1259 lines
40 KiB
Python
1259 lines
40 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Source-aware structured cleaning (MySQL).
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Input table: messages
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Output table: structured_jobs
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"""
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import json
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import logging
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import os
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import re
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from dataclasses import dataclass
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import pymysql
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CONFIG_FILE = "config.json"
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PIPELINE_NAME = "structured_cleaner_v1"
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URL_RE = re.compile(r"https?://[^\s)]+", re.IGNORECASE)
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EMAIL_RE = re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.IGNORECASE)
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TG_RE = re.compile(r"(?<!\w)@[a-zA-Z0-9_]{4,}")
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HASHTAG_RE = re.compile(r"#([A-Za-z0-9_\-\u4e00-\u9fff·]+)")
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MD_TAG_RE = re.compile(r"#(?:\*\*)?([A-Za-z0-9_\-\u4e00-\u9fff·]+)(?:\*\*)?")
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SECTION_KEYS = (
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"简介",
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"合作方式",
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"待招岗位",
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"薪酬福利",
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"岗位职责",
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"岗位要求",
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"申请方式",
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"岗位来源",
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)
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@dataclass
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class StructuredJob:
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source: str
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source_channel: str | None
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parser_name: str
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parser_version: str
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chat_id: int | None
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message_id: int
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message_date: str
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job_type: str | None
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company_name: str | None
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industry_tags: list[str]
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company_intro: str | None
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company_url: str | None
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work_mode: str
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job_nature: str
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job_location_text: str | None
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job_location_tags: list[str] | None
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employment_type_raw: str | None
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position_name: str | None
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position_tags: list[str]
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salary_raw: str | None
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salary_currency: str | None
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salary_min: int | None
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salary_max: int | None
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salary_period: str | None
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responsibilities: list[str]
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requirements: list[str]
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apply_email: str | None
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apply_telegram: str | None
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job_source_url: str | None
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body_text: str
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raw_content: str
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def setup_logger() -> logging.Logger:
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os.makedirs("logs", exist_ok=True)
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logger = logging.getLogger("clean_to_structured")
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logger.setLevel(logging.INFO)
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if logger.handlers:
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return logger
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fmt = logging.Formatter(
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"[%(asctime)s] [%(levelname)s] %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
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)
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ch = logging.StreamHandler()
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ch.setFormatter(fmt)
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fh = logging.FileHandler("logs/clean_to_structured.log", encoding="utf-8")
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fh.setFormatter(fmt)
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logger.addHandler(ch)
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logger.addHandler(fh)
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return logger
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logger = setup_logger()
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def load_mysql_config() -> dict:
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if not os.path.exists(CONFIG_FILE):
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raise FileNotFoundError(f"未找到配置文件: {CONFIG_FILE}")
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with open(CONFIG_FILE, "r", encoding="utf-8") as f:
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cfg = json.load(f)
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mysql_cfg = cfg.get("mysql", {})
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if not isinstance(mysql_cfg, dict):
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raise ValueError("配置错误: mysql 必须是对象")
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result = {
