概述
使用脚本生成Datax使用的json文件，导出MySQL数据到HDFS。
安装依赖
pip install pymysql
相关脚本
#!/usr/bin/env python
# coding=utf-8
# -*- coding=utf-8
# coding=utf-8
# python gen_import_config.py -d 数据库 -t 表
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 = ""
mysql_port = 3306
mysql_user = ""
mysql_passwd = ""
#HDFS NameNode相关配置，需根据实际情况作出修改
hdfs_nn_host = "master"
hdfs_nn_port = "8020"
#生成配置文件的目标路径，可根据实际情况作出修改
output_path = "/home/bigdata/test"
#获取表格的元数据 包含列名和数据类型
def get_mysql_meta(database, table):
connection = pymysql.connect(
host=mysql_host, # 连接地址, 本地
user=mysql_user, # 用户
password=mysql_passwd, # 数据库密码,记得修改为自己本机的密码
port=mysql_port,
connect_timeout=10000
)
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 fetchall
#获取mysql表的列名
def get_mysql_columns(database, table):
return map(lambda x: x[0], get_mysql_meta(database, table))
#将获取的元数据中mysql的数据类型转换为hive的数据类型 写入到hdfswriter中
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 map(lambda x: {"name": x[0], "type": type_mapping(x[1].lower())}, meta)
#生成json文件
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": list(get_mysql_columns(source_database, source_table)),
"splitPk": "",
"connection": [{
"table": [source_table],
"jdbcUrl": ["jdbc:mysql://" + mysql_host + ":" + str(mysql_port) + "/" + source_database]
}]
}
},
"writer": {
"name": "hdfswriter",
"parameter": {
"defaultFS": "hdfs://" + hdfs_nn_host + ":" + hdfs_nn_port,
"fileType": "text",
"path": "${targetdir}",
"fileName": source_table,
"column": list(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_value
if opt_name in ('-t', '--sourcetbl'):
source_table = opt_value
generate_json(source_database, source_table)
if __name__ == '__main__':
main(sys.argv[1:])
脚本的使用
#!/bin/bash
python gen_import_config.py -d 数据库 -t 表名
python gen_import_config.py -d 数据库 -t 表名
python gen_import_config.py -d 数据库 -t 表名
python gen_import_config.py -d 数据库 -t 表名
python gen_import_config.py -d 数据库 -t 表名
批量导出数据到HDFS案例
#!/bin/bash
# mysql_to_hdfs_full.sh all 使用例子,改datax的home,还有改配置文件的地址就可以用了
DATAX_HOME=/home/bigdata/datax/datax
# 如果传入日期则do_date等于传入的日期，否则等于前一天日期,也就是昨天
if [ -n "$2" ] ;then
do_date=$2
else
do_date=`date -d "-1 day" +%F`
fi
#处理目标路径，此处的处理逻辑是，如果目标路径不存在，则创建；若存在，则清空，目的是保证同步任务可重复执行
handle_targetdir() {
hadoop fs -test -e $1
if [[ $? -eq 1 ]]; then
echo "路径$1不存在，正在创建......"
hadoop fs -mkdir -p $1
else
echo "路径$1已经存在"
fs_count=$(hadoop fs -count $1)
content_size=$(echo $fs_count | awk '{print $3}')
if [[ $content_size -eq 0 ]]; then
echo "路径$1为空"
else
echo "路径$1不为空，正在清空......"
hadoop fs -rm -r -f $1/*
fi
fi
}
#数据同步
import_data() {
#$1 /home/bigdata/datax/datax/job/pyjson/bigdata.activity_info.json
#$2 /origin_data/bigdata/db/activity_info_full/$do_date
datax_config=$1
target_dir=$2
handle_targetdir $target_dir
python $DATAX_HOME/bin/datax.py -p"-Dtargetdir=$target_dir" $datax_config
}
case $1 in
"activity_info")
#/home/bigdata/datax/datax/job/pyjson改成自己文件生成的路径
import_data /home/bigdata/datax/datax/job/pyjson/bigdata.activity_info.json /origin_data/bigdata/full_db/activity_info_full/$do_date
;;
"all")
import_data /home/bigdata/datax/datax/job/pyjson/bigdata.activity_info.json /origin_data/bigdata/full_db/activity_info_full/$do_date
;;
esac
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