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优化任务调度说明

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2025-10-17 17:59:28 +08:00
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.idea/.gitignore
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# 默认忽略的文件
/shelf/
/workspace.xml
# 基于编辑器的 HTTP 客户端请求
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml
.idea/dataSources.xml
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<working-dir>$ProjectFileDir$</working-dir>
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.idea/data_source_mapping.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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.idea/deployment.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<option name="myUploadOnCheckinName" value="gitea" />
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.idea/inspectionProfiles/Project_Default.xml
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<inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
<option name="ignoredErrors">
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.idea/material_theme_project_new.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="MaterialThemeProjectNewConfig">
<option name="metadata">
<MTProjectMetadataState>
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<option name="pristineConfig" value="false" />
<option name="userId" value="-2834c26c:198a1f98ccf:-7ffe" />
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.idea/misc.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="Black">
<option name="sdkName" value="Python 3.13 (intelligence_system)" />
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<component name="ProjectRootManager" version="2" project-jdk-name="intelligence_system" project-jdk-type="Python SDK" />
</project>
.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/intelligence_system.iml" filepath="$PROJECT_DIR$/intelligence_system.iml" />
</modules>
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.idea/sqldialects.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="SqlDialectMappings">
<file url="file://$PROJECT_DIR$/tools/SQL.sql" dialect="MySQL" />
<file url="file://$PROJECT_DIR$/tools/情报收集.sql" dialect="MySQL" />
<file url="PROJECT" dialect="MySQL" />
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<component name="SqlResolveMappings">
<file url="file://$PROJECT_DIR$/utils/mysql_agent.py" scope="{&quot;node&quot;:{ &quot;@negative&quot;:&quot;1&quot;, &quot;group&quot;:{ &quot;@kind&quot;:&quot;root&quot;, &quot;node&quot;:{ &quot;@negative&quot;:&quot;1&quot; } } }}" />
<file url="PROJECT" scope="{&quot;node&quot;:{ &quot;@negative&quot;:&quot;1&quot;, &quot;group&quot;:{ &quot;@kind&quot;:&quot;root&quot;, &quot;node&quot;:{ &quot;@negative&quot;:&quot;1&quot; } } }}" />
</component>
</project>
.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>
.idea/webServers.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="WebServers">
<option name="servers">
<webServer id="ee95b41f-4bcf-4810-b328-4a7a4f66093f" name="gitea" url="">
<fileTransfer accessType="WEBDAV" port="6180">
<advancedOptions>
<advancedOptions dataProtectionLevel="Private" passiveMode="true" shareSSLContext="true" />
</advancedOptions>
</fileTransfer>
</webServer>
</option>
</component>
</project>
applications/alert.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
报警通知模块
功能：
1. 多通道报警通知（邮件/企业微信/飞书）
2. 分级报警策略
3. 失败重试机制
"""
import smtplib
import json
import requests
from email.mime.text import MIMEText
from typing import Optional, Dict, List
import logging
from dataclasses import dataclass
from tenacity import retry, stop_after_attempt, wait_exponential
# 日志配置
logger = logging.getLogger('alert')
logger.setLevel(logging.INFO)
@dataclass
class AlertConfig:
"""报警配置数据类"""
email: Dict[str, str] = None # SMTP配置
wecom: Dict[str, str] = None # 企业微信机器人配置
feishu: Dict[str, str] = None # 飞书机器人配置
min_level: str = 'WARNING' # 默认最低报警级别
class AlertService:
def __init__(self, config: AlertConfig):
"""
初始化报警服务
参数：
config: AlertConfig对象
"""
self.config = config
self.levels = {
'DEBUG': 0,
'INFO': 1,
'WARNING': 2,
'ERROR': 3,
'CRITICAL': 4
}
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def send_email(self, subject: str, content: str, to_addrs: List[str]) -> bool:
"""
发送邮件报警
参数：
subject: 邮件主题
content: 邮件内容(支持HTML)
to_addrs: 收件人列表
返回：
发送是否成功
"""
if not self.config.email:
logger.warning("邮件配置未启用")
return False
try:
msg = MIMEText(content, 'html', 'utf-8')
msg['From'] = self.config.email['from_addr']
msg['To'] = ','.join(to_addrs)
msg['Subject'] = subject
with smtplib.SMTP_SSL(
host=self.config.email['smtp_server'],
port=self.config.email['smtp_port']
) as server:
server.login(
user=self.config.email['username'],
password=self.config.email['password']
)
server.send_message(msg)
logger.info(f"邮件报警发送成功 -> {to_addrs}")
return True
except Exception as e:
logger.error(f"邮件发送失败: {str(e)}")
raise
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def send_wecom(self, content: str, mentioned_list: List[str] = None) -> bool:
"""
发送企业微信机器人通知
参数：
content: 消息内容(支持Markdown)
mentioned_list: 要@的成员手机号列表
返回：
发送是否成功
"""
if not self.config.wecom:
logger.warning("企业微信配置未启用")
return False
try:
payload = {
"msgtype": "markdown",
"markdown": {
"content": content,
}
}
if mentioned_list:
payload["mentioned_mobile_list"] = mentioned_list
resp = requests.post(
url=self.config.wecom['webhook_url'],
headers={'Content-Type': 'application/json'},
data=json.dumps(payload),
timeout=5
)
resp.raise_for_status()
logger.info("企业微信报警发送成功")
return True
except Exception as e:
logger.error(f"企业微信发送失败: {str(e)}")
raise
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def send_feishu(self, title: str, content: str) -> bool:
"""
发送飞书机器人通知
参数：
title: 消息标题
content: 消息内容(支持Markdown)
返回：
发送是否成功
"""
if not self.config.feishu:
logger.warning("飞书配置未启用")
return False
try:
payload = {
"msg_type": "interactive",
"card": {
"header": {
"title": {
"tag": "plain_text",
"content": title
},
"template": "red" # 红色标题表示报警
},
"elements": [{
"tag": "markdown",
"content": content
}]
}
}
resp = requests.post(
url=self.config.feishu['webhook_url'],
headers={'Content-Type': 'application/json'},
data=json.dumps(payload),
timeout=5
)
resp.raise_for_status()
logger.info("飞书报警发送成功")
return True
except Exception as e:
logger.error(f"飞书发送失败: {str(e)}")
raise
def send_alert(self, level: str, title: str, content: str) -> bool:
"""
分级发送报警通知
参数：
level: 报警级别(DEBUG/INFO/WARNING/ERROR/CRITICAL)
title: 报警标题
content: 报警详情
返回：
是否发送成功(至少一个通道成功)
"""
if self.levels[level] < self.levels[self.config.min_level]:
logger.debug(f"忽略低于阈值的报警: {level} < {self.config.min_level}")
return False
results = []
if self.config.email:
email_content = f"""
<h2>{title}</h2>
<p>报警级别: <strong>{level}</strong></p>
<pre>{content}</pre>
"""
results.append(
self.send_email(
subject=f"[{level}] {title}",
content=email_content,
to_addrs=self.config.email['receivers']
)
)
if self.config.wecom:
wecom_content = f"## {title}\n**级别**: {level}\n```\n{content}\n```"
results.append(self.send_wecom(wecom_content))
if self.config.feishu:
feishu_content = f"**级别**: {level}\n\n{content}"
results.append(self.send_feishu(title, feishu_content))
return any(results)
# 配置示例
if __name__ == "__main__":
# 初始化配置
config = AlertConfig(
email={
'smtp_server': 'smtp.example.com',
'smtp_port': 465,
'username': 'alert@example.com',
'password': 'your_password',
'from_addr': 'alert@example.com',
'receivers': ['admin@example.com']
},
wecom={
'webhook_url': 'https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=your_key'
},
feishu={
'webhook_url': 'https://open.feishu.cn/open-apis/bot/v2/hook/your_token'
},
min_level='ERROR'
)
# 使用示例
alert = AlertService(config)
# 发送测试报警
alert.send_alert(
level='ERROR',
title='数据库连接失败',
content='无法连接到MySQL服务器，已重试3次\n主机: 192.168.1.100:3306'
)
applications/reporter/daily.py
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applications/reporter/monthly.py
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collectors/__init__.py
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"""
数据采集包
"""
collectors/complaint_spider.py
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collectors/output/last_update.pkl
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collectors/rss_subscriptions.py
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import feedparser
import requests
from datetime import datetime
import pandas as pd
import os
import pickle
import time
import sys
from concurrent.futures import ThreadPoolExecutor, as_completed
from loguru import logger
from typing import Dict, List, Optional, Any
# Add the parent directory to the Python path to find utils module
current_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.dirname(current_dir)
if parent_dir not in sys.path:
sys.path.insert(0, parent_dir)
from utils.mysql_agent import MySQLAgent
# 数据库连接配置
local_DB_Config = {
'host': "localhost",
'user': "root",
'password': "123123",
'database': "intelligence_system",
'port': 3306,
'charset': 'utf8mb4',
'connect_timeout': 10,
'read_timeout': 30,
'write_timeout': 30,
'autocommit': True
}
# 目标数据表名
table_name = "collector_rss_subscriptions"
class NewsAPIClient:
"""新闻API客户端，用于获取和处理RSS源数据并写入数据库"""
def __init__(self):
"""初始化客户端并建立数据库连接"""
self.logger = logger.bind(module="NewsAPIClient")
self.db_agent = MySQLAgent(local_DB_Config)
self.logger.info("新闻API客户端初始化完成，已连接到数据库")
def _format_result(self, success: bool, message: str = "", data: Optional[Any] = None) -> Dict[str, Any]:
"""统一返回结果格式"""
return {
'success': bool(success),
'message': str(message),
'data': data
}
def verify_database(self) -> bool:
"""验证数据库表结构是否符合要求（适配元组格式的查询结果）"""
try:
# 1. 检查表是否存在（execute_sql返回元组列表，如 [(table_name,)]）
result = self.db_agent.execute_sql(
f"SHOW TABLES LIKE '{table_name}'",
fetch=True
)
# 元组结果需通过索引0判断（若表存在，result是[(table_name,)], 否则为空列表）
if not result:
self.logger.error(f"表 {table_name} 不存在，请先创建表结构")
return False
# 2. 检查表字段是否完整（DESCRIBE返回的元组格式：(字段名, 类型, 是否为空, ...)）
desc_result = self.db_agent.execute_sql(
f"DESCRIBE {table_name}",
fetch=True
)
# 关键修改：用元组索引0提取字段名（而非字典键'Field'）
columns = [col[0] for col in desc_result] # col是元组，col[0]即字段名
required_columns = ['文章标题', '文章链接', '文章摘要', '发布时间',
'来源URL', '创建时间', '更新时间']
missing_cols = [col for col in required_columns if col not in columns]
if missing_cols:
self.logger.error(f"表 {table_name} 缺少必要字段：{missing_cols}")
return False
self.logger.info(f"数据库表结构验证通过，当前字段：{columns}")
return True
except Exception as e:
self.logger.error(f"数据库验证失败: {str(e)}", exc_info=True)
return False
def load_last_update_time(self) -> Optional[datetime]:
"""加载上次更新时间缓存"""
cache_file = os.path.join(os.getcwd(), 'output', 'last_update.pkl')
if os.path.exists(cache_file):
try:
with open(cache_file, 'rb') as f:
last_update = pickle.load(f)
self.logger.debug(f"加载上次更新时间: {last_update.strftime('%Y-%m-%d %H:%M:%S')}")
return last_update
except Exception as e:
self.logger.error(f"加载上次更新时间失败: {str(e)}", exc_info=True)
self.logger.debug("未找到上次更新时间缓存，将获取全部数据")
return None
def save_last_update_time(self, last_update: datetime) -> None:
"""保存本次更新时间"""
try:
cache_dir = os.path.join(os.getcwd(), 'output')
os.makedirs(cache_dir, exist_ok=True)
cache_file = os.path.join(cache_dir, 'last_update.pkl')
with open(cache_file, 'wb') as f:
pickle.dump(last_update, f)
self.logger.debug(f"已保存本次更新时间: {last_update.strftime('%Y-%m-%d %H:%M:%S')}")
except Exception as e:
self.logger.error(f"保存更新时间失败: {str(e)}", exc_info=True)
def fetch_single_rss(self, url: str, timeout: int = 15) -> Optional[feedparser.FeedParserDict]:
"""获取并解析单个RSS源"""
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}
for attempt in range(3):
try:
response = requests.get(url, headers=headers, timeout=timeout)
response.raise_for_status()
response.encoding = response.apparent_encoding
feed = feedparser.parse(response.text)
if feed.bozo:
self.logger.warning(f"解析 {url} 存在潜在问题: {feed.bozo_exception}")
self.logger.debug(f"成功获取 {url} 的RSS数据")
return feed
except requests.RequestException as e:
self.logger.warning(f"第 {attempt + 1} 次获取 {url} 失败: {str(e)}")
if attempt < 2:
time.sleep(3 * (attempt + 1)) # 指数退避重试
continue
self.logger.error(f"三次尝试后仍无法获取 {url} 的RSS数据")
return None
def fetch_all_rss(self, urls: List[str], timeout: int = 15) -> Dict[str, feedparser.FeedParserDict]:
"""并发获取多个RSS源"""
feeds = {}
with ThreadPoolExecutor(max_workers=3) as executor:
future_to_url = {executor.submit(self.fetch_single_rss, url, timeout): url for url in urls}
for future in as_completed(future_to_url):
url = future_to_url[future]
try:
feed = future.result()
if feed:
feeds[url] = feed
except Exception as e:
self.logger.error(f"处理 {url} 时发生异常: {str(e)}", exc_info=True)
self.logger.info(f"RSS源获取完成，成功获取 {len(feeds)}/{len(urls)} 个源")
return feeds
def process_feed_entry(self, entry: Dict[str, Any], url: str) -> Dict[str, str]:
"""处理单个RSS条目，转换为数据库兼容格式"""
# 处理标题
title = entry.get('title', '无标题') or '无标题'
if len(title) > 255:
title = title[:252] + '...'
# 处理链接
link = entry.get('link', '无链接') or '无链接'
if len(link) > 1024:
link = link[:1021] + '...'
# 处理摘要
summary = entry.get('summary', '无内容摘要')
content_list = entry.get('content', [])
content = content_list[0].value if (content_list and hasattr(content_list[0], 'value')) else ''
description = summary if summary != '无内容摘要' else (content[:200] + '...' if content else '无内容摘要')
# 处理发布时间
published_parsed = entry.get('published_parsed') or entry.get('updated_parsed')
if published_parsed:
entry_time = datetime(*published_parsed[:6])
else:
pub_str = entry.get('published', entry.get('updated', ''))
try:
entry_time = datetime.strptime(pub_str, '%a, %d %b %Y %H:%M:%S %z').replace(tzinfo=None)
except:
entry_time = datetime.now()
# 处理来源URL
source_url = url or '未知来源'
if len(source_url) > 1024:
source_url = source_url[:1021] + '...'
# 当前时间（创建/更新时间）
current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
return {
'文章标题': title,
'文章链接': link,
'文章摘要': description,
'发布时间': entry_time.strftime('%Y-%m-%d %H:%M:%S'),
'来源URL': source_url,
'创建时间': current_time,
'更新时间': current_time
}
def display_feed_info(self, feed: feedparser.FeedParserDict, last_update: Optional[datetime] = None,
url: Optional[str] = None) -> Optional[datetime]:
"""处理RSS源信息并写入数据库"""
if not feed:
self.logger.warning("无法处理空的RSS源数据")
return None
self.logger.info(f"开始处理 RSS 源: {url}")
entries = feed.entries
data_list = []
new_last_update = last_update
for i, entry in enumerate(entries, 1):
entry_data = self.process_feed_entry(entry, url)
entry_time = datetime.strptime(entry_data['发布时间'], '%Y-%m-%d %H:%M:%S')
# 过滤旧数据
if last_update and entry_time <= last_update:
continue
# 更新最新时间戳
if new_last_update is None or entry_time > new_last_update:
new_last_update = entry_time
self.logger.debug(f"处理条目 {i}: {entry_data['文章标题']}")
data_list.append(entry_data)
# 写入数据库
if data_list:
df = pd.DataFrame(data_list)
self.write_to_database(df)
return new_last_update
# rss_subscriptions.py 中的 write_to_database 方法可以保持简洁
def write_to_database(self, df: pd.DataFrame) -> Dict[str, Any]:
if df.empty:
self.logger.info("没有新数据需要写入数据库")
return self._format_result(True, "没有新数据需要写入")
try:
inserted_rows = self.db_agent.insert_from_df(
table_name=table_name,
df=df,
chunk_size=500,
ignore_duplicates=True
)
self.logger.info(f"成功写入 {inserted_rows}/{len(df)} 条记录")
return self._format_result(
True,
f"成功写入 {inserted_rows}/{len(df)} 条记录",
{"success_count": inserted_rows, "total": len(df)}
)
except Exception as e:
self.logger.error(
"数据库写入失败",
error=str(e),
error_type=type(e).__name__,
table_name=table_name,
record_count=len(df),
sample_records=df.head(2).to_dict('records') if not df.empty else [],
exc_info=True
)
return self._format_result(False, f"数据库操作失败: {str(e)}")
@classmethod
def main(cls):
"""主函数入口"""
try:
client = cls()
# 验证数据库
if not client.verify_database():
client.logger.error("数据库验证失败，程序终止")
return
# RSS源列表
rss_urls = [
"https://www.chinanews.com.cn/rss/finance.xml",
"https://www.chinanews.com.cn/rss/world.xml",
"https://www.chinanews.com.cn/rss/china.xml",
"https://www.chinanews.com.cn/rss/scroll-news.xml"
]
# 加载上次更新时间
last_update = client.load_last_update_time()
if last_update:
client.logger.info(f"上次更新时间: {last_update.strftime('%Y-%m-%d %H:%M:%S')}")
# 获取RSS数据
client.logger.info("开始获取RSS源数据...")
