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Hadoop 架构初探

对流行Hadoop做了一些最基本的了解,暂时没太大感觉,恩先记点笔记吧. = =

Hadoop 基本命令及环境安装

一、下载虚拟机镜像

目前比较流行的有以下三个:

(CHD) http://www.cloudera.com (HDP)  http://hortonworks.com/ (MapR) http://www.mapr.com

本文使用HDP的沙盘 下载地址 http://hortonworks.com/products/hortonworks-sandbox/#install 我使用的是 Hyper-V 的镜像 , 配置可以查看下载地址旁边的文档

二、使用HDP沙盘

三、 使用hue ui 的文件浏览器操作文件 根据沙盘的提示访问 http://192.168.56.101:8000/filebrowser/#/  我们可以看到刚才建立的目录。 (还是UI方便点啊) image

使用Hive并且将数据导入仓库

一、先看一下Demo里面的Hive目录

hadoop fs -ls /apps/hive/warehouse
Found 3 items
drwxrwxrwx   - hive hdfs          0 2015-08-20 09:05 /apps/hive/warehouse/sample_07
drwxrwxrwx   - hive hdfs          0 2015-08-20 09:05 /apps/hive/warehouse/sample_08
drwxrwxrwx   - hive hdfs          0 2015-08-20 08:58 /apps/hive/warehouse/xademo.db
hadoop fs -ls /apps/hive/warehouse/sample_07
Found 1 items
-rwxr-xr-x   1 hue hue      46055 2015-08-20 08:46 /apps/hive/warehouse/sample_07/sample_07
查看文件内容
hadoop fs -cat /apps/hive/warehouse/sample_07/sample_07 | less
二、使用hive命令

进入hive数据库

hive
显示hive中的数据库
show databases;
显示表格
show tables; 
show tables '*08*';
清空屏幕
!clear;
进一步查看表格结构
describe sample_07;
describe extended sample_07 ;
创建数据库
create database bihell;
使用hadoop fs命令查看下hive 目录,我们刚才创建的数据库文件应该在里面了
!hadoop fs -ls /apps/hive/warehouse/;
结果如下:
Found 4 items
drwxrwxrwx   - root hdfs          0 2015-09-12 08:57 /apps/hive/warehouse/bihell.db
drwxrwxrwx   - hive hdfs          0 2015-08-20 09:05 /apps/hive/warehouse/sample_07
drwxrwxrwx   - hive hdfs          0 2015-08-20 09:05 /apps/hive/warehouse/sample_08
drwxrwxrwx   - hive hdfs          0 2015-08-20 08:58 /apps/hive/warehouse/xademo.db
三、使用建立的数据库 一直用命令行比较吃力,我们也可用ui界面 [![image](http://www.bihell.com/wp-content/uploads/2015/09/image_thumb1.png "image")](http://www.bihell.com/wp-content/uploads/2015/09/image1.png) 在我们新建的bihell数据库中建立表格
CREATE TABLE movies (
     movie_id INT,
     movie_title STRING,
     release_date STRING,
     video_release_date STRING,
     imdb_url STRING,
     unknown INT,
     action INT,
     adventure INT,
     animation INT,
     children INT,
     comedy INT,
     crime INT,
     documentary INT,
     drama INT,
     fantasy INT,
     film_noir INT,
     horror INT,
     musical INT,
     mystery INT,
     romance INT,
     sci_fi INT,
     thriller INT,
     war INT,
     Western INT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '|'
STORED AS TEXTFILE;
创建完毕以后点击Tables可以看到我们刚才创建的表格 [![image](http://www.bihell.com/wp-content/uploads/2015/09/image_thumb2.png "image")](http://www.bihell.com/wp-content/uploads/2015/09/image2.png) 在SSH执行文件命令,我们可以看到bihell.db下面多了一个目录
hadoop fs -ls /apps/hive/warehouse/bihell.db
Found 1 items
drwxrwxrwx   - hive hdfs          0 2015-09-12 09:09 /apps/hive/warehouse/bihell.db/movies
四、进入hive ,我们导入一些数据进去 导入数据
lOAD DATA INPATH '/bihell/userinfo' INTO TABLE movies;
清空数据
truncate table movies;
导入并覆盖原有数据
load data inpath '/bihell/movies' overwrite into table movies;
四、建立External表与RCFile 表 前面我们建立表以后导入数据到表中, 目录中的文件会被删除,现在我们直接建立表并指向我们所在的文件目录,建立外部表.

