Hive学习之路 （二）Hive安装

Hive的下载

下载地址http://mirrors.hust.edu.cn/apache/

选择合适的Hive版本进行下载，进到stable-2文件夹可以看到稳定的2.x的版本是2.3.3

Hive的安装

1、本人使用MySQL做为Hive的元数据库，所以先安装MySQL。

MySql安装过程http://www.cnblogs.com/qingyunzong/p/8294876.html

2、上传Hive安装包

3、解压安装包

[hadoop@hadoop3 ~]$ tar -zxvf apache-hive-2.3.3-bin.tar.gz -C apps/

4、修改配置文件

配置文件所在目录apache-hive-2.3.3-bin/conf

[hadoop@hadoop3 apps]$ cd apache-hive-2.3.3-bin/
[hadoop@hadoop3 apache-hive-2.3.-bin]$ ls
bin  binary-package-licenses  conf  examples  hcatalog  jdbc  lib  LICENSE  NOTICE  RELEASE_NOTES.txt  scripts
[hadoop@hadoop3 apache-hive-2.3.-bin]$ cd conf/
[hadoop@hadoop3 conf]$ ls
beeline-log4j2.properties.template    ivysettings.xml
hive-default.xml.template             llap-cli-log4j2.properties.template
hive-env.sh.template                  llap-daemon-log4j2.properties.template
hive-exec-log4j2.properties.template  parquet-logging.properties
hive-log4j2.properties.template
[hadoop@hadoop3 conf]$ pwd
/home/hadoop/apps/apache-hive-2.3.-bin/conf
[hadoop@hadoop3 conf]$ 

新建hive-site.xml并添加以下内容

[hadoop@hadoop3 conf]$ touch hive-site.xml
[hadoop@hadoop3 conf]$ vi hive-site.xml 

<configuration>
        <property>
                <name>javax.jdo.option.ConnectionURL</name>
                <value>jdbc:mysql://hadoop1:3306/hivedb?createDatabaseIfNotExist=true</value>
                <description>JDBC connect string for a JDBC metastore</description>
                <!-- 如果 mysql 和 hive 在同一个服务器节点，那么请更改 hadoop02 为 localhost -->
        </property>
        <property>
                <name>javax.jdo.option.ConnectionDriverName</name>
                <value>com.mysql.jdbc.Driver</value>
                <description>Driver class name for a JDBC metastore</description>
        </property>
        <property>
                <name>javax.jdo.option.ConnectionUserName</name>
                <value>root</value>
                <description>username to use against metastore database</description>
        </property>
        <property>
                <name>javax.jdo.option.ConnectionPassword</name>
                <value>root</value>
        <description>password to use against metastore database</description>
        </property>
</configuration>

以下可选配置，该配置信息用来指定 Hive 数据仓库的数据存储在 HDFS 上的目录

        <property>
                <name>hive.metastore.warehouse.dir</name>
                <value>/hive/warehouse</value>
                <description>hive default warehouse, if nessecory, change it</description>
        </property>    

5、 一定要记得加入 MySQL 驱动包（mysql-connector-java-5.1.40-bin.jar）该 jar 包放置在 hive 的根路径下的 lib 目录

6、 安装完成，配置环境变量

[hadoop@hadoop3 lib]$ vi ~/.bashrc 

#Hive
export HIVE_HOME=/home/hadoop/apps/apache-hive-2.3.3-bin
export PATH=$PATH:$HIVE_HOME/bin

使修改的配置文件立即生效

[hadoop@hadoop3 lib]$ source ~/.bashrc 

7、 验证 Hive 安装

[hadoop@hadoop3 ~]$ hive --help
Usage ./hive <parameters> --service serviceName <service parameters>
Service List: beeline cleardanglingscratchdir cli hbaseimport hbaseschematool help hiveburninclient hiveserver2 hplsql jar lineage llapdump llap llapstatus metastore metatool orcfiledump rcfilecat schemaTool version
Parameters parsed:
  --auxpath : Auxiliary 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
[hadoop@hadoop3 ~]$ 

8、 初始化元数据库

　　注意：当使用的 hive 是 2.x 之前的版本，不做初始化也是 OK 的，当 hive 第一次启动的 时候会自动进行初始化，只不过会不会生成足够多的元数据库中的表。在使用过程中会 慢慢生成。但最后进行初始化。如果使用的 2.x 版本的 Hive，那么就必须手动初始化元 数据库。使用命令：

[hadoop@hadoop3 ~]$ schematool -dbType mysql -initSchema
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/apps/apache-hive-2.3.-bin/lib/log4j-slf4j-impl-2.6..jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7./share/hadoop/common/lib/slf4j-log4j12-1.7..jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
Metastore connection URL:     jdbc:mysql://hadoop1:3306/hivedb?createDatabaseIfNotExist=true
Metastore Connection Driver :     com.mysql.jdbc.Driver
Metastore connection User:     root
Starting metastore schema initialization to 2.3.
Initialization script hive-schema-2.3..mysql.sql
Initialization script completed
schemaTool completed
[hadoop@hadoop3 ~]$ 