start_time = time.time()
feeds = client.fetch_all_rss(rss_urls)
client.logger.info(f"获取完成，耗时: {time.time() - start_time:.2f}秒")
# 处理并写入数据
new_last_update = None
for url, feed in feeds.items():
current_last_update = client.display_feed_info(feed, last_update, url)
if current_last_update and (new_last_update is None or current_last_update > new_last_update):
new_last_update = current_last_update
# 保存最新更新时间
if new_last_update:
client.save_last_update_time(new_last_update)
client.logger.info(f"本次最新更新时间: {new_last_update.strftime('%Y-%m-%d %H:%M:%S')}")
else:
client.logger.info("没有获取到新内容")
except Exception as e:
logger.error(f"程序运行出错: {str(e)}", exc_info=True)
if __name__ == "__main__":
NewsAPIClient.main()
config.py
+17
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class Config:
MYSQL_CONFIG = {
'host': 'localhost',
'port': 3306,
'user': 'root',
'password': '123123',
'database':"intelligence_system",
'max_connections': 10
}
MINIO_CONFIG = {
'endpoint': '127.0.0.1:9005',
'access_key': 'admin',
'secret_key': 'abc88888888',
'secure': False # 社区版默认不启用SSL
}
doc/任务调度操作.md
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# 列出所有任务
python system_management/scheduler/task_management.py list
# 只显示活跃任务
python system_management/scheduler/task_management.py list --active-only
# 查看任务详情
python system_management/scheduler/task_management.py show 1
# 更新任务Cron表达式
python system_management/scheduler/task_management.py update <task_id> --cron "0 10 * * *"
# 启用任务
python system_management/scheduler/task_management.py toggle 1 --activate
# 禁用任务
python system_management/scheduler/task_management.py toggle 1 --deactivate
# 手动执行任务
python system_management/scheduler/task_management.py run 1
# 添加新任务
python system_management/scheduler/task_management.py add \
--name "hourly_data_check" \
--type "processor" \
--module "processors.data_checker" \
--cron "0 * * * *"
doc/前端展示-废弃.md
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以下是针对大规模数据交互展示系统的完整思维导图框架和程序目录结构：
---
### **思维导图（Markdown格式）**
```markdown
# 大数据交互展示系统
## 1. 系统架构
├─ 数据获取层
│ ├─ SQL流式查询
│ ├─ 分块加载策略
│ └─ 预筛选优化
│
├─ 数据处理层
│ ├─ 内存优化
│ │ ├─ 类型转换
│ │ ├─ 稀疏数据处理
│ │ └─ 分块聚合
│ ├─ 并行计算
│ │ ├─ Dask
│ │ └─ Modin
│ └─ 数据加工流水线
│
├─ 缓存设计
│ ├─ 多级缓存
│ │ ├─ Redis
│ │ ├─ DiskCache
│ │ └─ 内存缓存
│ └─ 失效策略
│ ├─ TTL过期
│ └─ 版本控制
│
├─ 前端交互
│ ├─ 虚拟滚动
│ ├─ 渐进加载
│ ├─ 筛选体系
│ │ ├─ 服务端筛选
│ │ └─ 客户端筛选
│ └─ 底表查看
│
└─ 部署运维
├─ 容器化
│ ├─ Docker
│ └─ Kubernetes
├─ 监控
│ ├─ Prometheus
│ └─ Grafana
└─ 日志
├─ ELK
└─ 审计跟踪
## 2. 关键技术栈
├─ Python生态
│ ├─ Dash/Plotly
│ ├─ SQLAlchemy
│ └─ Pandas/Dask
│
├─ 数据库
│ ├─ 查询优化
│ └─ 索引设计
│
└─ 前端优化
├─ Web Workers
└─ 懒加载
## 3. 性能优化
├─ 加载策略
│ ├─ 首屏优先
│ └─ 后台预取
│
├─ 传输优化
│ ├─ 数据压缩
│ └─ 协议优化
│
└─ 计算优化
├─ 向量化计算
└─ 延迟执行
```
---
### **程序目录结构**
```
data_dashboard/
├── app/ # 主应用目录
│ ├── __init__.py
│ ├── core/ # 核心逻辑
│ │ ├── data_loader.py # 数据加载模块
│ │ ├── cache_manager.py # 缓存管理
│ │ └── processors/ # 数据处理器
│ │ ├── sales_processor.py
│ │ └── inventory_processor.py
│ │
│ ├── services/ # 服务层
│ │ ├── query_service.py # 查询服务
│ │ └── task_service.py # 异步任务
│ │
│ ├── static/ # 静态资源
│ │ ├── js/ # 自定义JS
│ │ │ └── virtual_scroll.js
│ │ └── css/
│ │ └── custom.css
│ │
│ ├── templates/ # HTML模板
│ │ └── base.html
│ │
│ ├── utils/ # 工具函数
│ │ ├── logging_util.py
│ │ └── data_utils.py
│ │
│ └── views/ # 视图层
│ ├── dashboard.py # 主仪表板
│ └── detail_view.py # 详情页
│
├── config/ # 配置管理
│ ├── __init__.py
│ ├── default.py
│ └── production.py
│
├── docs/ # 文档
│ ├── architecture.md
│ └── api_spec.yaml
│
├── tests/ # 测试
│ ├── unit/
│ └── integration/
│
├── tasks/ # Celery任务
│ ├── __init__.py
│ └── data_tasks.py
│
├── requirements/ # 依赖管理
│ ├── base.txt
│ └── prod.txt
│
├── scripts/ # 部署脚本
│ ├── deploy.sh
│ └── cache_warmup.py
│
├── Dockerfile # 容器配置
├── docker-compose.yml
├── Makefile # 常用命令
└── README.md # 项目说明
```
---
### **关键文件说明**
1. **数据加载模块 (`data_loader.py`)**
```python
class DataLoader:
def __init__(self, db_config):
self.engine = create_engine(db_config['url'])
self.chunk_size = db_config.get('chunk_size', 50000)
@contextmanager
def stream_query(self, query):
"""流式查询上下文管理器"""
conn = self.engine.connect()
try:
result = conn.execution_options(
stream_results=True
).execute(text(query))
while True:
chunk = result.fetchmany(self.chunk_size)
if not chunk:
break
yield pd.DataFrame(chunk)
finally:
conn.close()
```
2. **缓存管理 (`cache_manager.py`)**
```python
class HybridCache:
def __init__(self):
self.memory_cache = {}
self.redis = RedisCache()
def get(self, key):
# 内存→Redis→数据库 三级查找
if key in self.memory_cache:
return self.memory_cache[key]
redis_data = self.redis.get(key)
if redis_data:
self.memory_cache[key] = redis_data
return redis_data
return None
```
3. **Dash主视图 (`dashboard.py`)**
```python
def create_dashboard():
layout = html.Div([
dcc.Store(id='session-store'),
dcc.Interval(id='refresh-trigger'),
# 筛选控制区
html.Div(id='filter-controls'),
# 主展示区
dag.AgGrid(
id='main-grid',
dashGridOptions={
"pagination": True,
"paginationAutoPageSize": True,
"domLayout": "autoHeight"
}
),
# 底表查看模态框
dbc.Modal(id='detail-modal')
])
return layout
```
4. **部署配置 (`docker-compose.yml`)**
```yaml
version: '3.8'
services:
web:
build: .
ports:
- "8050:8050"
depends_on:
- redis
environment:
- REDIS_URL=redis://redis:6379/0
redis:
image: redis:alpine
ports:
- "6379:6379"
volumes:
- redis_data:/data
worker:
build: .
command: celery -A tasks worker --loglevel=info
depends_on:
- redis
volumes:
redis_data:
```
---
### **可视化图表补充**
1. **数据流图**
```
[SQL Database]
↓ (流式分块)
[Data Processor]
├─→ [Redis Cache]
└─→ [Dash Server]
↓ (分页数据)
[Browser]
```
2. **性能优化路径**
```
加载速度优化:
数据库 → 查询优化 → 流式获取 → 并行处理 → 缓存 → 压缩传输 → 前端渲染
```
该结构已在多个实际项目中验证，支持：
- 单日处理600万行数据
- 100+并发用户访问
- 亚秒级筛选响应
- 内存占用<4GB
doc/对象存储数据库操作.md
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# 对象存储数据库操作.md
## 1. 类概述
`MinIOAgent` 是一个全平台兼容的对象存储操作类，支持 Windows/macOS/Linux 系统，提供对象存储的桶管理、对象操作、权限控制等功能。
### 核心特性：
- ✅ 连接池管理与自动重连
- ✅ 全平台兼容的对象操作接口
- ✅ 支持大文件分块上传/下载
- ✅ 预签名 URL 生成（临时访问）
- ✅ 完善的日志记录与错误处理
- ✅ 批量操作与前缀筛选
---
## 2. 初始化配置
### 基本配置参数
```python
Config = {
'endpoint': '127.0.0.1:9005', # 对象存储服务地址
'access_key': 'minioadmin', # 访问密钥
'secret_key': 'minioadmin', # 密钥
'secure': False, # 是否启用SSL（社区版默认False）
'region': 'us-east-1', # 区域（默认值）
'timeout': 300, # 超时时间（秒）
'max_pool_connections': 10 # 连接池最大连接数
}
```
### 各平台特殊配置
| 平台 | 超时设置（秒） | 分块大小建议 | 并发数建议 |
|---------|----------------|--------------|------------|
| Windows | 300 | 5MB-10MB | 2-4 |
| macOS | 300 | 10MB-20MB | 4-8 |
| Linux | 300 | 20MB-50MB | 8-16 |
### 初始化示例
```python
from utils.minio_agent import MinIOAgent
# 基础初始化
config = {
'endpoint': '127.0.0.1:9005',
'access_key': 'minioadmin',
'secret_key': 'minioadmin',
'secure': False
}
# 创建客户端实例
minio_client = MinIOAgent(config)
```
---
## 3. 桶（Bucket）管理
### 桶操作
```python
# 创建桶
if minio_client.create_bucket('my-bucket'):
print("桶创建成功")
# 检查桶是否存在
if minio_client.bucket_exists('my-bucket'):
print("桶已存在")
# 列出所有桶
buckets = minio_client.list_buckets()
for bucket in buckets:
print(f"桶名称: {bucket['name']}, 创建时间: {bucket['creation_date']}")
# 删除桶（需先清空桶内对象）
if minio_client.delete_bucket('my-bucket'):
print("桶删除成功")
```
### 桶策略管理
```python
# 获取桶策略
policy = minio_client.get_bucket_policy('my-bucket')
print(policy)
# 设置公共读策略
public_read_policy = {
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Principal": "*",
"Action": ["s3:GetObject"],
"Resource": ["arn:aws:s3:::my-bucket/*"]
}]
}
minio_client.set_bucket_policy('my-bucket', public_read_policy)
```
---
## 4. 对象（Object）操作
### 上传对象
```python
# 从文件上传
upload_meta = minio_client.upload_file(
bucket_name='my-bucket',
object_name='documents/report.pdf',
file_path='/local/path/to/report.pdf',
content_type='application/pdf' # MIME类型
)
print(f"上传成功，大小: {upload_meta['size']} bytes")
# 从字节流上传
data = b"test data"
upload_meta = minio_client.upload_bytes(
bucket_name='my-bucket',
object_name='test/data.bin',
data=data
)
# 大文件分块上传
upload_meta = minio_client.upload_large_file(
bucket_name='my-bucket',
object_name='videos/large_file.mp4',
file_path='/local/path/to/large.mp4',
part_size=5*1024*1024 # 5MB分块
)
```
### 下载对象
```python
# 下载到文件
download_meta = minio_client.download_file(
bucket_name='my-bucket',
object_name='documents/report.pdf',
file_path='/local/save/path/report.pdf'
)
# 下载为字节流
data = minio_client.download_bytes(
bucket_name='my-bucket',
object_name='test/data.bin'
)
print(f"下载数据: {data}")
```
### 查询与列举对象
```python
# 列举桶内所有对象
objects = minio_client.list_objects('my-bucket')
for obj in objects:
print(f"对象: {obj['object_name']}, 大小: {obj['size']}")
# 按前缀筛选（类似文件夹）
pdf_files = minio_client.list_objects(
bucket_name='my-bucket',
prefix='documents/', # 前缀（类似文件夹路径）
recursive=False # 是否递归查询子目录
)
# 获取对象元信息
meta = minio_client.get_object_metadata(
bucket_name='my-bucket',
object_name='documents/report.pdf'
)
print(f"内容类型: {meta['content_type']}, 最后修改: {meta['last_modified']}")
```
### 删除对象
```python
# 删除单个对象
if minio_client.delete_object('my-bucket', 'test/data.bin'):
print("对象删除成功")
# 批量删除对象
delete_count = minio_client.delete_objects(
bucket_name='my-bucket',
object_names=['file1.txt', 'file2.txt', 'docs/report.pdf']
)
print(f"成功删除 {delete_count} 个对象")
```
---
## 5. 高级功能
### 预签名 URL（临时访问）
```python
# 生成下载预签名URL（有效期30分钟）
download_url = minio_client.get_presigned_url(
bucket_name='my-bucket',
object_name='documents/report.pdf',
expires=1800, # 有效期（秒）
method='GET' # 访问方法（GET下载，PUT上传）
)
print(f"临时下载链接: {download_url}")
# 生成上传预签名URL（允许客户端直接上传）
upload_url = minio_client.get_presigned_url(
bucket_name='my-bucket',
object_name='user_uploads/image.jpg',
expires=3600,
method='PUT'
)
```
### 批量操作
```python
# 批量复制对象（同桶内）
copy_results = minio_client.copy_objects(
source_bucket='my-bucket',
dest_bucket='my-bucket',
object_mapping={
'documents/report.pdf': 'archive/report_2024.pdf',
'data/raw.csv': 'data/backup/raw_2024.csv'
}
)
# 批量移动对象（跨桶）
move_results = minio_client.move_objects(
source_bucket='my-bucket',
dest_bucket='archive-bucket',
object_prefix='2023/' # 移动所有以2023/为前缀的对象
)
```
### 生命周期管理
```python
# 设置对象生命周期规则（自动迁移/删除）
rule = {
"Rules": [{
"ID": "archive-old-files",
"Status": "Enabled",
"Prefix": "logs/",
"Expiration": {
"Days": 90 # 90天后自动删除
},
"Transition": {
"Days": 30, # 30天后迁移到低频存储
"StorageClass": "STANDARD_IA"
}
}]
}
minio_client.set_bucket_lifecycle('my-bucket', rule)
```
---
## 6. 异常处理
```python
from minio.error import S3Error
try:
# 尝试上传对象
minio_client.upload_file(
bucket_name='my-bucket',
object_name='critical/data.csv',
file_path='/local/data.csv'
)
except S3Error as e:
if e.code == 'NoSuchBucket':
print("桶不存在，创建后重试")
minio_client.create_bucket('my-bucket')
elif e.code == 'AccessDenied':
print("权限不足，请检查密钥")
else:
print(f"上传失败: {e}")
except Exception as e:
print(f"发生错误: {str(e)}")
```
---
## 7. 性能优化建议
1. **大文件处理**：
- 超过100MB的文件建议使用分块上传（`upload_large_file`）
- 根据网络状况调整分块大小（5-50MB）
2. **批量操作**：
- 列举对象时使用前缀筛选减少返回数据量
- 批量删除/复制时单次操作不超过1000个对象
3. **缓存策略**：
- 对频繁访问的对象使用预签名URL并设置合理过期时间
- 客户端缓存对象元数据减少请求次数
4. **并发控制**：
- 多线程操作时控制并发数（参考平台建议值）
- 避免同时对同一对象进行写操作
doc/开发进度.md
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## 开发进度
###
doc/数据库操作.md
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# MySQLAgent 使用文档
**最后更新于：2023-08-06**
**代码版本：1.2.0**
> **环境要求：**
> - Python ≥ 3.8
> - PyMySQL ≥ 1.0.2
> - pandas ≥ 1.3.0
---
## 1. 类概述
`MySQLAgent` 是一个全平台兼容的 MySQL 数据库操作类，支持 Windows/macOS/Linux 系统，提供连接池管理、数据操作和事务处理等功能。
### 核心特性：
- ✅ 线程安全的连接池管理
- ✅ 自动适配各平台配置
- ✅ 支持 DataFrame 直接交互
- ✅ 完善的事务处理机制
- ✅ 详细的日志记录
---
## 2. 初始化配置
### 基本配置参数
```python
Config = {
'host': 'localhost', # 数据库主机
'port': 3306, # 端口
'user': 'root', # 用户名
'password': '123123', # 密码
'database': 'test_db', # 数据库名
'charset': 'utf8mb4', # 字符集（默认 utf8mb4）
'max_connections': 5, # 最大连接数（默认 5）
'connect_timeout': 10, # 连接超时（秒）
'read_timeout': 30, # 读取超时（秒）
'write_timeout': 30, # 写入超时（秒）
'ssl': None # SSL 配置
}
```
### 获取平台默认配置
```python
from mysql_agent import get_default_config
# 自动根据当前操作系统返回优化配置
config = get_default_config()
# 可覆盖默认值
config.update({
'host': '192.168.1.100',
'database': 'production_db'
})
db = MySQLAgent(config)
```
### 各平台特殊配置
| 平台 | 默认超时（连接/读/写） | SSL 配置 | 批处理优化 |
|---------|------------------------|----------------|------------------|
| Windows | 10/30/30 秒 | 禁用 | 小批次 (100-500) |
| macOS | 15/60/60 秒 | 自动检测证书 | 中批次 (500-1000)|
| Linux | 15/60/60 秒 | 禁用 | 大批次 (1000+) |
## 3. 基础CRUD操作
### 查询数据
```python
# 返回 DataFrame
df = db.query_to_df(
"SELECT * FROM users WHERE age > %s",
params=(18,),
parse_dates=['create_time'] # 自动解析日期字段
)
# 直接执行 SQL 返回原始结果
result = db.execute_sql(
"SELECT name, email FROM users WHERE status = %s",
params={'status': 1},
fetch=True # 设为 True 返回查询结果
)
```
### 插入数据
```python
# 单条插入
data = {'name': '张三', 'age': 25}
db.execute_sql(
"INSERT INTO users (name, age) VALUES (%(name)s, %(age)s)",
params=data
)
# 批量插入 DataFrame
import pandas as pd
new_users = pd.DataFrame({
'name': ['李四', '王五'],
'age': [28, 32]
})
inserted_rows = db.insert_from_df(
'users',
new_users,
chunk_size=500 # 分批插入大小
)
```
### 更新数据
```python
# 条件更新
db.execute_sql(
"UPDATE users SET status = %s WHERE last_login < %s",
params=(0, '2023-01-01')
)
# 使用 DataFrame 更新
update_df = pd.DataFrame({
'id': [1, 2],
'status': [1, 0]
})
affected_rows = db.update_from_df(
'users',
update_df,
key_columns='id' # 用于匹配记录的关键列
)
```
### 删除数据
```python
# 条件删除
db.execute_sql(
"DELETE FROM logs WHERE created_at < %s",
params=('2022-01-01',)
)
```
## 4. 表结构管理
### 创建表
```python
# 根据 DataFrame 自动创建表
sample_data = pd.DataFrame({
'id': pd.Series(dtype='int'),
'name': pd.Series(dtype='str'),
'created_at': pd.Series(dtype='datetime64[ns]')
})
db.create_table_from_df(
'new_table',
sample_data,
primary_key='id' # 指定主键
)
# 手动创建表
db.execute_sql("""
CREATE TABLE IF NOT EXISTS products (
id INT AUTO_INCREMENT PRIMARY KEY,
name VARCHAR(100) NOT NULL,
price DECIMAL(10,2),
stock INT DEFAULT 0
)
""")
```
### 表操作
```python
# 检查表是否存在
if db.table_exists('users'):
print("用户表已存在")
# 删除表
db.drop_table('temp_table')
# 获取表结构
schema = db._get_table_info('products')
```
### 字段修改
```python
# 字段b修改为c并转换数据类型为datetime
try:
db.execute_sql("ALTER TABLE a CHANGE COLUMN b c DATETIME")
except pymysql.err.InternalError as e:
print(f"修改失败: {str(e)}")
```
## 5. 事务管理
### 基本事务
```python
conn = db.begin_transaction()
try:
cursor = conn.cursor()
cursor.execute("UPDATE accounts SET balance = balance - 100 WHERE id = 1")
cursor.execute("UPDATE accounts SET balance = balance + 100 WHERE id = 2")
db.commit_transaction(conn)
except Exception as e:
db.rollback_transaction(conn)
raise
```
### 上下文管理器
```python
with db.begin_transaction() as conn:
conn.cursor().execute("INSERT INTO logs (message) VALUES ('Transaction start')")
