原始链接：https://www.pianshen.com/article/931082274/
技术标签： hive
0元数据，上传到服务器上：
{"movie":"2717","rate":"3","timeStamp":"978298196","uid":"2"}
{"movie":"2571","rate":"4","timeStamp":"978299773","uid":"2"}
{"movie":"1917","rate":"3","timeStamp":"978300174","uid":"2"}
{"movie":"2396","rate":"4","timeStamp":"978299641","uid":"2"}
{"movie":"3735","rate":"3","timeStamp":"978298814","uid":"2"}
{"movie":"1953","rate":"4","timeStamp":"978298775","uid":"2"}
{"movie":"1597","rate":"3","timeStamp":"978300025","uid":"2"}
{"movie":"3809","rate":"3","timeStamp":"978299712","uid":"2"}
{"movie":"1954","rate":"5","timeStamp":"978298841","uid":"2"}
{"movie":"1955","rate":"4","timeStamp":"978299200","uid":"2"}
{"movie":"235","rate":"3","timeStamp":"978299351","uid":"2"}
{"movie":"1124","rate":"5","timeStamp":"978299418","uid":"2"}
{"movie":"1957","rate":"5","timeStamp":"978298750","uid":"2"}
{"movie":"163","rate":"4","timeStamp":"978299809","uid":"2"}
#创建t_json表
0: jdbc:hive2://localhost:10000> create table t_json(line string)
0: jdbc:hive2://localhost:10000> row format delimited;
#导入数据
0: jdbc:hive2://localhost:10000> load data local inpath '/home/hadoop/data.json' into table t_json;
导入后结果：
以上数据初始化话完成。
1、创建java项目。不介绍，项目创建好后，可以加入pom依赖，或者直接把hive依赖的jar包拷贝项目里，
2、创建java类JsonParser
package com.zsy.hive.udf;
import java.io.IOException;
import org.apache.hadoop.hive.ql.exec.UDF;
import parquet.org.codehaus.jackson.map.ObjectMapper;
public class JsonParser extends UDF {
ObjectMapper objectMapper = new ObjectMapper();
public String evaluate(String jsonLine) {
MovieRateBean readValue;
try {
readValue = objectMapper.readValue(jsonLine, MovieRateBean.class);
return readValue.toString();
} catch (IOException e) {
}
return "";
}
}
3、创建bean对象：
package com.zsy.hive.udf;
public class MovieRateBean {
private String movie;
private String rate;
private String timeStamp;
private String uid;
public String getMovie() {
return movie;
}
public void setMovie(String movie) {
this.movie = movie;
}
public String getRate() {
return rate;
}
public void setRate(String rate) {
this.rate = rate;
}
public String getTimeStamp() {
return timeStamp;
}
public void setTimeStamp(String timeStamp) {
this.timeStamp = timeStamp;
}
public String getUid() {
return uid;
}
public void setUid(String uid) {
this.uid = uid;
}
@Override
public String toString() {
return movie + "," + rate + "," + timeStamp + "," + uid;
}
}
4、打包，放到hive所在的服务器。
5、执行命令，把jar包加入classpath里：
0: jdbc:hive2://localhost:10000> add JAR /home/hadoop/udf.jar;6、创建函数：
0: jdbc:hive2://localhost:10000> create temporary function parseJson as 'com.zsy.hive.udf.JsonParser';7、测试：
跟进逗号进行分割：select split(parseJson(line),',')[0] movieId,split(parseJson(line),',')[1] rate,split(parseJson(line),',')[2] time,split(parseJson(line),',')[3] id from t_json limit 10;
查询的结果保存到hive表里：
create table t_rate as select split(parseJson(line),',')[0] movieId,split(parseJson(line),',')[1] rate,split(parseJson(line),',')[2] time,split(parseJson(line),',')[3] id from t_json ;
运行过程：
结果：
以上是通过自定义hive函数来解决的方式，其实hive有一个内置函数，接下来演示下内置函数：
select get_json_object(line,'$.movie') as movie from t_json limit 10;
内置的函数只能实现简单的json格式，复杂的json格式还是要定义函数来实现
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本文链接：https://blog.csdn.net/u011410254/article/details/83279620
