Hive 之 Java API 操作

Java 想要訪問Hive，需要通過beeline的方式連接Hive，hiveserver2提供了一個新的命令行工具beeline，hiveserver2 對 之前的hive做了升級，功能更加強大，它增加了權限控制，要使用beeline需要先啟動hiverserver2，再使用beeline連接

1.beeline 的 使用

啟動hiverserver2

$ hiveserver2

使用beeline連接hive

$ beeline -u jdbc:hive2://hdpcomprs:10000/db_comprs -n hadoop -p

參數解釋： 
-u：連接url，可以使用IP，也可以使用主機名，端口默認為10000 
-n：連接的用戶名（注：不是登錄hive的用戶名，是hive所在服務器登錄用戶名） 
-p：密碼，可以不用輸入

可以使用如下命令來修改端口

hiveserver2 --hiveconf hive.server2.thrift.port=14000

如果不知道beeline怎么使用，可以使用如下命令來查看beeline的使用幫助

[hadoop@hdpcomprs ~]$ beeline --help
Usage: java org.apache.hive.cli.beeline.BeeLine 
   -u <database url> the JDBC URL to connect to -r reconnect to last saved connect url (in conjunction with !save) -n <username> the username to connect as -p <password> the password to connect as -d <driver class> the driver class to use -i <init file> script file for initialization -e <query> query that should be executed -f <exec file> script file that should be executed -w (or) --password-file <password file> the password file to read password from --hiveconf property=value Use value for given property --hivevar name=value hive variable name and value This is Hive specific settings in which variables can be set at session level and referenced in Hive commands or queries. --property-file=<property-file> the file to read connection properties (url, driver, user, password) from --color=[true/false] control whether color is used for display --showHeader=[true/false] show column names in query results --headerInterval=ROWS; the interval between which heades are displayed --fastConnect=[true/false] skip building table/column list for tab-completion --autoCommit=[true/false] enable/disable automatic transaction commit --verbose=[true/false] show verbose error messages and debug info --showWarnings=[true/false] display connection warnings --showDbInPrompt=[true/false] display the current database name in the prompt --showNestedErrs=[true/false] display nested errors --numberFormat=[pattern] format numbers using DecimalFormat pattern --force=[true/false] continue running script even after errors --maxWidth=MAXWIDTH the maximum width of the terminal --maxColumnWidth=MAXCOLWIDTH the maximum width to use when displaying columns --silent=[true/false] be more silent --autosave=[true/false] automatically save preferences --outputformat=[table/vertical/csv2/tsv2/dsv/csv/tsv] format mode for result display Note that csv, and tsv are deprecated - use csv2, tsv2 instead --incremental=[true/false] Defaults to false. When set to false, the entire result set is fetched and buffered before being displayed, yielding optimal display column sizing. When set to true, result rows are displayed immediately as they are fetched, yielding lower latency and memory usage at the price of extra display column padding. Setting --incremental=true is recommended if you encounter an OutOfMemory on the client side (due to the fetched result set size being large). Only applicable if --outputformat=table. --incrementalBufferRows=NUMROWS the number of rows to buffer when printing rows on stdout, defaults to 1000; only applicable if --incremental=true and --outputformat=table --truncateTable=[true/false] truncate table column when it exceeds length --delimiterForDSV=DELIMITER specify the delimiter for delimiter-separated values output format (default: |) --isolation=LEVEL set the transaction isolation level --nullemptystring=[true/false] set to true to get historic behavior of printing null as empty string --maxHistoryRows=MAXHISTORYROWS The maximum number of rows to store beeline history. --help display this message Example: 1. Connect using simple authentication to HiveServer2 on localhost:10000 $ beeline -u jdbc:hive2://localhost:10000 username password 2. Connect using simple authentication to HiveServer2 on hs.local:10000 using -n for username and -p for password $ beeline -n username -p password -u jdbc:hive2://hs2.local:10012 3. Connect using Kerberos authentication with hive/[e-mail] as HiveServer2 principal $ beeline -u "jdbc:hive2://hs2.local:10013/default;principal=hive/[e-mail]" 4. Connect using SSL connection to HiveServer2 on localhost at 10000 $ beeline "jdbc:hive2://localhost:10000/default;ssl=true;sslTrustStore=/usr/local/truststore;trustStorePassword=mytruststorepassword" 5. Connect using LDAP authentication $ beeline -u jdbc:hive2://hs2.local:10013/default <ldap-username> <ldap-password> 

如果使用beeline連接時報了如下錯 
hadoop is not allowed to impersonate hadoop (state=08S01,code=0)