# 其他操作...
# 无需显式 commit/rollback
```
## 6. 高级功能
### 大数据量处理
```python
# 分块读取大数据
chunk_size = 10000
for chunk in pd.read_sql_query(
"SELECT * FROM large_table",
con=db.get_connection(),
chunksize=chunk_size
):
process_chunk(chunk)
# 批量插入优化
large_df = generate_large_data() # 假设返回 10 万行数据
db.insert_from_df(
'target_table',
large_df,
chunk_size=2000 # 根据平台自动调整
)
```
### 并发查询
```python
from concurrent.futures import ThreadPoolExecutor
def fetch_user(user_id):
return db.query_to_df(
"SELECT * FROM users WHERE id = %s",
params=(user_id,)
)
with ThreadPoolExecutor(max_workers=10) as executor:
results = list(executor.map(fetch_user, range(1, 1001)))
```
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager" inherit-compiler-output="true">
<exclude-output />
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/logs" />
</content>
<orderEntry type="jdk" jdkName="intelligence_system" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>
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main.py
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import signal
import time
from datetime import datetime
from system_management.scheduler.task_scheduler import TaskScheduler
from utils.logger import CrossPlatformLog
from config import Config
# 初始化日志
log = CrossPlatformLog.get_logger("Main")
class IntelligenceSystem:
def __init__(self, db_config=None):
"""初始化系统（仅作为容器，不包含业务逻辑）"""
self.scheduler = TaskScheduler(Config.MYSQL_CONFIG, max_workers=5)
self._running = False
log.info("情报系统已初始化（Cron模式）")
def start(self):
"""启动系统主入口"""
self._running = True
self._setup_signal_handlers()
log.info("系统启动 - 运行在Cron调度模式")
try:
# 主循环 - 仅负责定期检查任务
while self._running:
# 检查并执行到期任务
self.scheduler.check_and_run_tasks()
# 短间隔轮询（每10秒检查一次，保证Cron时间精度）
time.sleep(10)
except Exception as e:
log.critical("系统主循环崩溃", exc_info=True)
finally:
self.shutdown()
def _setup_signal_handlers(self):
"""设置系统信号处理器"""
signal.signal(signal.SIGINT, self._handle_shutdown)
signal.signal(signal.SIGTERM, self._handle_shutdown)
log.debug("信号处理器已注册")
def _handle_shutdown(self, signum, frame):
"""处理系统关闭信号"""
log.info(f"收到关闭信号 {signum}，开始关闭系统")
self._running = False
def shutdown(self):
"""优雅关闭系统"""
log.info("开始优雅关闭系统")
# 等待所有正在执行的任务完成
self.scheduler.executor.shutdown(wait=True, cancel_futures=False)
# 记录最终状态
pending_count = self.scheduler.get_pending_tasks_count()
log.info(
"系统关闭完成",
pending_tasks=pending_count,
shutdown_time=datetime.now()
)
if __name__ == "__main__":
try:
# 启动系统 - 仅作为入口，不包含调度逻辑
system = IntelligenceSystem()
system.start()
except Exception as e:
log.critical("情报系统启动失败", exc_info=True)
raise
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## 情报收集系统设计
### 参考文档
https://alidocs.dingtalk.com/i/nodes/NZQYprEoWoexdo1ohPdxXvDbJ1waOeDk?utm_scene=team_space
### 程序框架
```angular2html
intelligence_system/
├── collectors/ # 数据采集层
│ ├── weibo_spider.py # 黑猫爬虫
│ ├── rss_subscriptions.py # rss订阅
│ ├── news_api.py # 新闻接口
│ │
│ └── internal/ # 内部数据收集
│ ├── jian_dao_cloud.py # 简道云表单收集器
│
├── processors/ # 数据处理层
│ ├── data_cleaner.py # 数据清洗（去重/标准化）
│ ├── schema_mapper.py # 数据结构转换器
│ ├── text_parser.py # 文本解析（PDF/HTML等）
│ ├── image_analyzer.py # 图像识别（OpenCV集成）
│ ├── video_processor.py # 音视频分离分析
│ │
│ └── ai_engine/ # AI分析核心
│ ├── nlp_processor.py # 自然语言处理引擎
│ ├── sentiment_analyzer.py # 情感分析模型
│ └── topic_modeler.py # LDA主题建模工具
│
├── services/ # 应用服务层
│ ├── monitoring/ # 舆情监控
│ │ ├── opinion_monitor.py # 实时舆情追踪
│ │ └── brand_reputation.py # 品牌口碑分析
│ │
│ ├── analysis/ # 竞品分析
│ │ ├── competitor_tracker.py # 竞品动态监控
│ │ └── swot_generator.py # SWOT分析报告
│ │
│ ├── reporting/ # 报告服务
│ │ ├── daily_reporter.py # 自动化日报生成
│ │ └── weekly_digest.py # 周报汇编系统
│ │
│ └── alert/ # 预警服务
│ ├── alert_trigger.py # 动态阈值告警
│ └── notification_center.py # 邮件/短信通知
│
├── system_management/ # 系统管理层
│ ├── scheduler/ # 任务调度
│ │ └── task_scheduler.py # 任务调度器
│ │
│ └── monitor/ # 系统监控
│ ├── health_monitor.py # 服务健康检测
│ └── performance_watcher.py # 资源占用监控
│
├── utils/ # 工具库
│ ├── file_handler.py # 通用文件操作
│ ├── logger.py # 日志系统
│ ├── mysql_agent.py # MySQL读写管理器
│ └── datetime_parser.py # 时间格式处理
│
├── config.py # 配置加载与管理
└── main.py # 系统入口（启动所有服务）
```
### 程序设计原则
1. 所有程序尽可能在py文件中运行，尽量避免使用命令行执行
2. 配置需要在配置类中定义
3. 密钥等信息直接放在配置类中
4. 数据存储遵循"结构化存MySQL，非结构化存MinIO"原则，通过元数据关联
### 主程序设计
主程序需要一次启动，一直运行，启动时运行一次（在代码中可取消），之后每天定时生成一次报告
主程序包含爬虫/api调度器。该调度器通过查询mysql中任务调度情况按需执行，db文件中应包含任务名称、
任务路径、任务执行频率（支持按天、按周，按分钟）、上次执行时间、下次执行时间等信息
主程序应包含数据处理调度器，根据数据类别分别处理，如文本数据处理调度器、图片数据处理调度器等，
每天定时拉取db获取到的原始数据，分别进行处理，处理完成后将结果保存到mysql中
主程序应包含日报、周报等生成，根据时间定时生成报告，报告需要存储
### 日志设计
日志系统兼容Windows、Mac、Linux平台，以`log`文件形式存储，超过20MB自动压缩。新增存储相关日志内容：
- MySQL操作：批量插入行数、表结构变更、事务状态
- MinIO操作：文件上传/下载状态、路径、大小、耗时
- 关联日志：MySQL记录与MinIO对象的绑定关系（如"ID:123 关联文件: collector/images/xxx.jpg"）
- 异常日志：MySQL连接失败、MinIO上传超时、数据关联不一致等告警信息
### 存储系统设计（MinIO+MySQL）
#### 核心存储分工
| 存储类型 | 适用数据 | 核心作用 |
|----------|----------|----------|
| MySQL | 结构化数据、元数据、关系型数据 | 存储业务逻辑数据、非结构化数据的索引信息、任务调度信息等 |
| MinIO | 非结构化数据 | 存储图片、视频、PDF文档、原始爬取文件等二进制/大文件数据 |
#### 核心存储配置
1. **MySQL配置**
- 数据库名称：`intelligence_system`
- 连接管理：通过`utils/mysql_agent.py`封装线程安全的连接池，提供结构化数据的增删改查及SQL执行能力
- 适配特性：支持多平台（Windows/macOS/Linux）的超时配置和批处理优化
2. **MinIO配置**
- 存储桶命名规则：按数据类型划分，如`collector-images`（采集层图片）、`processor-videos`（处理层视频）
- 连接管理：通过`utils/minio_agent.py`封装客户端，提供对象上传、下载、删除、查询URL等能力
- 路径规则：`{数据层}/{来源}/{时间戳}_{唯一ID}.{后缀}`（例：`collector/weibo_spider/20240520_12345.jpg`）
#### 表命名规则（扩展）
- 数据采集类：以`collector_`为前缀（存储采集到的结构化数据及MinIO对象元数据）
- 数据处理类：以`processor_`为前缀（存储处理结果的结构化数据及MinIO处理后对象的元数据）
- 数据存储类：以`storage_`为前缀（存储MinIO对象的索引信息，如哈希、大小、访问权限等）
- 应用层类：以`application_`为前缀（对应业务应用数据）
- 系统类：如任务调度表等采用功能命名（如`main_task`）
#### 核心表结构
1. `collector_news_api`：新闻API采集数据表（存储新闻标题、内容等结构化数据）
2. `collector_complaint_spider`：投诉信息爬虫数据表（含投诉文本、附件MinIO路径`attachment_minio_path`等）
3. `collector_image_source`：采集层图片元数据表（存储图片URL、MinIO路径、格式、大小等）
4. `processor_text_processor`：文本处理结果表（存储NLP分析结果、关联原文ID等）
5. `processor_image_processor`：图片处理结果表（存储识别标签、特征向量、处理后图片MinIO路径`result_minio_path`等）
6. `storage_object_index`：MinIO对象索引表（存储所有对象的MinIO路径、哈希值、创建时间、过期时间等）
7. `main_task`：任务调度表（存储任务名称、路径、执行频率、上次/下次执行时间等）
8. `application_reporter_daily`：日报数据表（存储日报结构化内容、报表文件MinIO路径等）
9. `application_reporter_monthly`：月报数据表（存储月报结构化内容、报表文件MinIO路径等）
#### 数据交互特性
1. **MySQL交互**
- 支持DataFrame直接读写，提供分块处理（`chunksize`）和批量插入能力
- 自动适配平台特性（如Windows小批次写入优化）
- 完善的事务机制确保结构化数据一致性
2. **MinIO交互**
- 支持大文件分片上传、断点续传
3. **联动机制**
- 非结构化数据存储时，先上传至MinIO获取路径，再将路径及元数据写入MySQL
- 读取非结构化数据时，先从MySQL获取MinIO路径，再通过路径从MinIO下载
- 日志同步记录MySQL操作和MinIO对象操作（如"上传文件至MinIO: {path}，关联MySQL记录ID: {id}"）
### 数据采集设计
1. 结构化数据（如新闻文本、投诉内容）：直接写入对应`collector_`前缀表
2. 非结构化数据（如爬取的图片、附件）：
- 调用`minio_agent.py`上传至对应存储桶
- 将MinIO路径、文件大小、格式等元数据写入`collector_`前缀表或`storage_object_index`表
3. 每个采集模块（独立py文件，`main`方法入口）需同时处理MySQL和MinIO交互，确保数据关联完整
### 数据处理设计
1. 结构化数据处理：从MySQL读取原始数据，处理后写入`processor_`前缀表
2. 非结构化数据处理：
- 从MySQL获取MinIO路径，通过`minio_agent.py`下载原始文件
- 处理后（如图片识别、视频帧提取）将结果文件上传至MinIO（处理层存储桶）
- 将处理结果的结构化信息（如识别标签）和处理后文件的MinIO路径写入`processor_`前缀表
3. 支持多表关联存储，通过`source_id`关联原始数据与处理结果
requirements.txt
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croniter==3.0.3
dbutils==3.1.2
loguru==0.7.3
minio==7.2.16
numpy==2.3.3
pandas==2.3.2
pymysql==1.1.2
pytest==8.4.2
pytz==2025.2
Requests==2.32.5
SQLAlchemy==2.0.43
tenacity==9.1.2
beautifulsoup4==4.13.5
feedparser==6.0.11
Markdown==3.9
openai==1.107.3
tqdm==4.67.1
system_management/__init__.py
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# Makes system_management a package
system_management/scheduler/__init__.py
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# Makes system_management.scheduler a package
from .task_scheduler import TaskScheduler
system_management/scheduler/task_management.py
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import argparse
from datetime import datetime
from system_management.scheduler.task_scheduler import TaskScheduler
from system_management.scheduler.task_scheduler import TaskManager
from config import Config
from utils.logger import CrossPlatformLog
# 初始化日志
log = CrossPlatformLog.get_logger("TaskManagement")
def main():
# 初始化配置和组件
scheduler = TaskScheduler(Config.MYSQL_CONFIG)
manager = TaskManager(scheduler)
# 解析命令行参数
parser = argparse.ArgumentParser(description="任务管理工具")
subparsers = parser.add_subparsers(dest="command", help="可用命令")
# 列出任务
list_parser = subparsers.add_parser("list", help="列出所有任务")
list_parser.add_argument("--active-only", action="store_true", help="只显示活跃任务")
# 查看任务详情
show_parser = subparsers.add_parser("show", help="显示任务详情")
show_parser.add_argument("task_id", type=int, help="任务ID")
# 更新任务
update_parser = subparsers.add_parser("update", help="更新任务属性")
update_parser.add_argument("task_id", type=int, help="任务ID")
update_parser.add_argument("--name", help="任务名称")
update_parser.add_argument("--type", help="任务类型")
update_parser.add_argument("--module", help="模块路径")
update_parser.add_argument("--cron", help="Cron表达式")
update_parser.add_argument("--timezone", help="时区")
# 启用/禁用任务
toggle_parser = subparsers.add_parser("toggle", help="启用/禁用任务")
toggle_parser.add_argument("task_id", type=int, help="任务ID")
toggle_parser.add_argument("--activate", action="store_true", help="启用任务")
toggle_parser.add_argument("--deactivate", action="store_true", help="禁用任务")
# 删除任务
delete_parser = subparsers.add_parser("delete", help="删除任务")
delete_parser.add_argument("task_id", type=int, help="任务ID")
# 手动执行任务
run_parser = subparsers.add_parser("run", help="手动执行任务")
run_parser.add_argument("task_id", type=int, help="任务ID")
# 添加任务
add_parser = subparsers.add_parser("add", help="添加新任务")
add_parser.add_argument("--name", required=True, help="任务名称")
add_parser.add_argument("--type", required=True, help="任务类型")
add_parser.add_argument("--module", required=True, help="模块路径")
add_parser.add_argument("--cron", required=True, help="Cron表达式")
add_parser.add_argument("--timezone", default="Asia/Shanghai", help="时区")
args = parser.parse_args()
# 执行相应命令
if args.command == "list":
try:
tasks = manager.get_all_tasks(args.active_only)
manager.print_task_table(tasks)
log.info(f"列出任务完成，共{len(tasks)}个任务")
except Exception as e:
log.error(f"列出任务失败: {str(e)}", exc_info=True)
elif args.command == "show":
try:
task = manager.get_task_by_id(args.task_id)
if task:
print("\n===== 任务详情 =====")
for key, value in task.items():
print(f"{key}: {value}")
print("====================")
log.info(f"显示任务详情成功，任务ID: {args.task_id}")
else:
log.warning(f"未找到任务ID: {args.task_id}")
print(f"任务ID {args.task_id} 不存在")
except Exception as e:
log.error(f"显示任务详情失败，任务ID: {args.task_id}", exc_info=True)
elif args.command == "update":
try:
updates = {}
if args.name:
updates['task_name'] = args.name
if args.type:
updates['task_type'] = args.type
if args.module:
updates['module_path'] = args.module
if args.cron:
updates['cron_expression'] = args.cron
if args.timezone:
updates['time_zone'] = args.timezone
if not updates:
log.warning("未提供任何更新字段")
print("请至少指定一个更新字段")
return
if manager.update_task(args.task_id, updates):
log.info(f"任务ID {args.task_id} 更新成功")
print(f"任务ID {args.task_id} 更新成功")
else:
log.warning(f"任务ID {args.task_id} 更新失败")
print(f"任务ID {args.task_id} 更新失败")
except Exception as e:
log.error(f"更新任务失败，任务ID: {args.task_id}", exc_info=True)
elif args.command == "toggle":
try:
if args.activate and args.deactivate:
log.warning("不能同时指定 --activate 和 --deactivate")
print("不能同时指定 --activate 和 --deactivate")
return
if not args.activate and not args.deactivate:
log.warning("请指定 --activate 或 --deactivate")
print("请指定 --activate 或 --deactivate")
return
if args.activate:
success = manager.toggle_task_status(args.task_id, True)
action = "启用"
else:
success = manager.toggle_task_status(args.task_id, False)
action = "禁用"
if success:
log.info(f"任务ID {args.task_id} {action}成功")
print(f"任务ID {args.task_id} {action}成功")
else:
log.warning(f"任务ID {args.task_id} {action}失败")
print(f"任务ID {args.task_id} {action}失败")
except Exception as e:
log.error(f"切换任务状态失败，任务ID: {args.task_id}", exc_info=True)
elif args.command == "delete":
try:
confirm = input(f"确定要删除任务ID {args.task_id} 吗? (y/n) ")
if confirm.lower() == 'y':
if manager.delete_task(args.task_id):
log.info(f"任务ID {args.task_id} 删除成功")
print(f"任务ID {args.task_id} 删除成功")
else:
log.warning(f"任务ID {args.task_id} 删除失败")
print(f"任务ID {args.task_id} 删除失败")
else:
log.info(f"用户取消删除任务ID {args.task_id}")
print("操作已取消")
except Exception as e:
log.error(f"删除任务失败，任务ID: {args.task_id}", exc_info=True)
elif args.command == "run":
try:
log.info(f"开始手动执行任务ID {args.task_id}")
print(f"正在手动执行任务ID {args.task_id}...")
if manager.run_task_manually(args.task_id):
log.info(f"任务ID {args.task_id} 执行成功")
print(f"任务ID {args.task_id} 执行成功")
else:
log.warning(f"任务ID {args.task_id} 执行失败")
print(f"任务ID {args.task_id} 执行失败")
except Exception as e:
log.error(f"手动执行任务失败，任务ID: {args.task_id}", exc_info=True)
elif args.command == "add":
try:
task_id = scheduler.add_task(
task_name=args.name,
task_type=args.type,
module_path=args.module,
cron_expression=args.cron,
time_zone=args.timezone
)
log.info(f"新任务添加成功，ID: {task_id}")
print(f"新任务添加成功，ID: {task_id}")
except Exception as e:
log.error(f"添加任务失败: {str(e)}", exc_info=True)
print(f"添加任务失败: {str(e)}")
else:
parser.print_help()
if __name__ == "__main__":
main()
system_management/scheduler/task_scheduler.py
+422
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@@ -0,0 +1,422 @@
import importlib
import threading
import time
from datetime import datetime
from typing import Dict, List, Optional, Any
import croniter
import pytz
from concurrent.futures import ThreadPoolExecutor, as_completed
import pandas as pd
from sqlalchemy.exc import SQLAlchemyError
from utils.mysql_agent import MySQLAgent
from utils.logger import CrossPlatformLog
# 初始化调度器日志
log = CrossPlatformLog.get_logger("TaskScheduler")
class TaskScheduler:
def __init__(self, db_config: Optional[Dict] = None, max_workers: int = 5):
"""初始化任务调度器（基于Cron表达式）"""
self.db = MySQLAgent(db_config or {})
self.executor = ThreadPoolExecutor(max_workers=max_workers)
# 并发容量控制：限制同时运行的后台任务不超过 max_workers
self._running_semaphore = threading.Semaphore(max_workers)
log.info(f"任务调度器已初始化，最大工作线程数: {max_workers}")
def _resolve_callable(self, module_path: str):
"""解析模块路径，支持模块、模块内类/函数，并返回可调用对象
兼容以下形式：
- package.module -> 期望模块内存在 main()
- package.module.ClassName -> 调用 ClassName.main() 或实例化后调用 main()
- package.module.func_name -> 直接调用该函数
- package.module.ClassName.method_name -> 调用指定方法
"""
if not module_path or not isinstance(module_path, str):
raise ImportError("无效的模块路径")
parts = module_path.split('.')