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"host": mysql_cfg.get("host") or os.getenv("MYSQL_HOST", "127.0.0.1"),
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"port": int(mysql_cfg.get("port") or os.getenv("MYSQL_PORT", "3306")),
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"user": mysql_cfg.get("user") or os.getenv("MYSQL_USER", "jobs_user"),
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"password": mysql_cfg.get("password") or os.getenv("MYSQL_PASSWORD", ""),
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"database": mysql_cfg.get("database") or os.getenv("MYSQL_DATABASE", "jobs"),
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"charset": mysql_cfg.get("charset") or os.getenv("MYSQL_CHARSET", "utf8mb4"),
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}
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if not result["password"]:
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raise ValueError("配置错误: mysql.password 不能为空")
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return result
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def connect_mysql(cfg: dict):
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conn = pymysql.connect(
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host=cfg["host"],
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port=cfg["port"],
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user=cfg["user"],
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password=cfg["password"],
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database=cfg["database"],
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charset=cfg["charset"],
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autocommit=True,
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)
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with conn.cursor() as cur:
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cur.execute("SET time_zone = '+00:00'")
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return conn
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def init_target_db(conn):
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with conn.cursor() as cur:
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cur.execute(
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"""
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CREATE TABLE IF NOT EXISTS structured_jobs (
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id BIGINT PRIMARY KEY AUTO_INCREMENT,
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source VARCHAR(255) NOT NULL,
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source_channel VARCHAR(255) NULL,
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parser_name VARCHAR(64) NOT NULL,
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parser_version VARCHAR(32) NOT NULL,
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chat_id BIGINT NULL,
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message_id BIGINT NOT NULL,
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message_date DATETIME NOT NULL,
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job_type VARCHAR(64) NULL,
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company_name VARCHAR(255) NULL,
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industry_tags_json JSON NOT NULL,
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company_intro LONGTEXT NULL,
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company_url TEXT NULL,
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work_mode VARCHAR(32) NOT NULL,
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job_nature VARCHAR(32) NOT NULL,
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job_location_text VARCHAR(255) NULL,
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job_location_tags_json JSON NULL,
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employment_type_raw TEXT NULL,
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position_name VARCHAR(255) NULL,
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position_tags_json JSON NOT NULL,
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salary_raw TEXT NULL,
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salary_currency VARCHAR(16) NULL,
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salary_min BIGINT NULL,
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salary_max BIGINT NULL,
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salary_period VARCHAR(16) NULL,
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responsibilities_json JSON NOT NULL,
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requirements_json JSON NOT NULL,
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apply_email VARCHAR(255) NULL,
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apply_telegram VARCHAR(255) NULL,
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job_source_url TEXT NULL,
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body_text LONGTEXT NOT NULL,
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raw_content LONGTEXT NOT NULL,
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cleaned_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP
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ON UPDATE CURRENT_TIMESTAMP,
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UNIQUE KEY uk_source_message (source, message_id),
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KEY idx_structured_source_date (source, message_date)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
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"""
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)
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cur.execute(
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"""
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CREATE TABLE IF NOT EXISTS clean_state (
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pipeline_name VARCHAR(128) PRIMARY KEY,
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last_message_row_id BIGINT NOT NULL DEFAULT 0,
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updated_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP
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ON UPDATE CURRENT_TIMESTAMP
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
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"""
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)
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def dedupe(values: list[str]) -> list[str]:
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seen = set()
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out = []
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for v in values:
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if not v:
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continue