复原文件

!hadoop fs -put /home/bihell/ml-100k/u.user /bihell/userinfo;
建立另外一个表格,注意有指定路径
CREATE EXTERNAL TABLE users (
user_id INT,
age INT,
gender STRING,
occupation STRING,
zip_code STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '|'
STORED AS TEXTFILE
LOCATION '/bihell/userinfo';
查看users的schema
describe formatted users;
查询表
SELECT * FROM users limit 100;
创建 RCFile 表格
CREATE TABLE occupation_count 
STORED AS RCFile 
AS SELECT COUNT(*), occupation FROM users GROUP BY occupation;
引用另外一个表创建一个空表
CREATE TABLE occupation2 LIKE occupation_count;

Hive 查询语言

我们之前已经用了部分hive查询,现在深入一下

一、复杂类型 Arrays – ARRAY<data_type> Maps  -- MAP<primitive,data_type> Struct  -- STRUCT<col_name:data_type[COMMENT col_comment],…> Union Type – UNIONYTPE<data_type,data_type,…>

create table movies ( movie_name string, participants ARRAY <string>, release_dates MAP <string,timestamp>, studio_addr STRUCT  <state:string,city:string,zip:string,streetnbr:int,streetname:string,unit:string>, complex_participants MAP<string,STRUCT<address:string,attributes MAP<string,string>>> misc UNIONTYPE <int,string,ARRAY<double>>

);

查询方式

select movie_name, participants[0], release_dates[“USA”], studio_addr.zip, complex_participants[“Leonardo DiCaprio”].attributes[“fav_color”], misc from movies;
二、Partitioned Tables 这个章节主要讲述加载与管理Hive中的数据 前面我们使用了CREATE TABLE 以及 CREATE EXTERNAL TABLE 本文我们要看下Table Partitions 创建分区表:
CREATE TABLE page_views( eventTime STRING, userid STRING) PARTITIONED BY (dt STRING, applicationtype STRING) STORED AS TEXTFILE;
数据库文件默认地址 : /apps/hive/warehouse/page_views 当你每次导入数据的时候都会为你建立partition ,比如
LOAD DATA INPATH ‘/mydata/android'/Aug_10_2013/pageviews/’ INTO TABLE page_views PARTITION (dt = ‘2013-08-10’, applicationtype = ‘android’);
生成分区如下: /apps/hive/warehouse/page_views/dt=2013-08-10/application=android 当然我们也可以覆盖导入
LOAD DATA INPATH ‘/mydata/android'/Aug_10_2013/pageviews/’ OVERWRITE INTO TABLE page_views PARTITION (dt = ‘2013-08-10’, applicationtype = ‘android’);
image

创建语句中dt和applicationtype 是virtual partition columns. 如果你describe table,会发现所有字段显示和正常表一样 eventTime STRING userid STRING page STRING dt STRING applicationtype STRING

可以直接用于查询

select dt as eventDate,page,count(*) as pviewCount From page_views where applicationtype = ‘iPhone’;
三、External Partitioned Tables 相比分区表,只是多了一个EXTERNAL ,我们注意到这里没有指定location ,添加文件的时候才需要指定
CREATE  EXTERNAL TABLE page_views( eventTime STRING, userid STRING) PARTITIONED BY (dt STRING, applicationtype STRING) STORED AS TEXTFILE;
添加文件
ALTER TABLE page_views ADD PARTITION ( dt = ‘2013-09-09’, applicationtype = ‘Windows Phone 8’) LOCATION ‘/somewhere/on/hdfs/data/2013-09-09/wp8’;