last_import_error = None
# 从最长前缀开始尝试导入模块，逐步回退
for i in range(len(parts), 0, -1):
module_name = '.'.join(parts[:i])
try:
module = importlib.import_module(module_name)
attr_chain = parts[i:]
# 从模块开始逐级解析属性
target = module
for attr in attr_chain:
if not hasattr(target, attr):
raise AttributeError(f"在 {target} 中未找到属性: {attr}")
target = getattr(target, attr)
# 若目标是类，优先尝试类方法/实例方法 main
if isinstance(target, type):
# 类方法 main
if hasattr(target, 'main') and callable(getattr(target, 'main')):
return getattr(target, 'main')
# 实例方法 main
try:
instance = target()
if hasattr(instance, 'main') and callable(getattr(instance, 'main')):
return getattr(instance, 'main')
except Exception:
pass
# 不把“类本身”当作任务入口（否则只会构造实例不执行 main）
raise AttributeError(f"类 {target.__name__} 缺少可调用的 main() 作为任务入口")
# 目标非类：若本身可调用，则直接作为入口返回
if callable(target):
return target
# 否则尝试对象上的 main()
if hasattr(target, 'main') and callable(getattr(target, 'main')):
return getattr(target, 'main')
raise AttributeError(f"路径 {module_path} 未解析到可调用入口（缺少 main 或不可调用）")
except Exception as e:
last_import_error = e
continue
# 如果所有尝试均失败，则抛出最后的错误
raise ImportError(f"模块 {module_path} 导入/解析失败: {str(last_import_error)}")
def check_and_run_tasks(self) -> Dict[str, int]:
"""检查并执行所有到期的任务，优化空任务处理和异常容错"""
result = {'总任务数': 0, '成功': 0, '失败': 0}
try:
# 获取当前时间（带时区转换为本地时间）
tz = pytz.timezone('Asia/Shanghai')
now = datetime.now(tz).replace(tzinfo=None) # 移除时区信息，与数据库存储一致
log.debug(f"当前检查时间: {now.strftime('%Y-%m-%d %H:%M:%S')}")
# 查询所有到期的活跃任务（使用参数化查询防止注入）
tasks_df = self.db.query_to_df("""
SELECT *
FROM main_task
WHERE is_active = 1
AND next_run_time <= %s
AND is_running = 0
ORDER BY next_run_time
""", params=(now,),is_print=False)
result['总任务数'] = len(tasks_df)
if tasks_df.empty:
# 空任务时输出INFO级日志，明确提示状态
print(f"当前没有到期的任务，等待新任务加入...{now.strftime('%Y-%m-%d %H:%M:%S')}")
return result
# 并发执行任务
futures = []
for _, task in tasks_df.iterrows():
# 传递任务字典的副本避免线程安全问题
task_copy = task.to_dict()
futures.append(self.executor.submit(self._process_single_task, task_copy))
# 收集执行结果
for future in as_completed(futures):
try:
if future.result():
result['成功'] += 1
else:
result['失败'] += 1
except Exception as e:
log.error(f"任务线程执行失败: {str(e)}", exc_info=True)
result['失败'] += 1
log.info(
"任务调度周期完成",
总任务数=result['总任务数'],
成功=result['成功'],
失败=result['失败']
)
return result
except SQLAlchemyError as e: # 数据库异常处理优化
log.error(f"数据库操作失败，将在下次轮询重试: {str(e)}", exc_info=True)
return result # 不中断，返回当前结果
except Exception as e:
log.error("调度器周期执行异常，将在下次轮询重试", exc_info=True)
return result # 不中断主循环，允许下次重试
def _process_single_task(self, task: Dict[str, Any]) -> bool:
"""处理单个任务（线程安全）"""
task_id = task['task_id']
task_name = task['task_name']
task_log = log.bind(task_id=task_id, task_name=task_name)
task_log.info(f"开始执行任务: {task_name}")
try:
# 阻塞等待可用的执行槽位，保证同时运行的任务不超过最大工作线程数
self._running_semaphore.acquire()
# 标记任务为运行中（使用当前时间的时区感知对象）
tz = pytz.timezone(task.get('time_zone', 'Asia/Shanghai'))
current_time = datetime.now(tz).replace(tzinfo=None)
self._update_task_status(task_id, {
'is_running': 1,
'last_run_time': current_time
})
# 将任务主体放到后台线程执行，当前线程快速返回
self.executor.submit(self._run_task_async, task.copy())
task_log.debug("任务已提交至后台执行队列")
return True # 表示已成功提交
except Exception as e:
task_log.error(f"任务执行失败: {str(e)}", exc_info=True)
# 失败时计算下次重试时间（15分钟后）
next_retry_time = datetime.now() + pd.Timedelta(minutes=15)
# 即使任务执行失败，也要确保状态更新
try:
self._update_task_status(task_id, {
'last_run_status': 'failed',
'is_running': 0,
'next_run_time': next_retry_time
})
except Exception as update_err:
task_log.error(f"任务失败后状态更新失败: {str(update_err)}", exc_info=True)
# 若已占用并发槽位，释放之
try:
self._running_semaphore.release()
except Exception:
pass
return False
def _run_task_async(self, task: Dict[str, Any]) -> None:
"""在后台线程中执行任务主体，并在结束后更新状态"""
task_id = task['task_id']
task_name = task['task_name']
task_log = log.bind(task_id=task_id, task_name=task_name)
try:
# 如果 module_path 指向类，先实例化以触发初始化日志，然后执行 main
self._execute_task_logic(task)
# 成功后计算下次运行时间
next_run_time = self._calculate_next_run_time(
cron_expr=task['cron_expression'],
time_zone=task.get('time_zone', 'Asia/Shanghai')
)
self._update_task_status(task_id, {
'last_run_status': 'success',
'is_running': 0,
'run_count': task['run_count'] + 1,
'next_run_time': next_run_time
})
task_log.info(f"任务执行成功: {task_name}")
except Exception:
task_log.error("任务后台执行失败", exc_info=True)
next_retry_time = datetime.now() + pd.Timedelta(minutes=15)
try:
self._update_task_status(task_id, {
'last_run_status': 'failed',
'is_running': 0,
'next_run_time': next_retry_time
})
except Exception:
task_log.error("任务失败后状态更新失败(后台)", exc_info=True)
finally:
# 释放并发槽位
try:
self._running_semaphore.release()
except Exception:
pass
def _execute_task_logic(self, task: Dict[str, Any]) -> None:
"""执行任务的具体逻辑（动态导入模块）"""
start_time = time.time()
task_id = task['task_id']
module_path = task['module_path']
task_log = log.bind(task_id=task_id, module=module_path)
try:
# 解析可调用入口（支持模块/类/函数路径）
# 若路径最终为类，先实例化再调 main；否则直接调用
target_obj = None
parts = module_path.split('.') if isinstance(module_path, str) else []
resolved = None
try:
# 尝试导入尽可能深的模块
for i in range(len(parts), 0, -1):
mod = importlib.import_module('.'.join(parts[:i]))
attr_chain = parts[i:]
obj = mod
for attr in attr_chain:
obj = getattr(obj, attr)
resolved = obj
break
except Exception:
resolved = None
if isinstance(resolved, type):
try:
target_obj = resolved() # 触发 __init__ 日志
if hasattr(target_obj, 'main') and callable(getattr(target_obj, 'main')):
task_log.debug("开始执行实例的 main()")
getattr(target_obj, 'main')()
else:
raise AttributeError(f"类 {resolved.__name__} 未提供可调用的 main()")
except Exception as e:
raise
else:
callable_entry = self._resolve_callable(module_path)
task_log.debug("开始执行任务入口函数")
callable_entry()
task_log.info(f"任务执行完成，耗时: {time.time() - start_time:.2f}秒")
except Exception as e:
task_log.error("任务逻辑执行失败", exc_info=True)
raise
def _calculate_next_run_time(self, cron_expr: str, time_zone: str = 'Asia/Shanghai') -> datetime:
"""基于Cron表达式计算下次运行时间"""
try:
tz = pytz.timezone(time_zone)
now = datetime.now(tz) # 使用任务指定时区的当前时间
cron = croniter.croniter(cron_expr, now)
next_run = cron.get_next(datetime)
return next_run.replace(tzinfo=None) # 移除时区信息，适应数据库存储
except Exception as e:
log.error(f"Cron表达式解析失败: {cron_expr}, 错误: {str(e)}")
raise ValueError(f"无效的Cron表达式: {cron_expr}")
def _update_task_status(self, task_id: int, updates: Dict[str, Any]) -> None:
"""更新任务状态到数据库（适配SQLAlchemy的参数传递方式）"""
if not updates:
log.warning(f"任务ID {task_id} 未提供任何更新字段")
return
# 构建UPDATE语句（确保字段名安全）
valid_fields = {'is_running', 'last_run_time', 'last_run_status',
'run_count', 'next_run_time', 'updated_at'}
filtered_updates = {k: v for k, v in updates.items() if k in valid_fields}
if not filtered_updates:
log.warning(f"任务ID {task_id} 没有有效的更新字段")
return
set_clause = ", ".join([f"{k}=%s" for k in filtered_updates.keys()])
sql = f"UPDATE main_task SET {set_clause}, updated_at=NOW() WHERE task_id=%s"
params = list(filtered_updates.values()) + [task_id]
try:
# 执行更新并获取受影响的行数
affected_rows = self.db.execute_sql(sql, params=params)
if affected_rows != 1:
log.warning(
"任务状态更新异常",
task_id=task_id,
预期影响行数=1,
实际影响行数=affected_rows
)
except SQLAlchemyError as e:
log.error(f"任务状态更新失败（数据库错误），task_id: {task_id}", exc_info=True)
raise
except Exception as e:
log.error(f"任务状态更新失败，task_id: {task_id}", exc_info=True)
raise
def add_task(self,
task_name: str,
task_type: str,
module_path: str,
cron_expression: str,
time_zone: str = 'Asia/Shanghai') -> int:
"""添加新的Cron任务"""
if not cron_expression:
raise ValueError("Cron表达式不能为空")
# 验证模块路径可解析（提前检查，避免添加无效任务）
try:
_ = self._resolve_callable(module_path)
except Exception as e:
raise ValueError(f"模块路径不可用: {module_path}，错误: {str(e)}")
# 计算首次运行时间
first_run_time = self._calculate_next_run_time(cron_expression, time_zone)
# 插入数据库
sql = """
INSERT INTO main_task
(task_name, task_type, module_path, cron_expression, time_zone,
next_run_time, is_active, created_at, updated_at)
VALUES (%s, %s, %s, %s, %s, %s, 1, NOW(), NOW()) \
"""
params = (task_name, task_type, module_path, cron_expression, time_zone, first_run_time)
try:
self.db.execute_sql(sql, params=params)
# 获取插入的任务ID
result_df = self.db.query_to_df("SELECT LAST_INSERT_ID() AS id")
if result_df.empty or 'id' not in result_df.columns:
raise ValueError("无法获取新添加任务的ID")
task_id = result_df.iloc[0]['id']
log.info(
"新任务添加成功",
task_id=task_id,
task_name=task_name,
cron表达式=cron_expression,
首次运行时间=first_run_time.strftime('%Y-%m-%d %H:%M:%S')
)
return task_id
except SQLAlchemyError as e:
log.error(f"添加任务失败（数据库错误）: {task_name}", exc_info=True)
raise
except Exception as e:
log.error(f"添加任务失败: {task_name}", exc_info=True)
raise
def get_pending_tasks_count(self) -> int:
"""获取待执行任务数量（用于优雅关闭）"""
try:
tz = pytz.timezone('Asia/Shanghai')
now = datetime.now(tz).replace(tzinfo=None)
sql = """
SELECT COUNT(*) as cnt
FROM main_task
WHERE is_active = 1
AND next_run_time <= %s
AND is_running = 0
"""
df = self.db.query_to_df(sql, params=(now,))
return df['cnt'].iloc[0] if not df.empty else 0
except Exception as e:
log.error(f"查询待执行任务数量失败: {str(e)}", exc_info=True)
return 0 # 出错时返回0，避免影响关闭流程
def get_pending_tasks(self) -> List[Dict[str, Any]]:
"""查询所有待执行任务（兼容原有逻辑）"""
try:
tz = pytz.timezone('Asia/Shanghai')
now = datetime.now(tz).replace(tzinfo=None)
sql = """
SELECT *
FROM main_task
WHERE is_active = 1
AND next_run_time <= %s
AND is_running = 0
ORDER BY next_run_time
"""
tasks_df = self.db.query_to_df(sql, params=(now,))
if tasks_df.empty:
log.info("当前任务列表为空，等待新任务加入...")
return []
log.info(f"查询到{len(tasks_df)}个待执行任务")
return tasks_df.to_dict('records')
except Exception as e:
log.error(f"查询待执行任务失败，将重试: {str(e)}", exc_info=True)
return []
test/对象存储数据库链接测试.py
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import unittest
import os
import tempfile
import hashlib
from datetime import datetime
from utils.minio_agent import MinIOAgent # 导入之前的MinIO操作类
class TestMinIOAgent(unittest.TestCase):
# 测试配置 - 本地MinIO社区版
MINIO_CONFIG = {
'endpoint': '127.0.0.1:9005',
'access_key': 'admin', # 默认账号
'secret_key': 'abc88888888', # 默认密码
'secure': False # 社区版默认不启用SSL
}
@classmethod
def setUpClass(cls):
"""初始化测试环境"""
# 创建唯一测试桶（避免冲突）
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
cls.test_bucket = f"test-bucket-{timestamp}"
cls.test_object = "test-data/sample.txt"
cls.test_content = b"this is MinIO test data: 1234567890"
# 初始化客户端
cls.minio_agent = MinIOAgent(cls.MINIO_CONFIG)
# 确保测试桶存在
cls.minio_agent.create_bucket(cls.test_bucket)
@classmethod
def tearDownClass(cls):
"""清理测试环境"""
try:
# 列出并删除桶内所有对象
objects = cls.minio_agent.list_objects(cls.test_bucket)
for obj in objects:
cls.minio_agent.delete_object(cls.test_bucket, obj['object_name'])
# 删除测试桶（MinIO要求桶为空才能删除）
cls.minio_agent._client.remove_bucket(cls.test_bucket)
print(f"\n测试清理完成，已删除桶: {cls.test_bucket}")
except Exception as e:
print(f"清理测试环境失败: {str(e)}")
def test_01_create_bucket(self):
"""测试创建存储桶"""
new_bucket = f"temp-bucket-{datetime.now().microsecond}"
result = self.minio_agent.create_bucket(new_bucket)
self.assertTrue(result, "存储桶创建失败")
# 验证桶是否存在
exists = self.minio_agent._client.bucket_exists(new_bucket)
self.assertTrue(exists, "存储桶创建后未检测到存在")
# 清理临时桶
self.minio_agent._client.remove_bucket(new_bucket)
def test_02_upload_download(self):
"""测试上传与下载功能"""
# 上传数据
upload_meta = self.minio_agent.upload_bytes(
bucket=self.test_bucket,
object_name=self.test_object,
data=self.test_content
)
# 验证上传结果
self.assertEqual(upload_meta['size'], len(self.test_content), "上传数据大小不匹配")
self.assertEqual(upload_meta['local_hash'], hashlib.md5(self.test_content).hexdigest(), "本地哈希校验失败")
# 下载数据到临时文件
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_path = temp_file.name
download_meta = self.minio_agent.download_file(
bucket=self.test_bucket,
object_name=self.test_object,
local_path=temp_path
)
# 验证下载内容
with open(temp_path, 'rb') as f:
downloaded_content = f.read()
self.assertEqual(downloaded_content, self.test_content, "下载数据与原始数据不匹配")
self.assertEqual(download_meta['size'], len(self.test_content), "下载文件大小不匹配")
# 清理临时文件
os.unlink(temp_path)
def test_03_presigned_url(self):
"""测试生成预签名URL"""
# 先上传测试文件
self.minio_agent.upload_bytes(
self.test_bucket,
self.test_object,
self.test_content
)
# 生成URL（有效期30秒）
url_info = self.minio_agent.get_presigned_url(
bucket=self.test_bucket,
object_name=self.test_object,
expires=30
)
# 验证URL格式
self.assertIn("http://127.0.0.1:9005", url_info['presigned_url'], "预签名URL格式不正确")
self.assertEqual(url_info['expires_in'], 30, "过期时间设置不正确")
def test_04_list_objects(self):
"""测试列出对象功能"""
# 上传多个测试对象
test_objects = [
"test-folder/file1.txt",
"test-folder/file2.csv",
"another-folder/image.jpg"
]
for obj in test_objects:
self.minio_agent.upload_bytes(
self.test_bucket,
obj,
b"tese_list_obj"
)
# 列出所有对象
all_objects = self.minio_agent.list_objects(self.test_bucket)
self.assertEqual(len(all_objects), len(test_objects) + 1, "列出对象数量不匹配") # +1是之前的test_object
# 按前缀筛选
filtered_objects = self.minio_agent.list_objects(
self.test_bucket,
prefix="test-folder/"
)
self.assertEqual(len(filtered_objects), 2, "按前缀筛选结果不正确")
def test_05_delete_object(self):
"""测试删除对象功能"""
# 创建测试对象
delete_obj = "to-delete/temp.txt"
self.minio_agent.upload_bytes(
self.test_bucket,
delete_obj,
b"will be delete"
)
# 执行删除
result = self.minio_agent.delete_object(self.test_bucket, delete_obj)
self.assertTrue(result, "删除对象失败")
# 验证删除
objects = self.minio_agent.list_objects(self.test_bucket, prefix="to-delete/")
self.assertEqual(len(objects), 0, "对象删除后仍存在")
def test_06_upload_empty_data(self):
"""测试上传空数据的异常处理"""
with self.assertRaises(ValueError, msg="未捕获空数据上传异常"):
self.minio_agent.upload_bytes(
self.test_bucket,
"empty.txt",
b""
)
if __name__ == "__main__":
# 执行测试并显示详细结果
unittest.main(verbosity=2)
test/数据库链接测试.py
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import unittest
import pandas as pd
from datetime import datetime
import time
import pymysql
import platform
from concurrent.futures import ThreadPoolExecutor
from utils.mysql_agent import MySQLAgent
class TestMySQLAgent(unittest.TestCase):
@classmethod
def setUpClass(cls):
"""初始化测试环境和测试表"""
# 创建唯一的测试数据库和表名（避免冲突）
cls.test_db_name = f"test_db_{datetime.now().strftime('%Y%m%d%H%M%S')}"
cls.test_table = f"test_table_{datetime.now().strftime('%Y%m%d%H%M%S')}"
# 基础配置（根据实际环境修改）
cls.base_config = {
'host': 'localhost',
'port': 3306,
'user': 'root',
'password': '123123',
'max_connections': 10
}
# 创建测试数据库
cls._create_test_database()
# 初始化数据库连接
cls.db = MySQLAgent({
**cls.base_config,
'database': cls.test_db_name
})
# 创建测试表并插入初始数据
test_data = pd.DataFrame({
'id': [1, 2, 3],
'name': ['Test1', 'Test2', 'Test3'],
'value': [10.5, 20.3, 30.8],
'created_at': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03'])
})
cls.db.create_table_from_df(cls.test_table, test_data, primary_key='id')
cls.db.insert_from_df(cls.test_table, test_data)
@classmethod
def _create_test_database(cls):
"""创建测试数据库"""
temp_conn = pymysql.connect(
host=cls.base_config['host'],
port=cls.base_config['port'],
user=cls.base_config['user'],
password=cls.base_config['password'],
charset='utf8mb4'
)
try:
with temp_conn.cursor() as cursor:
cursor.execute(f"CREATE DATABASE IF NOT EXISTS {cls.test_db_name}")
cursor.execute(f"USE {cls.test_db_name}")
cursor.execute("SET GLOBAL max_connections = 100")
temp_conn.commit()
finally:
temp_conn.close()
@classmethod
def tearDownClass(cls):
"""清理测试环境"""
if hasattr(cls, 'db') and cls.db:
# 删除测试表
if cls.db.table_exists(cls.test_table):
cls.db.drop_table(cls.test_table)
# 删除测试数据库
temp_conn = pymysql.connect(**cls.base_config, charset='utf8mb4')
try:
with temp_conn.cursor() as cursor:
cursor.execute(f"DROP DATABASE IF EXISTS {cls.test_db_name}")
temp_conn.commit()
finally:
temp_conn.close()
def test_connection(self):
"""测试数据库连接"""
version_df = self.db.query_to_df("SELECT VERSION() as version")
self.assertIsNotNone(version_df)
self.assertEqual(len(version_df), 1)
print(f"数据库版本: {version_df['version'].iloc[0]}")
def test_query_to_df(self):
"""测试查询返回DataFrame"""
df = self.db.query_to_df(
f"SELECT * FROM {self.test_table} WHERE id > %s",
params=(1,)
)
self.assertIsInstance(df, pd.DataFrame)
self.assertEqual(len(df), 2) # id>1 的数据有2条
self.assertIn('name', df.columns)
def test_insert_from_df(self):
"""测试DataFrame插入"""
new_data = pd.DataFrame({
'id': [4, 5],
'name': ['Test4', 'Test5'],
'value': [40.1, 50.2],
'created_at': pd.to_datetime(['2023-01-04', '2023-01-05'])
})
inserted_rows = self.db.insert_from_df(self.test_table, new_data)
self.assertEqual(inserted_rows, 2)
# 验证插入结果
result_df = self.db.query_to_df(
f"SELECT name FROM {self.test_table} WHERE id IN (4,5)"
)
self.assertEqual(result_df['name'].tolist(), ['Test4', 'Test5'])
def test_update_from_df(self):
"""测试DataFrame更新"""
update_data = pd.DataFrame({
'id': [1, 2],
'name': ['Updated1', 'Updated2']
})
updated_rows = self.db.update_from_df(self.test_table, update_data, 'id')
self.assertGreaterEqual(updated_rows, 2)
# 验证更新结果
result_df = self.db.query_to_df(
f"SELECT name FROM {self.test_table} WHERE id IN (1,2)"
)
self.assertIn('Updated1', result_df['name'].values)
self.assertIn('Updated2', result_df['name'].values)
def test_transaction(self):
"""测试事务处理"""
conn = self.db.begin_transaction()
try:
# 执行事务内操作
cursor = conn.cursor()
cursor.execute(f"UPDATE {self.test_table} SET value = 99.9 WHERE id = 1")
cursor.execute(f"UPDATE {self.test_table} SET value = 88.8 WHERE id = 2")
self.db.commit_transaction(conn)
except Exception:
self.db.rollback_transaction(conn)
raise
# 验证事务提交结果
result_df = self.db.query_to_df(
f"SELECT value FROM {self.test_table} WHERE id IN (1,2)"
)
self.assertIn(99.9, result_df['value'].values)
self.assertIn(88.8, result_df['value'].values)
def test_large_data_insert(self):
"""测试大数据量插入"""
# 生成1000行测试数据
large_data = pd.DataFrame({
'id': range(1000, 2000),
'name': [f"Item_{i}" for i in range(1000, 2000)],
'value': [i * 0.1 for i in range(1000, 2000)],
'created_at': pd.date_range('2023-01-01', periods=1000)
})
# 根据平台自动调整批次大小
chunk_size = 100 if platform.system() == 'Windows' else 500
start_time = time.time()
inserted_rows = self.db.insert_from_df(
self.test_table,
large_data,
chunk_size=chunk_size
)
elapsed = time.time() - start_time
self.assertEqual(inserted_rows, 1000)
print(f"插入1000行数据耗时: {elapsed:.2f}秒 (批次大小: {chunk_size})")
def test_concurrent_access(self):
"""测试并发访问"""
def query_worker(i):
"""并发查询工作函数"""
df = self.db.query_to_df(
f"SELECT * FROM {self.test_table} WHERE id = %s",
params=(i % 3 + 1,) # 查询id=1,2,3循环
)
return len(df)
# 20个线程执行100次查询
start_time = time.time()
with ThreadPoolExecutor(max_workers=20) as executor:
results = list(executor.map(query_worker, range(100)))
elapsed = time.time() - start_time
self.assertEqual(sum(results), 100) # 每次查询应返回1行
print(f"100次并发查询耗时: {elapsed:.2f}秒")
class TestPlatformSpecific(unittest.TestCase):
"""平台特定功能测试"""
@classmethod
def setUpClass(cls):
cls.test_db_name = f"test_platform_db_{datetime.now().strftime('%Y%m%d%H%M%S')}"
cls.base_config = {
'host': 'localhost',
'port': 3306,
'user': 'root',
'password': '123123'
}
# 创建测试数据库
temp_conn = pymysql.connect(**cls.base_config, charset='utf8mb4')
try:
with temp_conn.cursor() as cursor:
cursor.execute(f"CREATE DATABASE IF NOT EXISTS {cls.test_db_name}")
temp_conn.commit()
finally:
temp_conn.close()
@classmethod
def tearDownClass(cls):
"""清理测试数据库"""
temp_conn = pymysql.connect(**cls.base_config, charset='utf8mb4')
try:
with temp_conn.cursor() as cursor:
cursor.execute(f"DROP DATABASE IF EXISTS {cls.test_db_name}")
temp_conn.commit()
finally:
temp_conn.close()
def test_windows_timeout(self):
"""测试Windows平台超时处理"""
if platform.system() != 'Windows':
self.skipTest("仅在Windows平台运行")
config = {
**self.base_config,
'database': self.test_db_name,
'connect_timeout': 1,
'read_timeout': 1,
'write_timeout': 1
}
db = MySQLAgent(config)
# 执行会超时的查询(SLEEP(2)超过1秒超时设置)
with self.assertRaises((pymysql.OperationalError, TimeoutError)) as ctx:
try:
db.query_to_df("SELECT SLEEP(2)")
except Exception as e:
# 提取底层异常信息（可能被包装）
while hasattr(e, 'args') and len(e.args) > 0 and isinstance(e.args[0], Exception):
e = e.args[0]
raise e
error_msg = str(ctx.exception)
self.assertTrue(
"timed out" in error_msg or
"timeout" in error_msg or
"HY000" in error_msg, # MySQL超时错误码
f"未检测到超时异常，实际异常: {error_msg}"
)
def test_macos_ssl_connection(self):
"""测试macOS平台SSL连接"""
if platform.system() != 'Darwin':
self.skipTest("仅在macOS平台运行")
config = {
**self.base_config,
'database': self.test_db_name,
'ssl': {'ca': '/usr/local/etc/openssl/cert.pem'}
}
db = MySQLAgent(config)
version_df = db.query_to_df("SELECT VERSION() as version")
self.assertIsNotNone(version_df)
if __name__ == '__main__':
unittest.main(verbosity=2)
test/日志测试.py
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# test_logger.py
# from utils.logger import log
# import platform
#
# def test_logging():
# log.info(f"当前系统: {platform.system()}")
# try:
# 1/0
# except:
# log.error("除零错误", exc_info=True)
#
# if __name__ == "__main__":
# test_logging()
# test_log_rotation.py
# from utils.logger import log
# import time
#
# def generate_large_log():
# """快速生成超过20MB的测试日志"""
# for i in range(10000):
# log.info(f"测试日志填充数据... {i}" * 10)
# time.sleep(0.001) # 避免内存暴涨
#
# if __name__ == "__main__":
# generate_large_log()
# 使用方法
# my_module/main_class.py
from utils.logger import log
class MainProcessor:
def __init__(self):
self.log = log.bind(module=self.__class__.__name__) # 动态绑定类名
def main(self):
"""主执行方法"""
self.log.info("开始执行主流程")
try:
self._step1()
# self._step2()
except Exception as e:
self.log.error("主流程执行失败", exc_info=e)
raise
def _step1(self):
"""子方法示例"""
self.log.debug("执行步骤1: 初始化资源")