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if v in seen:
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continue
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seen.add(v)
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out.append(v)
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return out
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def clean_md_text(s: str) -> str:
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s = re.sub(r"\*+", "", s)
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s = re.sub(r"~+", "", s)
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s = s.replace("`", "").strip()
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return re.sub(r"\s+", " ", s).strip()
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def normalize_md_line(s: str) -> str:
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s = s.replace("**", "").replace("`", "")
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s = s.replace("~~", "")
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s = s.replace("\u3000", " ")
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return re.sub(r"\s+", " ", s).strip()
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def clean_company_name(s: str | None) -> str | None:
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if not s:
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return None
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s = clean_md_text(s)
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s = s.strip(" -|:：#")
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return s or None
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def infer_salary_currency(text: str) -> str | None:
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low = text.lower()
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if any(k in low for k in ["usd", "us$", "dollar"]) or "$" in text:
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return "USD"
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if any(k in text for k in ["¥", "￥", "人民币"]) or "cny" in low:
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return "CNY"
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if "元" in text or "万" in text:
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return "CNY"
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if "k" in low and any(k in low for k in ["month", "year", "day"]):
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return "USD"
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if "eur" in low or "€" in text:
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return "EUR"
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if "hkd" in low or "hk$" in low:
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return "HKD"
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if "sgd" in low or "s$" in low:
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return "SGD"
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# No explicit marker: infer by language.
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if re.search(r"[\u4e00-\u9fff]", text):
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return "CNY"
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return "USD"
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def parse_salary(
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raw: str | None,
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) -> tuple[str | None, str | None, int | None, int | None, str | None]:
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if not raw:
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return None, None, None, None, None
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text = clean_md_text(raw)
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lower = text.lower()
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currency = infer_salary_currency(text)
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num_tokens = re.findall(r"(\d+(?:\.\d+)?)\s*([kKwW万]?)", text.replace(",", ""))
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salary_min = None
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salary_max = None
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if num_tokens:
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vals: list[tuple[float, str]] = [(float(n), u) for n, u in num_tokens]
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if len(vals) >= 2:
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u1 = vals[0][1]
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u2 = vals[1][1]
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if not u1 and u2:
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vals[0] = (vals[0][0], u2)
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if not u2 and u1:
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vals[1] = (vals[1][0], u1)
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def scaled(v: float, unit: str) -> int:
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m = 1
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if unit in ("k", "K"):
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m = 1000
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elif unit in ("w", "W", "万"):
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m = 10000
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return int(v * m)
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salary_min = scaled(vals[0][0], vals[0][1]) if len(vals) >= 1 else None
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salary_max = scaled(vals[1][0], vals[1][1]) if len(vals) >= 2 else None
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period = None
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if "month" in lower or "每月" in text or "月" in text:
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period = "month"
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elif "year" in lower or "年" in text:
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period = "year"
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elif "day" in lower or "日" in text:
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period = "day"
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return text, currency, salary_min, salary_max, period
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def strip_meta_lines(content: str) -> str:
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lines = []
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for ln in (content or "").splitlines():
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if ln.startswith("[MEDIA_TYPE] "):
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continue
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if ln.startswith("[ACTION_TYPE] "):
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continue
