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290 lines
11 KiB
290 lines
11 KiB
# coding=utf-8
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from __future__ import division
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import json
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from datetime import timedelta,datetime
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from collections import defaultdict
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jobnum_region_dict = {
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"10": "省公司",
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"110": "武汉分公司",
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"170": "襄阳分公司",
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"130": "鄂州分公司",
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"260": "孝感分公司",
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"250": "黄冈分公司",
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"120": "黄石分公司",
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"190": "咸宁分公司",
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"200": "荆州分公司",
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"140": "宜昌分公司",
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"150": "恩施分公司",
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"160": "十堰分公司",
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"240": "随州分公司",
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"230": "荆门分公司",
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"1801": "江汉分公司",
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"1802": "潜江分公司",
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"1803": "天门分公司"
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}
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def keep_digits_filter(code):
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"""
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工号可能有字面，去掉字面，以数字起算
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"""
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return ''.join(filter(str.isdigit, str(code)))
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#安全除
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def safe_divide(numerator, denominator):
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if denominator == 0:
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return
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else:
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return numerator / denominator
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def find_region_by_code(code, region_dict):
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"""
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查询工号对应公司
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未查询到 返回错误工号
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"""
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code_str = keep_digits_filter(code)
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# 使用生成器表达式和next函数尝试找到匹配的前缀
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company = next(
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(region_dict.get(code_str[:i]) for i in range(2, min(len(code_str), 5)) if code_str[:i] in region_dict),
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"错误工号")
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return company
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def ip_summary_data_format(ip_summary_data):
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"""
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ip维度数据转换方法
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"""
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result = {"summary": {"ip": []}, "detail": {"ip": {}}}
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grouped_data = defaultdict(lambda: {"reqs": 0, "ips": set()})
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ip_detail_dict = defaultdict(lambda: defaultdict(lambda: {"req_frequency": 0}))
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# 全部账号元组
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ips_total = set()
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for ip_data in ip_summary_data:
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company = find_region_by_code(ip_data["jobnum"], jobnum_region_dict)
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count = ip_data["count"]
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ip = ip_data["ip"]
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jobnum = ip_data["jobnum"]
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ip_detail_dict_key = "{}{}".format(ip, jobnum)
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# 更新统计数据
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grouped_data[company]["reqs"] += count
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grouped_data[company]["ips"].add(ip)
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ips_total.add(ip)
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# 构建下钻详情
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ip_detail_dict[company][ip_detail_dict_key]["req_ip"] = ip
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ip_detail_dict[company][ip_detail_dict_key]["req_jobnum"] = jobnum
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ip_detail_dict[company][ip_detail_dict_key]["req_frequency"] += count
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# 统计总请求次数和独立IP数
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reqs_total = sum(data["reqs"] for data in grouped_data.values())
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# 请求为0抛出
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if reqs_total == 0:
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return result
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# 构建summary部分
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ip_data_list = [
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{
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"company": company,
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"req_frequency": data["reqs"],
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# 本公司的 请求次数/所有公司 请求次数的合计
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"frequency_rate": round(data["reqs"] / reqs_total, 4),
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"ip_count": len(data["ips"]),
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# 本公司的 ip个数/所有公司 ip个数的合计
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"ip_rate": round(len(data["ips"]) / len(ips_total), 4),
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# 本公司的 请求次数/本公司 ip个数的合计
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"ip_avg": safe_divide(data["reqs"],len(data["ips"])),
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}
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for company, data in grouped_data.items()
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]
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result["summary"]["ip"] = sorted(ip_data_list, key=lambda x: x["req_frequency"], reverse=True)
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# 构建detail部分
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result["detail"]["ip"] = {
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company: sorted(data.values(), key=lambda x: x['req_frequency'], reverse=True)[:500]
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for company, data in ip_detail_dict.items()
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}
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return result
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def account_summary_data_format(account_summary_data):
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"""
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账号维度数据转换方法
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"""
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result = {"summary": {"account": []}, "detail": {"account": {}}}
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grouped_data = defaultdict(lambda: {"reqs": 0, "accounts": set()})
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account_detail_dict = defaultdict(lambda: defaultdict(lambda: {"req_frequency": 0}))
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accounts_total = set()
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for account_data in account_summary_data:
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company = find_region_by_code(account_data["jobnum"], jobnum_region_dict)
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count = account_data["count"]
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account = account_data["account"]
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jobnum = account_data["jobnum"]
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account_detail_dict_key = "{}{}".format(account, jobnum)
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# 更新统计数据
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grouped_data[company]["reqs"] += count
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grouped_data[company]["accounts"].add(account)
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accounts_total.add(account)
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# 更新下钻详情
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account_detail_dict[company][account_detail_dict_key]["req_account"] = account
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account_detail_dict[company][account_detail_dict_key]["req_jobnum"] = jobnum
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account_detail_dict[company][account_detail_dict_key]["req_frequency"] += count
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# 统计总请求次数和独立账号数
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reqs_total = sum(data["reqs"] for data in grouped_data.values())
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# 请求为0抛出
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if reqs_total == 0:
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return result
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# 构建summary部分
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account_data_list = [
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{
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"company": company,
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"req_frequency": data["reqs"],
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# 本公司的 请求次数/所有公司 请求次数的合计
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"frequency_rate": round(data["reqs"] / reqs_total, 4),
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"account_count": len(data["accounts"]),