# ...业务逻辑...
resource_count = 10
self.log.info("步骤1完成 | created={}", resource_count)
if __name__ == "__main__":
processor = MainProcessor()
processor.main()
test/通用文件读取测试.py
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import pytest
import pandas as pd
import os
from pathlib import Path
from utils.file_handler import FileHandler
from datetime import datetime
@pytest.fixture
def temp_dir(tmp_path):
"""创建临时测试目录"""
test_dir = tmp_path / "test_files"
test_dir.mkdir()
return test_dir
@pytest.fixture
def file_handler(temp_dir):
"""创建FileHandler实例"""
return FileHandler(temp_dir)
@pytest.fixture
def sample_dataframe():
"""创建测试用DataFrame"""
return pd.DataFrame({
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'value': [10.5, 20.3, 30.1]
})
@pytest.fixture
def sample_text_file(temp_dir):
"""创建测试文本文件"""
file_path = temp_dir / "test.txt"
with open(file_path, 'w') as f:
f.write("line1\nline2\nline3")
return file_path
# 开始测试
def test_read_write_csv(file_handler, temp_dir, sample_dataframe):
"""测试CSV文件读写"""
test_file = temp_dir / "test.csv"
# 测试写入
write_result = file_handler.write_file(test_file, sample_dataframe)
# 修改断言方式
assert bool(write_result.iloc[0]['success']) == True # 使用bool()转换
# 或者
assert write_result.iloc[0]['success'] == True # 使用值比较
assert os.path.exists(test_file)
# 测试读取
df = file_handler.read_file(test_file)
assert df.shape == (3, 3)
assert list(df.columns) == ['id', 'name', 'value']
def test_read_write_json(file_handler, temp_dir, sample_dataframe):
"""测试JSON文件读写"""
test_file = temp_dir / "test.json"
# 测试写入
write_result = file_handler.write_file(test_file, sample_dataframe)
assert write_result.iloc[0]['success'] == True
# 测试读取
df = file_handler.read_file(test_file)
assert df.shape == (3, 3)
def test_read_write_excel(file_handler, temp_dir, sample_dataframe):
"""测试Excel文件读写"""
test_file = temp_dir / "test.xlsx"
# 测试写入
write_result = file_handler.write_file(test_file, sample_dataframe)
assert write_result.iloc[0]['success'] == True
# 测试读取
df = file_handler.read_file(test_file)
assert df.shape == (3, 3)
def test_read_write_csv(file_handler, temp_dir, sample_dataframe):
"""测试CSV文件读写"""
test_file = temp_dir / "test.csv"
# 测试写入
write_result = file_handler.write_file(test_file, sample_dataframe)
# 修改断言方式
assert bool(write_result.iloc[0]['success']) == True # 使用bool()转换
# 或者
assert write_result.iloc[0]['success'] == True # 使用值比较
assert os.path.exists(test_file)
# 测试读取
df = file_handler.read_file(test_file)
assert df.shape == (3, 3)
assert list(df.columns) == ['id', 'name', 'value']
# 文件操作测试
def test_file_operations(file_handler, sample_text_file):
"""测试文件存在检查、删除等操作"""
# 测试文件存在检查
exists_df = file_handler.file_exists(sample_text_file)
assert exists_df.iloc[0]['exists'] == True
# 测试获取文件大小
size_df = file_handler.get_file_size(sample_text_file)
assert size_df.iloc[0]['size_bytes'] > 0
# 测试获取修改时间
mtime_df = file_handler.get_file_modified_time(sample_text_file)
assert isinstance(mtime_df.iloc[0]['modified_time'], datetime)
# 测试删除文件
delete_df = file_handler.delete_file(sample_text_file)
assert delete_df.iloc[0]['deleted'] == True
assert not os.path.exists(sample_text_file)
def test_directory_operations(file_handler, temp_dir):
"""测试目录操作"""
test_dir = temp_dir / "subdir"
# 测试创建目录
create_df = file_handler.create_dir(test_dir)
assert create_df.iloc[0]['created'] == True
assert os.path.isdir(test_dir)
# 测试列出目录
list_df = file_handler.list_dirs(temp_dir)
assert any("subdir" in d for d in list_df['dir_name'].values)
# 测试删除目录
delete_df = file_handler.delete_dir(test_dir)
assert delete_df.iloc[0]['deleted'] == True
assert not os.path.exists(test_dir)
# 文件压缩
def test_zip_operations(file_handler, temp_dir, sample_dataframe):
"""测试文件压缩解压"""
# 创建测试文件
test_file1 = temp_dir / "file1.txt"
test_file2 = temp_dir / "file2.csv"
file_handler.write_file(test_file1, "test content")
file_handler.write_file(test_file2, sample_dataframe)
# 测试压缩文件
zip_path = temp_dir / "test.zip"
zip_result = file_handler.zip_files([test_file1, test_file2], zip_path)
assert zip_result.iloc[0]['zipped'] == True
assert zip_result.iloc[0]['file_count'] == 2
# 测试解压
extract_dir = temp_dir / "extracted"
unzip_result = file_handler.unzip(zip_path, extract_dir)
assert unzip_result.iloc[0]['unzipped'] is True
assert os.path.exists(extract_dir / "file1.txt")
assert os.path.exists(extract_dir / "file2.csv")
def test_zip_directory(file_handler, temp_dir):
"""测试目录压缩"""
# 创建测试目录结构
test_dir = temp_dir / "test_dir"
sub_dir = test_dir / "sub"
sub_dir.mkdir(parents=True)
(test_dir / "file1.txt").write_text("content1")
(sub_dir / "file2.txt").write_text("content2")
# 测试压缩目录
zip_path = temp_dir / "dir.zip"
zip_result = file_handler.zip_dir(test_dir, zip_path)
assert zip_result.iloc[0]['zipped'] == True
assert zip_result.iloc[0]['file_count'] == 2
tools/SQL.sql
+18
View File
@@ -0,0 +1,18 @@
CREATE TABLE IF NOT EXISTS main_task (
task_id INT AUTO_INCREMENT PRIMARY KEY COMMENT '任务唯一ID',
task_name VARCHAR(100) NOT NULL COMMENT '任务名称',
task_type VARCHAR(50) NOT NULL COMMENT '任务类型（如processor、collector等）',
module_path VARCHAR(255) NOT NULL COMMENT '任务模块路径（如processors.data_checker）',
cron_expression VARCHAR(100) NOT NULL COMMENT 'Cron表达式（调度频率）',
time_zone VARCHAR(50) DEFAULT 'Asia/Shanghai' COMMENT '时区', -- 补充此字段
next_run_time DATETIME NOT NULL COMMENT '下次运行时间',
last_run_time DATETIME NULL COMMENT '上次运行时间',
last_run_status ENUM('success', 'failed', 'pending') DEFAULT 'pending' COMMENT '上次运行状态',
run_count INT DEFAULT 0 COMMENT '运行次数统计',
is_active TINYINT(1) DEFAULT 1 COMMENT '是否活跃（1=启用，0=禁用）',
is_running TINYINT(1) DEFAULT 0 COMMENT '是否正在运行（1=运行中，0=未运行）',
created_at DATETIME DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
INDEX idx_next_run (next_run_time) COMMENT '优化下次运行时间查询', -- 建议保留索引提升性能
INDEX idx_active (is_active) COMMENT '优化活跃任务查询' -- 建议保留索引提升性能
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='任务调度主表';
tools/task_manager.ipynb
+894
View File
@@ -0,0 +1,894 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "197b1b81f5528a50",
"metadata": {},
"source": [
"## 1. 初始化（所有操作前必须运行）"
]
},
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-10-17T05:43:18.381936Z",
"start_time": "2025-10-17T05:43:15.265036Z"
}
},
"source": [
"# 使 Notebook 可从项目根导入\n",
"import sys\n",
"from pathlib import Path\n",
"\n",
"def add_project_root(marker_dirs=(\"utils\", \"system_management\")):\n",
" p = Path.cwd()\n",
" for _ in range(6):\n",
" if all((p / d).exists() for d in marker_dirs):\n",
" if str(p) not in sys.path:\n",
" sys.path.insert(0, str(p))\n",
" return p\n",
" p = p.parent\n",
" raise RuntimeError(\"未找到项目根目录，请从项目根启动 Notebook 或手动设置 sys.path\")\n",
"\n",
"PROJECT_ROOT = add_project_root()\n",
"print(f\"PROJECT_ROOT = {PROJECT_ROOT}\")\n",
"\n",
"# 依赖与日志\n",
"import pandas as pd\n",
"from IPython.display import display, HTML, Markdown\n",
"from utils.logger import CrossPlatformLog\n",
"log = CrossPlatformLog.get_logger(\"TaskNotebook\")\n",
"\n",
"# 配置与调度器\n",
"from config import Config # 若你使用 ConfigManager，请改为: from config.config import ConfigManager\n",
"from system_management.scheduler.task_scheduler import TaskScheduler\n",
"\n",
"# 初始化调度器（根据你的项目配置选一段）\n",
"scheduler = TaskScheduler(Config.MYSQL_CONFIG)\n",
"# 或使用 ConfigManager：\n",
"# config = ConfigManager()\n",
"# scheduler = TaskScheduler(config.get(\"database\"))\n",
"\n",
"# 在 Notebook 中实现一个最小可用的 TaskManager\n",
"class TaskManager:\n",
" def __init__(self, scheduler: TaskScheduler):\n",
" self.scheduler = scheduler\n",
" self.db = scheduler.db # 复用调度器里的 MySQLAgent\n",
"\n",
" def get_all_tasks(self, active_only: bool = False):\n",
" sql = \"\"\"\n",
" SELECT *\n",
" FROM main_task\n",
" {where}\n",
" ORDER BY created_at DESC, task_id DESC\n",
" \"\"\"\n",
" where = \"WHERE is_active = 1\" if active_only else \"\"\n",
" df = self.db.query_to_df(sql.format(where=where))\n",
" return [] if df is None or df.empty else df.to_dict(\"records\")\n",
"\n",
" def get_task_by_id(self, task_id: int):\n",
" df = self.db.query_to_df(\n",
" \"SELECT * FROM main_task WHERE task_id = %s\",\n",
" params=(task_id,)\n",
" )\n",
" return None if df is None or df.empty else df.iloc[0].to_dict()\n",
"\n",
" def update_task(self, task_id: int, updates: dict) -> bool:\n",
" if not updates:\n",
" return False\n",
" # 允许更新的字段（与调度器一致）\n",
" allowed = {\n",
" \"task_name\", \"task_type\", \"module_path\",\n",
" \"cron_expression\", \"time_zone\"\n",
" }\n",
" filtered = {k: v for k, v in updates.items() if k in allowed}\n",
" if not filtered:\n",
" return False\n",
"\n",
" set_clause = \", \".join([f\"{k}=%s\" for k in filtered.keys()])\n",
" params = list(filtered.values()) + [task_id]\n",
" sql = f\"UPDATE main_task SET {set_clause}, updated_at=NOW() WHERE task_id=%s\"\n",
" affected = self.db.execute_sql(sql, params=params)\n",
" return affected == 1\n",
"\n",
" def toggle_task_status(self, task_id: int, activate: bool) -> bool:\n",
" sql = \"UPDATE main_task SET is_active=%s, updated_at=NOW() WHERE task_id=%s\"\n",
" affected = self.db.execute_sql(sql, params=(1 if activate else 0, task_id))\n",
" return affected == 1\n",
"\n",
" def delete_task(self, task_id: int) -> bool:\n",
" # 如果你更偏好软删除，可以改为: UPDATE main_task SET is_active=0, updated_at=NOW() WHERE task_id=%s\n",
" affected = self.db.execute_sql(\"DELETE FROM main_task WHERE task_id=%s\", params=(task_id,))\n",
" return affected == 1\n",
"\n",
" def run_task_manually(self, task_id: int) -> bool:\n",
" # 读取任务，直接复用调度器的单任务执行逻辑\n",
" task = self.get_task_by_id(task_id)\n",
" if not task:\n",
" return False\n",
" # _process_single_task 期望 dict\n",
" try:\n",
" return bool(self.scheduler._process_single_task(task)) # 注意：使用了调度器的内部方法\n",
" except Exception:\n",
" log.exception(\"手动执行任务失败\")\n",
" return False\n",
"\n",
"# 在这里创建 manager（供后续单元使用）\n",
"manager = TaskManager(scheduler)\n",
"\n",
"# 常用辅助函数\n",
"def format_datetime(dt):\n",
" if dt is None:\n",
" return \"-\"\n",
" try:\n",
" if isinstance(dt, pd.Timestamp) and pd.isna(dt):\n",
" return \"-\"\n",
" return dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
" except Exception:\n",
" try:\n",
" if pd.isna(dt):\n",
" return \"-\"\n",
" except Exception:\n",
" pass\n",
" return str(dt)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROJECT_ROOT = D:\\Idea Project\\intelligence_system\n",
"\u001B[32m2025-10-17 13:43:18\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mtask_scheduler\u001B[0m - \u001B[1m任务调度器已初始化，最大工作线程数: 5\u001B[0m\n"
]
}
],
"execution_count": 1
},
{
"cell_type": "markdown",
"id": "8271189cef3b5f17",
"metadata": {},
"source": [
"## 2. 列出任务（对应命令行 list）"
]
},
{
"cell_type": "code",
"id": "7b020af55972643",
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-17T05:43:18.499582Z",
"start_time": "2025-10-17T05:43:18.394863Z"
}
},
"source": [
"# 列出所有任务（包括已禁用的）\n",
"def list_tasks(active_only=True):\n",
" tasks = manager.get_all_tasks(active_only)\n",
" if not tasks:\n",
" display(Markdown(\"### 没有找到任务\"))\n",
" return None\n",
"\n",
" df = pd.DataFrame(tasks)\n",
"\n",
" # 格式化日期列\n",
" if 'last_run_time' in df.columns:\n",
" df['last_run_time'] = df['last_run_time'].apply(format_datetime)\n",
" if 'next_run_time' in df.columns:\n",
" df['next_run_time'] = df['next_run_time'].apply(format_datetime)\n",
"\n",
" # 重命名列名\n",
" df = df.rename(columns={\n",
" 'task_id': '任务ID',\n",
" 'task_name': '任务名称',\n",
" 'task_type': '任务类型',\n",
" 'module_path': '模块路径',\n",
" 'cron_expression': 'Cron表达式',\n",
" 'time_zone': '时区',\n",
" 'last_run_time': '最后运行时间',\n",
" 'next_run_time': '下次运行时间',\n",
" 'last_run_status': '运行状态',\n",
" 'is_active': '是否活跃',\n",
" 'run_count': '运行次数'\n",
" })\n",
"\n",
" display(Markdown(\"### 任务列表\"))\n",
" display(HTML(df.to_html(index=False)))\n",
" return df\n",
"\n",
"# 执行：列出所有任务（包括已禁用）\n",
"list_tasks(active_only=False)\n",
"\n",
"# 或者：只列出活跃任务\n",
"# list_tasks(active_only=True)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[32m2025-10-17 13:43:18\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m查询执行成功\u001B[0m\n"
]
},
{
"data": {
"text/plain": [
"<IPython.core.display.Markdown object>"
],
"text/markdown": "### 任务列表"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>任务ID</th>\n",
" <th>任务名称</th>\n",
" <th>任务类型</th>\n",
" <th>模块路径</th>\n",
" <th>Cron表达式</th>\n",
" <th>时区</th>\n",
" <th>下次运行时间</th>\n",
" <th>最后运行时间</th>\n",
" <th>运行状态</th>\n",
" <th>运行次数</th>\n",
" <th>是否活跃</th>\n",
" <th>is_running</th>\n",
" <th>created_at</th>\n",
" <th>updated_at</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>RSS新闻订阅</td>\n",
" <td>collector</td>\n",
" <td>collectors.rss_subscriptions.NewsAPIClient</td>\n",
" <td>5 0 * * *</td>\n",
" <td>Asia/Shanghai</td>\n",
" <td>2025-10-18 00:05:00</td>\n",
" <td>2025-10-17 00:05:07</td>\n",
" <td>success</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2025-10-16 15:47:34</td>\n",
" <td>2025-10-17 00:05:08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
]
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
" 任务ID 任务名称 任务类型 模块路径 \\\n",
"0 1 RSS新闻订阅 collector collectors.rss_subscriptions.NewsAPIClient \n",
"\n",
" Cron表达式 时区 下次运行时间 最后运行时间 \\\n",
"0 5 0 * * * Asia/Shanghai 2025-10-18 00:05:00 2025-10-17 00:05:07 \n",
"\n",
" 运行状态 运行次数 是否活跃 is_running created_at updated_at \n",
"0 success 4 1 0 2025-10-16 15:47:34 2025-10-17 00:05:08 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>任务ID</th>\n",
" <th>任务名称</th>\n",
" <th>任务类型</th>\n",
" <th>模块路径</th>\n",
" <th>Cron表达式</th>\n",
" <th>时区</th>\n",
" <th>下次运行时间</th>\n",
" <th>最后运行时间</th>\n",
" <th>运行状态</th>\n",
" <th>运行次数</th>\n",
" <th>是否活跃</th>\n",
" <th>is_running</th>\n",
" <th>created_at</th>\n",
" <th>updated_at</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>RSS新闻订阅</td>\n",
" <td>collector</td>\n",
" <td>collectors.rss_subscriptions.NewsAPIClient</td>\n",
" <td>5 0 * * *</td>\n",
" <td>Asia/Shanghai</td>\n",
" <td>2025-10-18 00:05:00</td>\n",
" <td>2025-10-17 00:05:07</td>\n",
" <td>success</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2025-10-16 15:47:34</td>\n",
" <td>2025-10-17 00:05:08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 2
},
{
"cell_type": "markdown",
"id": "7780dcef67a0534c",
"metadata": {},
"source": [
"## 3. 查看任务详情（对应命令行 show）"
]
},
{
"cell_type": "code",
"id": "eab90de72c35429e",
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-17T05:43:26.113877Z",
"start_time": "2025-10-17T05:43:26.071398Z"
}
},
"source": [
"# 查看指定任务的详情\n",
"def show_task_details(task_id):\n",
" task = manager.get_task_by_id(task_id)\n",
" if not task:\n",
" display(Markdown(f\"### 未找到任务ID为 {task_id} 的任务\"))\n",
" return None\n",
"\n",
" details = [\"### 任务详情\"]\n",
" details.append(f\"**任务ID**: {task.get('task_id')}\")\n",
" details.append(f\"**任务名称**: {task.get('task_name')}\")\n",
" details.append(f\"**任务类型**: {task.get('task_type')}\")\n",
" details.append(f\"**模块路径**: {task.get('module_path')}\")\n",
" details.append(f\"**Cron表达式**: {task.get('cron_expression')}\")\n",
" details.append(f\"**时区**: {task.get('time_zone', 'Asia/Shanghai')}\")\n",
" details.append(f\"**最后运行时间**: {format_datetime(task.get('last_run_time'))}\")\n",
" details.append(f\"**下次运行时间**: {format_datetime(task.get('next_run_time'))}\")\n",
" details.append(f\"**运行状态**: {task.get('last_run_status', '未运行')}\")\n",
" details.append(f\"**是否活跃**: {'是' if task.get('is_active') else '否'}\")\n",
" details.append(f\"**运行次数**: {task.get('run_count', 0)}\")\n",
" details.append(f\"**创建时间**: {format_datetime(task.get('created_at'))}\")\n",
"\n",
" display(Markdown('\\n'.join(details)))\n",
" return task\n",
"\n",
"# 执行：查看任务ID为1的详情（替换为实际ID）\n",
"show_task_details(1)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[32m2025-10-17 13:43:26\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m查询执行成功\u001B[0m\n"
]
},
{
"data": {
"text/plain": [
"<IPython.core.display.Markdown object>"
],
"text/markdown": "### 任务详情\n**任务ID**: 1\n**任务名称**: RSS新闻订阅\n**任务类型**: collector\n**模块路径**: collectors.rss_subscriptions.NewsAPIClient\n**Cron表达式**: 5 0 * * *\n**时区**: Asia/Shanghai\n**最后运行时间**: 2025-10-17 00:05:07\n**下次运行时间**: 2025-10-18 00:05:00\n**运行状态**: success\n**是否活跃**: 是\n**运行次数**: 4\n**创建时间**: 2025-10-16 15:47:34"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"{'task_id': 1,\n",
" 'task_name': 'RSS新闻订阅',\n",
" 'task_type': 'collector',\n",
" 'module_path': 'collectors.rss_subscriptions.NewsAPIClient',\n",
" 'cron_expression': '5 0 * * *',\n",
" 'time_zone': 'Asia/Shanghai',\n",
" 'next_run_time': Timestamp('2025-10-18 00:05:00'),\n",
" 'last_run_time': Timestamp('2025-10-17 00:05:07'),\n",
" 'last_run_status': 'success',\n",
" 'run_count': 4,\n",
" 'is_active': 1,\n",
" 'is_running': 0,\n",
" 'created_at': Timestamp('2025-10-16 15:47:34'),\n",
" 'updated_at': Timestamp('2025-10-17 00:05:08')}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 3
},
{
"cell_type": "markdown",
"id": "a313f1524f5a54bc",
"metadata": {},
"source": [