9、 启动 Hive 客户端

hive --service cli和hive效果一样

[hadoop@hadoop3 ~]$ hive --service cli
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/apps/apache-hive-2.3.-bin/lib/log4j-slf4j-impl-2.6..jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7./share/hadoop/common/lib/slf4j-log4j12-1.7..jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]

Logging initialized using configuration in jar:file:/home/hadoop/apps/apache-hive-2.3.-bin/lib/hive-common-2.3..jar!/hive-log4j2.properties Async: true
Hive-on-MR is deprecated in Hive  and may not be available in the future versions. Consider using a different execution engine (i.e. spark, tez) or using Hive .X releases.
hive> 

基本使用

现有一个文件student.txt，将其存入hive中，student.txt数据格式如下：

,刘晨,女,,IS
,王风娟,女,,IS
,王一,女,,IS
,冯伟,男,,CS
,王小丽,女,,CS
,邢小丽,女,,IS
,赵钱,男,,IS
,王敏,女,,MA
,张立,男,,IS
,孙花,女,,CS
,孔小涛,男,,CS
,刘刚,男,,MA
,孙庆,男,,CS
,易思玲,女,,MA
,李娜,女,,CS
,周二,男,,MA
,郑明,男,,MA
,李勇,男,,CS
,包小柏,男,,MA
,梦圆圆,女,,MA
,王君,男,,MA

1、创建一个数据库myhive

hive> create database myhive;
OK
Time taken: 7.847 seconds
hive> 

2、使用新的数据库myhive

hive> use myhive;
OK
Time taken: 0.047 seconds
hive> 

3、查看当前正在使用的数据库

hive> select current_database();
OK
myhive
Time taken: 0.728 seconds, Fetched:  row(s)
hive> 

4、在数据库myhive创建一张student表

hive> create table student(id int, name string, sex string, age int, department string) row format delimited fields terminated by ",";
OK
Time taken: 0.718 seconds
hive> 

5、往表中加载数据

hive> load data local inpath "/home/hadoop/student.txt" into table student;
Loading data to table myhive.student
OK
Time taken: 1.854 seconds
hive> 

6、查询数据

hive> select * from student;
OK
    刘晨    女        IS
    王风娟    女        IS
    王一    女        IS
    冯伟    男        CS
    王小丽    女        CS
    邢小丽    女        IS
    赵钱    男        IS
    王敏    女        MA
    张立    男        IS
    孙花    女        CS
    孔小涛    男        CS
    刘刚    男        MA
    孙庆    男        CS
    易思玲    女        MA
    李娜    女        CS
    周二    男        MA
    郑明    男        MA
    李勇    男        CS
    包小柏    男        MA
    梦圆圆    女        MA
    王君    男        MA
Time taken: 2.455 seconds, Fetched:  row(s)
hive> 

7、查看表结构

hive> desc student;
OK
id                      int
name                    string
sex                     string
age                     int
department              string
Time taken: 0.102 seconds, Fetched:  row(s)
hive> 

hive> desc extended student;
OK
id                      int
name                    string
sex                     string
age                     int
department              string                                      

Detailed Table Information    Table(tableName:student, dbName:myhive, owner:hadoop, createTime:, lastAccessTime:, retention:, sd:StorageDescriptor(cols:[FieldSchema(name:id, type:int, comment:null), FieldSchema(name:name, type:string, comment:null), FieldSchema(name:sex, type:string, comment:null), FieldSchema(name:age, type:int, comment:null), FieldSchema(name:department, type:string, comment:null)], location:hdfs://myha01/user/hive/warehouse/myhive.db/student, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, parameters:{serialization.format=,, field.delim=,}), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{transient_lastDdlTime=1522750695, totalSize=523, numRows=0, rawDataSize=0, numFiles=1}, viewOriginalText:null, viewExpandedText:null, tableType:MANAGED_TABLE, rewriteEnabled:false)
Time taken: 0.127 seconds, Fetched:  row(s)
hive> 

hive> desc formatted student;
OK
# col_name                data_type               comment             

id                      int
name                    string
sex                     string
age                     int
department              string                                      

# Detailed Table Information
Database:               myhive
Owner:                  hadoop
CreateTime:             Tue Apr  :: CST
LastAccessTime:         UNKNOWN
Retention:
Location:               hdfs://myha01/user/hive/warehouse/myhive.db/student
Table Type:             MANAGED_TABLE
Table Parameters:
    numFiles
    numRows
    rawDataSize
    totalSize
    transient_lastDdlTime              

# Storage Information
SerDe Library:          org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
InputFormat:            org.apache.hadoop.mapred.TextInputFormat
OutputFormat:           org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
Compressed:             No
Num Buckets:            -
Bucket Columns:         []
Sort Columns:           []
Storage Desc Params:
    field.delim             ,
    serialization.format    ,
Time taken: 0.13 seconds, Fetched:  row(s)
hive>