当前位置：首页 > news >正文

基于Python的高效批量处理Splunk Session ID并写入MySQL的解决方案

news 来源：原创 2025/5/13 8:27:55

已经用Python实现对Splunk通过session id获取查询数据，现在要实现Python批量数据获取，通过一个列表中的大量Session ID，快速高效地获取一个数据表，考虑异常处理，多线程和异步操作以提高性能，同时将数据表写入MySQL表，获取数据的成功和失败的状态信息写入.log日志文本文件。

这个方案提供了灵活的可扩展性，可以根据具体业务需求调整数据处理逻辑、数据库结构和并发策略。

import logging
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from queue import Queue
import requests
import pymysql
from pymysql import MySQLError
from pymysql.cursors import DictCursor# 配置日志记录
logging.basicConfig(level=logging.INFO,format='%(asctime)s - %(levelname)s - %(message)s',handlers=[logging.FileHandler('splunk_import.log'),logging.StreamHandler()]
)# Splunk配置
SPLUNK_API_URL = "https://your.splunk.url/services/search/results"
SPLUNK_HEADERS = {"Content-Type": "application/json",# 其他需要的请求头
}# MySQL配置
MYSQL_CONFIG = {"host": "localhost","user": "root","password": "password","database": "splunk_data","charset": "utf8mb4","cursorclass": DictCursor
}# 线程安全的MySQL连接池
class MySQLPool:_lock = threading.Lock()_pool = None@classmethoddef get_connection(cls):with cls._lock:if not cls._pool:cls._pool = pymysql.ConnectionPool(min=2,max=10,**MYSQL_CONFIG)return cls._pool.get_connection()# Splunk数据获取函数
def fetch_splunk_data(session_id):try:params = {"output_mode": "json","session_id": session_id}response = requests.get(SPLUNK_API_URL,headers=SPLUNK_HEADERS,params=params,timeout=30)response.raise_for_status()return {"session_id": session_id,"data": response.json(),"success": True}except Exception as e:return {"session_id": session_id,"error": str(e),"success": False}# 数据处理和存储函数
def process_session(session_id):result = fetch_splunk_data(session_id)if not result['success']:logging.error(f"Session {session_id} failed: {result.get('error')}")return Falsetry:# 解析数据（根据实际数据结构调整）parsed_data = {"session_id": session_id,"event_count": len(result['data'].get('results', [])),"status": "success",# 添加其他需要存储的字段}# 写入MySQLwith MySQLPool.get_connection() as conn:with conn.cursor() as cursor:sql = """INSERT INTO splunk_results (session_id, event_count, status)VALUES (%s, %s, %s)"""cursor.execute(sql, (parsed_data['session_id'],parsed_data['event_count'],parsed_data['status']))conn.commit()logging.info(f"Session {session_id} processed successfully")return Trueexcept MySQLError as e:logging.error(f"MySQL error on {session_id}: {str(e)}")return Falseexcept Exception as e:logging.error(f"Processing error on {session_id}: {str(e)}")return False# 批量处理控制器
def batch_process(session_ids, max_workers=10):success_count = 0failure_count = 0with ThreadPoolExecutor(max_workers=max_workers) as executor:futures = {executor.submit(process_session, sid): sid for sid in session_ids}for future in as_completed(futures):sid = futures[future]try:if future.result():success_count += 1else:failure_count += 1except Exception as e:logging.error(f"Unexpected error processing {sid}: {str(e)}")failure_count += 1logging.info(f"Processing completed. Success: {success_count}, Failed: {failure_count}")# 示例使用
if __name__ == "__main__":# 从文件读取Session ID列表（示例）with open("session_ids.txt") as f:session_ids = [line.strip() for line in f if line.strip()]# 启动批量处理（控制并发数）batch_process(session_ids, max_workers=15)

关键功能说明：

多线程处理：
- 使用ThreadPoolExecutor管理线程池
- 可配置最大并发数（max_workers）
- 实现任务提交和结果跟踪
MySQL连接管理：
- 实现简单的连接池模式
- 确保线程安全的数据库连接
- 自动管理连接获取和释放
异常处理：
- 网络请求异常处理
- 数据库操作异常处理
- 通用异常捕获和日志记录
日志记录：
- 同时输出到文件和终端
- 包含时间戳和错误详情
- 线程安全的日志记录
性能优化：
- 连接复用减少开销
- 异步任务处理
- 批量结果处理

使用说明：

安装依赖：

pip install requests pymysql

准备MySQL表：

CREATE TABLE splunk_results (id INT AUTO_INCREMENT PRIMARY KEY,session_id VARCHAR(255) NOT NULL,event_count INT,status VARCHAR(50),created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

配置文件：

修改SPLUNK_API_URL和请求头
调整MySQL连接参数
根据实际数据结构修改数据解析逻辑

优化建议：

速率限制：

# 在fetch_splunk_data中添加延迟
import time
time.sleep(0.1)  # 控制请求频率

重试机制：

from tenacity import retry, stop_after_attempt, wait_exponential@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def fetch_splunk_data(session_id):# 原有代码

批量写入优化：

# 在process_session中改为批量插入
batch_size = 100
batch = []def save_batch():with MySQLPool.get_connection() as conn:with conn.cursor() as cursor:sql = "INSERT ..."cursor.executemany(sql, batch)conn.commit()# 在处理过程中积累数据
batch.append(data)
if len(batch) >= batch_size:save_batch()batch = []