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if ln.startswith("[MEDIA_JSON] "):
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continue
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if ln.startswith("[ACTION_JSON] "):
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continue
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if ln.startswith("phones="):
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continue
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if ln.startswith("[MEDIA_TEXT] "):
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continue
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lines.append(ln.rstrip())
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return "\n".join(lines).strip()
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def preprocess_body_text(body_text: str) -> str:
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text = re.sub(r"\[([^\]]+)\]\((https?://[^)\s]+)\)", r"\1 \2", body_text)
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text = text.replace("**", "").replace("__", "").replace("~~", "").replace("`", "")
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lines = [re.sub(r"[ \t]+", " ", ln).strip() for ln in text.splitlines()]
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return "\n".join(lines).strip()
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def extract_section(body_text: str, section_name: str) -> str | None:
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lines = body_text.splitlines()
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start = None
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for i, ln in enumerate(lines):
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if section_name in ln:
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start = i + 1
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break
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if start is None:
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return None
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collected = []
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for ln in lines[start:]:
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if any(k in ln for k in SECTION_KEYS):
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break
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collected.append(ln)
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text = "\n".join(collected).strip()
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return text or None
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def extract_first_url(text: str | None) -> str | None:
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if not text:
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return None
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urls = URL_RE.findall(text)
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return urls[0] if urls else None
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def extract_job_source_url(body_text: str) -> str | None:
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for ln in body_text.splitlines():
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if "岗位来源" in ln:
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urls = URL_RE.findall(ln)
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if urls:
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return urls[0]
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return None
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def extract_company_name_dejob(body_text: str) -> str | None:
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for ln in body_text.splitlines():
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if "🏡" in ln:
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no_md = clean_md_text(ln)
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no_md = no_md.replace("🏡", "").strip()
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if "#" in no_md:
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no_md = no_md.split("#", 1)[0].strip()
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return clean_company_name(no_md)
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return None
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def extract_tags_after_key(line: str, key: str) -> list[str]:
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if key not in line:
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return []
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frag = normalize_md_line(line.split(key, 1)[1])
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tags = [clean_md_text(t).replace("·", " ").strip() for t in MD_TAG_RE.findall(frag)]
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return dedupe([t for t in tags if t])
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def extract_list_section(body_text: str, key: str) -> list[str]:
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sec = extract_section(body_text, key)
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if not sec:
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return []
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items = []
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for ln in sec.splitlines():
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t = clean_md_text(ln)
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t = re.sub(r"^\d+️⃣?\s*", "", t)
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t = re.sub(r"^\d+[\.、]\s*", "", t)
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if t:
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items.append(t)
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return items
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def extract_position_name_dejob(body_text: str) -> str | None:
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for ln in body_text.splitlines():
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if "待招岗位" in ln:
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tags = extract_tags_after_key(ln, "待招岗位")
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if tags:
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return tags[0]
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return None
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def extract_apply_email(body_text: str) -> str | None:
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emails = EMAIL_RE.findall(body_text)
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return emails[0] if emails else None
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def extract_apply_telegram(body_text: str) -> str | None:
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for ln in body_text.splitlines():