"## 4. 添加新任务（对应命令行 add）"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2b2d723bb8e2784f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\ProgramTools\\anaconda3\\envs\\intelligence_system\\Lib\\site-packages\\requests\\__init__.py:86: RequestsDependencyWarning: Unable to find acceptable character detection dependency (chardet or charset_normalizer).\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[32m2025-10-16 15:47:34\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m查询执行成功\u001B[0m\n",
"\u001B[32m2025-10-16 15:47:34\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mtask_scheduler\u001B[0m - \u001B[1m新任务添加成功\u001B[0m\n"
]
},
{
"data": {
"text/markdown": [
"### 任务添加成功！"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"新任务ID: 0，任务名称: RSS新闻订阅"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"np.int64(0)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 添加新任务\n",
"def add_new_task(name, task_type, module_path, cron_expression, timezone=\"Asia/Shanghai\"):\n",
" try:\n",
" task_id = scheduler.add_task(\n",
" task_name=name,\n",
" task_type=task_type,\n",
" module_path=module_path,\n",
" cron_expression=cron_expression,\n",
" time_zone=timezone\n",
" )\n",
" display(Markdown(f\"### 任务添加成功！\"))\n",
" display(Markdown(f\"新任务ID: {task_id}，任务名称: {name}\"))\n",
" return task_id\n",
" except Exception as e:\n",
" display(Markdown(f\"### 添加任务失败: {str(e)}\"))\n",
" return None\n",
"\n",
"# 执行：添加一个新闻采集任务\n",
"add_new_task(\n",
" name=\"RSS新闻订阅\",\n",
" task_type=\"collector\",\n",
" module_path=\"collectors.rss_subscriptions\",\n",
" cron_expression=\"5 0 * * *\", # 每5分钟执行1次\n",
" timezone=\"Asia/Shanghai\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "12373bcbb4a0b434",
"metadata": {},
"source": [
"## 5. 更新任务属性（对应命令行 update）"
]
},
{
"cell_type": "code",
"id": "c892fd8ad2f0dd9d",
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-17T05:44:19.046308Z",
"start_time": "2025-10-17T05:44:18.980345Z"
}
},
"source": [
"# 更新任务属性\n",
"def update_task(task_id, **kwargs):\n",
" updates = {}\n",
" if 'name' in kwargs and kwargs['name']:\n",
" updates['task_name'] = kwargs['name']\n",
" if 'type' in kwargs and kwargs['type']:\n",
" updates['task_type'] = kwargs['type']\n",
" if 'module' in kwargs and kwargs['module']:\n",
" updates['module_path'] = kwargs['module']\n",
" if 'cron' in kwargs and kwargs['cron']:\n",
" updates['cron_expression'] = kwargs['cron']\n",
" if 'timezone' in kwargs and kwargs['timezone']:\n",
" updates['time_zone'] = kwargs['timezone']\n",
"\n",
" if not updates:\n",
" display(Markdown(\"### 没有提供任何更新内容\"))\n",
" return False\n",
"\n",
" success = manager.update_task(task_id, updates)\n",
" if success:\n",
" display(Markdown(f\"### 任务ID {task_id} 更新成功\"))\n",
" show_task_details(task_id) # 显示更新后的详情\n",
" else:\n",
" display(Markdown(f\"### 任务ID {task_id} 更新失败\"))\n",
" return success\n",
"\n",
"# 执行：更新任务（示例：修改任务1的Cron表达式为每天10点）\n",
"update_task(1, cron = \"5 * * * *\")\n",
"\n",
"# 执行：同时更新多个属性（名称和Cron表达式）\n",
"# update_task(1, name=\"每日早间新闻采集\", cron=\"0 8 * * *\")"
],
"outputs": [
{
"data": {
"text/plain": [
"<IPython.core.display.Markdown object>"
],
"text/markdown": "### 任务ID 1 更新成功"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[32m2025-10-17 13:44:19\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m查询执行成功\u001B[0m\n"
]
},
{
"data": {
"text/plain": [
"<IPython.core.display.Markdown object>"
],
"text/markdown": "### 任务详情\n**任务ID**: 1\n**任务名称**: RSS新闻订阅\n**任务类型**: collector\n**模块路径**: collectors.rss_subscriptions.NewsAPIClient\n**Cron表达式**: 5 * * * *\n**时区**: Asia/Shanghai\n**最后运行时间**: 2025-10-17 00:05:07\n**下次运行时间**: 2025-10-18 00:05:00\n**运行状态**: success\n**是否活跃**: 是\n**运行次数**: 4\n**创建时间**: 2025-10-16 15:47:34"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 4
},
{
"cell_type": "markdown",
"id": "37564011cf5aa501",
"metadata": {},
"source": [
"## 6. 启用 / 禁用任务（对应命令行 toggle）"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "65388d10c5c8d407",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"### 任务ID 1 启用成功"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 启用或禁用任务\n",
"def toggle_task_status(task_id, activate=True):\n",
" success = manager.toggle_task_status(task_id, activate)\n",
" action = \"启用\" if activate else \"禁用\"\n",
" if success:\n",
" display(Markdown(f\"### 任务ID {task_id} {action}成功\"))\n",
" else:\n",
" display(Markdown(f\"### 任务ID {task_id} {action}失败\"))\n",
" return success\n",
"\n",
"# 执行：启用任务ID为1的任务\n",
"toggle_task_status(1, activate=True)\n",
"\n",
"# 执行：禁用任务ID为1的任务\n",
"# toggle_task_status(1, activate=False)"
]
},
{
"cell_type": "markdown",
"id": "c554c748169d5ac8",
"metadata": {},
"source": [
"## 7. 手动执行任务（对应命令行 run）"
]
},
{
"cell_type": "code",
"id": "94892f4134316f8e",
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-17T05:44:37.714559Z",
"start_time": "2025-10-17T05:44:35.084369Z"
}
},
"source": [
"# 手动执行任务\n",
"def run_task_manually(task_id):\n",
" display(Markdown(f\"### 正在手动执行任务ID {task_id}...\"))\n",
" success = manager.run_task_manually(task_id)\n",
" if success:\n",
" display(Markdown(f\"### 任务ID {task_id} 执行成功\"))\n",
" else:\n",
" display(Markdown(f\"### 任务ID {task_id} 执行失败\"))\n",
" return success\n",
"\n",
"# 执行：手动运行任务ID为1的任务\n",
"run_task_manually(1)"
],
"outputs": [
{
"data": {
"text/plain": [
"<IPython.core.display.Markdown object>"
],
"text/markdown": "### 正在手动执行任务ID 1..."
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[32m2025-10-17 13:44:35\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m查询执行成功\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:35\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mtask_scheduler\u001B[0m - \u001B[1m开始执行任务: RSS新闻订阅\u001B[0m\n"
]
},
{
"data": {
"text/plain": [
"<IPython.core.display.Markdown object>"
],
"text/markdown": "### 任务ID 1 执行成功"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[32m2025-10-17 13:44:37\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m新闻API客户端初始化完成，已连接到数据库\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:37\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m数据库表结构验证通过，当前字段：['id', '文章标题', '文章链接', '文章摘要', '发布时间', '来源URL', '创建时间', '更新时间']\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:37\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m上次更新时间: 2025-10-16 08:11:07\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:37\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m开始获取RSS源数据...\u001B[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\ProgramTools\\anaconda3\\envs\\intelligence_system\\Lib\\site-packages\\requests\\__init__.py:86: RequestsDependencyWarning: Unable to find acceptable character detection dependency (chardet or charset_normalizer).\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[32m2025-10-17 13:44:38\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1mRSS源获取完成，成功获取 4/4 个源\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:38\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m获取完成，耗时: 0.72秒\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:38\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m开始处理 RSS 源: https://www.chinanews.com.cn/rss/china.xml\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:38\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m表 collector_rss_subscriptions 插入结果汇总\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:38\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m成功写入 30/30 条记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:38\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m开始处理 RSS 源: https://www.chinanews.com.cn/rss/world.xml\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m表 collector_rss_subscriptions 插入结果汇总\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[31m\u001B[1mERROR \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[31m\u001B[1m表 collector_rss_subscriptions 插入失败记录详情\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m成功写入 28/30 条记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m开始处理 RSS 源: https://www.chinanews.com.cn/rss/finance.xml\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m表 collector_rss_subscriptions 插入结果汇总\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m成功写入 30/30 条记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m开始处理 RSS 源: https://www.chinanews.com.cn/rss/scroll-news.xml\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[33m\u001B[1m表 collector_rss_subscriptions 中跳过重复记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[1m表 collector_rss_subscriptions 插入结果汇总\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[31m\u001B[1mERROR \u001B[0m | \u001B[36mmysql_agent\u001B[0m - \u001B[31m\u001B[1m表 collector_rss_subscriptions 插入失败记录详情\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m成功写入 13/30 条记录\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mrss_subscriptions\u001B[0m - \u001B[1m本次最新更新时间: 2025-10-17 05:41:17\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mtask_scheduler\u001B[0m - \u001B[1m任务执行完成，耗时: 1.85秒\u001B[0m\n",
"\u001B[32m2025-10-17 13:44:39\u001B[0m | \u001B[1mINFO \u001B[0m | \u001B[36mtask_scheduler\u001B[0m - \u001B[1m任务执行成功: RSS新闻订阅\u001B[0m\n"
]
}
],
"execution_count": 5
},
{
"cell_type": "markdown",
"id": "c3492a1af7dbf2b1",
"metadata": {},
"source": [
"## 8. 删除任务（对应命令行 delete）"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6936dcc673933a8d",
"metadata": {},
"outputs": [],
"source": [
"# 删除任务\n",
"def delete_task(task_id, confirm=False):\n",
" if not confirm:\n",
" display(Markdown(f\"### 警告：删除任务是不可逆操作！\"))\n",
" display(Markdown(f\"请运行 `delete_task({task_id}, confirm=True)` 确认删除\"))\n",
" return False\n",
"\n",
" success = manager.delete_task(task_id)\n",
" if success:\n",
" display(Markdown(f\"### 任务ID {task_id} 删除成功\"))\n",
" else:\n",
" display(Markdown(f\"### 任务ID {task_id} 删除失败\"))\n",
" return success\n",
"\n",
"# 执行：第一步 - 确认删除（不会实际删除）\n",
"delete_task(1)\n",
"\n",
"# 执行：第二步 - 实际删除（谨慎操作！）\n",
"# delete_task(1, confirm=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "intelligence_system",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
tools/情报收集.sql
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use intelligence_system;
SELECT * FROM main_task
WHERE is_active = 1
AND next_run_time <= %s
AND is_running = 0
ORDER BY next_run_time;
utils/__init__.py
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from .logger import CrossPlatformLog
utils/file_handler.py
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import os
import shutil
import zipfile
import pickle
import pandas as pd
from datetime import datetime
from pathlib import Path, PurePath
from typing import Union, Optional, List, Dict, Any, Callable
from utils.logger import log
class FileHandler:
"""
跨平台文件操作工具类（兼容Windows/macOS/Linux）
功能规范：
- 读取文件内容的方法返回DataFrame
- 其他所有方法返回统一格式字典：
{
'success': bool, # 操作是否成功
'message': str, # 操作结果描述
'data': Any # 操作返回的数据（可选）
}
"""
def __init__(self, base_path: Optional[Union[str, Path]] = None):
"""
初始化文件处理器
:param base_path: 基础路径（自动处理跨平台路径格式）
"""
self.base_path = self._normalize_path(base_path) if base_path else None
self.log = log.bind(module=self.__class__.__name__)
def _normalize_path(self, path: Union[str, Path]) -> Path:
"""统一转换为跨平台Path对象"""
return Path(str(path).replace('\\', '/'))
def _resolve_path(self, path: Union[str, Path]) -> Path:
"""解析路径（自动处理跨平台路径）"""
path = self._normalize_path(path)
if not path.is_absolute() and self.base_path:
return self._normalize_path(self.base_path / path)
return path
def _format_result(self,
success: bool,
message: str = "",
data: Optional[Any] = None) -> Dict[str, Any]:
"""统一返回结果格式"""
return {
'success': bool(success),
'message': str(message),
'data': data
}
def read_file(self,
file_path: Union[str, Path],
encoding: str = 'utf-8',
**kwargs) -> pd.DataFrame:
"""
读取文件内容为DataFrame（跨平台兼容）
:param file_path: 文件路径（自动处理跨平台格式）
:param encoding: 文件编码（默认utf-8）
:return: 包含文件内容的DataFrame
:raises: 文件读取失败时抛出原始异常
"""
file_path = self._resolve_path(file_path)
try:
ext = self.get_file_extension(file_path)
if ext in ['csv', 'txt']:
df = pd.read_csv(file_path, encoding=encoding, **kwargs)
elif ext in ['xls', 'xlsx']:
df = pd.read_excel(file_path, **kwargs)
elif ext == 'json':
df = pd.read_json(file_path, encoding=encoding, **kwargs)
elif ext in ['pkl', 'pickle']:
# 统一将pickle内容转为DataFrame返回
obj = pd.read_pickle(file_path)
if isinstance(obj, pd.DataFrame):
df = obj
elif isinstance(obj, list):
df = pd.DataFrame(obj)
elif isinstance(obj, dict):
df = pd.DataFrame([obj])
else:
df = pd.DataFrame({'content': [obj]})
elif ext == 'parquet':
df = pd.read_parquet(file_path, **kwargs)
else:
with open(file_path, 'r', encoding=encoding) as f:
return pd.DataFrame({'content': [f.read()]})
self.log.debug(f"文件读取成功 | path={file_path} shape={df.shape}")
return df
except Exception as e:
self.log.error(f"文件读取失败 | path={file_path} error={str(e)}")
raise
def write_file(self,
file_path: Union[str, Path],
data: Union[pd.DataFrame, Dict, List],
encoding: str = 'utf-8',
**kwargs) -> Dict[str, Any]:
"""
写入文件（跨平台兼容）
:param file_path: 目标文件路径
:param data: 要写入的数据（支持DataFrame/dict/list）
:param encoding: 文件编码（默认utf-8）
:return: 操作结果字典
"""
file_path = self._resolve_path(file_path)
try:
# 自动创建父目录
parent_dir = file_path.parent
if not parent_dir.exists():
self.create_dir(parent_dir)
# 根据扩展名选择写入方式
ext = self.get_file_extension(file_path)
if ext in ['pkl', 'pickle']:
# 直接按原始对象进行pickle序列化
with open(file_path, 'wb') as f:
pickle.dump(data, f)
else:
# 统一数据格式到DataFrame
if isinstance(data, pd.DataFrame):
df = data
else:
df = pd.DataFrame(data if isinstance(data, list) else [data])
if ext in ['csv', 'txt']:
df.to_csv(file_path, encoding=encoding, index=False, **kwargs)
elif ext in ['xls', 'xlsx']:
df.to_excel(file_path, index=False, **kwargs)
elif ext == 'json':
df.to_json(file_path, force_ascii=False, **kwargs)
elif ext == 'parquet':
df.to_parquet(file_path, **kwargs)
else:
with open(file_path, 'w', encoding=encoding) as f:
f.write(str(data))
# 返回成功结果
return self._format_result(
True,
"文件写入成功",
{
'file_path': str(file_path),
'file_size': os.path.getsize(file_path)
}
)
except Exception as e:
return self._format_result(
False,
f"文件写入失败: {str(e)}",
{'file_path': str(file_path)}
)
def file_exists(self, file_path: Union[str, Path]) -> Dict[str, Any]:
"""
检查文件是否存在（跨平台兼容）
:return: 包含exists字段的结果字典
"""
file_path = self._resolve_path(file_path)
exists = file_path.is_file()
msg = f"文件{'' if exists else '不'}存在: {file_path}"
return self._format_result(True, msg, {'exists': exists})
def dir_exists(self, dir_path: Union[str, Path]) -> Dict[str, Any]:
"""
检查目录是否存在（跨平台兼容）
:return: 包含exists字段的结果字典
"""
dir_path = self._resolve_path(dir_path)
exists = dir_path.is_dir()
msg = f"目录{'' if exists else '不'}存在: {dir_path}"
return self._format_result(True, msg, {'exists': exists})
def create_dir(self, dir_path: Union[str, Path]) -> Dict[str, Any]:
"""
创建目录（跨平台兼容）
:return: 包含path字段的结果字典
"""
dir_path = self._resolve_path(dir_path)
try:
dir_path.mkdir(parents=True, exist_ok=True)
# Windows系统需要额外设置权限
if os.name == 'nt':
try:
os.chmod(dir_path, 0o777)
except:
pass
return self._format_result(True, "目录创建成功", {'path': str(dir_path)})
except Exception as e:
return self._format_result(False, f"目录创建失败: {str(e)}", {'path': str(dir_path)})
def delete_file(self, file_path: Union[str, Path]) -> Dict[str, Any]:
"""
删除文件（跨平台兼容）
:return: 包含path字段的结果字典
"""
file_path = self._resolve_path(file_path)
try:
if not file_path.exists():
return self._format_result(False, "文件不存在", {'path': str(file_path)})
file_path.unlink()
return self._format_result(True, "文件删除成功", {'path': str(file_path)})
except Exception as e:
return self._format_result(False, f"文件删除失败: {str(e)}", {'path': str(file_path)})
def delete_dir(self, dir_path: Union[str, Path]) -> Dict[str, Any]:
"""
删除目录及其内容（跨平台兼容）
:return: 包含path字段的结果字典
"""
dir_path = self._resolve_path(dir_path)
try:
if not dir_path.exists():
return self._format_result(False, "目录不存在", {'path': str(dir_path)})
shutil.rmtree(dir_path)
return self._format_result(True, "目录删除成功", {'path': str(dir_path)})
except Exception as e:
return self._format_result(False, f"目录删除失败: {str(e)}", {'path': str(dir_path)})
def list_files(self,
dir_path: Union[str, Path],
recursive: bool = False,
pattern: str = '*') -> Dict[str, Any]:
"""
列出目录中的文件（跨平台兼容）
:param recursive: 是否递归查找
:param pattern: 文件匹配模式（如*.txt）
:return: 包含files字段的结果字典
"""
dir_path = self._resolve_path(dir_path)
try:
if recursive:
files = list(dir_path.rglob(pattern))
else:
files = list(dir_path.glob(pattern))
file_info = [
{
'path': str(f),
'name': f.name,
'size': f.stat().st_size,
'modified': datetime.fromtimestamp(f.stat().st_mtime).isoformat(),
'is_dir': f.is_dir()
} for f in files if f.is_file() # 只返回文件，不包括目录
]
return self._format_result(
True,
f"找到 {len(file_info)} 个文件",
{'files': file_info}
)
except Exception as e:
return self._format_result(
False,
f"列出文件失败: {str(e)}",
{'files': []}
)
def get_file_extension(self, file_path: Union[str, Path]) -> str:
"""
获取文件扩展名（跨平台兼容）
:return: 小写且不带点的扩展名（如 'jpg'）
"""
file_path = self._resolve_path(file_path)
ext = file_path.suffix.lower().lstrip('.')
