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Github： https://github.com/thinkgamer
前言
hive 2.x版本出来已经有一段时间了，目前的2.x中的稳定版本为2.1.0
github地址：https://github.com/apache/hive/tree/master
官方下载地址为：https://mirrors.tuna.tsinghua.edu.cn/apache/hive/
工作之余，我们就来看看hive2.1.0这个版本相对于1.2来说的change
部署
之前的一篇hive1.2的mysql部署文章：
http://blog.csdn.net/gamer_gyt/article/details/52032579
hive2.1.0相对1.2来讲部署上并没有什么变化，但就算是温习吧，我们依旧走一遍这个过程，看看有哪些坑等着我们去踩。
1：下载文件
2：解压至指定目录
我这里hive的存放目录是/opt/bigdata/hive，并重命名为hive
tar -zxvf /home/thinkgamer/下载/apache-hive-2.1.0-bin.tar.gz -C /opt/bigdata/
mv apache-hive-2.1.0-bin/ hive
3：Mysql创建hive21用户
创建hive21用户，赋予权限，清除缓存
CREATE USER 'hive21' IDENTIFIED BY 'hive21';
grant all privileges on *.* to 'hive21' with grant option;
flush privileges;4：拷贝msyql jar包到hive/lib
cp /path/to/mysql-connector-java-5.1.38-bin.jar hive/lib
5：修改配置文件
(1)：javax.jdo.option.ConnectionURL
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://localhost:3306/hive21?createDatabaseIfNotExist=true&useUnicode=true&characterEncoding=UTF-8</value>
(2)：javax.jdo.option.ConnectionDriverName
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>(3)：javax.jdo.option.ConnectionUserName
<name>javax.jdo.option.ConnectionUserName</name>
<value>hive21</value> (4)：javax.jdo.option.ConnectionPassword
<name>javax.jdo.option.ConnectionPassword</name>
<value>hive21</value>6：启动/测试
bin/hive
hive> show databases;
OK
default
Time taken: 1.123 seconds, Fetched: 1 row(s)
hive> create table table_name (
> id int,
> dtDontQuery string,
> name string
> );
OK
Time taken: 0.983 seconds
hive> show tables;
OK
table_name
Time taken: 0.094 seconds, Fetched: 1 row(s)这个时候进入mysql数据库有一个hive21的数据库
mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| hive |
| hive21 |
| mysql |
| performance_schema |
+--------------------+7：踩过的坑
(1)：没有初始化hive元数据库
报错如下：
Caused by: MetaException(message:Hive metastore database is not initialized. Please use schematool (e.g. ./schematool -initSchema -dbType ...) to create the schema.
If needed, don't forget to include the option to auto-create the underlying database in your JDBC connection string (e.g. ?createDatabaseIfNotExist=true for mysql)) 解决办法：
bin/schematool -initSchema -dbType mysql --verbose 此问题解决时在网上查阅资料有人说这里要初始化derby数据库，个人认为这是不正确的，因为我们已经配置使用了mysql作为元数据库
(2)：未配置日志和缓存目录
报错如下：
Logging initialized using configuration in file:/opt/bigdata/hive/conf/hive-log4j2.properties Async: true
Exception in thread "main" java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: ${system:java.io.tmpdir%7D/$%7Bsystem:user.name%7D
at org.apache.hadoop.fs.Path.initialize(Path.java:205)
at org.apache.hadoop.fs.Path.<init>(Path.java:171)
at org.apache.hadoop.hive.ql.session.SessionState.createSessionDirs(SessionState.java:631)
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:550)
at org.apache.hadoop.hive.ql.session.SessionState.beginStart(SessionState.java:518)
at org.apache.hadoop.hive.cli.CliDriver.run(CliDriver.java:705)
at org.apache.hadoop.hive.cli.CliDriver.main(CliDriver.java:641)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
Caused by: java.net.URISyntaxException: Relative path in absolute URI: ${system:java.io.tmpdir%7D/$%7Bsystem:user.name%7D
at java.net.URI.checkPath(URI.java:1823)
at java.net.URI.<init>(URI.java:745)
at org.apache.hadoop.fs.Path.initialize(Path.java:202)
... 12 more解决办法：
修改 hive-site.xml 替换${system:java.io.tmpdir} 和 ${system:user.name}为/opt/bigdata/hive/tmp
hive2.1 和hive1.2的简单比较
hive1.2
[master@master1 hive]$ bin/hive --service help
Usage ./hive <parameters> --service serviceName <service parameters>
Service List: beeline cli help hiveburninclient hiveserver2 hiveserver hwi jar lineage metastore metatool orcfiledump rcfilecat schemaTool version
Parameters parsed:
--auxpath : Auxillary jars
--config : Hive configuration directory
--service : Starts specific service/component. cli is default
Parameters used:
HADOOP_HOME or HADOOP_PREFIX : Hadoop install directory
HIVE_OPT : Hive options
For help on a particular service:
./hive --service serviceName --help
Debug help: ./hive --debug --help hive2.1
root@thinkgamer-pc:/opt/bigdata/hive# bin/hive --service help
Usage ./hive <parameters> --service serviceName <service parameters>
Service List: beeline cleardanglingscratchdir cli hbaseimport hbaseschematool help hiveburninclient hiveserver2 hplsql hwi jar lineage llapdump llap llapstatus metastore metatool orcfiledump rcfilecat schemaTool version
Parameters parsed:
--auxpath : Auxillary jars
--config : Hive configuration directory
--service : Starts specific service/component. cli is default
Parameters used:
HADOOP_HOME or HADOOP_PREFIX : Hadoop install directory
HIVE_OPT : Hive options
For help on a particular service:
./hive --service serviceName --help
Debug help: ./hive --debug --help我们可以看到在hive2.1中增加了对hbase的支持，同时还增加了hplsql等等，这些都是hive2.1的新特性，这里介绍几个常用的
beeline：和hive1.2中beeline使用方法应该是一样的，至于性能方面的提升肯定是有的，beeline的使用，参考
http://blog.csdn.net/gamer_gyt/article/details/52062460cleardanglingscratchdir：scratch directory（清楚缓存）
使用方法： bin/hive –service cleardanglingscratchdirhbaseimport/hbaseschematool：与Hbase进行交互
hiveserver2：提供一个JDBC接口，供外部程序操作hive
hplsql：一个工具，实现sql在Apache hive，sparkSql，以及其他基于hadoop的sql，Nosql和关系数据库的使用
官方解释：
HPL/SQL (previously known as PL/HQL) is an open source tool (Apache License 2.0) that implements procedural SQL language for Apache Hive, SparkSQL as well as any other SQL-on-Hadoop implementations, NoSQL and RDBMS.
HPL/SQL language is compatible to a large extent with Oracle PL/SQL, ANSI/ISO SQL/PSM (IBM DB2, MySQL, Teradata i.e), PostgreSQL PL/pgSQL (Netezza), Transact-SQL (Microsoft SQL Server and Sybase) that allows you leveraging existing SQL/DWH skills and familiar approach to implement data warehouse solutions on Hadoop. It also facilitates migration of existing business logic to Hadoop.
HPL/SQL is an efficient way to implement ETL processes in Hadoop.- LLAP：也是hive2.1引入的新特性，大概就是提升hive2.1的执行时间，具体可参考：
http://zh.hortonworks.com/blog/llap-enables-sub-second-sql-hadoop/
下面附一张从网上看到的图片：