ALTER TABLE page_view ADD PARTITION (dt=’2013-09-09’,applicationtype=’iPhone’) LOCATION ‘hdfs://NameNode/somewhere/on/hdfs/data/iphone/current’;

ALTER TABLE page_views ADD IF NOT EXSTS PARTITION (dt=’2013-09-09’,applicationtype=’iPhone’) LOCATION ‘/somewhere/on/hdfs/data/iphone/current’; PARTITION (dt=’2013-09-08’,applicationtype=’iPhone’) LOCATION ‘/somewhere/on/hdfs/data/prev1/iphone; PARTITION (dt=’2013-09-07’,applicationtype=’iPhone’) LOCATION ‘/somewhere/on/hdfs/data/iphone/prev2;

四、实际操作 EXTERNAL PARTITION TABLE
--建立目录 hadoop fs -mkdir /bihell/logs/pv_ext/somedatafor_7_11 /bihell/logs/pv_ext/2013/08/11/log/data

--建立EXTERNAL TABLE CREATE EXTERNAL TABLE page_views_ext (logtime STRING, userid INT, ip STRING, page STRING, ref STRING, os STRING, os_ver STRING, agent STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LOCATION '/bihell/logs/pv_ext/';

--查看表格详细信息 DESCRIBE FORMATTED page_views_ext;

--查看执行计划 EXPLAIN SELECT * FROM page_views_ext WHERE userid = 13;

--删除表 DROP TABLE page_views_ext;

--创建EXTERNAL Partition Table CREATE EXTERNAL TABLE page_views_ext (logtime STRING, userid INT, ip STRING, page STRING, ref STRING, os STRING, os_ver STRING, agent STRING) PARTITIONED BY (y STRING, m STRING, d STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LOCATION '/bihell/logs/pv_ext/';

--将日志传送至Hadoop目录 !hadoop fs -put /media/sf_VM_Share/LogFiles/log_2013711_155354.log /bihell/logs/pv_ext/somedatafor_7_11

--因为是partition table 所以此时查询该表是没有任何内容的 SELECT * FROM page_views_ext;

--添加文件 ALTER TABLE page_views_ext ADD PARTITION (y='2013', m='07', d='11') LOCATION '/bihell/logs/pv_ext/somedatafor_7_11';

--再次查询 SELECT * FROM page_views_ext LIMIT 100;

--describe table DESCRIBE FORMATTED page_views_ext;

--再次查看执行计划 我们发现predicate还是13, 并没有加上 m,d EXPLAIN SELECT * FROM page_views_ext WHERE userid=13 AND m='07'AND d='11' LIMIT 100;

--再添加一个文件 !hadoop fs -put /media/sf_VM_Share/LogFiles/log_2013811_16136.log /bihell/logs/pv_ext/2013/08/11/log/data ALTER TABLE page_views_ext ADD PARTITION (y='2013', m='08', d='11') LOCATION '/bihell/logs/pv_ext/2013/08/11/log/data';

--查询 SELECT COUNT(*) as RecordCount, m FROM page_views_ext WHERE d='11' GROUP BY m;

--另一种方式添加数据 !hadoop fs -put /media/sf_VM_Share/LogFiles/log_2013720_162256.log /bihell/logs/pv_ext/y=2013/m=07/d=20/data.log SELECT * FROM page_views_ext WHERE m='07' AND d='20' LIMIT 100; MSCK REPAIR TABLE page_views_ext; SELECT * FROM page_views_ext WHERE m='07' AND d='20' LIMIT 100;

PARTITION TABLE
CREATE TABLE page_views (logtime STRING, userid INT, ip STRING, page STRING, ref STRING, os STRING, os_ver STRING, agent STRING) PARTITIONED BY (y STRING, m STRING, d STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';

