数据库操作说明

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├── system_management/ # 系统管理层
│ ├── scheduler/ # 任务调度
│ │ ── task_scheduler.py # 分布式任务调度器
│ │ └── cron_manager.py # 定时规则配置
│ │ ── task_scheduler.py # 任务调度器
│ │
│ └── monitor/ # 系统监控
│ ├── health_monitor.py # 服务健康检测
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目录
1.
类概述
2.
初始化配置
3.
基础CRUD操作
4.
表结构管理
5.
事务管理
6.
高级功能
7.
注意事项
8.
示例代码
9.
性能优化
10.
错误处理
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。
# MySQLAgent 使用文档
​核心特性:​
**最后更新于:2023-08-06**
**代码版本:1.2.0**
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
初始化配置
基本配置参数
python
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> **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
{
'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)
'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
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```
### 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
@@ -70,35 +58,35 @@ config.update({
})
db = MySQLAgent(config)
各平台特殊配置
平台 默认超时 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+)
基础CRUD操作
查询数据
python
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```
### 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,), # 参数可以是元组或字典
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}, # 使用字典参数
params={'status': 1},
fetch=True # 设为 True 返回查询结果
)
插入数据
python
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```
### 插入数据
```python
# 单条插入
data = {'name': '张三', 'age': 25}
db.execute_sql(
@@ -117,11 +105,10 @@ inserted_rows = db.insert_from_df(
new_users,
chunk_size=500 # 分批插入大小
)
更新数据
python
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```
### 更新数据
```python
# 条件更新
db.execute_sql(
"UPDATE users SET status = %s WHERE last_login < %s",
@@ -138,22 +125,20 @@ affected_rows = db.update_from_df(
update_df,
key_columns='id' # 用于匹配记录的关键列
)
删除数据
python
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```
### 删除数据
```python
# 条件删除
db.execute_sql(
"DELETE FROM logs WHERE created_at < %s",
params=('2022-01-01',)
)
表结构管理
创建表
python
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```
## 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'),
@@ -175,11 +160,10 @@ price DECIMAL(10,2),
stock INT DEFAULT 0
)
""")
表操作
python
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```
### 表操作
```python
# 检查表是否存在
if db.table_exists('users'):
print("用户表已存在")
@@ -189,12 +173,19 @@ db.drop_table('temp_table')
# 获取表结构
schema = db._get_table_info('products')
事务管理
基本事务
python
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```
### 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()
@@ -204,21 +195,19 @@ db.commit_transaction(conn)
except Exception as e:
db.rollback_transaction(conn)
raise
上下文管理器
python
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```
### 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
高级功能
大数据量处理
python
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```
## 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(
@@ -235,11 +224,10 @@ db.insert_from_df(
large_df,
chunk_size=2000 # 根据平台自动调整
)
并发查询
python
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```
### 并发查询
```python
from concurrent.futures import ThreadPoolExecutor
def fetch_user(user_id):
@@ -250,149 +238,4 @@ params=(user_id,)
with ThreadPoolExecutor(max_workers=10) as executor:
results = list(executor.map(fetch_user, range(1, 1001)))
注意事项
1.
​连接管理​
获取连接后必须确保关闭
推荐使用with语句或try/finally
2.
​事务隔离​
长时间事务会占用连接池资源
复杂事务考虑使用存储过程
3.
​性能要点​
Windows平台减少批次大小
macOS注意SSL证书路径
Linux可增大连接池大小
4.
​类型映射​
Pandas类型 MySQL类型
int64 BIGINT
float64 DOUBLE
datetime64 DATETIME
object TEXT
示例代码
完整业务场景
python
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class OrderSystem:
def __init__(self):
self.db = MySQLAgent(get_default_config())
def create_order(self, user_id, items):
"""创建订单(完整事务示例)"""
conn = self.db.begin_transaction()
try:
# 1. 插入订单主表
cursor = conn.cursor()
cursor.execute(
"INSERT INTO orders (user_id, total) VALUES (%s, %s)",
(user_id, sum(item['price']*item['quantity'] for item in items))
)
order_id = cursor.lastrowid
# 2. 插入订单明细
order_items = pd.DataFrame([{
'order_id': order_id,
'product_id': item['product_id'],
'quantity': item['quantity'],
'price': item['price']
} for item in items])
self.db.insert_from_df('order_items', order_items, conn=conn)
# 3. 更新库存
for item in items:
cursor.execute(
"UPDATE products SET stock = stock - %s WHERE id = %s",
(item['quantity'], item['product_id'])
)
self.db.commit_transaction(conn)
return order_id
except Exception as e:
self.db.rollback_transaction(conn)
raise
性能优化
1.