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if "Telegram" in ln:
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m = TG_RE.search(ln)
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if m:
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return m.group(0)
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handles = TG_RE.findall(body_text)
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return handles[0] if handles else None
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def extract_urls(body_text: str) -> list[str]:
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return dedupe(URL_RE.findall(body_text))
|
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|
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|
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def extract_first_url_by_keyword(body_text: str, keywords: list[str]) -> str | None:
|
||
urls = extract_urls(body_text)
|
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for u in urls:
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lu = u.lower()
|
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if any(k.lower() in lu for k in keywords):
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return u
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return None
|
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|
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|
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def extract_first_nonempty_line(body_text: str) -> str | None:
|
||
for ln in body_text.splitlines():
|
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t = clean_md_text(ln)
|
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if t:
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return t
|
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return None
|
||
|
||
|
||
def normalize_possible_url(raw: str) -> str | None:
|
||
token = clean_md_text(raw or "")
|
||
if not token:
|
||
return None
|
||
token = token.strip("()[]<>.,;\"' ")
|
||
if not token:
|
||
return None
|
||
if token.lower().startswith(("http://", "https://")):
|
||
return token
|
||
if token.lower().startswith("www."):
|
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return "https://" + token
|
||
# simple domain-style fallback, e.g. company.com/apply
|
||
if " " not in token and "." in token and "/" in token:
|
||
return "https://" + token
|
||
if " " not in token and re.fullmatch(r"[A-Za-z0-9.-]+\.[A-Za-z]{2,}", token):
|
||
return "https://" + token
|
||
return None
|
||
|
||
|
||
def extract_apply_link(body_text: str) -> str | None:
|
||
# Priority 1: explicit apply-like lines.
|
||
for ln in body_text.splitlines():
|
||
low = ln.lower()
|
||
if "apply" not in low and "申请" not in ln and "投递" not in ln:
|
||
continue
|
||
|
||
# direct URL in line
|
||
line_urls = URL_RE.findall(ln)
|
||
if line_urls:
|
||
return line_urls[0]
|
||
|
||
# try parse right side after ':' / '-'
|
||
if ":" in ln:
|
||
rhs = ln.split(":", 1)[1]
|
||
elif "：" in ln:
|
||
rhs = ln.split("：", 1)[1]
|
||
elif "-" in ln:
|
||
rhs = ln.split("-", 1)[1]
|
||
else:
|
||
rhs = ln
|
||
|
||
for token in re.split(r"\s+", rhs.strip()):
|
||
u = normalize_possible_url(token)
|
||
if u:
|
||
return u
|
||
|
||
# Priority 2: first URL that looks like an apply page.
|
||
for u in extract_urls(body_text):
|
||
lu = u.lower()
|
||
if "apply" in lu or "job" in lu or "careers" in lu:
|
||
return u
|
||
return None
|
||
|
||
|
||
def infer_employment_fields(
|
||
tags: list[str], raw_line: str | None
|
||
) -> tuple[str, str, str | None, list[str] | None, str | None]:
|
||
mode_remote = {"远程", "remote", "居家", "在家办公", "home office", "wfh"}
|
||
mode_onsite = {"实地", "现场", "线下", "onsite", "on-site", "坐班", "到岗"}
|
||
nature_map = {
|
||
"全职": "full_time",
|
||
"兼职": "part_time",
|
||
"实习": "intern",
|
||
"合同": "contract",
|
||
"contract": "contract",
|
||
"自由职业": "freelance",
|
||
"freelance": "freelance",
|
||
}
|
||
nature_priority = ["full_time", "part_time", "contract", "intern", "freelance"]
|
||
|
||
normalized = []
|
||
for t in tags:
|
||
n = clean_md_text(t).replace("·", " ").strip()
|
||
if n:
|
||
normalized.append(n)
|
||
normalized = dedupe(normalized)
|
||
|
||
has_remote = False
|
||
has_onsite = False
|
||
natures_found = []
|
||
locations = []
|
||
|
||
for tag in normalized:
|
||
low = tag.lower()
|
||
if low in mode_remote or tag in mode_remote:
|
||
has_remote = True
|
||
continue
|
||
if low in mode_onsite or tag in mode_onsite:
|
||
has_onsite = True
|
||
continue
|
||
|
||
mapped = nature_map.get(tag) or nature_map.get(low)
|
||
if mapped:
|
||
natures_found.append(mapped)
|
||
continue
|
||
|
||
locations.append(tag)
|
||
|
||
if has_remote and has_onsite:
|
||
work_mode = "hybrid"
|
||
elif has_remote:
|
||
work_mode = "remote"
|
||
elif has_onsite:
|
||
work_mode = "onsite"
|
||
else:
|
||
work_mode = "unknown"
|
||
|
||
job_nature = "unknown"
|
||
for cand in nature_priority:
|
||
if cand in natures_found:
|
||
job_nature = cand
|
||
break
|
||
|
||
location_tags_raw = dedupe(locations)
|
||
location_text = location_tags_raw[0] if location_tags_raw else None
|
||
location_tags: list[str] | None = location_tags_raw if location_tags_raw else None
|
||
raw = clean_md_text(raw_line) if raw_line else None
|
||
|
||
return work_mode, job_nature, location_text, location_tags, raw
|
||
|
||
|
||
def parse_dejob_official(
|
||
source: str,
|
||
chat_id: int | None,
|
||
message_id: int,
|
||
message_date: str,
|
||
body_text: str,
|
||
raw_content: str,
|
||
) -> StructuredJob:
|
||
job_type = "招聘" if ("招聘" in body_text or "Recruitment" in body_text) else None
|
||
|
||
company_name = extract_company_name_dejob(body_text)
|
||
|
||
industry_tags = []
|
||
for ln in body_text.splitlines():
|
||
if "🏡" in ln:
|
||
norm_ln = normalize_md_line(ln)
|
||
industry_tags = [
|
||
clean_md_text(t).replace("·", " ") for t in MD_TAG_RE.findall(norm_ln)
|
||
]
|
||
industry_tags = dedupe([t for t in industry_tags if t])
|
||
break
|
||
|
||
cooperation_tags = []
|
||
cooperation_line = None
|
||
for ln in body_text.splitlines():
|
||
if "合作方式" in ln:
|
||
cooperation_line = ln
|
||
norm_ln = normalize_md_line(ln)
|
||
cooperation_tags = [
|
||
clean_md_text(t).replace("·", " ") for t in MD_TAG_RE.findall(norm_ln)
|
||
]
|
||
cooperation_tags = dedupe([t for t in cooperation_tags if t])
|
||
break
|
||
(
|
||
work_mode,
|
||
job_nature,
|
||
job_location_text,
|
||
job_location_tags,
|
||
employment_type_raw,
|
||
) = infer_employment_fields(cooperation_tags, cooperation_line)
|
||
|
||
position_tags = []
|
||
for ln in body_text.splitlines():
|
||
if "待招岗位" in ln:
|
||
norm_ln = normalize_md_line(ln)
|
||
position_tags = [
|
||
clean_md_text(t).replace("·", " ") for t in MD_TAG_RE.findall(norm_ln)
|
||
]
|
||
position_tags = dedupe([t for t in position_tags if t])
|
||
break
|
