|
|
result = milvus_instance.query(query_vectors, top_k, 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_stability",
|
|
"value": {
|
|
"during_time": during_time,
|
|
"start_mem_usage": start_mem_usage,
|
|
"end_mem_usage": end_mem_usage,
|
|
"diff_mem": end_mem_usage - start_mem_usage
|
|
}
|
|
}
|
|
report(metric)
|
|
|
|
elif run_type == "stability":
|
|
(data_type, collection_size, index_file_size, dimension, metric_type) = parser.collection_parser(collection_name)
|
|
search_params = collection["search_params"]
|
|
insert_xb = collection["insert_xb"]
|
|
insert_interval = collection["insert_interval"]
|
|
delete_xb = collection["delete_xb"]
|
|
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.preload_collection()
|
|
start_mem_usage = milvus_instance.get_mem_info()["memory_used"]
|
|
start_row_count = milvus_instance.count()
|
|
logger.debug(milvus_instance.describe_index())
|
|
logger.info(start_row_count)
|
|
start_time = time.time()
|
|
i = 0
|
|
ids = []
|
|
insert_vectors = [[random.random() for _ in range(dimension)] for _ in range(insert_xb)]
|
|
query_vectors = [[random.random() for _ in range(dimension)] for _ in range(10000)]
|
|
while time.time() < start_time + during_time * 60:
|
|
i = i + 1
|
|
for j in range(insert_interval):
|
|
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)))
|
|
result = milvus_instance.query(query_vectors[0:nq], top_k, search_param=search_param)
|
|
count = milvus_instance.count()
|
|
insert_ids = [(count+x) for x in range(len(insert_vectors))]
|
|
ids.extend(insert_ids)
|
|
status, res = milvus_instance.insert(insert_vectors, ids=insert_ids)
|
|
logger.debug("%d, row_count: %d" % (i, milvus_instance.count()))
|
|
milvus_instance.delete_vectors(ids[-delete_xb:])
|
|
milvus_instance.flush()
|
|
milvus_instance.compact()
|
|
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,
|
|
"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
|
|
}
|
|
}
|
|
report(metric)
|
|
|
|
else:
|
|
logger.warning("Run type not defined")
|
|
return
|
|
logger.debug("Test finished")
|