转自:http://blog.csdn.net/ningguixin/article/details/12852051
有一张很大的表:TRLOG
该表大概有2T左右
TRLOG:
CREATE TABLE TRLOG
(PLATFORM string,
USER_ID int,
CLICK_TIME string,
CLICK_URL string)
row format delimited
fields terminated by ' ';
数据:
PLATFORM USER_ID CLICK_TIME CLICK_URL
WEB 12332321 2013-03-21 13:48:31.324 /home/
WEB 12332321 2013-03-21 13:48:32.954 /selectcat/er/
WEB 12332321 2013-03-21 13:48:46.365 /er/viewad/12.html
WEB 12332321 2013-03-21 13:48:53.651 /er/viewad/13.html
WEB 12332321 2013-03-21 13:49:13.435 /er/viewad/24.html
WEB 12332321 2013-03-21 13:49:35.876 /selectcat/che/
WEB 12332321 2013-03-21 13:49:56.398 /che/viewad/93.html
WEB 12332321 2013-03-21 13:50:03.143 /che/viewad/10.html
WEB 12332321 2013-03-21 13:50:34.265 /home/
WAP 32483923 2013-03-21 23:58:41.123 /m/home/
WAP 32483923 2013-03-21 23:59:16.123 /m/selectcat/fang/
WAP 32483923 2013-03-21 23:59:45.123 /m/fang/33.html
WAP 32483923 2013-03-22 00:00:23.984 /m/fang/54.html
WAP 32483923 2013-03-22 00:00:54.043 /m/selectcat/er/
WAP 32483923 2013-03-22 00:01:16.576 /m/er/49.html
…… …… …… ……
需要把上述数据处理为如下结构的表ALLOG:
CREATE TABLE ALLOG
(PLATFORM string,
USER_ID int,
SEQ int,
FROM_URL string,
TO_URL string)
row format delimited
fields terminated by ' ';
整理后的数据结构:
PLATFORM USER_ID SEQ FROM_URL TO_URL
WEB 12332321 1 NULL /home/
WEB 12332321 2 /home/ /selectcat/er/
WEB 12332321 3 /selectcat/er/ /er/viewad/12.html
WEB 12332321 4 /er/viewad/12.html /er/viewad/13.html
WEB 12332321 5 /er/viewad/13.html /er/viewad/24.html
WEB 12332321 6 /er/viewad/24.html /selectcat/che/
WEB 12332321 7 /selectcat/che/ /che/viewad/93.html
WEB 12332321 8 /che/viewad/93.html /che/viewad/10.html
WEB 12332321 9 /che/viewad/10.html /home/
WAP 32483923 1 NULL /m/home/
WAP 32483923 2 /m/home/ /m/selectcat/fang/
WAP 32483923 3 /m/selectcat/fang/ /m/fang/33.html
WAP 32483923 4 /m/fang/33.html /m/fang/54.html
WAP 32483923 5 /m/fang/54.html /m/selectcat/er/
WAP 32483923 6 /m/selectcat/er/ /m/er/49.html
…… …… …… ……
PLATFORM和USER_ID还是代表平台和用户ID;SEQ字段代表用户按时间排序后的访问顺序,FROM_URL和TO_URL分别代表用户从哪一页跳转到哪一页。对于某个平台上某个用户的第一条访问记录,其FROM_URL是NULL(空值)。
面试官说需要用两种办法做出来:
1、实现一个能加速上述处理过程的Hive Generic UDF,并给出使用此UDF实现ETL过程的Hive SQL
2、实现基于纯Hive SQL的ETL过程,从TRLOG表生成ALLOG表;(结果是一套SQL)
答案:
1.