||
|
||
position_name = extract_position_name_dejob(body_text)
|
||
|
||
intro_sec = extract_section(body_text, "简介")
|
||
company_url = extract_first_url(intro_sec) or extract_first_url(body_text)
|
||
company_intro = None
|
||
if intro_sec:
|
||
intro_lines = [ln for ln in intro_sec.splitlines() if not URL_RE.search(ln)]
|
||
company_intro = clean_md_text("\n".join(intro_lines)) or None
|
||
|
||
salary_line = None
|
||
for ln in body_text.splitlines():
|
||
if "薪酬" in ln or "Salary" in ln or "salary" in ln:
|
||
salary_line = ln
|
||
break
|
||
|
||
salary_raw, salary_currency, salary_min, salary_max, salary_period = parse_salary(
|
||
salary_line
|
||
)
|
||
|
||
responsibilities = extract_list_section(body_text, "岗位职责")
|
||
requirements = extract_list_section(body_text, "岗位要求")
|
||
|
||
apply_email = extract_apply_email(body_text)
|
||
apply_tg = extract_apply_telegram(body_text)
|
||
job_source_url = extract_job_source_url(body_text)
|
||
|
||
return StructuredJob(
|
||
source=source,
|
||
source_channel="DeJob",
|
||
parser_name="dejob_official",
|
||
parser_version="v1",
|
||
chat_id=chat_id,
|
||
message_id=message_id,
|
||
message_date=message_date,
|
||
job_type=job_type,
|
||
company_name=company_name,
|
||
industry_tags=industry_tags,
|
||
company_intro=company_intro,
|
||
company_url=company_url,
|
||
work_mode=work_mode,
|
||
job_nature=job_nature,
|
||
job_location_text=job_location_text,
|
||
job_location_tags=job_location_tags,
|
||
employment_type_raw=employment_type_raw,
|
||
position_name=position_name,
|
||
position_tags=position_tags,
|
||
salary_raw=salary_raw,
|
||
salary_currency=salary_currency,
|
||
salary_min=salary_min,
|
||
salary_max=salary_max,
|
||
salary_period=salary_period,
|
||
responsibilities=responsibilities,
|
||
requirements=requirements,
|
||
apply_email=apply_email,
|
||
apply_telegram=apply_tg,
|
||
job_source_url=job_source_url,
|
||
body_text=body_text or "empty_message",
|
||
raw_content=raw_content,
|
||
)
|
||
|
||
|
||
def parse_generic(
|
||
source: str,
|
||
chat_id: int | None,
|
||
message_id: int,
|
||
message_date: str,
|
||
body_text: str,
|
||
raw_content: str,
|
||
) -> StructuredJob:
|
||
hashtags = [h.replace("·", " ").strip() for h in HASHTAG_RE.findall(body_text)]
|
||
hashtags = dedupe([h for h in hashtags if h])
|
||
|
||
urls = URL_RE.findall(body_text)
|
||
emails = EMAIL_RE.findall(body_text)
|
||
tgs = TG_RE.findall(body_text)
|
||
|
||
title = None
|
||
for ln in body_text.splitlines():
|
||
t = clean_md_text(ln)
|
||
if t:
|
||
title = t[:120]
|
||
break
|
||
|
||
salary_line = None
|
||
for ln in body_text.splitlines():
|
||
if any(k in ln.lower() for k in ("salary", "薪资", "薪酬", "k/", "$")):
|
||
salary_line = ln
|
||
break
|
||
|
||
salary_raw, salary_currency, salary_min, salary_max, salary_period = parse_salary(
|
||
salary_line
|
||
)
|
||
|
||
job_type = "招聘" if ("招聘" in body_text or "recruit" in body_text.lower()) else None
|
||
(
|
||
work_mode,
|
||
job_nature,
|
||
job_location_text,
|
||
job_location_tags,
|
||
employment_type_raw,
|
||
) = infer_employment_fields(hashtags, None)
|
||
|
||
return StructuredJob(
|
||
source=source,
|
||
source_channel=None,
|
||
parser_name="generic",
|
||
parser_version="v1",
|
||
chat_id=chat_id,
|
||
message_id=message_id,
|
||
message_date=message_date,
|
||
job_type=job_type,
|
||
company_name=None,
|
||
industry_tags=hashtags,
|
||
company_intro=None,
|
||
company_url=urls[0] if urls else None,
|
||
work_mode=work_mode,
|
||
job_nature=job_nature,
|
||
job_location_text=job_location_text,
|
||
job_location_tags=job_location_tags,
|
||
employment_type_raw=employment_type_raw,
|
||
position_name=title,
|
||
position_tags=hashtags,
|
||
salary_raw=salary_raw,
|
||
salary_currency=salary_currency,
|
||
salary_min=salary_min,
|
||
salary_max=salary_max,
|
||
salary_period=salary_period,
|
||
responsibilities=[],
|
||
requirements=[],
|
||
apply_email=emails[0] if emails else None,
|
||
apply_telegram=tgs[0] if tgs else None,
|
||
job_source_url=None,
|
||
body_text=body_text or "empty_message",
|
||
raw_content=raw_content,
|
||
)
|
||
|
||
|
||
def parse_dejob_global(
|
||
source: str,
|
||
chat_id: int | None,
|
||
message_id: int,
|
||
message_date: str,
|
||
body_text: str,
|
||
raw_content: str,
|
||
) -> StructuredJob:
|
||
job_type = "招聘" if ("招聘" in body_text or "recruit" in body_text.lower()) else None
|
||
|
||
company_name = extract_company_name_dejob(body_text)
|
||
|
||
industry_tags = []
|
||
for ln in body_text.splitlines():
|
||
if "🏡" in ln:
|
||
norm_ln = normalize_md_line(ln)
|
||
industry_tags = [
|
||
clean_md_text(t).replace("·", " ") for t in MD_TAG_RE.findall(norm_ln)
|
||
]
|
||
industry_tags = dedupe([t for t in industry_tags if t])
|
||
break
|
||
if not industry_tags:
|
||
industry_tags = [h.replace("·", " ").strip() for h in HASHTAG_RE.findall(body_text)]
|
||
industry_tags = dedupe([h for h in industry_tags if h])
|
||
|
||
cooperation_tags = []
|
||
cooperation_line = None
|
||
for ln in body_text.splitlines():
|
||
low = ln.lower()
|
||
if "合作方式" in ln or "fulltime" in low or "parttime" in low or "remote" in low:
|
||
cooperation_line = ln
|
||
norm_ln = normalize_md_line(ln)
|
||
cooperation_tags = [
|
||
clean_md_text(t).replace("·", " ") for t in MD_TAG_RE.findall(norm_ln)
|
||
]
|
||
cooperation_tags = dedupe([t for t in cooperation_tags if t])
|
||
break
|
||
(
|
||
work_mode,
|
||
job_nature,
|
||
job_location_text,
|
||
job_location_tags,
|
||
employment_type_raw,
|
||
) = infer_employment_fields(cooperation_tags, cooperation_line)
|
||
|
||
position_tags = []
|
||
for ln in body_text.splitlines():
|
||
if "待招岗位" in ln or "📚" in ln:
|
||
norm_ln = normalize_md_line(ln)
|
||
position_tags = [
|
||
clean_md_text(t).replace("·", " ") for t in MD_TAG_RE.findall(norm_ln)
|
||
]
|
||
position_tags = dedupe([t for t in position_tags if t])
|
||
break
|
||
if not position_tags:
|
||
position_tags = industry_tags
|
||
|
||
position_name = position_tags[0] if position_tags else extract_first_nonempty_line(body_text)
|
||
|
||
intro_sec = extract_section(body_text, "Introduction") or extract_section(body_text, "简介")
|
||
urls = extract_urls(body_text)
|
||
company_url = extract_first_url_by_keyword(body_text, ["dejob.top/jobDetail"])
|
||
if company_url and urls:
|
||
for u in urls:
|
||
if "dejob.top/jobDetail" not in u:
|
||
company_url = u
|
||
break
|
||
if not company_url:
|
||
company_url = extract_first_url(intro_sec) or (urls[0] if urls else None)
|
||
|
||
company_intro = None
|
||
if intro_sec:
|
||
intro_lines = [ln for ln in intro_sec.splitlines() if not URL_RE.search(ln)]
|
||
company_intro = clean_md_text("\n".join(intro_lines)) or None
|
||
|
||
salary_line = None
|
||
for ln in body_text.splitlines():
|
||
if "薪酬" in ln or "salary" in ln.lower():
|
||
salary_line = ln
|
||
break
