9.1、全量表数据同步
1、数据通道
全量表数据由DataX从MySQL业务数据库直接同步到HDFS，具体数据流向如下图所示。
2、 DataX配置文件
我们需要为每张全量表编写一个DataX的json配置文件，此处以activity_info为例，配置文件内容如下：
{
"job": {
"content": [
{
"reader": {
"name": "mysqlreader",
"parameter": {
"column": [
"id",
"activity_name",
"activity_type",
"activity_desc",
"start_time",
"end_time",
"create_time"
],
"connection": [
{
"jdbcUrl": [
"jdbc:mysql://hadoop102:3306/gmall"
],
"table": [
"activity_info"
]
}
],
"password": "Zan_1234",
"splitPk": "",
"username": "root"
}
},
"writer": {
"name": "hdfswriter",
"parameter": {
"column": [
{
"name": "id",
"type": "bigint"
},
{
"name": "activity_name",
"type": "string"
},
{
"name": "activity_type",
"type": "string"
},
{
"name": "activity_desc",
"type": "string"
},
{
"name": "start_time",
"type": "string"
},
{
"name": "end_time",
"type": "string"
},
{
"name": "create_time",
"type": "string"
}
],
"compress": "gzip",
"defaultFS": "hdfs://hadoop102:8020",
"fieldDelimiter": "\t",
"fileName": "activity_info",
"fileType": "text",
"path": "${targetdir}",
"writeMode": "append"
}
}
}
],
"setting": {
"speed": {
"channel": 1
}
}
}
}注：由于目标路径包含一层日期，用于对不同天的数据加以区分，故path参数并未写死，需在提交任务时通过参数动态传入，参数名称为targetdir。
3、DataX配置文件生成脚本
方便起见，此处提供了DataX配置文件批量生成脚本，脚本内容及使用方式如下。
1）在~/bin目录下创建gen_import_config.py脚本
[shuidi@hadoop102 bin]$ vim ~/bin/gen_import_config.py 脚本内容如下
# ecoding=utf-8
import json
import getopt
import os
import sys
import MySQLdb
#MySQL相关配置，需根据实际情况作出修改
mysql_host = "hadoop102"
mysql_port = "3306"
mysql_user = "root"
mysql_passwd = "Zan_1234"
#HDFS NameNode相关配置，需根据实际情况作出修改
hdfs_nn_host = "hadoop102"
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 = "/opt/module/datax/job/import"
def get_connection():
return MySQLdb.connect(host=mysql_host, port=int(mysql_port), user=mysql_user, passwd=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 fetchall
def get_mysql_columns(database, table):
return 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 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": "${targetdir}",
"fileName": source_table,
"column": get_hive_columns(source_database, source_table),
"writeMode": "append",
"fieldDelimiter": "\t",
"compress": "gzip"
}
}
}]
}
}
if not os.path.exists(output_path):
os.makedirs(output_path)
with open(os.path.join(output_path, ".".join([source_database, source_table, "json"])), "w") as f:
json.dump(job, f)
def main(args):
source_database = ""
source_table = ""
options, arguments = getopt.getopt(args, '-d:-t:', ['sourcedb=', 'sourcetbl='])
for opt_name, opt_value in options:
if opt_name in ('-d', '--sourcedb'):
source_database = opt_value
if opt_name in ('-t', '--sourcetbl'):
source_table = opt_value
generate_json(source_database, source_table)
if __name__ == '__main__':
main(sys.argv[1:])注：
（1）安装Python Mysql驱动
由于需要使用Python访问Mysql数据库，故需安装驱动，命令如下：
[shuidi@hadoop102 bin]$ sudo yum install -y MySQL-python（2）脚本使用说明
python gen_import_config.py -d database -t table通过-d传入数据库名，-t传入表名，执行上述命令即可生成该表的DataX同步配置文件。
2）在~/bin目录下创建gen_import_config.sh脚本
[shuidi@hadoop102 bin]$ vim ~/bin/gen_import_config.sh脚本内容如下
#!/bin/bash
python ~/bin/gen_import_config.py -d gmall -t activity_info
python ~/bin/gen_import_config.py -d gmall -t activity_rule
python ~/bin/gen_import_config.py -d gmall -t base_category1
python ~/bin/gen_import_config.py -d gmall -t base_category2
python ~/bin/gen_import_config.py -d gmall -t base_category3
