1.需求
從離線Hive數倉ads層抽取數據到Mysql
2.參考DataX官方Github實例
DataX官網
從hive讀數據
{
"job": {
"setting": {
"speed": {
"channel": 3
}
},
"content": [
{
"reader": {
"name": "hdfsreader",
"parameter": {
"path": "/user/hive/warehouse/mytable01/*",
"defaultFS": "hdfs://xxx:port",
"column": [
{
"index": 0,
"type": "long"
},
{
"index": 1,
"type": "boolean"
},
{
"type": "string",
"value": "hello"
},
{
"index": 2,
"type": "double"
}
],
"fileType": "orc",
"encoding": "UTF-8",
"fieldDelimiter": ","
}
},
"writer": {
"name": "streamwriter",
"parameter": {
"print": true
}
}
}
]
}
}
寫入MySQL
{
"job": {
"setting": {
"speed": {
"channel": 1
}
},
"content": [
{
"reader": {
"name": "streamreader",
"parameter": {
"column" : [
{
"value": "DataX",
"type": "string"
},
{
"value": 19880808,
"type": "long"
},
{
"value": "1988-08-08 08:08:08",
"type": "date"
},
{
"value": true,
"type": "bool"
},
{
"value": "test",
"type": "bytes"
}
],
"sliceRecordCount": 1000
}
},
"writer": {
"name": "mysqlwriter",
"parameter": {
"writeMode": "insert",
"username": "root",
"password": "root",
"column": [
"id",
"name"
],
"session": [
"set session sql_mode='ANSI'"
],
"preSql": [
"delete from test"
],
"connection": [
{
"jdbcUrl": "jdbc:mysql://127.0.0.1:3306/datax?useUnicode=true&characterEncoding=gbk",
"table": [
"test"
]
}
]
}
}
}
]
}
}
HA設置
"hadoopConfig":{
"dfs.nameservices": "testDfs",
"dfs.ha.namenodes.testDfs": "namenode1,namenode2",
"dfs.namenode.rpc-address.aliDfs.namenode1": "",
"dfs.namenode.rpc-address.aliDfs.namenode2": "",
"dfs.client.failover.proxy.provider.testDfs": "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider"
}
3.最終json配置
{
"job": {
"setting": {
"speed": {
"channel": 3
}
},
"content": [
{
"reader": {
"name": "hdfsreader",
"parameter": {
"path": "hdfs://mycluster/user/hive/warehouse/ads.db/ads_paper_avgtimeandscore/dt=${dt}/dn=${dn}",
"defaultFS": "hdfs://mycluster",
"hadoopConfig":{
"dfs.nameservices": "mycluster",
"dfs.ha.namenodes.mycluster": "nn1,nn2,nn3",
"dfs.namenode.rpc-address.mycluster.nn1": "hadoop101:8020",
"dfs.namenode.rpc-address.mycluster.nn2": "hadoop102:8020",
"dfs.namenode.rpc-address.mycluster.nn3": "hadoop103:8020",
"dfs.client.failover.proxy.provider.mycluster": "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider"
},
"column": [
{
"index": 0,
"type": "string"
},
{
"index": 1,
"type": "string"
},
{
"index": 2,
"type": "string"
},
{
"index": 3,
"type": "string"
}
,
{
"value": "${dt}",
"type": "string"
},
{
"value": "${dn}",
"type": "string"
}
],
"fileType": "text",
"encoding": "UTF-8",
"fieldDelimiter": "\t"
}
},
"writer": {
"name": "mysqlwriter",
"parameter": {
"writeMode": "insert",
"username": "root",
"password": "123456",
"column": [
"paperviewid",
"paperviewname",
"avgscore",
"avgspendtime",
"dt",
"dn"
],
"preSql": [
"delete from paper_avgtimeandscore where dt=${dt}"
],
"connection": [
{
"jdbcUrl": "jdbc:mysql://hadoop101:3306/qz_paper?useUnicode=true&characterEncoding=utf8",
"table": [
"paper_avgtimeandscore"
]
}
]
}
}
}
]
}
}
4.執行腳本
./datax.py ./work_json/paper_avgtimeandscore.json -p "-Ddt=20190722 -Ddn=webA"
5.執行結果
6.注意
向MySQL中導入數據前，要先建好表，不然會報錯！找不到表！
CREATE TABLE paper_avgtimeandscore(
paperviewid INT,
paperviewname VARCHAR(100),
avgscore decimal(4,1),
avgspendtime decimal(10,1),
dt VARCHAR(100),
dn VARCHAR(100)
);