LOAD DATA LOCAL INPATH '/media/sf_VM_Share/LogFiles/log_2013805_16210.log' OVERWRITE INTO TABLE page_views PARTITION (y='2013', m='08', d='05');

!hadoop fs -ls /apps/hive/warehouse/bihell.db/page_views/;

批量插入及动态分区表插入

Multiple Inserts --Syntax FROM form_statement INSERT OVERWRITE TABLE table1 [PARTITION(partcol1=val1,partcol2=val2)] select_statement1 INSERT INTO TABLE table2 [PARTITION(partcol1=val1,partcol2=val2)[IF NOT EXISTS]] select_statements2 INSERT OVERWRITE DIRECTORY ‘path’ select_statement3;

-- 提取操作 FROM movies INSERT OVERWRITE TABLE horror_movies SELECT * WHERE horror = 1 AND release_date=’8/23/2013’ INSERT INTO action_movies SELECT * WHERE action = 1 AND release_date = ‘8/23/2013’;

FROM (SELECT * FROM movies WHERE release_date =’8/23/2013’) src INSERT OVERWRITE TABLE horror_movies SELECT * WHERE horror =1 INSERT INTO action_movies SELECT * WHERE action = 1;

Dynamic Partition Inserts
CREATE TABLE views_stg (eventTime STRING, userid STRING)
PARTITIONED BY(dt STRING,applicationtype STRING,page STRING);

FROM page_views src
INSERT OVERWRITE TABLE views_stg PARTITION (dt=’2013-09-13’,applicationtype=’Web’,page=’Home’)
    SELECT src.eventTime,src.userid WHERE dt=’2013-09-13’ AND applicationtype=’Web’,page=’Home’
INSERT OVERWRITE TABLE views_stg PARTITION (dt=’2013-09-14,applicationtype=’Web’,page=’Cart’)
    SELECT src.eventTime,src.userid WHERE dt=’2013-09-14’ AND applicationtype=’Web’,page=’Cart’
INSERT OVERWRITE TABLE views_stg PARTITION (dt=’2013-09-15’,applicationtype=’Web’,page=’Checkout’)
    SELECT src.eventTime,src.userid WHERE dt=’2013-09-15’ AND applicationtype=’Web’,page=’Checkout’

FROM page_views src
INSERT OVERWRITE TABLE views_stg PARTITION (applicationtype=’Web’,dt,page)
SELECT src.eventTime,src.userid,src.dt,src.page WHERE applicationtype=’Web’
实例
!hadoop fs -mkdir /bihell/logs/multi_insert;

!hadoop fs -put /media/sf_VM_Share/LogFiles/log_2012613_161117.log /media/sf_VM_Share/LogFiles/log_2013803_15590.log /bihell/logs/multi_insert

-- 创建EXTERNAL TABLE 
CREATE EXTERNAL TABLE staging (logtime STRING, userid INT, ip STRING, page STRING, ref STRING, os STRING, os_ver STRING, agent STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
LOCATION '/bihell/logs/multi_insert';

--批量插入 PARTITION
INSERT INTO TABLE page_views PARTITION (y, m, d)
SELECT logtime, userid, ip, page, ref, os, os_ver, agent, substr(logtime, 7, 4), substr(logtime, 1, 2), substr(logtime, 4, 2)
FROM staging;

SET hive.exec.dynamic.partition.mode=nonstrict;

INSERT INTO TABLE page_views PARTITION (y, m, d)
SELECT logtime, userid, ip, page, ref, os, os_ver, agent, substr(logtime, 7, 4), substr(logtime, 1, 2), substr(logtime, 4, 2)
FROM staging;

SELECT * FROM page_views WHERE y='2012' LIMIT 100;

select regexp_replace(logtime, '/', '-') from staging;
select substr(logtime, 7, 4), substr(logtime, 1, 2), substr(logtime, 4, 2) from staging;
参考

Hadoop Admin Commands

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