|
||
salary_raw, salary_currency, salary_min, salary_max, salary_period = parse_salary(
|
||
salary_line
|
||
)
|
||
|
||
responsibilities = extract_list_section(body_text, "岗位职责") or extract_list_section(
|
||
body_text, "Responsibilities"
|
||
)
|
||
requirements = extract_list_section(body_text, "岗位要求") or extract_list_section(
|
||
body_text, "Requirements"
|
||
)
|
||
|
||
apply_email = extract_apply_email(body_text)
|
||
apply_tg = extract_apply_telegram(body_text)
|
||
job_source_url = extract_first_url_by_keyword(body_text, ["dejob.top/jobDetail"])
|
||
if not job_source_url:
|
||
urls = extract_urls(body_text)
|
||
job_source_url = urls[0] if urls else None
|
||
|
||
return StructuredJob(
|
||
source=source,
|
||
source_channel="DeJob",
|
||
parser_name="dejob_global",
|
||
parser_version="v1",
|
||
chat_id=chat_id,
|
||
message_id=message_id,
|
||
message_date=message_date,
|
||
job_type=job_type,
|
||
company_name=company_name,
|
||
industry_tags=industry_tags,
|
||
company_intro=company_intro,
|
||
company_url=company_url,
|
||
work_mode=work_mode,
|
||
job_nature=job_nature,
|
||
job_location_text=job_location_text,
|
||
job_location_tags=job_location_tags,
|
||
employment_type_raw=employment_type_raw,
|
||
position_name=position_name,
|
||
position_tags=position_tags,
|
||
salary_raw=salary_raw,
|
||
salary_currency=salary_currency,
|
||
salary_min=salary_min,
|
||
salary_max=salary_max,
|
||
salary_period=salary_period,
|
||
responsibilities=responsibilities,
|
||
requirements=requirements,
|
||
apply_email=apply_email,
|
||
apply_telegram=apply_tg,
|
||
job_source_url=job_source_url or company_url,
|
||
body_text=body_text or "empty_message",
|
||
raw_content=raw_content,
|
||
)
|
||
|
||
|
||
def parse_remote_cn(
|
||
source: str,
|
||
chat_id: int | None,
|
||
message_id: int,
|
||
message_date: str,
|
||
body_text: str,
|
||
raw_content: str,
|
||
) -> StructuredJob:
|
||
lines = [clean_md_text(ln) for ln in body_text.splitlines() if clean_md_text(ln)]
|
||
title = lines[0] if lines else None
|
||
|
||
hashtags = [h.replace("·", " ").strip() for h in HASHTAG_RE.findall(body_text)]
|
||
hashtags = dedupe([h for h in hashtags if h])
|
||
|
||
(
|
||
work_mode,
|
||
job_nature,
|
||
job_location_text,
|
||
job_location_tags,
|
||
employment_type_raw,
|
||
) = infer_employment_fields(hashtags, None)
|
||
|
||
summary_line = None
|
||
for ln in lines:
|
||
if ln.startswith("摘要:"):
|
||
summary_line = ln
|
||
break
|
||
salary_raw, salary_currency, salary_min, salary_max, salary_period = parse_salary(
|
||
summary_line
|
||
)
|
||
|
||
urls = extract_urls(body_text)
|
||
apply_email = extract_apply_email(body_text)
|
||
apply_tg = extract_apply_telegram(body_text)
|
||
|
||
# remote_cn often places the detail link right below the title line.
|
||
top_url = None
|
||
raw_lines = [ln.strip() for ln in body_text.splitlines() if ln.strip()]
|
||
for ln in raw_lines[:6]:
|
||
found = URL_RE.findall(ln)
|
||
if found:
|
||
top_url = found[0]
|
||
break
|
||
|
||
job_source_url = (
|
||
top_url
|
||
or extract_first_url_by_keyword(body_text, ["remote-info.cn/jobs/"])
|
||
or (urls[0] if urls else None)
|
||
)
|
||
|
||
job_type = "招聘" if ("招聘" in body_text or "job" in body_text.lower()) else None
|
||
|
||
return StructuredJob(
|
||
source=source,
|
||
source_channel="remote_cn",
|
||
parser_name="remote_cn",
|
||
parser_version="v1",
|
||
chat_id=chat_id,
|
||
message_id=message_id,
|
||
message_date=message_date,
|
||
job_type=job_type,
|
||
company_name=None,
|
||
industry_tags=hashtags,
|
||
company_intro=summary_line.replace("摘要:", "", 1).strip() if summary_line else None,
|
||
company_url=job_source_url or (urls[0] if urls else None),
|
||
work_mode=work_mode,
|
||
job_nature=job_nature,
|
||
job_location_text=job_location_text,
|
||
job_location_tags=job_location_tags,
|
||
employment_type_raw=employment_type_raw,
|
||
position_name=title,
|
||
position_tags=hashtags,
|
||
salary_raw=salary_raw,
|
||
salary_currency=salary_currency,
|
||
salary_min=salary_min,
|
||
salary_max=salary_max,
|
||
salary_period=salary_period,
|
||
responsibilities=[],
|
||
requirements=[],
|
||
apply_email=apply_email,
|
||
apply_telegram=apply_tg,
|
||
job_source_url=job_source_url,
|
||
body_text=body_text or "empty_message",
|
||
raw_content=raw_content,
|
||
)
|
||
|
||
|
||
def parse_cryptojobslist_source(
|
||
source: str,
|
||
chat_id: int | None,
|
||
message_id: int,
|
||
message_date: str,
|
||
body_text: str,
|
||
raw_content: str,
|
||
) -> StructuredJob:
|
||
lines = [clean_md_text(ln) for ln in body_text.splitlines() if clean_md_text(ln)]
|
||
title = lines[0] if lines else None
|
||
urls = extract_urls(body_text)
|
||
hashtags = [h.replace("·", " ").strip() for h in HASHTAG_RE.findall(body_text)]
|
||
hashtags = dedupe([h for h in hashtags if h])
|
||
|
||
(
|
||
work_mode,
|
||
job_nature,
|
||
job_location_text,
|
||
job_location_tags,
|
||
employment_type_raw,
|
||
) = infer_employment_fields(hashtags, None)
|
||
|
||
salary_line = None
|
||
for ln in lines:
|
||
if any(k in ln.lower() for k in ("salary", "$", "usd")):
|
||
salary_line = ln
|
||
break
|
||
salary_raw, salary_currency, salary_min, salary_max, salary_period = parse_salary(
|
||
salary_line
|
||
)
|
||
|
||
apply_email = extract_apply_email(body_text)
|
||
apply_tg = extract_apply_telegram(body_text)
|
||
apply_link = extract_apply_link(body_text)
|
||
job_source_url = (
|
||
apply_link
|
||
or extract_first_url_by_keyword(body_text, ["cryptojobslist.com"])
|
||
or (urls[0] if urls else None)
|
||
)
|
||
|
||
job_type = "招聘" if ("job" in body_text.lower() or "hiring" in body_text.lower()) else None
|
||
|
||
return StructuredJob(
|
||
source=source,
|
||
source_channel="cryptojobslist",
|
||
parser_name="cryptojobslist",
|
||
parser_version="v1",
|
||
chat_id=chat_id,
|
||
message_id=message_id,
|
||
message_date=message_date,
|
||
job_type=job_type,
|
||
company_name=None,
|
||
industry_tags=hashtags,
|
||
company_intro=None,
|
||
company_url=job_source_url or (urls[0] if urls else None),
|
||
work_mode=work_mode,
|
||
job_nature=job_nature,
|
||
job_location_text=job_location_text,
|
||
job_location_tags=job_location_tags,
|
||
employment_type_raw=employment_type_raw,
|
||
position_name=title,
|
||
position_tags=hashtags,
|
||
salary_raw=salary_raw,
|
||
salary_currency=salary_currency,
|
||
salary_min=salary_min,
|
||
salary_max=salary_max,
|
||
salary_period=salary_period,
|
||
responsibilities=[],
|
||
requirements=[],
|
||
apply_email=apply_email,
|
||
apply_telegram=apply_tg,
|
||
job_source_url=job_source_url,
|
||
body_text=body_text or "empty_message",
|
||
raw_content=raw_content,
|
||
)
|
||
|
||
|
||
def route_parse(row: tuple) -> StructuredJob:
|
||
source, chat_id, message_id, content, message_date = row
|
||
raw_content = content or ""
|
||