​连接池调优​
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# 生产环境推荐配置
config = {
**get_default_config(),
'max_connections': 20, # 根据服务器配置调整
'maxcached': 15, # 最大空闲连接
'ping': 2 # 连接检查级别
}
2.
​查询优化​
使用EXPLAIN分析慢查询
添加适当索引
避免SELECT *
3.
​批处理建议​
操作类型 Windows macOS/Linux
插入批次 100-500 1000-5000
更新批次 50-200 500-2000
错误处理
常见错误码处理
python
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try:
db.execute_sql("INSERT INTO...")
except pymysql.err.IntegrityError as e:
# 唯一键冲突
if e.args[0] == 1062:
handle_duplicate_entry()
except pymysql.err.OperationalError as e:
# 连接超时
if "timed out" in str(e):
retry_connection()
except Exception as e:
logger.error(f"Database error: {str(e)}")
raise
连接重试机制
python
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def safe_query(sql, params=None, max_retries=3):
for attempt in range(max_retries):
try:
return db.query_to_df(sql, params)
except (pymysql.err.OperationalError, pymysql.err.InterfaceError) as e:
if attempt == max_retries - 1:
raise
time.sleep(2 ** attempt) # 指数退避
​提示​:本文档对应代码版本1.2.0,最后更新于2023-08-06。使用前请确保您的环境满足:
Python ≥ 3.8
PyMySQL ≥ 1.0.2
pandas ≥ 1.3.0
```
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
情报收集系统主程序(明文配置版)
功能:
1. 调度数据采集、处理、存储流程
2. 生成日报/月报
3. 异常监控和报警
"""
import sys
import logging
# main.py
import signal
import time
import threading
from datetime import datetime
from typing import Dict, List, Any
from system_management.scheduler.task_scheduler import TaskScheduler
from utils.logger import CrossPlatformLog
# 初始化日志
log = CrossPlatformLog.get_logger("Main")
# 自定义模块
from processors.data_processor import DataProcessor
from storage.mysql_agent import IntelligenceDB
from applications.reporter import ReportGenerator
from applications.alert import AlertService
from utils.logger import setup_logging
class IntelligenceSystem:
def __init__(self):
# 初始化核心组件
setup_logging()
self.logger = logging.getLogger(__name__)
self.db = IntelligenceDB()
self.processor = DataProcessor()
self.alert = AlertService()
def __init__(self, db_config=None):
self.scheduler = TaskScheduler(db_config)
self._running = False
log.info("IntelligenceSystem initialized")
def run(self):
"""启动系统主循环"""
self._running = True
self._register_signal_handlers()
log.info("Starting main loop")
def run_daily_pipeline(self):
"""每日数据采集处理流程"""
try:
self.logger.info("开始执行每日数据采集流程")
while self._running:
start_time = time.time()
self._run_cycle()
# 阶段1:数据采集
raw_data = self._collect_data()
# 精确控制循环间隔(扣除执行时间)
elapsed = time.time() - start_time
sleep_time = max(0, 60 - elapsed)
time.sleep(sleep_time)
# 阶段2:数据处理
processed_data = self._process_data(raw_data)
# 阶段3:数据存储
self._store_data(processed_data)
# 阶段4:生成日报
self._generate_reports()
# 阶段5:异常检测
self._check_alerts()
self.logger.info("每日流程执行完成")
except Exception as e:
self.logger.error(f"主流程执行失败: {str(e)}", exc_info=True)
self.alert.send_critical(f"系统异常: {str(e)}")
def _collect_data(self) -> Dict[str, List[Dict]]:
"""执行所有数据采集任务"""
collected = {}
for name, collector in self.collectors.items():
try:
self.logger.info(f"开始采集 {name} 数据...")
