AutoScraperX/spiders/base_spider.py

import asyncio
import random
import time
import traceback
import uuid
from abc import ABC
from typing import List, Dict, Optional

import aiohttp

from core.models.video_item import VideoItem
from core.utils.helpers import generate_titles
from core.utils.spider_config import SpiderConfig
from core.utils.extractors import safe_extract, extract_fields
from core.utils.log.logger_manager import LoggerManager
from core.utils.template_resolver import resolve_request_body_template
from services.async_mysql_service import AsyncMysqlService
from services.pipeline import PiaoQuanPipeline
from core.base.async_request_client import AsyncRequestClient
from services.async_mq_producer import AsyncMQProducer


import re
PLACEHOLDER_PATTERN = re.compile(r"\{\{\s*([a-zA-Z0-9_.]+)(\|[^}]+)?\s*\}\}")
class BaseSpider(ABC):
    """
    通用爬虫基类：支持严格顺序执行流程
    """

    MAX_RETRIES = 3  # 单个请求最大重试次数
    TIMEOUT = 30  # 请求超时时间（秒）

    def __init__(self, rule_dict: Dict, user_list: List, trace_id: str, env: str = "prod"):
        self.trace_id = trace_id
        self.env = env
        self.user_list = user_list
        self.rule_dict = rule_dict
        self.class_name = self.__class__.__name__  # 获取子类类名

        # 根据类名自动获取配置
        self.platform_config = SpiderConfig.get_platform_config(classname=str(self.class_name.lower()))
        if not self.platform_config:
            raise ValueError(f"找不到对应配置: {self.class_name}")

        # 初始化日志和MQ
        self.platform = self.platform_config.platform
        self.mode = self.platform_config.mode
        self.logger = LoggerManager.get_logger(platform=self.platform, mode=self.mode)
        self.logger.info(f"{trace_id}--初始化爬虫类: {self.class_name}")
        self.aliyun_logr = LoggerManager.get_aliyun_logger(platform=self.platform, mode=self.mode)
        self.mq_producer = AsyncMQProducer(topic_name="topic_crawler_etl_prod_v2",platform=self.platform,mode=self.mode)

        # 请求配置
        self.method = self.platform_config.method.upper()
        self.url = self.platform_config.url
        self.headers = self.platform_config.headers
        self.body = self.platform_config.request_body


        self.response =self.platform_config.response_parse
        self.field_map =  self.response.get("fields", {})
        self.data_path =  self.response.get("data_path")
        self.next_cursor_path = self.response.get("next_cursor")


        # 流程控制配置
        self.loop_times = self.platform_config.loop_times  # 循环次数
        self.loop_interval = self.platform_config.loop_interval  # 循环间隔（秒）

        self.db_service = AsyncMysqlService(platform=self.platform, mode=self.mode)
        self.request_client = AsyncRequestClient(logger=self.logger,aliyun_log=self.aliyun_logr)

        self.logger.info(
            f"{self.trace_id}--配置: 循环{self.loop_times}次，间隔{self.loop_interval}秒")

        self.session = None
        self.feishu_sheetid = self.platform_config.feishu_sheetid

        # 动态检测分页变量名
        self.cursor_variable_name = None
        body_str = str(self.body)
        match = PLACEHOLDER_PATTERN.search(body_str)
        if match:
            self.cursor_variable_name = match.group(1)
            self.logger.info(f"{self.trace_id}--检测到分页变量名: {self.cursor_variable_name}")
        else:
            self.logger.info(f"{self.trace_id}--未检测到分页变量名，默认不分页")

        self.current_cursor = None


    async def crawl_data(self,session,dynamic_variables=None) -> Optional[List[Dict]]:
        dynamic_variables = dynamic_variables or {}
        if self.cursor_variable_name:
            dynamic_variables[self.cursor_variable_name] = self.current_cursor or ""

        resolved_body = resolve_request_body_template(self.body, dynamic_variables)

        response = await self.request_client.request(
            session=session,
            method=self.method,
            url=self.url,
            headers=self.headers,
            json=resolved_body
        )

        if self.next_cursor_path:
            extracted_cursor = safe_extract(response, self.next_cursor_path)
            self.current_cursor = extracted_cursor if extracted_cursor else None
            self.logger.info(f"{self.trace_id}--解析到下一页 {self.cursor_variable_name}: {self.current_cursor}")

        data = safe_extract(response, self.data_path)
        return data if data else []

    async def filter_data(self, video: Dict) -> bool:
        """校验视频是否符合规则"""
        pipeline = PiaoQuanPipeline(
            platform=self.platform,
            mode=self.mode,
            rule_dict=self.rule_dict,
            env=self.env,
            item=video,
            trace_id=self.platform + str(uuid.uuid1())
        )
        return await pipeline.process_item()

    async def is_video_count_sufficient(self) -> bool:
        """
        校验视频是否达到当日最大量
        :return:True False
        """
        rule_videos_cnt = self.rule_dict.get("videos_cnt")
        if not rule_videos_cnt:
            return True
        async with AsyncMysqlService(self.platform, self.mode) as mysql:
            video_count = await mysql.get_today_videos()
        if video_count >= rule_videos_cnt.get("min", 200):
            self.logger.info(f"{self.trace_id}--今日视频已达到最大量{video_count}")
            self.aliyun_logr.logging(
                code="1011",
                message=f"视频数量达到当日最大值",
                data=f"<今日视频数量>{video_count}"
            )
            return False
        self.logger.info(f"{self.trace_id}--今日视频已入库{video_count}")
        return True