body_text = preprocess_body_text(strip_meta_lines(raw_content))
|
||
|
||
if source == "@DeJob_official":
|
||
return parse_dejob_official(
|
||
source, chat_id, message_id, message_date, body_text, raw_content
|
||
)
|
||
if source == "@DeJob_Global_group":
|
||
return parse_dejob_global(
|
||
source, chat_id, message_id, message_date, body_text, raw_content
|
||
)
|
||
if source == "@remote_cn":
|
||
return parse_remote_cn(
|
||
source, chat_id, message_id, message_date, body_text, raw_content
|
||
)
|
||
if source == "@cryptojobslist":
|
||
return parse_cryptojobslist_source(
|
||
source, chat_id, message_id, message_date, body_text, raw_content
|
||
)
|
||
|
||
return parse_generic(source, chat_id, message_id, message_date, body_text, raw_content)
|
||
|
||
|
||
def upsert_structured(conn, item: StructuredJob):
|
||
with conn.cursor() as cur:
|
||
cur.execute(
|
||
"""
|
||
INSERT INTO structured_jobs (
|
||
source, source_channel, parser_name, parser_version, chat_id, message_id,
|
||
message_date, job_type, company_name, industry_tags_json, company_intro,
|
||
company_url, work_mode, job_nature, job_location_text, job_location_tags_json, employment_type_raw,
|
||
position_name, position_tags_json,
|
||
salary_raw, salary_currency, salary_min, salary_max, salary_period,
|
||
responsibilities_json, requirements_json, apply_email, apply_telegram,
|
||
job_source_url, body_text, raw_content
|
||
) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
|
||
ON DUPLICATE KEY UPDATE
|
||
source_channel=VALUES(source_channel),
|
||
parser_name=VALUES(parser_name),
|
||
parser_version=VALUES(parser_version),
|
||
chat_id=VALUES(chat_id),
|
||
message_date=VALUES(message_date),
|
||
job_type=VALUES(job_type),
|
||
company_name=VALUES(company_name),
|
||
industry_tags_json=VALUES(industry_tags_json),
|
||
company_intro=VALUES(company_intro),
|
||
company_url=VALUES(company_url),
|
||
work_mode=VALUES(work_mode),
|
||
job_nature=VALUES(job_nature),
|
||
job_location_text=VALUES(job_location_text),
|
||
job_location_tags_json=VALUES(job_location_tags_json),
|
||
employment_type_raw=VALUES(employment_type_raw),
|
||
position_name=VALUES(position_name),
|
||
position_tags_json=VALUES(position_tags_json),
|
||
salary_raw=VALUES(salary_raw),
|
||
salary_currency=VALUES(salary_currency),
|
||
salary_min=VALUES(salary_min),
|
||
salary_max=VALUES(salary_max),
|
||
salary_period=VALUES(salary_period),
|
||
responsibilities_json=VALUES(responsibilities_json),
|
||
requirements_json=VALUES(requirements_json),
|
||
apply_email=VALUES(apply_email),
|
||
apply_telegram=VALUES(apply_telegram),
|
||
job_source_url=VALUES(job_source_url),
|
||
body_text=VALUES(body_text),
|
||
raw_content=VALUES(raw_content),
|
||
cleaned_at=CURRENT_TIMESTAMP
|
||
""",
|
||
(
|
||
item.source,
|
||
item.source_channel,
|
||
item.parser_name,
|
||
item.parser_version,
|
||
item.chat_id,
|
||
item.message_id,
|
||
item.message_date,
|
||
item.job_type,
|
||
item.company_name,
|
||
json.dumps(item.industry_tags, ensure_ascii=False),
|
||
item.company_intro,
|
||
item.company_url,
|
||
item.work_mode,
|
||
item.job_nature,
|
||
item.job_location_text,
|
||
json.dumps(item.job_location_tags, ensure_ascii=False)
|
||
if item.job_location_tags is not None
|
||
else None,
|
||
item.employment_type_raw,
|
||
item.position_name,
|
||
json.dumps(item.position_tags, ensure_ascii=False),
|
||
item.salary_raw,
|
||
item.salary_currency,
|
||
item.salary_min,
|
||
item.salary_max,
|
||
item.salary_period,
|
||
json.dumps(item.responsibilities, ensure_ascii=False),
|
||
json.dumps(item.requirements, ensure_ascii=False),
|
||
item.apply_email,
|
||
item.apply_telegram,
|
||
item.job_source_url,
|
||
item.body_text,
|
||
item.raw_content,
|
||
),
|
||
)
|
||
|
||
|
||
def is_recruitment_job(item: StructuredJob) -> bool:
|
||
return item.job_type == "招聘"
|
||
|
||
|
||
def has_usable_job_link(item: StructuredJob) -> bool:
|
||
return bool((item.job_source_url or "").strip())
|
||
|
||
|
||
def get_last_processed_row_id(conn, pipeline_name: str) -> int:
|
||
with conn.cursor() as cur:
|
||
cur.execute(
|
||
"SELECT COALESCE(last_message_row_id, 0) FROM clean_state WHERE pipeline_name=%s",
|
||
(pipeline_name,),
|
||
)
|
||
row = cur.fetchone()
|
||
return int(row[0]) if row else 0
|
||
|
||
|
||
def set_last_processed_row_id(conn, pipeline_name: str, row_id: int):
|
||
with conn.cursor() as cur:
|
||
cur.execute(
|
||
"""
|
||
INSERT INTO clean_state (pipeline_name, last_message_row_id, updated_at)
|
||
VALUES (%s, %s, NOW())
|
||
ON DUPLICATE KEY UPDATE
|
||
last_message_row_id=VALUES(last_message_row_id),
|
||
updated_at=NOW()
|
||
""",
|
||
(pipeline_name, row_id),
|
||
)
|
||
|
||
|
||
def main():
|
||
mysql_cfg = load_mysql_config()
|
||
conn = connect_mysql(mysql_cfg)
|
||
|
||
try:
|
||
init_target_db(conn)
|
||
last_row_id = get_last_processed_row_id(conn, PIPELINE_NAME)
|
||
logger.info(f"增量清洗起点 messages.id > {last_row_id}")
|
||
|
||
with conn.cursor() as src_cur:
|
||
src_cur.execute(
|
||
"""
|
||
SELECT id, source, chat_id, message_id, content, date
|
||
FROM messages
|
||
WHERE id > %s
|
||
ORDER BY id ASC
|
||
""",
|
||
(last_row_id,),
|
||
)
|
||
rows = src_cur.fetchall()
|
||
|
||
processed = 0
|
||
inserted = 0
|
||
skipped_non_recruit = 0
|
||
skipped_no_link = 0
|
||
by_parser = {}
|
||
max_row_id = last_row_id
|
||
|
||
for row in rows:
|
||
row_id, source, chat_id, message_id, content, message_date = row
|
||
item = route_parse((source, chat_id, message_id, content, message_date))
|
||
processed += 1
|
||
by_parser[item.parser_name] = by_parser.get(item.parser_name, 0) + 1
|
||
if row_id > max_row_id:
|
||
max_row_id = row_id
|
||
|
||
if not is_recruitment_job(item):
|
||
skipped_non_recruit += 1
|
||
continue
|
||
|
||
if not has_usable_job_link(item):
|
||
skipped_no_link += 1
|
||
continue
|
||
|
||
upsert_structured(conn, item)
|
||
inserted += 1
|
||
|
||
if processed % 500 == 0:
|
||
logger.info(
|
||
f"[clean] processed={processed}, inserted={inserted}, "
|
||
f"skipped_non_recruit={skipped_non_recruit}, skipped_no_link={skipped_no_link}"
|
||
)
|
||
|
||
if max_row_id > last_row_id:
|
||
set_last_processed_row_id(conn, PIPELINE_NAME, max_row_id)
|
||
logger.info(f"更新检查点 last_message_row_id={max_row_id}")
|
||
|
||
with conn.cursor() as cur:
|
||
cur.execute("SELECT count(*) FROM structured_jobs")
|
||
total = cur.fetchone()[0]
|
||
|
||
logger.info(
|
||
"[done] "
|
||
f"structured_jobs={total}, inserted={inserted}, skipped_non_recruit={skipped_non_recruit}, "
|
||
f"skipped_no_link={skipped_no_link}, "
|
||
f"target=mysql.structured_jobs, parsers={by_parser}"
|
||
)
|
||
if processed == 0:
|
||
logger.info("无新增消息，清洗完成")
|
||
except Exception:
|
||
logger.exception("清洗任务失败")
|
||
raise
|
||
finally:
|
||
conn.close()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|