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
File->Project Structure
add Artifacts
不用填写Main Class,直接点击OK
jar包配置
生成jar包
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。