UDF
package org.apache.hadoop.hive.udf; public class RowNumber extends org.apache.hadoop.hive.ql.exec.UDF { private static int MAX_VALUE = 50; private static String comparedColumn[] = new String[MAX_VALUE]; private static int rowNum = 1; public int evaluate(Object... args) { String columnValue[] = new String[args.length]; for (int i = 0; i < args.length; i++) columnValue[i] = args[i].toString(); if (rowNum == 1) { for (int i = 0; i < columnValue.length; i++) comparedColumn[i] = columnValue[i]; } for (int i = 0; i < columnValue.length; i++) { if (!comparedColumn[i].equals(columnValue[i])) { for (int j = 0; j < columnValue.length; j++) { comparedColumn[j] = columnValue[j]; } rowNum = 1; return rowNum++; } } return rowNum++; } public static void main(String[] args) { RowNumber aRowNumber = new RowNumber(); System.out.println(aRowNumber.evaluate("12332321")); System.out.println(aRowNumber.evaluate("12332321")); System.out.println(aRowNumber.evaluate("12332321")); System.out.println(aRowNumber.evaluate("12332321")); System.out.println(aRowNumber.evaluate("12332321")); } }
INSERT OVERWRITE TABLE ALLOG
SELECT t1.platform,t1.user_id,RowNumber(t1.user_id)seq,t2.click_url FROM_URL,t1.click_url TO_URL FROM
(select *,RowNumber(user_id)seq from trlog)t1
LEFT OUTER JOIN
(select *,RowNumber(user_id)seq from trlog)t2
on t1.user_id = t2.user_id and t1.seq=t2.seq+1;
2.
INSERT OVERWRITE TABLE ALLOG
SELECT t1.platform,t1.user_id,t1.seq,t2.click_url FROM_URL,t1.click_url TO_URL FROM
(SELECT platform,user_id,click_time,click_url,count(1) seq FROM (SELECT a.*,b.click_time click_time1,b.click_url click_url2 FROM trlog a left outer join trlog b on a.user_id = b.user_id)t WHERE click_time>=click_time1 GROUP BY platform,user_id,click_time,click_url)t1
LEFT OUTER JOIN
(SELECT platform,user_id,click_time,click_url,count(1) seq FROM (SELECT a.*,b.click_time click_time1,b.click_url click_url2 FROM trlog a left outer join trlog b on a.user_id = b.user_id)t WHERE click_time>=click_time1 GROUP BY platform,user_id,click_time,click_url )t2
on t1.user_id = t2.user_id and t1.seq = t2.seq + 1;
使用到的知识点为:
left outer join 左表全部显示,右表只显示满足条件的
3、对于以上的文本处理 我们可以很快的联想到shell中awk的处理
利用awk 中数组的相关操作,方法如下
cat url.txt |awk -F\t 'BEGIN{OFS=" "}{a[$1]++;b[a[$1]]=$4;print a[$1],$1,$2,$3,b[a[$1]-1],$4}'
其中OFS为输出的字段的定界符,这里利用了2个数组,a和b
输出为:
1 WEB 12332321 2013-03-21 13:48:31.324 /home 2 WEB 12332321 2013-03-21 13:48:32.954 /home /selectcat/er 3 WEB 12332321 2013-03-21 13:48:46.365 /selectcat/er /er/viewad/12.html 4 WEB 12332321 2013-03-21 13:48:53.651 /er/viewad/12.html /er/viewad/13.html 5 WEB 12332321 2013-03-21 13:49:13.435 /er/viewad/13.html /er/viewad/24.html 6 WEB 12332321 2013-03-21 13:49:35.876 /er/viewad/24.html /selectcat/che/ 7 WEB 12332321 2013-03-21 13:49:56.398 /selectcat/che/ /che/viewad/93.html 8 WEB 12332321 2013-03-21 13:50:03.143 /che/viewad/93.html /che/viewad/10.html 9 WEB 12332321 2013-03-21 13:50:34.265 /che/viewad/10.html /home/ 1 WAP 32483923 2013-03-21 23:58:41.123 /m/home/ 2 WAP 32483923 2013-03-21 23:59:16.123 /m/home/ /m/selectcat/fang/ 3 WAP 32483923 2013-03-21 23:59:45.123 /m/selectcat/fang/ /m/fang/33.html 4 WAP 32483923 2013-03-22 00:00:23.984 /m/fang/33.html /m/fang/54.html 5 WAP 32483923 2013-03-22 00:00:54.043 /m/fang/54.html /m/selectcat/er/ 6 WAP 32483923 2013-03-22 00:01:16.576 /m/selectcat/er/ /m/er/49.html