Python——脚本实现datax全量同步mysql到hive

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前言

在我们构建离线数仓时或者迁移数据时，通常选用sqoop和datax等工具进行操作，sqoop和datax各有优点，datax优点也很明显，基于内存，所以速度上很快，那么在进行全量同步时编写json文件是一项很繁琐的事，是否可以编写脚本来把繁琐事来简单化，接下来我将分享这样一个mysql全量同步到hive自动生成json文件的python脚本。

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一、展示脚本

# coding=utf-8
import json
import getopt
import os
import sys
import pymysql# MySQL 相关配置，需根据实际情况作出修改
mysql_host = "XXXXXX"
mysql_port = "XXXX"
mysql_user = "XXX"
mysql_passwd = "XXXXXX"# HDFS NameNode 相关配置，需根据实际情况作出修改
hdfs_nn_host = "XXXXXX"
hdfs_nn_port = "XXXX"# 生成配置文件的目标路径，可根据实际情况作出修改
output_path = "/XXX/XXX/XXX"def get_connection():return pymysql.connect(host=mysql_host, port=int(mysql_port), user=mysql_user, password=mysql_passwd)def get_mysql_meta(database, table):connection = get_connection()cursor = connection.cursor()sql = "SELECT COLUMN_NAME,DATA_TYPE from information_schema.COLUMNS WHERE TABLE_SCHEMA=%s AND TABLE_NAME=%s ORDER BY ORDINAL_POSITION"cursor.execute(sql, [database, table])fetchall = cursor.fetchall()cursor.close()connection.close()return fetchalldef get_mysql_columns(database, table):return list(map(lambda x: x[0], get_mysql_meta(database, table)))def get_hive_columns(database, table):def type_mapping(mysql_type):mappings = {"bigint": "bigint","int": "bigint","smallint": "bigint","tinyint": "bigint","decimal": "string","double": "double","float": "float","binary": "string","char": "string","varchar": "string","datetime": "string","time": "string","timestamp": "string","date": "string","text": "string"}return mappings[mysql_type]meta = get_mysql_meta(database, table)return list(map(lambda x: {"name": x[0], "type": type_mapping(x[1].lower())}, meta))def generate_json(source_database, source_table):job = {"job": {"setting": {"speed": {"channel": 3},"errorLimit": {"record": 0,"percentage": 0.02}},"content": [{"reader": {"name": "mysqlreader","parameter": {"username": mysql_user,"password": mysql_passwd,"column": get_mysql_columns(source_database, source_table),"splitPk": "","connection": [{"table": [source_table],"jdbcUrl": ["jdbc:mysql://" + mysql_host + ":" + mysql_port + "/" + source_database]}]}},"writer": {"name": "hdfswriter","parameter": {"defaultFS": "hdfs://" + hdfs_nn_host + ":" + hdfs_nn_port,"fileType": "text","path": "${targetdir}","fileName": source_table,"column": get_hive_columns(source_database, source_table),"writeMode": "append","fieldDelimiter": "\t","compress": "gzip"}}}]}}if not os.path.exists(output_path):os.makedirs(output_path)with open(os.path.join(output_path, ".".join([source_database, source_table, "json"])), "w") as f:json.dump(job, f)def main(args):source_database = ""source_table = ""options, arguments = getopt.getopt(args, '-d:-t:', ['sourcedb=', 'sourcetbl='])for opt_name, opt_value in options:if opt_name in ('-d', '--sourcedb'):source_database = opt_valueif opt_name in ('-t', '--sourcetbl'):source_table = opt_valuegenerate_json(source_database, source_table)if __name__ == '__main__':main(sys.argv[1:])

二、使用准备

1、安装python环境

这里我安装的是python3环境

sudo yum install -y python3

2、安装EPEL

EPEL（Extra Packages for Enterprise Linux）是一个由 Fedora Special Interest Group 维护的软件仓库，提供了大量在官方 RHEL 或 CentOS 软件仓库中没有的软件包。当你在 CentOS 或 RHEL 系统上需要安装一些不在官方软件仓库中的软件时，通常会先安装epel - release

sudo yum install -y epel-release

3、安装脚本执行需要的第三方模块

pip3 install pymysql
pip3 install cryptography

这里可能由于斑纹问题cryptography安装不上去更新一下pip和setuptools

pip3 install --upgrade pip
pip3 install --upgrade setuptools

重新安装cryptography

pip3 install cryptography

三、脚本使用方法

1、配置脚本

首先根据自己服务器修改脚本相关配置

2、创建.py文件

vim /xxx/xxx/xxx/gen_import_config.py

3、执行脚本

python3 /脚本路径/gen_import_config.py -d 数据库名 -t 表名

4、测试生成json文件是否可用

datax.py -p"-Dtargetdir=/表在hdfs存放路径" /生成的json文件路径

执行时首先要确保targetdir目标地址在hdfs上存在，如果没有需要创建后再次执行