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标题: datax做增量导入数据到hive:mysql＞hive [打印本页]

作者: 傲渊山岳 时间: 2024-12-17 22:12
标题: datax做增量导入数据到hive:mysql＞hive
为什么要做增量导入? 例如mysql表中的数据导入hive，如果第一天抽取了mysql中t_user表中的全部数据，则第二天只必要抽取新增数据即可！增长导入是利用where 条件查询实现的，查询条件一般是自增的id或者时间列下面演示基于时间列的数据增量抽取。
1.数据准备

# 1. 在mysql数据库创建如下表结构：
create table t_order(
id int primary key auto_increment,
amt decimal(10,2),
`status` int default 0,
user_id int,
create_time timestamp DEFAULT CURRENT_TIMESTAMP,
modify_time timestamp DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
);
# 2.插入数据
insert into t_order values(null,100,0,1001,'2023-07-01 10:10:10','2023-07-01 10:10:10');
insert into t_order values(null,99,0,1002,'2023-07-01 10:10:10','2023-07-01 10:10:10');
select *
from t_order;
-- 2.在hive创建如下表结构
create table t_order(
id int,
amt decimal(10,2),
`status` int,
user_id int,
create_time string,
modify_time string
)partitioned by (dt string)
row format delimited fields terminated by '\t';
-- 手动添加分区
alter table t_order add partition (dt='2023-07-01');
show partitions t_order;

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2.编写增量数据导入datax配置文件

{
"job": {
"content": [
{
"reader": {
"name": "mysqlreader",
"parameter": {
"connection": [
{
"jdbcUrl": ["jdbc:mysql://hadoop11:3306/test1"],
"querySql": [
"select id,amt,status,user_id,create_time,modify_time from t_order where DATE_FORMAT(modify_time, '%Y-%m-%d') = '$dt'"
]
}
],
"password": "123456",
"username": "root",
}
},
"writer": {
"name": "hdfswriter",
"parameter": {
"column": [
{"name": "id","type": "int"},
{"name": "amt","type": "double"},
{"name": "status","type": "int"},
{"name": "user_id","type": "int"},
{"name": "create_time","type": "string"},
{"name": "modify_time","type": "string"}
],
"defaultFS": "hdfs://hdfs-cluster",
"hadoopConfig":{
"dfs.nameservices": "hdfs-cluster",
"dfs.ha.namenodes.hdfs-cluster": "nn1,nn2",
"dfs.namenode.rpc-address.hdfs-cluster.nn1": "hadoop11:8020",
"dfs.namenode.rpc-address.hdfs-cluster.nn2": "hadoop12:8020",
"dfs.client.failover.proxy.provider.hdfs-cluster": "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider"
},
"path": "/user/hive/warehouse/t_order/dt=$dt",
"fieldDelimiter": "\t",
"fileName": "t_order",
"fileType": "text",
"writeMode": "append"
}
}
}
],
"setting": {
"speed": {
"channel": "1"
}
}
}
}
3.测试增量导入

执行：python /opt/installs/datax/bin/datax.py /opt/installs/datax/job/test_job.json -p "-Ddt='2023-07-01'" 原有的两条数据已经导入证明json文件无误

# 再添加到mysql一天的测试数据
insert into t_order values(null,220,0,1001,'2023-07-02 10:10:10','2023-07-02 10:10:10');
update t_order set `status` = 2 , modify_time = '2023-07-02 11:00:00' where id = 2;

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-- 手动创建hive分区 2023-07-02
alter table t_order add partition (dt='2023-07-02');

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执行：python /opt/installs/datax/bin/datax.py /opt/installs/datax/job/test_job.json -p "-Ddt='2023-07-02'"
查询结果：已经完成数据导入

4.编写对应的shell脚本执行下令

#! /bin/bash
# 1. 要求用户提供日期如果没有提供，则使用昨天日期
dt=$1
if [ 'x'$1 == 'x' ];then
dt=$(date -d'-1 day' +%Y-%m-%d)
fi
# 2. 查询dt对应日期的分区是否存在，默认返回结果表里面的列名需要去掉，只保留表中的数据赋值给x1变量
x1=$(hive -e "set hive.cli.print.header=false;show partitions t_order partition(dt='$dt')")
# 3. 如果x1变量等于空，说明分区不存在，则创建分区
echo $x1
if [ "$x1" == "" ]
then
hive -e "alter table t_order add partition(dt='$dt')"
fi
# 4. 执行py文件
python /opt/installs/datax/bin/datax.py -p "-Ddt=$dt" /opt/installs/datax/job/test_job.json

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测试shell脚本，mysql增长2023-07-03的数据增量导入到hive：

insert into t_order values(null,330,0,1003,'2023-07-03 10:10:10','2023-07-03 10:10:10');
update t_order set `status` = 2 , modify_time = '2023-07-03 11:00:00' where id = 3;

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测试成功！

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