model_script/feature_spark_monitor.py

import configparser
from datetime import datetime, timedelta

from client import YarnClient
from util import dateutil, feishu_inform_util
import pandas as pd

yarn_client = YarnClient.YarnClient("121.40.173.140")

table_list = [
    "alg_cid_feature_basic_info",
    "alg_cid_feature_adver_action",
    "alg_cid_feature_cid_action",
    "alg_cid_feature_region_action",
    "alg_cid_feature_app_action",
    "alg_cid_feature_week_action",
    "alg_cid_feature_hour_action",
    "alg_cid_feature_brand_action",
    "alg_cid_feature_weChatVersion_action",
    "alg_cid_feature_vid_cf",
    "alg_cid_feature_vid_cf_rank",
    "alg_mid_feature_ad_action",
    "alg_vid_feature_all_exp",
    "alg_vid_feature_all_share",
    "alg_vid_feature_all_return",
    "alg_vid_feature_exp2share",
    "alg_vid_feature_share2return",
    "alg_vid_feature_feed_noflow_exp",
    "alg_vid_feature_feed_noflow_root_share",
    "alg_vid_feature_feed_noflow_root_return",
    "alg_vid_feature_feed_flow_exp",
    "alg_vid_feature_feed_flow_root_share",
    "alg_vid_feature_feed_flow_root_return",
    "alg_vid_feature_feed_province_exp",
    "alg_vid_feature_feed_province_root_share",
    "alg_vid_feature_feed_province_root_return",
    "alg_vid_feature_basic_info",
    "alg_recsys_feature_cf_i2i_new",
    "alg_vid_feature_all_exp_v2",
    "alg_vid_feature_exp2share_v2",
    "alg_vid_feature_feed_noflow_exp_v2",
    "alg_vid_feature_feed_noflow_root_share_v2",
    "alg_vid_feature_feed_noflow_root_return_v2",
    "alg_vid_feature_feed_flow_exp_v2",
    "alg_vid_feature_feed_flow_root_share_v2",
    "alg_vid_feature_feed_flow_root_return_v2",
    "alg_vid_feature_feed_province_exp_v2",
    "alg_vid_feature_feed_province_root_share_v2",
    "alg_vid_feature_feed_province_root_return_v2",
    "alg_recsys_feature_cf_i2i_new_v2",
    "alg_mid_feature_play",
    "alg_mid_feature_share_and_return",
    "alg_mid_feature_play_tags",
    "alg_mid_feature_return_tags",
    "alg_mid_feature_share_tags",
    "alg_mid_feature_feed_exp_share_tags",
    "alg_mid_feature_feed_exp_return_tags",
    "alg_mid_feature_sharecf",
    "alg_mid_feature_returncf",
    "alg_mid_feature_feed_exp_return_tags_v2",
    "alg_mid_feature_feed_exp_share_tags_v2"
]

filter_date = datetime(2024, 1, 1)


def df_print(result):
    df = pd.DataFrame(result)
    # 过滤出 name 中包含 'cid' 的行
    filtered_df = df[df['name'].str.contains('cid')].copy()  # 使用 .copy() 生成副本以避免警告
    filtered_df.loc[:, 'name'] = filtered_df['name'].str.replace('odps sync to redis : ', '', regex=False)

    sorted_df = filtered_df.sort_values(by="startedTime")

    # 获取表头
    header = ' | '.join(sorted_df.columns)

    def format_row(row):
        return ' | '.join([str(row[col]) for col in sorted_df.columns])

    # 获取数据行
    rows = filtered_df.apply(format_row, axis=1).tolist()

    # 打印输出
    print(header)
    print('-' * len(header))
    print('\n'.join(rows))


def handle_table(table_name: str, spark_task_list: list[dict]) -> (bool, str):
    filtered_data = [
        item for item in spark_task_list
        if table_name in item['name'] and dateutil.str_cover_date(item['startedTime']) > filter_date
    ]
    if filtered_data:
        latest_started_time = max(
            [dateutil.str_cover_date(item['startedTime']) for item in filtered_data])
        print(f"表: {table_name}, 最后一次同步时间为: {latest_started_time}")
        now = datetime.now()
        time_difference = now - latest_started_time
        if time_difference > timedelta(minutes=140):
            return True, latest_started_time

        return False, filtered_data

    return False, ""


def send_error_info(table_name: str, latest_started_time: str):
    mgs_text = f"\n- 大数据表名: {table_name}" \
               f"\n- 最后一次同步时间: {latest_started_time}" \
               f"\n- 超过两个小时没有同步，请关注"
    card_json = {
        "config": {},
        "i18n_elements": {
            "zh_cn": [
                {
                    "tag": "markdown",
                    "content": "",
                    "text_align": "left",
                    "text_size": "normal"
                },
                {
                    "tag": "markdown",
                    "content": mgs_text,
                    "text_align": "left",
                    "text_size": "normal"
                }
            ]
        },
        "i18n_header": {
            "zh_cn": {
                "title": {
                    "tag": "plain_text",
                    "content": "【测试】大数据表同步延迟告警"
                },
                "subtitle": {
                    "tag": "plain_text",
                    "content": ""
                },
                "template": "red"
            }
        }
    }


def _main():
    # 读取配置文件
    config = configparser.ConfigParser()
    config.read("config/config.ini")
    print(config.get("feishu", 'model.webhook'))

    # 获取最近七小时的Spark任务
    hours_7_early = int((datetime.now() - timedelta(hours=7)).timestamp()) * 1000
    result = yarn_client.get_apps(started_time_begin=hours_7_early)

    for table_name in table_list:
        # 判断最后一次同步是否为两个小时以内
        b, latest_started_time = handle_table(table_name, result)
        if b:
            print(f"表: {table_name}, 最后一次同步时间距当前时间超过140分钟")
            send_error_info(table_name, latest_started_time)


if __name__ == '__main__':
    _main()