Request Analysis

本文介绍了一个使用Python编写的简单并发性能测试脚本,该脚本能够同时向多个网站发送请求并记录响应时间和状态码,从而评估系统的并发处理能力。
用法:

python bencher.py threadcount duration hitfile


TEST SUMMARY
============
21 REQUESTS in 3s (7.00/s)
7.00 CYCLES in 3s (2.33/s)
http://www.tl50.com/ status_count: 200=9 (avg 1338ms/req)
http://www.google.com.hk/ status_count: 200=6 (avg 671ms/req)
http://www.baidu.com/ status_count: 200=6 (avg 156ms/req)


编写test.txt 内容如下

http://www.google.com.hk/
http://www.baidu.com/



import socket
import thread
import httplib
import time
import sys
import urlparse
import Queue
import os

def check_urls(urls):
for u in urls:
url_o = urlparse.urlparse(u)
conn = httplib.HTTPConnection(url_o.hostname, url_o.port)
try:
conn.request("GET", "%s%s?%s" % (url_o.path, url_o.params, url_o.query))
except socket.gaierror:
error("the url '%s' contains an unknown hostname" % u)
except socket.error:
error("the url '%s' contains an host/port combo that did not respond" % u)

resp = conn.getresponse()

if resp.status != 200:
error("the url '%s' did not return 200 is %s" % (u,resp.status))

from itertools import cycle
def test_thread(q, duration, urls):
looper = cycle(urls)
ind_bench = dict((u, []) for u in urls)
start = time.time()
count = 0
while time.time() - start < duration:
url = looper.next()
url_o = urlparse.urlparse(url)
m_s = time.time()
conn = httplib.HTTPConnection(url_o.hostname, url_o.port)
conn.request("GET", "%s%s?%s" % (url_o.path, url_o.params, url_o.query))
resp = conn.getresponse()
ind_bench[url].append((time.time() - m_s, resp.status))
count += 1
q.put((count, ind_bench))

def thread_details(num, amt, urls, duration, details):
def out(s):
if 'VERBOSE' in os.environ:
print s
out('- Thread #%d -' % num)
cycles = amt / float(len(urls))
out(' %d total requests (%.2f/s), %.1f cycles (%.1f/s)' % (amt, float(amt) / duration,
cycles, cycles / duration))
url_data = {}
for url in urls:
stats = details[url]
status_acc = {}
tot_time = 0
for tm, status in stats:
tot_time += tm
try:
status_acc[status] += 1
except KeyError:
status_acc[status] = 1
avg_time = tot_time / float(len(stats))
out(' %s status_count: %s (avg %dms/req)' % (
url, ', '.join([('%s=%s' % (k, v)) for k, v in status_acc.iteritems()]),
avg_time * 1000))
url_data[url] = (avg_time, status_acc)
return cycles, url_data

def run_test(count, duration, urls):
check_urls(urls)
rec_q = Queue.Queue()
for x in xrange(count):
thread.start_new_thread(test_thread, (rec_q, duration, urls))

results = []
for x in xrange(count):
results.append(rec_q.get())

tot = 0
cycles = 0
total_acc = dict((u, {}) for u in urls)
times = dict((u, 0) for u in urls)
for x, res in enumerate(results):
amt, details = res
cyc, url_data = thread_details(x + 1, amt, urls, duration, details)
tot += amt
cycles += cyc
for url, (avg_time, status_acc) in url_data.iteritems():
times[url] += avg_time
for code, c_count in status_acc.iteritems():
try:
total_acc[url][code] += c_count
except KeyError:
total_acc[url][code] = c_count

print "TEST SUMMARY"
print "============"
print "%s REQUESTS in %ss (%.2f/s)" % (tot, duration, tot / float(duration))
print "%.2f CYCLES in %ss (%.2f/s)" % (cycles, duration, cycles / float(duration))
for url in urls:
print ' %s status_count: %s (avg %dms/req)' % (
url, ', '.join([('%s=%s' % (k, v)) for k, v in total_acc[url].iteritems()]),
(times[url] / float(count)) * 1000)


def error(msg):
sys.stderr.write('error: %s\n' % msg)
sys.stderr.write('usage: %s threadcount duration hitfile\n' % sys.argv[0])
raise SystemExit, 1

def get_urls_from_file(fn):
return [line.strip() for line in open(fn) if line.strip()]

def main_with_argv():
args = sys.argv[1:]
if len(args) != 3:
error("exactly three arguments are required")
count, duration, hitfile = args
try:
count = int(count)
except ValueError:
error("argument #1 (count) must be an int")
try:
duration = int(duration)
except ValueError:
error("argument #2 (duration) must be an int")
try:
urls = get_urls_from_file(hitfile)
except IOError:
error("could not retrieve URL list from file '%s'" % hitfile)
run_test(count, duration, urls)

if __name__ == '__main__':
main_with_argv()

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow框架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值