原因：hiveserver2增加了權限控制，需要在hadoop的配置文件中配置 
解決方法：在hadoop的core-site.xml中添加如下內容，然后重啟hadoop，再使用beeline連接即可

參考官網： 
https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/Superusers.html

<property> <name>hadoop.proxyuser.hadoop.hosts</name> <value>*</value> </property> <property> <name>hadoop.proxyuser.hadoop.groups</name> <value>*</value> </property>

連接成功后，和執行hive后相同執行shell命令即可，如果想要退出連接使用 !q 或 !quit 命令

2.Java API 操作 Hive

創建一個maven項目，pom.xml文件配置如下

<?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.bigdata.hadoop</groupId> <artifactId>hive</artifactId> <version>1.0-SNAPSHOT</version> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>org.apache.hive</groupId> <artifactId>hive-jdbc</artifactId> <version>2.3.0</version> </dependency> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.9</version> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <version>3.5.1</version> <configuration> <source>1.8</source> <target>1.8</target> </configuration> </plugin> </plugins> </build> </project>

創建測試類HiveJDBC，代碼如下 
官網參考：https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients

package com.bigdata.hadoop.hive; import org.junit.After; import org.junit.Before; import org.junit.Test; import java.sql.*; /** * JDBC 操作 Hive（注：JDBC 訪問 Hive 前需要先啟動HiveServer2） */ public class HiveJDBC { private static String driverName = "org.apache.hive.jdbc.HiveDriver"; private static String url = "jdbc:hive2://hdpcomprs:10000/db_comprs"; private static String user = "hadoop"; private static String password = ""; private static Connection conn = null; private static Statement stmt = null; private static ResultSet rs = null; // 加載驅動、創建連接 @Before public void init() throws Exception { Class.forName(driverName); conn = DriverManager.getConnection(url,user,password); stmt = conn.createStatement(); } // 創建數據庫 @Test public void createDatabase() throws Exception { String sql = "create database hive_jdbc_test"; System.out.println("Running: " + sql); stmt.execute(sql); } // 查詢所有數據庫 @Test public void showDatabases() throws Exception { String sql = "show databases"; System.out.println("Running: " + sql); rs = stmt.executeQuery(sql); while (rs.next()) { System.out.println(rs.getString(1)); } } // 創建表 @Test public void createTable() throws Exception { String sql = "create table emp(\n" + "empno int,\n" + "ename string,\n" + "job string,\n" + "mgr int,\n" + "hiredate string,\n" + "sal double,\n" + "comm double,\n" + "deptno int\n" + ")\n" + "row format delimited fields terminated by '\\t'"; System.out.println("Running: " + sql); stmt.execute(sql); } // 查詢所有表 @Test public void showTables() throws Exception { String sql = "show tables"; System.out.println("Running: " + sql); rs = stmt.executeQuery(sql); while (rs.next()) { System.out.println(rs.getString(1)); } } // 查看表結構 @Test public void descTable() throws Exception { String sql = "desc emp"; System.out.println("Running: " + sql); rs = stmt.executeQuery(sql); while (rs.next()) { System.out.println(rs.getString(1) + "\t" + rs.getString(2)); } } // 加載數據 @Test public void loadData() throws Exception { String filePath = "/home/hadoop/data/emp.txt"; String sql = "load data local inpath '" + filePath + "' overwrite into table emp"; System.out.println("Running: " + sql); stmt.execute(sql); } // 查詢數據 @Test public void selectData() throws Exception { String sql = "select * from emp"; System.out.println("Running: " + sql); rs = stmt.executeQuery(sql); System.out.println("員工編號" + "\t" + "員工姓名" + "\t" + "工作崗位"); while (rs.next()) { System.out.println(rs.getString("empno") + "\t\t" + rs.getString("ename") + "\t\t" + rs.getString("job")); } } // 統計查詢（會運行mapreduce作業） @Test public void countData() throws Exception { String sql = "select count(1) from emp"; System.out.println("Running: " + sql); rs = stmt.executeQuery(sql); while (rs.next()) { System.out.println(rs.getInt(1) ); } } // 刪除數據庫 @Test public void dropDatabase() throws Exception { String sql = "drop database if exists hive_jdbc_test"; System.out.println("Running: " + sql); stmt.execute(sql); } // 刪除數據庫表 @Test public void deopTable() throws Exception { String sql = "drop table if exists emp"; System.out.println("Running: " + sql); stmt.execute(sql); } // 釋放資源 @After public void destory() throws Exception { if ( rs != null) { rs.close(); } if (stmt != null) { stmt.close(); } if (conn != null) { conn.close(); } } }