data = collector.fetch_data({
'keywords': '汽车后市场',
'max_results': 100
})
collected[name] = data
self.logger.info(f"{name} 采集完成,共 {len(data)} 条数据")
except Exception as e:
self.logger.error(f"{name} 采集器异常: {str(e)}")
continue
return collected
def _process_data(self, raw_data: Dict) -> Dict:
"""处理原始数据"""
processed = {}
for data_type, items in raw_data.items():
processed[data_type] = []
for item in items:
try:
# 文本数据标准处理
if data_type in ['news', 'complaint']:
result = self.processor.process_text(item['content'])
processed_item = {
**item,
'keywords': result['keywords'],
'category': result['category']
}
processed[data_type].append(processed_item)
# 图像处理(预留接口)
elif data_type == 'images':
processed[data_type].append(
self.processor.image_to_text(item)
)
except Exception as e:
self.logger.warning(f"数据处理失败: {item.get('id', '')} - {str(e)}")
continue
return processed
def _store_data(self, processed_data: Dict):
"""存储到数据库"""
for data_type, items in processed_data.items():
success_count = 0
for item in items:
try:
if self.db.insert_data(data_type, item):
success_count += 1
except Exception as e:
self.logger.error(f"数据存储失败: {str(e)}")
self.logger.info(
f"{data_type} 数据存储完成,成功 {success_count}/{len(items)}"
)
def _generate_reports(self):
"""生成报告并发送"""
try:
# 日报生成
report_date = datetime.now().date()
report_html = ReportGenerator(self.db).generate_daily()
report_path = f"reports/daily_{report_date}.html"
with open(report_path, 'w', encoding='utf-8') as f:
f.write(report_html)
self.logger.info(f"日报已生成: {report_path}")
# 每月1号生成月报
if datetime.now().day == 1:
monthly_report = ReportGenerator(self.db).generate_monthly()
# 这里替换为实际的邮件发送逻辑
self.logger.info("月度报告已生成(邮件发送功能需配置)")
except Exception as e:
self.logger.error(f"报告生成失败: {str(e)}")
def _check_alerts(self):
"""检查预警信息"""
negative_keywords = ['投诉', '造假', '违规']
alerts = self.alert.check_negative(negative_keywords)
if alerts:
alert_msg = "\n".join([f"[{a['source']}] {a['content']}" for a in alerts])
self.logger.warning(f"发现负面舆情:\n{alert_msg}")
# 这里替换为实际的通知发送逻辑
self.alert.send_urgent("负面舆情警报", alert_msg)
def cleanup(self):
"""资源清理"""
self.db.close()
self.logger.info("系统资源已释放")
def run_scheduled():
"""定时任务执行入口"""
system = IntelligenceSystem()
try:
while True:
now = datetime.now()
if now.hour == 9 and now.minute == 0: # 每天9点执行
system.run_daily_pipeline()
time.sleep(60) # 避免重复执行
time.sleep(30)
except KeyboardInterrupt:
system.cleanup()
log.info("Received keyboard interrupt")
except Exception as e:
log.critical(
"System crashed",
exc_info=True
)
raise
finally:
self.shutdown()
def _run_cycle(self):
"""单个运行周期"""
try:
# 1. 执行任务调度
result = self.scheduler.run_pending_tasks()
# 2. 每小时记录系统状态
if datetime.now().minute == 0:
self._log_system_status()
except Exception as e:
log.error(
"Cycle execution failed",
exc_info=True
)
raise
def _log_system_status(self):
"""记录系统状态"""
try:
status_df = self.scheduler.get_task_status()
pending = len(status_df[status_df['next_run_time'] <= datetime.now()])
log.info(
"System status",
pending_tasks=pending,
active_tasks=len(status_df),
last_success=status_df['last_run_time'].max()
)
except Exception as e:
log.error(
"Failed to log system status",
exc_info=True
)
def _register_signal_handlers(self):
"""注册信号处理"""
signal.signal(signal.SIGINT, self._handle_shutdown)
signal.signal(signal.SIGTERM, self._handle_shutdown)
log.debug("Signal handlers registered")
def _handle_shutdown(self, signum, frame):
"""处理关闭信号"""
log.info(