python ~/bin/gen_import_config.py -d gmall -t base_dic
python ~/bin/gen_import_config.py -d gmall -t base_province
python ~/bin/gen_import_config.py -d gmall -t base_region
python ~/bin/gen_import_config.py -d gmall -t base_trademark
python ~/bin/gen_import_config.py -d gmall -t cart_info
python ~/bin/gen_import_config.py -d gmall -t coupon_info
python ~/bin/gen_import_config.py -d gmall -t sku_attr_value
python ~/bin/gen_import_config.py -d gmall -t sku_info
python ~/bin/gen_import_config.py -d gmall -t sku_sale_attr_value
python ~/bin/gen_import_config.py -d gmall -t spu_info3）为gen_import_config.sh脚本增加执行权限
[shuidi@hadoop102 bin]$ chmod 777 ~/bin/gen_import_config.sh 4）执行gen_import_config.sh脚本，生成配置文件
[shuidi@hadoop102 bin]$ gen_import_config.sh5）观察生成的配置文件
[shuidi@hadoop102 bin]$ ll /opt/module/datax/job/import/
总用量 60
-rw-rw-r-- 1 atguigu atguigu 957 10月 15 22:17 gmall.activity_info.json
-rw-rw-r-- 1 atguigu atguigu 1049 10月 15 22:17 gmall.activity_rule.json
-rw-rw-r-- 1 atguigu atguigu 651 10月 15 22:17 gmall.base_category1.json
-rw-rw-r-- 1 atguigu atguigu 711 10月 15 22:17 gmall.base_category2.json
-rw-rw-r-- 1 atguigu atguigu 711 10月 15 22:17 gmall.base_category3.json
-rw-rw-r-- 1 atguigu atguigu 835 10月 15 22:17 gmall.base_dic.json
-rw-rw-r-- 1 atguigu atguigu 865 10月 15 22:17 gmall.base_province.json
-rw-rw-r-- 1 atguigu atguigu 659 10月 15 22:17 gmall.base_region.json
-rw-rw-r-- 1 atguigu atguigu 709 10月 15 22:17 gmall.base_trademark.json
-rw-rw-r-- 1 atguigu atguigu 1301 10月 15 22:17 gmall.cart_info.json
-rw-rw-r-- 1 atguigu atguigu 1545 10月 15 22:17 gmall.coupon_info.json
-rw-rw-r-- 1 atguigu atguigu 867 10月 15 22:17 gmall.sku_attr_value.json
-rw-rw-r-- 1 atguigu atguigu 1121 10月 15 22:17 gmall.sku_info.json
-rw-rw-r-- 1 atguigu atguigu 985 10月 15 22:17 gmall.sku_sale_attr_value.json
-rw-rw-r-- 1 atguigu atguigu 811 10月 15 22:17 gmall.spu_info.json4、测试生成的DataX配置文件
以activity_info为例，测试用脚本生成的配置文件是否可用。
1）创建目标路径
由于DataX同步任务要求目标路径提前存在，故需手动创建路径，当前activity_info表的目标路径应为/origin_data/gmall/db/activity_info_full/2020-06-14。
[shuidi@hadoop102 bin]$ hadoop fs -mkdir -p /origin_data/gmall/db/activity_info_full/2020-06-142）执行DataX同步命令
[shuidi@hadoop102 bin]$ python /opt/module/datax/bin/datax.py -p"-Dtargetdir=/origin_data/gmall/db/activity_info_full/2020-06-14" /opt/module/datax/job/import/gmall.activity_info.json3）观察同步结果
观察HFDS目标路径是否出现数据。
5、全量表数据同步脚本
为方便使用以及后续的任务调度，此处编写一个全量表数据同步脚本。
1）在~/bin目录创建mysql_to_hdfs_full.sh
[shuidi@hadoop102 bin]$ vim ~/bin/mysql_to_hdfs_full.sh 脚本内容如下
#!/bin/bash
DATAX_HOME=/opt/module/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
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() {
datax_config=$1
target_dir=$2
handle_targetdir $target_dir
python $DATAX_HOME/bin/datax.py -p"-Dtargetdir=$target_dir" $datax_config
}
case $1 in
"activity_info")
import_data /opt/module/datax/job/import/gmall.activity_info.json /origin_data/gmall/db/activity_info_full/$do_date
;;
"activity_rule")
import_data /opt/module/datax/job/import/gmall.activity_rule.json /origin_data/gmall/db/activity_rule_full/$do_date
;;
"base_category1")
import_data /opt/module/datax/job/import/gmall.base_category1.json /origin_data/gmall/db/base_category1_full/$do_date
;;
"base_category2")
import_data /opt/module/datax/job/import/gmall.base_category2.json /origin_data/gmall/db/base_category2_full/$do_date
;;
"base_category3")
