Hive支持自定义map与reduce script。接下来我用一个简单的wordcount例子加以说明。
如果自己使用Java开发,需要处理System.in,System,out以及key/value的各种逻辑,比较麻烦。有人开发了一个小框架,可以让我们使用与Hadoop中map与reduce相似的写法,只关注map与reduce即可。如今此框架已经集成在Hive中,就是$HIVE_HOME/lib/hive-contrib-2.3.0.jar,hive版本不同,对应的contrib名字可能不同。
- 开发工具:intellij
- JDK:jdk1.7
- hive:2.3.0
- hadoop:2.8.1
一、开发map与reduce
- “map类
- public class WordCountMap {
- static void main(String args[]) throws Exception{
- new GenericMR().map(System.in,System.out,new Mapper() {
- @Override
- void map(String[] strings,Output output) Exception {
- for(String str:strings){
- String[] strs=str.split("\\W+");//如果源文本文件是以\t分隔的,则不需要再拆分,传入的strings就是每行拆分好的单词
- (String str_2:strs) {
- output.collect(new String[]{str_2,"1"});
- }
- }
- }
- });
- }
- }
- "reduce类
- WordCountReducer {
- new GenericMR().reduce(System.in,1)"> Reducer() {
- @Override
- void reduce(String s,Iterator<String[]> iterator,1)">int sum=0;
- while(iterator.hasNext()){
- Integer count=Integer.valueOf(iterator.next()[1]);
- sum+=count;
- }
- output.collect( String[]{s,String.valueOf(sum)});
- }
- });
- }
- }
二、导出jar包
然后导出Jar包(包含hive-contrib-2.3.0),假如导出jar包名为wordcount.jar
三、编写hive sql
- drop table if exists raw_lines;
- -- create table raw_line,and read all the lines in '/user/inputs',this is the path on your local HDFS
- create external table if not exists raw_lines(line string)
- ROW FORMAT DELIMITED
- stored as textfile
- location ;
- drop table exists word_count;
- -- create table word_count,this is the output table which will be put /user/outputs' as a text fileif not exists word_count(word string,count int)
- ROW FORMAT DELIMITED
- FIELDS TERMINATED BY \t
- lines terminated by \n' STORED AS TEXTFILE LOCATION /user/outputs/;
- -- add the mapper&reducer scripts as resources,please change your/local/path
- --must use "add file",not add jart find map and reduce main class
- add file your/local/path/wordcount.jar;
- from (
- from raw_lines
- map raw_lines.line
- --call the mapper here
- using java -cp wordcount.jar WordCountMap
- as word,count
- cluster by word) map_output
- insert overwrite table word_count
- reduce map_output.word,map_output.count
- --call the reducer here
- using java -cp wordcount.jar WordCountReducer
- as word,count;
此hive sql保存为wordcount.hql
四、执行hive sql
- beeline -u [hiveserver] -n username -f wordcount.hql
简单说下Hive的自定义map与reduce内部原理:
hive读取文本文件,然后将其一行行输入系统标准输入中,用户自定义的Map读取标准输入流中数据,一行行处理,然后将其按照一定格式(例如:"key\tvalue")输出到标准输出流中,然后hive会将输出的字符串进行排序,然后再送到标准输入流中,Reduce再从标准输入流中读取数据进行相应处理,处理完成后,再送到标准输出流中,Hive再对Reduce结果进行处理存入表中。