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# 本公司的 账号次数/所有公司 账号次数的合计
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"account_rate": round(len(data["accounts"]) / len(accounts_total), 4),
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# 本公司的 请求次数/本公司 账号次数的合计
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"account_avg": safe_divide(data["reqs"],len(data["accounts"])),
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}
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for company, data in grouped_data.items()
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]
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result["summary"]["account"] = sorted(account_data_list, key=lambda x: x["req_frequency"], reverse=True)
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# 构建detail部分
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result["detail"]["account"] = {company: sorted(data.values(), key=lambda x: x['req_frequency'], reverse=True)[:500]
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for company, data in account_detail_dict.items()}
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return result
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def interface_summary_data_format(interface_summary_data):
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"""
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接口维度数据转换方法
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"""
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result = {"summary": {"interface": []}, "detail": {"interface": {}}}
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grouped_data = defaultdict(lambda: {"reqs": 0})
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interface_detail_dict = defaultdict(lambda: defaultdict(lambda: {"req_frequency": 0}))
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for interface_data in interface_summary_data:
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count = interface_data["count"]
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interface = interface_data["interface"]
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jobnum = interface_data["jobnum"]
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account = interface_data["account"]
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ip = interface_data["ip"]
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interface_detail_dict_key = "{}{}".format(ip, account, jobnum)
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# 更新统计数据
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grouped_data[interface]["reqs"] += count
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# 构建下钻详情
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interface_detail_dict[interface][interface_detail_dict_key]["interface_addr"] = interface
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interface_detail_dict[interface][interface_detail_dict_key]["req_ip"] = ip
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interface_detail_dict[interface][interface_detail_dict_key]["req_account"] = account
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interface_detail_dict[interface][interface_detail_dict_key]["req_jobnum"] = jobnum
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interface_detail_dict[interface][interface_detail_dict_key]["req_frequency"] += count
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# 统计总请求次数
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reqs_total = sum(data["reqs"] for data in grouped_data.values())
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# 请求为0抛出
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if reqs_total == 0:
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return result
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# 构建summary部分
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interface_data_list = [
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{
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"interface_addr": interface,
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"req_frequency": data["reqs"],
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# 本接口的 请求次数/所有接口 请求次数的合计
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"frequency_rate": round(data["reqs"] / reqs_total, 4),
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# 本接口的 请求次数/ 20 查询top20接口
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"frequency_avg": safe_divide(data["reqs"],20),
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}
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for interface, data in grouped_data.items()
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]
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result["summary"]["interface"] = sorted(interface_data_list, key=lambda x: x["req_frequency"], reverse=True)[:500]
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# 构建detail部分
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result["detail"]["interface"] = {
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company: sorted(data.values(), key=lambda x: x["req_frequency"], reverse=True)
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for company, data in interface_detail_dict.items()
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}
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return result
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def menu_summary_data_format(menu_summary_data):
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"""
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菜单维度数据转换方法
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"""
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result = {"summary": {"menu": []}, "detail": {"menu": {}}}
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grouped_data = defaultdict(lambda: {"reqs": 0, "menu": set()})
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menu_detail_dict = defaultdict(lambda: defaultdict(lambda: {"req_frequency": 0}))
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menu_total = set()
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for menu_data in menu_summary_data:
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count = menu_data["count"]
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menu = menu_data["menu"]
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jobnum = menu_data["jobnum"]
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account = menu_data["account"]
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ip = menu_data["ip"]
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menu_detail_dict_key = "{}{}{}".format(ip, account, jobnum)
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# 更新统计数据和独立菜单数
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grouped_data[menu]["reqs"] += count
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grouped_data[menu]["menu"].add(menu)
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menu_total.add(menu)
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# 构建下钻详情
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menu_detail_dict[menu][menu_detail_dict_key]["menu_name"] = menu
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menu_detail_dict[menu][menu_detail_dict_key]["req_ip"] = ip
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menu_detail_dict[menu][menu_detail_dict_key]["req_account"] = account
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menu_detail_dict[menu][menu_detail_dict_key]["req_jobnum"] = jobnum
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menu_detail_dict[menu][menu_detail_dict_key]["req_frequency"] += count
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# 统计总请求次数
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reqs_total = sum(data["reqs"] for data in grouped_data.values())
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# 请求为0抛出
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if reqs_total == 0 or menu_total == 0:
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return result
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# 构建summary部分
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menu_data_list = [
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{
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"menu_name": menu,
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"req_frequency": data["reqs"],
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# 本菜单的 请求次数 /所有菜单 请求次数的合计
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"frequency_rate": round(data["reqs"] / reqs_total, 4),
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# 本菜单的 请求次数 /所有菜单 个数的合计
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"frequency_avg": safe_divide(data["reqs"],len(menu_total)),
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}
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for menu, data in grouped_data.items()
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]
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result["summary"]["menu"] = sorted(menu_data_list, key=lambda x: x["req_frequency"], reverse=True)
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# 构建detail部分
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result["detail"]["menu"] = {company: sorted(data.values(), key=lambda x: x["req_frequency"], reverse=True)[:500]
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for company, data in menu_detail_dict.items()}
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return result
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def adjust_times(start_time, end_time):
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start_time = datetime.strptime(start_time, "%Y-%m-%d")
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end_time = datetime.strptime(end_time, "%Y-%m-%d")
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delta_days = (end_time - start_time).days
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if delta_days == 0:
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pre_date = start_time-timedelta(1)
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pre_date = start_time-timedelta(1)
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return pre_date.strftime("%Y-%m-%d"),pre_date.strftime("%Y-%m-%d")
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if delta_days > 0:
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pre_start_date = start_time-timedelta(delta_days+1)
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pre_end_date = end_time-timedelta(delta_days+1)
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return pre_start_date.strftime("%Y-%m-%d"),pre_end_date.strftime("%Y-%m-%d")
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return start_time, end_time |