import_data /opt/module/datax/job/import/gmall.base_category3.json /origin_data/gmall/db/base_category3_full/$do_date
;;
"base_dic")
import_data /opt/module/datax/job/import/gmall.base_dic.json /origin_data/gmall/db/base_dic_full/$do_date
;;
"base_province")
import_data /opt/module/datax/job/import/gmall.base_province.json /origin_data/gmall/db/base_province_full/$do_date
;;
"base_region")
import_data /opt/module/datax/job/import/gmall.base_region.json /origin_data/gmall/db/base_region_full/$do_date
;;
"base_trademark")
import_data /opt/module/datax/job/import/gmall.base_trademark.json /origin_data/gmall/db/base_trademark_full/$do_date
;;
"cart_info")
import_data /opt/module/datax/job/import/gmall.cart_info.json /origin_data/gmall/db/cart_info_full/$do_date
;;
"coupon_info")
import_data /opt/module/datax/job/import/gmall.coupon_info.json /origin_data/gmall/db/coupon_info_full/$do_date
;;
"sku_attr_value")
import_data /opt/module/datax/job/import/gmall.sku_attr_value.json /origin_data/gmall/db/sku_attr_value_full/$do_date
;;
"sku_info")
import_data /opt/module/datax/job/import/gmall.sku_info.json /origin_data/gmall/db/sku_info_full/$do_date
;;
"sku_sale_attr_value")
import_data /opt/module/datax/job/import/gmall.sku_sale_attr_value.json /origin_data/gmall/db/sku_sale_attr_value_full/$do_date
;;
"spu_info")
import_data /opt/module/datax/job/import/gmall.spu_info.json /origin_data/gmall/db/spu_info_full/$do_date
;;
"all")
import_data /opt/module/datax/job/import/gmall.activity_info.json /origin_data/gmall/db/activity_info_full/$do_date
import_data /opt/module/datax/job/import/gmall.activity_rule.json /origin_data/gmall/db/activity_rule_full/$do_date
import_data /opt/module/datax/job/import/gmall.base_category1.json /origin_data/gmall/db/base_category1_full/$do_date
import_data /opt/module/datax/job/import/gmall.base_category2.json /origin_data/gmall/db/base_category2_full/$do_date
import_data /opt/module/datax/job/import/gmall.base_category3.json /origin_data/gmall/db/base_category3_full/$do_date
import_data /opt/module/datax/job/import/gmall.base_dic.json /origin_data/gmall/db/base_dic_full/$do_date
import_data /opt/module/datax/job/import/gmall.base_province.json /origin_data/gmall/db/base_province_full/$do_date
import_data /opt/module/datax/job/import/gmall.base_region.json /origin_data/gmall/db/base_region_full/$do_date
import_data /opt/module/datax/job/import/gmall.base_trademark.json /origin_data/gmall/db/base_trademark_full/$do_date
import_data /opt/module/datax/job/import/gmall.cart_info.json /origin_data/gmall/db/cart_info_full/$do_date
import_data /opt/module/datax/job/import/gmall.coupon_info.json /origin_data/gmall/db/coupon_info_full/$do_date
import_data /opt/module/datax/job/import/gmall.sku_attr_value.json /origin_data/gmall/db/sku_attr_value_full/$do_date
import_data /opt/module/datax/job/import/gmall.sku_info.json /origin_data/gmall/db/sku_info_full/$do_date
import_data /opt/module/datax/job/import/gmall.sku_sale_attr_value.json /origin_data/gmall/db/sku_sale_attr_value_full/$do_date
import_data /opt/module/datax/job/import/gmall.spu_info.json /origin_data/gmall/db/spu_info_full/$do_date
;;
esac2）为mysql_to_hdfs_full.sh增加执行权限
[shuidi@hadoop102 bin]$ chmod 777 ~/bin/mysql_to_hdfs_full.sh 3）测试同步脚本
[shuidi@hadoop102 bin]$ mysql_to_hdfs_full.sh all 2020-06-144）检查同步结果
查看HDFS目表路径是否出现全量表数据，全量表共15张。