    async def process_video(self, video: Dict) -> Optional[Dict]:
        """
        处理单条视频数据,字段映射关系，必要字段检验
        :param video:
        :return:
        """
        self.logger.debug(f"{self.trace_id}--开始处理视频: {video.get('title', '无标题')}")
        publish_user = random.choice(self.user_list)
        item_kwargs = extract_fields(video, self.field_map, logger=self.logger, trace_id=self.trace_id,aliyun_log=self.aliyun_logr)
        item_kwargs["user_id"] = publish_user["uid"]
        item_kwargs["user_name"] = publish_user["nick_name"]
        item_kwargs["platform"] = self.platform
        item_kwargs["strategy"] = self.mode

        try:
            item = VideoItem(**item_kwargs)
            video_dict = await item.produce_item()
            if not video_dict:
                self.logger.warning(f"{self.trace_id} 校验失败")
                return None
            return video_dict
        except Exception as e:
            self.logger.error(f"{self.trace_id} VideoItem 初始化失败: {e}")
            return None

    async def push_to_etl(self, item: Dict) -> bool:
        """推送数据到ETL（同步）"""
        self.logger.info(f"{self.trace_id}--开始推送数据到ETL: {item.get('video_id', item.get('title', '无标题'))}")
        try:
            await self.mq_producer.send_msg(item)
            self.aliyun_logr.logging(
                code="1009",
                message="成功发送至ETL",
                data=item,
                trace_id=self.trace_id
            )
            self.logger.info(f"{self.trace_id}--数据推送成功")
            return True
        except Exception as e:
            self.logger.exception(f"{self.trace_id}--数据推送失败: {e}, 内容: {item}")
            return False

    async def get_today_videos(self):
        """
        查询每天的爬虫爬取到的视频数量
        :return:
        """
        video_count = await self.db_service.get_today_videos()
        return video_count

    async def integrated_video_handling(self,video: Dict) -> Optional[Dict]:
        """
        视频处理
        :return:
        """
        await generate_titles(self.feishu_sheetid,video)

    async def run(self):
        """
        异步运行爬虫任务，严格按顺序执行
        1. 爬取
        2. 处理每条数据，字段校验
        3. 过滤（重复，平台规则，标题，发布时间）
        4. 标题处理
        5. 推送到ETL
        """
        try:
            total_success,total_failed= 0,0
            loop_start_time = time.time()
            async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=self.TIMEOUT)) as session:
                for loop_index in range(1, self.loop_times + 1):
                    # 判断当日视频数量已达到最大量
                    if not await self.is_video_count_sufficient():
                        return
                    self.logger.info(f"{self.trace_id}--步骤1: 开始第 {loop_index}/{self.loop_times} 次循环请求")
                    # 请求视频列表
                    video_list = await self.crawl_data(session)
                    if not video_list:
                        self.logger.warning(f"{self.trace_id}--未获取到视频数据，跳过当前循环")
                        await self._wait_for_next_loop(loop_index)
                        continue
                    success_count = 0
                    fail_count = 0


                    for video in video_list:
                        # 提取视频字段映射关系
                        video_obj = await self.process_video(video)
                        if not video_obj:
                            self.logger.warning(f"{self.trace_id}--视频处理失败，已跳过")
                            fail_count += 1
                            continue
                        # 视频过滤规则
                        if not await self.filter_data(video_obj):
                            self.logger.debug(f"{self.trace_id}--视频不符合规则，已跳过")
                            continue
                        # 视频处理
                        await self.integrated_video_handling(video_obj)

                        if await self.push_to_etl(video_obj):
                            success_count += 1
                        else:
                            fail_count += 1

                    total_success += success_count
                    total_failed += fail_count

                    loop_duration = time.time() - loop_start_time
                    self.logger.info(f"{self.trace_id}--第 {loop_index}/{self.loop_times} 次循环完成. "
                                     f"成功: {success_count}, 失败: {fail_count}, 耗时: {loop_duration:.2f}秒")

                    await self._wait_for_next_loop(loop_index)

                # 全局指标日志
                self.aliyun_logr.logging(
                    code="1003",
                    message="爬虫执行指标汇总",
                    data={
                        "trace_id": self.trace_id,
                        "classname": self.platform,
                        "success_count": total_success,
                        "fail_count": total_failed
                    },
                    trace_id=self.trace_id
                )

                self.logger.info(
                    f"{self.trace_id}--[{self.platform}] 爬虫任务全部完成，总成功: {total_success}, 总失败: {total_failed}")
                return True
        except Exception as e:
            self.logger.error(f"爬虫致命错误: {e}")
            raise

    async def _wait_for_next_loop(self, current_loop: int) -> None:
        """等待下一次循环请求"""
        if current_loop < self.loop_times and self.loop_interval > 0:
            self.logger.info(f"{self.trace_id}--等待 {self.loop_interval} 秒后进行下一次请求")
            await asyncio.sleep(self.loop_interval)