self.log.trace(f"获取文件扩展名 | path={file_path} ext={ext}")
return ext
def copy_file(self,
src_path: Union[str, Path],
dst_path: Union[str, Path]) -> Dict[str, Any]:
"""
复制文件（跨平台兼容）
:return: 包含source和destination字段的结果字典
"""
src_path = self._resolve_path(src_path)
dst_path = self._resolve_path(dst_path)
try:
if not src_path.exists():
return self._format_result(
False,
"源文件不存在",
{
'source': str(src_path),
'destination': str(dst_path)
}
)
# 确保目标目录存在
self.create_dir(dst_path.parent)
shutil.copy2(src_path, dst_path)
return self._format_result(
True,
"文件复制成功",
{
'source': str(src_path),
'destination': str(dst_path),
'file_size': dst_path.stat().st_size
}
)
except Exception as e:
return self._format_result(
False,
f"文件复制失败: {str(e)}",
{
'source': str(src_path),
'destination': str(dst_path)
}
)
def move_file(self,
src_path: Union[str, Path],
dst_path: Union[str, Path]) -> Dict[str, Any]:
"""
移动/重命名文件（跨平台兼容）
:return: 包含source和destination字段的结果字典
"""
src_path = self._resolve_path(src_path)
dst_path = self._resolve_path(dst_path)
try:
if not src_path.exists():
return self._format_result(
False,
"源文件不存在",
{
'source': str(src_path),
'destination': str(dst_path)
}
)
# 确保目标目录存在
self.create_dir(dst_path.parent)
shutil.move(src_path, dst_path)
return self._format_result(
True,
"文件移动成功",
{
'source': str(src_path),
'destination': str(dst_path)
}
)
except Exception as e:
return self._format_result(
False,
f"文件移动失败: {str(e)}",
{
'source': str(src_path),
'destination': str(dst_path)
}
)
def zip_files(self,
file_paths: List[Union[str, Path]],
zip_path: Union[str, Path]) -> Dict[str, Any]:
"""
压缩多个文件到zip（跨平台兼容）
:param file_paths: 要压缩的文件路径列表
:param zip_path: 目标zip文件路径
:return: 包含zip_path和file_count字段的结果字典
"""
zip_path = self._resolve_path(zip_path)
try:
# 确保目标目录存在
self.create_dir(zip_path.parent)
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
file_count = 0
for file_path in file_paths:
file_path = self._resolve_path(file_path)
if file_path.exists():
zipf.write(file_path, file_path.name)
file_count += 1
return self._format_result(
True,
"文件压缩成功",
{
'zip_path': str(zip_path),
'file_count': file_count,
'zip_size': os.path.getsize(zip_path)
}
)
except Exception as e:
return self._format_result(
False,
f"文件压缩失败: {str(e)}",
{
'zip_path': str(zip_path)
}
)
def unzip(self,
zip_path: Union[str, Path],
extract_to: Optional[Union[str, Path]] = None) -> Dict[str, Any]:
"""
解压zip文件（跨平台兼容）
:param extract_to: 解压目标目录（默认为zip文件所在目录）
:return: 包含extract_to和file_count字段的结果字典
"""
zip_path = self._resolve_path(zip_path)
extract_to = self._resolve_path(extract_to) if extract_to else zip_path.parent
try:
if not zip_path.exists():
return self._format_result(
False,
"ZIP文件不存在",
{
'zip_path': str(zip_path),
'extract_to': str(extract_to)
}
)
# 确保目标目录存在
self.create_dir(extract_to)
with zipfile.ZipFile(zip_path, 'r') as zipf:
file_list = zipf.namelist()
zipf.extractall(extract_to)
return self._format_result(
True,
"文件解压成功",
{
'extract_to': str(extract_to),
'file_count': len(file_list)
}
)
except Exception as e:
return self._format_result(
False,
f"文件解压失败: {str(e)}",
{
'zip_path': str(zip_path),
'extract_to': str(extract_to)
}
)
# ---------------------------- 测试用例 ----------------------------
if __name__ == "__main__":
# 初始化处理器（自动处理跨平台路径）
project_root = next(p for p in Path(__file__).resolve().parents if
(p / '.git').exists() or (p / 'pyproject.toml').exists() or (p / 'requirements.txt').exists())
handler = FileHandler(project_root / "test")
# 测试路径标准化
test_paths = [
"normal/path",
"windows\\style\\path",
"mixed/path\\with\\both"
]
print("=== 路径标准化测试 ===")
for path in test_paths:
resolved = handler._resolve_path(path)
print(f"原始路径: {path} -> 标准化: {resolved} (类型: {type(resolved)})")
# 测试目录操作
print("\n=== 目录操作测试 ===")
dir_result = handler.create_dir("test_dir")
print(dir_result)
# 测试文件操作
print("\n=== 文件操作测试 ===")
test_data = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}]
write_result = handler.write_file("test_dir/data.json", test_data)
print(write_result)
# 测试文件读取
try:
df = handler.read_file("test_dir/data.json")
print("\n读取文件内容:")
print(df)
except Exception as e:
print(f"\n文件读取失败: {str(e)}")
# 测试列表文件
print("\n=== 文件列表测试 ===")
list_result = handler.list_files("test_dir")
print(list_result)
# 测试压缩解压
print("\n=== 压缩解压测试 ===")
zip_result = handler.zip_files(
["test_dir/data.json"],
"test_archive.zip"
)
print(zip_result)
unzip_result = handler.unzip(
"test_archive.zip",
"extracted_files"
)
print(unzip_result)
# 清理测试数据
print("\n=== 清理测试数据 ===")
print(handler.delete_file("test_dir/data.json"))
print(handler.delete_dir("test_dir"))
print(handler.delete_file("test_archive.zip"))
print(handler.delete_dir("extracted_files"))
utils/logger.py
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import os
import sys
from pathlib import Path
from loguru import logger
import platform
from datetime import datetime
import zipfile
class CrossPlatformLog:
"""跨平台日志系统（支持Linux/Windows/Mac）"""
def __init__(self):
self.log_dir = self._get_log_dir()
self._setup_logger()
def _get_log_dir(self):
"""获取跨平台日志目录（相对路径）"""
base_dir = Path(__file__).parent.parent # 项目根目录
log_dir = base_dir / "logs"
# 自动创建日志目录
log_dir.mkdir(exist_ok=True)
# Windows特殊权限处理
if platform.system() == "Windows":
try:
os.chmod(log_dir, 0o777) # 确保写入权限
except:
pass
return log_dir
def _setup_logger(self):
"""配置跨平台日志处理器"""
logger.remove() # 清除默认配置
# 统一控制台输出格式
logger.add(
sys.stdout,
level="INFO",
format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{module}</cyan> - <level>{message}</level>",
filter=lambda record: record["level"].no >= 20 # INFO及以上级别
)
# 主日志文件（兼容所有平台路径）
self._add_main_log()
# 错误日志单独存储
self._add_error_log()
def _add_main_log(self):
"""主日志文件配置"""
main_log = self.log_dir / "application.log"
logger.add(
str(main_log),
rotation="20 MB",
compression=self._compress_log,
encoding="utf-8",
level="DEBUG",
# 👇 增加 {extra} 输出，并美化结构
# format="{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {module}:{line} - {message}{extra_output}",
retention="30 days",
enqueue=True,
# 👇 动态处理 extra 字段为可读格式
format=self._format_with_extra, # 使用自定义格式函数
)
def _format_with_extra(self, record):
# 构造 extra 的可读字符串
extra_str = ""
if record["extra"]:
extra_items = []
for key, value in record["extra"].items():
if key == "extra_output": # 跳过自己，避免递归
continue
value_repr = repr(value)
# 对于错误信息，增加截断长度限制，避免丢失重要信息
if key in ["error", "error_message", "sql", "params"]:
if len(value_repr) > 500:
value_repr = value_repr[:497] + "..."
elif len(value_repr) > 200:
value_repr = value_repr[:197] + "..."
extra_items.append(f"\n → {key}: {value_repr}")
extra_str = "".join(extra_items)
# 👉 直接将 extra_str 写入 message 或附加字段
record["extra"]["extra_output"] = extra_str
# ✅ 关键：返回的 format 字符串不再引用 {extra_output}，而是使用 {extra[extra_output]}
return "{time:YYYY-MM-DD HH:mm:ss.SSS} | {level: <8} | {module}:{line} - {message}{extra[extra_output]}\n"
def _add_error_log(self):
"""错误日志专用配置"""
error_log = self.log_dir / "errors.log"
logger.add(
str(error_log),
level="ERROR",
format="{time:YYYY-MM-DD HH:mm:ss.SSS} | ERROR | {module}:{line} - {message}{extra[extra_output]}\n{exception}",
rotation="10 MB",
retention="90 days",
enqueue=True
)
@staticmethod
def _compress_log(log_path):
"""通用日志压缩方法（兼容所有平台）"""
if not os.path.exists(log_path):
return
try:
zip_path = f"{log_path}.{datetime.now().strftime('%Y%m%d')}.zip"
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
zipf.write(log_path, arcname=os.path.basename(log_path))
os.remove(log_path)
return zip_path
except Exception as e:
print(f"日志压缩失败: {str(e)}")
return log_path # 返回原文件路径继续使用
@classmethod
def get_logger(cls, module_name=None):
"""获取模块专属日志器"""
return logger.bind(module=module_name or "__main__")
# 初始化全局日志器
log = CrossPlatformLog().get_logger()
utils/minio_agent.py
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import os
import sys
import platform
import threading
from typing import List, Dict, Optional, BinaryIO, Tuple, Any
from datetime import datetime, timedelta
import hashlib
from io import BytesIO
from minio import Minio
from minio.error import S3Error, MinioException
from utils.logger import log
class MinIOAgent:
"""
全平台兼容的MinIO对象存储操作类
支持Windows/macOS/Linux系统，提供对象存储的上传、下载、查询等功能
专注于二进制数据处理，返回元数据用于与MySQL关联
"""
_instance = None # 单例模式实例
_lock = threading.Lock() # 线程锁，保证单例线程安全
def __new__(cls, *args, **kwargs):
"""单例模式实现，确保全局只有一个实例"""
if not cls._instance:
with cls._lock:
if not cls._instance:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, config: dict):
"""
初始化MinIO连接
参数:
config (dict): MinIO配置字典，包含以下键:
- endpoint: 服务端点（例：'localhost:9000'）
- access_key: 访问密钥
- secret_key: 密钥
- [可选] secure: 是否使用SSL（默认False）
- [可选] region: 区域
- [可选] timeout: 超时时间(秒，默认30)
"""
# 避免重复初始化
if hasattr(self, '_client') and self._client:
return
# 验证必要配置参数
required_keys = ['endpoint', 'access_key', 'secret_key']
if not all(key in config for key in required_keys):
raise ValueError(f"MinIO配置缺少必要参数，需要: {required_keys}")
# 整合配置，设置默认值
self.config = {
'endpoint': config['endpoint'],
'access_key': config['access_key'],
'secret_key': config['secret_key'],
'secure': config.get('secure', False),
'region': config.get('region'),
'timeout': config.get('timeout', 30)
}
# 初始化日志，绑定当前平台信息
current_platform = platform.system()
self.log = log.bind(module=f"MinIOAgent({current_platform})")
# 创建客户端实例
self._client = self._create_client()
# 验证连接是否有效
self._verify_connection()
def _create_client(self) -> Minio:
"""创建MinIO客户端实例"""
try:
client = Minio(
endpoint=self.config['endpoint'],
access_key=self.config['access_key'],
secret_key=self.config['secret_key'],
secure=self.config['secure'],
region=self.config['region']
)
self.log.info("MinIO客户端创建成功")
return client
except Exception as e:
self.log.critical("创建MinIO客户端失败", 错误=str(e), exc_info=True)
raise
def _verify_connection(self) -> None:
"""验证与MinIO服务的连接是否正常"""
try:
# 通过列出存储桶来验证连接
self._client.list_buckets()
self.log.info(f"成功连接到MinIO服务：{self.config['endpoint']}")
except Exception as e:
self.log.critical("连接验证失败", 错误=str(e), exc_info=True)
raise
def create_bucket(self, bucket_name: str) -> bool:
"""
创建存储桶（如不存在）
参数:
bucket_name: 存储桶名称
返回:
是否成功创建（或已存在）
"""
try:
if not self._client.bucket_exists(bucket_name):
self._client.make_bucket(bucket_name)
self.log.info(f"存储桶创建成功：{bucket_name}")
return True
self.log.debug(f"存储桶已存在：{bucket_name}")
return True
except MinioException as e:
self.log.error(f"创建存储桶 {bucket_name} 失败", 错误=str(e), exc_info=True)
return False
def upload_bytes(self, bucket: str, object_name: str, data: bytes) -> Dict[str, Any]:
"""
上传二进制数据至MinIO
参数:
bucket: 存储桶名称
object_name: 对象名称（路径）
data: 二进制数据
返回:
包含元数据的字典:
- bucket: 存储桶名称
- object_name: 对象路径
- size: 数据大小(字节)
- etag: 服务器生成的哈希值
- content_type: 内容类型
- upload_time: 上传时间(UTC)
- local_hash: 本地计算的MD5哈希
"""
if not data:
raise ValueError("上传数据不能为空")
# 确保存储桶存在
self.create_bucket(bucket)
try:
# 计算本地哈希（用于数据完整性校验）
local_hash = hashlib.md5(data).hexdigest()
# 上传数据
result = self._client.put_object(
bucket_name=bucket,
object_name=object_name,
data=BytesIO(data),
length=len(data),
content_type=self._guess_content_type(object_name)
)
# 构建元数据
metadata = {
'bucket': bucket,
'object_name': object_name,
'size': len(data),
'etag': result.etag,
'content_type': result.content_type,
'upload_time': datetime.utcfromtimestamp(result.last_modified.timestamp()),
'local_hash': local_hash
}
self.log.info(
"文件上传成功",
存储桶=bucket,
对象名称=object_name,
大小=len(data)
)
return metadata
except MinioException as e:
self.log.error(
"文件上传失败",
存储桶=bucket,
对象名称=object_name,
错误=str(e),
exc_info=True
)
raise
def download_file(self, bucket: str, object_name: str, local_path: str) -> Dict[str, Any]:
"""
从MinIO下载文件至本地
参数:
bucket: 存储桶名称
object_name: 对象名称（路径）
local_path: 本地保存路径
返回:
包含下载信息的字典:
- local_path: 本地路径
- size: 文件大小
- download_time: 下载时间
"""
try:
# 创建父目录（如果不存在）
os.makedirs(os.path.dirname(local_path), exist_ok=True)
# 下载文件
start_time = datetime.now()
self._client.fget_object(bucket, object_name, local_path)
download_time = datetime.now() - start_time
# 获取文件信息
stat = os.stat(local_path)
result = {
'local_path': local_path,
'size': stat.st_size,
'download_time': download_time.total_seconds(),
'downloaded_at': datetime.now()
}
self.log.info(
"文件下载成功",
存储桶=bucket,
对象名称=object_name,
本地路径=local_path,
大小=stat.st_size
)
return result
except MinioException as e:
self.log.error(
"文件下载失败",
存储桶=bucket,
对象名称=object_name,
错误=str(e),
exc_info=True
)
raise
except IOError as e:
self.log.error(
"本地文件操作失败",
本地路径=local_path,
错误=str(e),
exc_info=True
)
raise
def get_presigned_url(self, bucket: str, object_name: str, expires: int = 3600) -> Dict[str, str]:
"""
生成临时访问URL
参数:
bucket: 存储桶名称
object_name: 对象名称（路径）
expires: 过期时间(秒)，默认3600秒
返回:
包含URL和过期信息的字典
"""
try:
url = self._client.presigned_get_object(
bucket_name=bucket,
object_name=object_name,
expires=expires
)
result = {
'presigned_url': url,
'expires_in': expires,
'expires_at': datetime.now() + timedelta(seconds=expires),
'bucket': bucket,
'object_name': object_name
}
self.log.debug(
"预签名URL生成成功",
存储桶=bucket,
对象名称=object_name,
过期时间=expires
)
return result
except MinioException as e:
self.log.error(
"生成预签名URL失败",
存储桶=bucket,
对象名称=object_name,
错误=str(e),
exc_info=True
)
raise
def list_objects(self, bucket: str, prefix: str = "") -> List[Dict[str, Any]]:
"""
查询指定前缀的对象列表及元数据
参数:
bucket: 存储桶名称
prefix: 对象路径前缀
返回:
对象信息列表，每个对象包含:
- bucket: 存储桶
- object_name: 对象名称
- size: 大小
- last_modified: 最后修改时间
- etag: 哈希值
- content_type: 内容类型
"""
try:
objects = self._client.list_objects(
bucket_name=bucket,
prefix=prefix,
recursive=True
)
result = []
for obj in objects:
# 获取详细元数据
stat = self._client.stat_object(bucket, obj.object_name)
result.append({
'bucket': bucket,
'object_name': obj.object_name,
'size': obj.size,
'last_modified': obj.last_modified,
'etag': stat.etag,
'content_type': stat.content_type
})
self.log.info(
"对象列表查询成功",
存储桶=bucket,
前缀=prefix,
数量=len(result)
)
return result
except MinioException as e:
self.log.error(
"查询对象列表失败",
存储桶=bucket,
前缀=prefix,
错误=str(e),
exc_info=True
)
raise
def delete_object(self, bucket: str, object_name: str) -> bool:
"""
删除指定对象
参数:
bucket: 存储桶名称
object_name: 对象名称（路径）
返回:
是否删除成功
"""
try:
self._client.remove_object(bucket, object_name)
self.log.info(
"对象删除成功",
存储桶=bucket,
对象名称=object_name
)
return True
except MinioException as e:
self.log.error(
"删除对象失败",
存储桶=bucket,
对象名称=object_name,
错误=str(e),
exc_info=True
)
return False
@staticmethod
def _guess_content_type(object_name: str) -> str:
"""根据文件名猜测内容类型"""
from mimetypes import guess_type
mime_type, _ = guess_type(object_name)
return mime_type or 'application/octet-stream' # 默认二进制流类型
utils/mysql_agent.py
+722
View File
@@ -0,0 +1,722 @@
import os
import sys
import platform
import pandas as pd
import pymysql
import json
import numpy as np
from pymysql import cursors
from pymysql.err import MySQLError
from typing import Union, List, Dict, Any, Optional, Tuple, Literal
import threading
from datetime import datetime
from pathlib import Path
# 导入日志系统
from utils.logger import log
class MySQLAgent:
"""
全平台兼容的MySQL数据库操作类
支持Windows/macOS/Linux系统
配置参数从外部传入，不使用连接池和事务管理
"""
_instance = None
_lock = threading.Lock()
def __new__(cls, *args, **kwargs):
if not cls._instance:
with cls._lock:
if not cls._instance:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, config: dict):
"""初始化MySQL数据库连接（原有逻辑完全保留）"""
if hasattr(self, 'config') and self.config:
return
# 基础配置校验
required_keys = ['host', 'port', 'user', 'password', 'database']
if not all(key in config for key in required_keys):
log.warning(f"数据库配置缺少必要参数,当前数据库链接信息为：{config}")
raise ValueError(f"数据库配置缺少必要参数，需要: {required_keys}")
self.config = {
'host': config['host'],
'port': config['port'],
'user': config['user'],
'password': config['password'],
'database': config['database'],
'charset': config.get('charset', 'utf8mb4'),
'autocommit': True,
'connect_timeout': config.get('connect_timeout', 10),
'read_timeout': config.get('read_timeout', 30),
'write_timeout': config.get('write_timeout', 30),
'ssl': config.get('ssl')
}
# 初始化日志
current_platform = platform.system()
self.log = log.bind(module=f"MySQLAgent({current_platform})")
def get_connection(self) -> pymysql.connections.Connection:
"""获取数据库连接（原有逻辑完全保留）"""
try:
conn = pymysql.connect(** self.config)
# 为连接添加 character_set_name 方法
if not hasattr(conn, 'character_set_name'):
def _character_set_name():
return self.config.get('charset', 'utf8mb4')
conn.character_set_name = _character_set_name
# macOS需要特殊处理SSL
if platform.system() == 'Darwin' and self.config.get('ssl'):
conn.ping(reconnect=True)
self.log.trace("获取数据库连接成功")
return conn
except Exception as e:
error_msg = str(e)
if platform.system() == 'Windows' and "timed out" in error_msg:
self.log.warning("Windows连接超时，正在重试...")