f"Processing shutdown signal {signum}",
signal=signum
)
self._running = False
def shutdown(self):
"""关闭系统"""
log.info("Performing system shutdown")
# 此处可添加其他清理逻辑
log.success("System shutdown completed")
if __name__ == "__main__":
if len(sys.argv) > 1 and sys.argv[1] == "--manual":
# 手动执行模式
system = IntelligenceSystem()
try:
system.logger.info("手动执行模式启动")
system.run_daily_pipeline()
finally:
system.cleanup()
else:
# 定时任务模式
print("情报收集系统已启动(定时模式)")
print("按 Ctrl+C 退出")
run_scheduled()
system = IntelligenceSystem()
system.run()
except Exception as e:
log.critical(
"System startup failed",
exc_info=True
)
raise
@@ -0,0 +1,277 @@
# system_management/scheduler/task_scheduler.py
import importlib
import time
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from storage.mysql_agent import MySQLAgent
from pathlib import Path
# 使用您的日志系统
from utils.logger import CrossPlatformLog
log = CrossPlatformLog.get_logger("TaskScheduler")
class TaskScheduler:
def __init__(self, db_config: Optional[Dict] = None):
"""
初始化任务调度器
Args:
db_config (Optional[Dict]): 可选的数据库配置,默认使用MySQLAgent默认配置
"""
self.db = MySQLAgent(db_config or {}) # 使用您提供的MySQLAgent
self._init_task_table()
log.info("TaskScheduler initialized")
def _init_task_table(self):
"""确保任务表存在并包含必要字段"""
if not self.db.table_exists("main_task"):
log.info("Creating main_task table")
create_sql = """
CREATE TABLE main_task (
task_id INT AUTO_INCREMENT PRIMARY KEY,
task_name VARCHAR(100) NOT NULL,
module_path VARCHAR(255) NOT NULL COMMENT '例如data_collection.spiders.weibo_spider',
frequency_type ENUM('minute','hourly','daily','weekly','monthly') NOT NULL,
frequency_value INT DEFAULT NULL COMMENT '间隔数值',
last_run_time DATETIME DEFAULT NULL,
next_run_time DATETIME DEFAULT NULL,
last_run_status VARCHAR(20) DEFAULT NULL,
is_active TINYINT(1) DEFAULT 1,
is_running TINYINT(1) DEFAULT 0,
run_count INT DEFAULT 0,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
INDEX idx_next_run (next_run_time),
INDEX idx_active (is_active)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
"""
self.db.execute_sql(create_sql)
log.success("main_task table created")
def run_pending_tasks(self) -> Dict[str, int]:
"""
执行所有到期的活跃任务
Returns:
Dict[str, int]: 包含执行结果的字典 {
'total': 总任务数,
'success': 成功数,
'failed': 失败数
}
"""
result = {'total': 0, 'success': 0, 'failed': 0}
try:
# 使用您提供的query_to_df方法获取任务
tasks_df = self.db.query_to_df(
"SELECT * FROM main_task "
"WHERE is_active = 1 AND next_run_time <= %s "
"ORDER BY next_run_time",
params=(datetime.now(),)
)
result['total'] = len(tasks_df)
if tasks_df.empty:
log.debug("No pending tasks found")
return result
for _, task in tasks_df.iterrows():
task_id = task['task_id']
log.bind(task_id=task_id).info(
f"Starting task {task['task_name']}"
)
# 标记任务为执行中
self._update_task_status(
task_id,
{'is_running': 1, 'last_run_time': datetime.now()}
)
try:
self._execute_single_task(task)
self._update_task_status(
task_id,
{
'last_run_status': 'success',
'is_running': 0,
'run_count': task['run_count'] + 1,
'next_run_time': self._calculate_next_run(
task['frequency_type'],
task['frequency_value']
)
}
)
result['success'] += 1
log.bind(task_id=task_id).success("Task completed")
except Exception as e:
log.bind(task_id=task_id).error(
f"Task failed: {str(e)}",
exc_info=True
)
self._update_task_status(
task_id,
{
'last_run_status': 'failed',
'is_running': 0,
'next_run_time': self._calculate_next_run(
task['frequency_type'],
task['frequency_value'],
