#!/usr/bin/env python3
from etls.comm.loggers import get_handler, get_logger
import sys
from etls.comm.job import exec_job, parse_sys_args
from etls.comm.jobflow import JobFlow
# import logging
# logger = logging.getLogger()
logger = get_logger()
if __name__ == '__main__':
if len(sys.argv) >= 3:
# 命令样式 etl.py tb_nm job_type 后面跟着 一一对应的kv参数(个数不限)
# tb_nm 表名 例如 dw.dim_cust(可以是多个表名用“,”分割，不能有空格)
# job_type job类型 可以取ddl/dml/batch_el/func/el/sql/flow, func是指对应python文件中的函数名称 el指同步表,batch_el批次同步
# 参数格式 参数名称需要以-或者--开头然后紧跟着参数值,例如-start_dt 2019-10-01 或者 --start_dt 2019-10-01
# etl.py task_infos deal -sys_dt 2019-12-10 执行目录下task_infos.py python文件中的deal函数 sys_dt是deal函数的参数
# etl.py edw.dim_cust dml -batch_dt 2019-12-10 执行的edw/目录下的 dw.dim_cust.sql batch_dt 是传入参数
# etl.py edw.fact_cust_leads dml -schm rtp -p_days 10
# etl.py hq_hr.estatus el -conn ibm -src_tb_nm ecology.estatus -src_db_cd hr
# etl.py T1 batch_el -batch_id 202008200101 和 etl.py batch_el el -batch_id 202008200101 -batch_nm T1 等效
# etl.py T1 flow 执行T1批处理
# 临时导入一张表
# python3 etl.py hq_hr.estatus el -conn ibm -src_tb_nm ecology.estatus -src_db_cd hr -c False
# 解析传入参数
conn, schm_tb_nm, job_type, params_dict = parse_sys_args(sys.argv)
log_file_nm = schm_tb_nm
log_file_nm = log_file_nm.split('/')[-1]
fh = get_handler(log_file_nm) # 输出到特定的日志文件
try:
if job_type == 'flow':
# cython: language_level=3
import copy
import time
from datetime import datetime
from queue import Queue
from etls.comm.dataxy import crt_el_log_tb
from etls.conf.settings import data_file_path
from etls.comm.conn import Conn
from etls.comm.job import exec_job, tb_task_log, exec_shell, parse_sys_args
from dask.multiprocessing import get # , config, get_context, initialize_worker_process
from etls.comm.loggers import get_logger
import os
import pandas as pd
import re
logger = get_logger('jobflows')
flows_path = os.path.join(data_file_path, 'flows')
# 以下用于基于依赖关系计算先后顺序3
def sort_by_depend(path):
# def get_jobs(path='/home/xzh/dps/etl/dpsetl/dpsetl/dw_daily.csv'):
df = pd.read_csv(path, index_col='job_nm')
no_dep_jobs = list(df[pd.isna(df['dependencies'])].index) # .sort()
# print(no_dep_jobs)
df.drop(index=no_dep_jobs, inplace=True)
df['dependencies'] = df['dependencies'].apply(
lambda x: str(x).replace("，", ",").strip().split())
dep = df.to_dict()['dependencies']
# print(dep['task_infos'])