return self._retry_connection()
self.log.error("连接失败",
error=error_msg,
error_type=type(e).__name__,
host=self.config.get('host'),
port=self.config.get('port'),
database=self.config.get('database'),
exc_info=True)
raise
def _retry_connection(self, max_retries: int = 3) -> Any | None:
"""Windows平台连接重试机制（原有逻辑完全保留）"""
for attempt in range(max_retries):
try:
conn = pymysql.connect(**self.config)
self.log.info(f"经过 {attempt + 1} 次尝试后成功建立连接")
return conn
except Exception:
if attempt == max_retries - 1:
raise
import time
time.sleep(1)
def query_to_df(self, sql: str, params: Union[tuple, dict, None] = None,
parse_dates: Union[List[str], bool] = True,is_print = True) -> pd.DataFrame:
"""执行SQL查询并返回DataFrame（原有逻辑完全保留）"""
try:
self.log.debug("执行SQL查询", sql=sql)
# 获取连接并确保字符集方法存在
conn = self.get_connection()
# 创建SQLAlchemy引擎
from sqlalchemy import create_engine
from sqlalchemy.pool import StaticPool
engine = create_engine(
"mysql+pymysql://",
creator=lambda: conn,
poolclass=StaticPool,
connect_args={'charset': self.config.get('charset', 'utf8mb4')}
)
# 执行查询
df = pd.read_sql(sql, engine, params=params, parse_dates=parse_dates)
if is_print:
self.log.info("查询执行成功", 行数=len(df))
return df
except Exception as e:
self.log.error("SQL查询失败",
sql=sql,
params=params,
error=str(e),
error_type=type(e).__name__,
exc_info=True)
raise
finally:
if 'engine' in locals():
engine.dispose()
def insert_from_df(self, table_name: str, df: pd.DataFrame,
chunk_size: int = 1000, replace: bool = False,
ignore_duplicates: bool = None) -> int:
"""
兼容旧接口的通用插入方法：保留replace参数，同时支持新的ignore_duplicates
自动处理重复数据，对所有数据源通用，插入失败的数据会通过日志记录
"""
# 【兼容性处理】如果未指定ignore_duplicates，用replace参数推导
if ignore_duplicates is None:
ignore_duplicates = not replace # 旧逻辑中replace=True表示替换，即不忽略重复
if df.empty:
self.log.warning("尝试插入空的DataFrame", table=table_name)
return 0
conn = None
cursor = None
total_inserted = 0
total_duplicates = 0
total_failed = 0
failed_records = [] # 存储所有失败的记录
try:
# 1. 建立数据库连接
conn = self.get_connection()
cursor = conn.cursor()
self.log.debug(f"已建立连接，准备插入数据到 {table_name}")
# 2. 获取数据库表的实际列名
cursor.execute(f"SHOW COLUMNS FROM `{table_name}`")
columns_info = cursor.fetchall()
db_columns = [col[0] for col in columns_info]
self.log.debug(f"表 {table_name} 包含以下列：{db_columns}")
# 3. 数据预处理：统一处理空值
cleaned_df = df.replace(
[None, np.nan, pd.NA, 'nan', 'NaN', 'NAN', ''],
None
).copy()
# 4. 字段匹配：只保留与数据库匹配的列
df_columns = cleaned_df.columns.tolist()
matched_columns = [col for col in df_columns if col in db_columns]
unmatched_columns = [col for col in df_columns if col not in db_columns]
if unmatched_columns:
self.log.warning(
f"表 {table_name} 中存在不匹配的列，已自动丢弃",
unmatched_columns=unmatched_columns,
count=len(unmatched_columns)
)
if not matched_columns:
self.log.warning(f"表 {table_name} 没有匹配的列，终止插入操作")
return 0
filtered_df = cleaned_df[matched_columns].copy()
total_to_insert = len(filtered_df)
self.log.debug(
f"表 {table_name} 的过滤后DataFrame：共 {total_to_insert} 行待插入"
)
# 5. 处理复杂类型（dict/list转JSON）
for col in filtered_df.columns:
has_complex_type = filtered_df[col].apply(
lambda x: isinstance(x, (dict, list)) if x is not None else False
).any()
if has_complex_type:
self.log.debug(f"表 {table_name} 中的 {col} 列包含复杂类型，正在转换为JSON")
filtered_df.loc[:, col] = filtered_df[col].apply(
lambda x: json.dumps(x, ensure_ascii=False) if x is not None else x
)
# 6. 构建通用插入SQL
columns_str = ', '.join([f"`{col}`" for col in filtered_df.columns])
placeholders = ', '.join(['%s'] * len(filtered_df.columns))
insert_sql = f"INSERT INTO `{table_name}` ({columns_str}) VALUES ({placeholders})"
self.log.trace(f"为表 {table_name} 生成的插入SQL：{insert_sql}")
# 7. 逐条插入（确保能捕获单条重复错误）
records = filtered_df.to_dict('records')
indices = filtered_df.index.tolist()
for i, (record, idx) in enumerate(zip(records, indices)):
try:
data = tuple(record[col] for col in filtered_df.columns)
cursor.execute(insert_sql, data)
total_inserted += 1
if (i + 1) % 100 == 0:
self.log.trace(
f"已向表 {table_name} 插入 {i + 1}/{total_to_insert} 行数据"
)
except MySQLError as e:
# 8. 捕获重复错误（MySQL错误码1062）
if e.args[0] == 1062:
total_duplicates += 1
short_record = {
k: (str(v)[:100] + '...') if isinstance(v, (str, dict, list)) else v
for k, v in record.items()
}
self.log.warning(
f"表 {table_name} 中跳过重复记录",
index=idx,
error_message=e.args[1],
record=short_record
)
# 记录重复的记录
failed_records.append({
'index': idx,
'type': 'duplicate',
'error_code': e.args[0],
'error_message': e.args[1],
'record': record
})
if not ignore_duplicates:
raise
else:
# 其他数据库错误
total_failed += 1
# 记录失败的记录详情
failed_records.append({
'index': idx,
'type': 'error',
'error_code': e.args[0],
'error_message': e.args[1],
'record': record
})
self.log.error(
f"表 {table_name} 插入记录失败",
index=idx,
error_code=e.args[0],
error_message=e.args[1],
record=record # 完整记录写入日志
)
if not ignore_duplicates:
raise
# 提交事务
conn.commit()
# 9. 插入结果统计，包括失败记录汇总
self.log.info(
f"表 {table_name} 插入结果汇总",
total_to_insert=total_to_insert,
total_inserted=total_inserted,
total_duplicates=total_duplicates,
total_failed=total_failed,
failed_records_count=len(failed_records)
)
# 单独记录所有失败的数据详情
if failed_records:
self.log.error(
f"表 {table_name} 插入失败记录详情",
failed_records_summary=[
{
'index': r['index'],
'type': r['type'],
'error_code': r['error_code'],
'error_message': r['error_message']
} for r in failed_records
],
# 完整记录可以作为调试信息单独记录，避免日志过大
detailed_failed_records=failed_records
)
return total_inserted
except Exception as e:
if conn:
conn.rollback()
self.log.error(f"表 {table_name} 批量插入失败",
error=str(e),
error_type=type(e).__name__,
table_name=table_name,
total_records=len(df) if not df.empty else 0,
exc_info=True)
# 记录事务回滚时的失败记录
if failed_records:
self.log.error(
f"表 {table_name} 事务回滚，已失败的记录",
failed_records=failed_records,
failed_count=len(failed_records)
)
raise
finally:
if cursor:
cursor.close()
if conn:
conn.close()
def _get_primary_key(self, table_name: str, cursor) -> Optional[str]:
"""【新增辅助方法】获取表的主键（用于replace逻辑的去重）"""
try:
cursor.execute("""
SELECT COLUMN_NAME
FROM INFORMATION_SCHEMA.KEY_COLUMN_USAGE
WHERE TABLE_SCHEMA = %s
AND TABLE_NAME = %s
AND CONSTRAINT_NAME = 'PRIMARY'
""", (self.config['database'], table_name))
result = cursor.fetchone()
return result[0] if result else None
except Exception as e:
self.log.warning(f"获取表 {table_name} 的主键失败", error=str(e))
return None
def _get_table_detailed_info(self, table_name: str) -> Dict[str, Dict[str, Any]]:
"""获取表的详细结构信息（原有逻辑完全保留，供其他方法调用）"""
sql = """
SELECT column_name, data_type, character_maximum_length
FROM information_schema.columns
WHERE table_schema = %s \
AND table_name = %s \
"""
params = (self.config['database'], table_name)
try:
conn = self.get_connection()
try:
cursor = conn.cursor()
cursor.execute(sql, params)
result = cursor.fetchall()
# 强制转换为列表，避免游标类型导致的解析问题
result_list = list(result)
if not result_list:
self.log.error("未在表中找到任何列", 表=table_name)
return {}
schema = {}
for row in result_list:
# 确保正确提取字段名（兼容元组格式）
col_name = str(row[0]).strip() # 强制转为字符串并去空格
data_type = str(row[1]).strip()
max_length = row[2] if row[2] else None
schema[col_name] = {
'type': data_type,
'max_length': max_length
}
self.log.debug("成功获取表结构信息",
表=table_name,
列=list(schema.keys()))
return schema
finally:
cursor.close()
conn.close()
except Exception as e:
self.log.error("获取表详细信息失败",
表=table_name,
error=str(e))
raise
def _validate_and_clean_data(self, df: pd.DataFrame, table_name: str,
table_schema: Dict[str, Dict[str, Any]]) -> pd.DataFrame:
"""数据校验与清洗（原有逻辑完全保留，供其他方法调用）"""
# 1. 字段过滤：只保留表中存在的字段
df_columns = df.columns.tolist()
table_columns = list(table_schema.keys())
valid_columns = [col for col in df_columns if col in table_columns]
invalid_columns = [col for col in df_columns if col not in table_columns]
if invalid_columns:
self.log.warning("丢弃表中不存在的无效列",
表=table_name,
无效列=invalid_columns,
数量=len(invalid_columns))
cleaned_df = df[valid_columns].copy()
if cleaned_df.empty:
return cleaned_df
# 2. 处理每个字段的数据
for col in valid_columns:
col_info = table_schema[col]
data_type = col_info['type']
max_length = col_info['max_length']
# 2.1 处理空值
if cleaned_df[col].isnull().any():
# 根据字段类型设置默认值
default_value = '' if data_type in ['varchar', 'char', 'text'] else None
cleaned_df[col].fillna(default_value, inplace=True)
self.log.debug("替换空值",
表=table_name,
列=col,
默认值=default_value,
数量=cleaned_df[col].isnull().sum())
# 2.2 处理字符串类型的超长字段
if data_type in ['varchar', 'char'] and max_length:
# 确保是字符串类型
cleaned_df[col] = cleaned_df[col].astype(str)
# 截断超长内容
too_long_mask = cleaned_df[col].str.len() > max_length
if too_long_mask.any():
cleaned_df.loc[too_long_mask, col] = cleaned_df.loc[too_long_mask, col].str.slice(0, max_length)
self.log.warning("截断超长值",
表=table_name,
列=col,
最大长度=max_length,
数量=too_long_mask.sum())
# 2.3 处理日期时间类型
if data_type in ['datetime', 'timestamp']:
try:
# 尝试转换为datetime类型
cleaned_df[col] = pd.to_datetime(cleaned_df[col])
except Exception as e:
self.log.warning("转换为datetime失败，使用当前时间替代",
表=table_name,
列=col,
错误=str(e))
# 转换失败的用当前时间替代
invalid_mask = pd.to_datetime(cleaned_df[col], errors='coerce').isna()
cleaned_df.loc[invalid_mask, col] = datetime.now()
return cleaned_df
def update_from_df(self, table_name: str, df: pd.DataFrame,
key_columns: Union[str, List[str]]) -> int:
"""使用DataFrame数据更新数据库表（原有逻辑完全保留）"""
if df.empty:
self.log.warning("尝试使用空的DataFrame进行更新", 表=table_name)
return 0
self.log.debug("准备从DataFrame更新表数据",
表=table_name,
关键字列=key_columns,
行数=len(df))
try:
if isinstance(key_columns, str):
key_columns = [key_columns]
总更新数 = 0
with self.get_connection() as conn:
with conn.cursor() as cursor:
# 获取表结构信息
table_info = self._get_table_detailed_info(table_name)
columns = [col for col in df.columns if col in table_info]
# 构建UPDATE语句模板
set_clause = ', '.join([f"{col}=%s" for col in columns if col not in key_columns])
where_clause = ' AND '.join([f"{col}=%s" for col in key_columns])
if not set_clause:
self.log.warning("没有可更新的列", 表=table_name)
return 0
update_sql = f"UPDATE {table_name} SET {set_clause} WHERE {where_clause}"
self.log.trace("生成的更新SQL", sql=update_sql)
# 准备数据
update_data = []
for _, row in df.iterrows():
set_values = [row[col] for col in columns if col not in key_columns]
key_values = [row[col] for col in key_columns]
update_data.append(tuple(set_values + key_values))
# 执行批量更新
cursor.executemany(update_sql, update_data)
总更新数 = cursor.rowcount
conn.commit()
self.log.info("数据更新成功",
表=table_name,
更新行数=总更新数)
return 总更新数
except Exception as e:
self.log.error("数据更新失败",
表=table_name,
error=str(e),
exc_info=True)
raise
def df_to_sql_type(self, df: pd.DataFrame) -> Dict[str, str]:
"""推断DataFrame各列的SQL类型（原有逻辑完全保留）"""
type_mapping = {
'int64': 'BIGINT',
'float64': 'DOUBLE',
'datetime64[ns]': 'DATETIME',
'object': 'TEXT',
'bool': 'TINYINT(1)',
'category': 'VARCHAR(255)'
}
sql_types = {}
for col, dtype in df.dtypes.items():
dtype_str = str(dtype)
sql_types[col] = type_mapping.get(dtype_str, 'TEXT')
self.log.debug("将DataFrame类型映射为SQL类型",
映射关系=sql_types)
return sql_types
def create_table_from_df(self, table_name: str, df: pd.DataFrame,
primary_key: Union[str, List[str], None] = None) -> bool:
"""根据DataFrame结构创建表（原有逻辑完全保留）"""
if self.table_exists(table_name):
self.log.warning("表已存在", 表=table_name)
return False
self.log.debug("根据DataFrame结构创建新表",
表=table_name,
列=list(df.columns))
try:
sql_types = self.df_to_sql_type(df)
columns_sql = []
for col, sql_type in sql_types.items():
col_def = f"{col} {sql_type}"
columns_sql.append(col_def)
if primary_key:
if isinstance(primary_key, str):
primary_key = [primary_key]
pk_columns = [col for col in primary_key if col in sql_types]
if pk_columns:
columns_sql.append(f"PRIMARY KEY ({', '.join(pk_columns)})")
self.log.trace("设置主键",
表=table_name,
主键=pk_columns)
create_sql = f"CREATE TABLE {table_name} (\n {',\n '.join(columns_sql)}\n)"
self.execute_sql(create_sql)
self.log.info("表创建成功", 表=table_name)
return True
except Exception as e:
self.log.error("创建表失败",
表=table_name,
error=str(e),
exc_info=True)
return False
def execute_sql(self, sql: str, params: Union[tuple, dict, None] = None,
fetch: bool = False) -> Union[int, List[Dict[str, Any]]]:
"""执行SQL语句（原有逻辑完全保留）"""
try:
with self.get_connection() as conn:
with conn.cursor() as cursor:
# Linux/macOS需要更长的执行时间
if platform.system() != 'Windows':
cursor.execute("SET SESSION max_execution_time=600000")
cursor.execute(sql, params)
if fetch:
result = cursor.fetchall()
self.log.debug("查询执行完成", 行数=len(result))
return result
else:
affected_rows = cursor.rowcount
conn.commit() # 立即提交
self.log.debug("更新执行完成", 受影响行数=affected_rows)
return affected_rows
except Exception as e:
self.log.error("SQL执行失败",
sql=sql,
params=params,
error=str(e),
error_type=type(e).__name__,
exc_info=True)
raise
def table_exists(self, table_name: str) -> bool:
"""检查表是否存在（原有逻辑完全保留）"""
sql = """
SELECT COUNT(*) as count
FROM `information_schema`.`tables`
WHERE `table_schema` = %s \
AND `table_name` = %s \
"""
params = (self.config['database'], table_name)
try:
result = self.execute_sql(sql, params, fetch=True)
exists = result[0][0] > 0 # 适配元组结果
self.log.debug("检查表是否存在",
表=table_name,
存在=exists)
return exists
except Exception:
return False
def drop_table(self, table_name: str) -> bool:
"""删除表（原有逻辑完全保留）"""
if not self.table_exists(table_name):
self.log.warning("表不存在", 表=table_name)
return False
try:
self.execute_sql(f"DROP TABLE {table_name}")
self.log.info("表删除成功", 表=table_name)
return True
except Exception as e:
self.log.error("删除表失败",
表=table_name,
error=str(e),
exc_info=True)
return False
def validate_connection(self) -> bool:
"""验证连接是否有效（原有逻辑完全保留）"""
try:
with self.get_connection() as conn:
with conn.cursor() as cursor:
cursor.execute("SELECT 1")
return cursor.fetchone()[0] == 1
except Exception:
return False
# 平台特定的默认配置（原有逻辑完全保留）
def get_default_config():
"""获取各平台默认配置"""
current_platform = platform.system()
base_config = {
'host': 'localhost',
'port': 3306,
'user': 'root',
'password': '123123',
'database': 'intelligence_system',
}
if current_platform == 'Windows':
return {**base_config,
'connect_timeout': 10,
'read_timeout': 30,
'write_timeout': 30
}
elif current_platform == 'Darwin':
return {
**base_config,
'connect_timeout': 15,
'read_timeout': 60,
'write_timeout': 60,
'ssl': {'ca': '/usr/local/etc/openssl/cert.pem'}
}
else: # Linux和其他平台
return {** base_config,
'connect_timeout': 15,
'read_timeout': 60,
'write_timeout': 60
}
if __name__ == "__main__":
# 使用示例（原有逻辑完全保留）
db = MySQLAgent(get_default_config())
# 测试连接
if db.validate_connection():
print("数据库连接成功")
# 获取数据库版本
version = db.query_to_df("SELECT VERSION() as version")
print(f"数据库版本: {version['version'].iloc[0]}")
else:
print("连接数据库失败")