retry=True
)
}
)
result['failed'] += 1
log.info(
"Scheduler cycle completed",
total_tasks=result['total'],
success=result['success'],
failed=result['failed']
)
return result
except Exception as e:
log.critical(
"Scheduler main loop failed",
exc_info=True
)
raise
def _execute_single_task(self, task: Dict) -> None:
"""执行单个任务模块"""
start_time = time.time()
task_log = log.bind(
task_id=task['task_id'],
module=task['module_path']
)
try:
module = importlib.import_module(task['module_path'])
if not hasattr(module, 'main'):
raise ImportError(f"Module has no main() function")
# 执行任务
task_log.debug("Task execution started")
module.main()
elapsed = time.time() - start_time
task_log.info(
f"Task completed in {elapsed:.2f}s",
duration=elapsed
)
except Exception as e:
task_log.error(
"Task execution failed",
exc_info=True
)
raise
def _update_task_status(self, task_id: int, updates: Dict) -> None:
"""更新任务状态"""
set_clause = ", ".join([f"{k}=%s" for k in updates.keys()])
sql = f"UPDATE main_task SET {set_clause} WHERE task_id=%s"
params = list(updates.values()) + [task_id]
try:
affected = self.db.execute_sql(sql, params=params)
if affected != 1:
log.warning(
"Unexpected row count in update",
task_id=task_id,
expected=1,
affected=affected
)
except Exception as e:
log.error(
"Failed to update task status",
task_id=task_id,
exc_info=True
)
raise
def _calculate_next_run(self, freq_type: str, freq_value: Optional[int] = None,
retry: bool = False) -> datetime:
"""
计算下次执行时间(带重试逻辑)
"""
base_time = datetime.now()
if retry:
# 失败后15分钟重试
log.debug("Calculating retry time")
return base_time + timedelta(minutes=15)
if freq_type == 'minute':
delta = timedelta(minutes=freq_value or 1)
elif freq_type == 'hourly':
delta = timedelta(hours=freq_value or 1)
elif freq_type == 'daily':
delta = timedelta(days=freq_value or 1)
elif freq_type == 'weekly':
delta = timedelta(weeks=freq_value or 1)
elif freq_type == 'monthly':
# 处理月末日期特殊情况
next_month = (base_time.replace(day=1) + timedelta(days=32)).replace(day=1)
last_day = (next_month - timedelta(days=1)).day
day = min(base_time.day, last_day)
return base_time.replace(day=1, month=next_month.month, day=day)
else:
raise ValueError(f"Unknown frequency type: {freq_type}")
return base_time + delta
def add_task(self, task_name: str, module_path: str, frequency_type: str,
frequency_value: Optional[int] = None) -> int:
"""
添加新任务到调度系统
"""
sql = """
INSERT INTO main_task
(task_name, module_path, frequency_type, frequency_value, next_run_time)
VALUES (%s, %s, %s, %s, %s)
"""
next_run = self._calculate_next_run(frequency_type, frequency_value)
params = (task_name, module_path, frequency_type, frequency_value, next_run)
try:
self.db.execute_sql(sql, params=params)
task_id = self.db.query_to_df("SELECT LAST_INSERT_ID() AS id").iloc[0]['id']
log.info(
"New task added",
task_id=task_id,
task_name=task_name,
next_run=next_run
)
return task_id
except Exception as e:
log.error(
"Failed to add new task",
task_name=task_name,
exc_info=True
)
raise
def get_task_status(self, active_only: bool = True) -> pd.DataFrame:
"""
获取任务状态
"""
where = "WHERE is_active = 1" if active_only else ""
log.debug("Fetching task status", active_only=active_only)
return self.db.query_to_df(
f"""
SELECT
task_id, task_name, module_path,
frequency_type, frequency_value,
last_run_time, next_run_time,
last_run_status, run_count,
is_active, is_running
FROM main_task
{where}
ORDER BY next_run_time
"""
)