Java + Spark SQL + Hive + Maven简单实现和异常问题

一. 前期准备

1.linux搭建Java和Scala环境搭建

2. linux搭建hadoop+spark+hive分布式集群

hadoop分布式集群搭建：hadoop分布式集群搭建

spark分布式集群搭建:spark分布式集群搭建

hive分布式集群搭建：待完善

二.项目代码实现

1 需求

通过Spark Sql 查询Hive数据库数据

数据库：bi_ods;

表：owms_m_locator

2 maven 项目搭建

新增一个Mave project工程

maven project工程

3 实现代码

package com.lm.hive.SparkHive;import org.apache.spark.SparkConf;import org.apache.spark.api.java.JavaSparkContext;import org.apache.spark.sql.SQLContext;import org.apache.spark.sql.hive.HiveContext;/** * Spark sql获取Hive数据 * */public class App {    public static void main( String[] args )     {        SparkConf sparkConf = new SparkConf().setAppName("SparkHive").setMaster("local[2]");        JavaSparkContext sc = new JavaSparkContext(sparkConf);                //不要使用SQLContext,部署异常找不到数据库和表        HiveContext hiveContext = new HiveContext(sc);        SQLContext sqlContext = new SQLContext(sc);        //查询表前10条数据        hiveContext.sql("select * from bi_ods.owms_m_locator limit 10").show();                sc.stop();    }}

4. pom.xml文件

<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.lm.hive</groupId><artifactId>SparkHive</artifactId><version>0.0.1-SNAPSHOT</version><packaging>jar</packaging><name>SparkHive</name><url>http://maven.apache.org</url><properties><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding></properties><dependencies><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>3.8.1</version><scope>test</scope></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>5.1.39</version></dependency><dependency><groupId>org.slf4j</groupId><artifactId>slf4j-log4j12</artifactId><version>1.7.22</version></dependency><dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>2.6.4</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_2.10</artifactId><version>1.6.0</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-hive_2.10</artifactId><version>1.6.0</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming_2.10</artifactId><version>1.6.0</version><exclusions><exclusion><artifactId>slf4j-log4j12</artifactId><groupId>org.slf4j</groupId></exclusion></exclusions></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-core_2.10</artifactId><version>1.6.0</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.hive/hive-jdbc --><dependency><groupId>org.apache.hive</groupId><artifactId>hive-jdbc</artifactId><version>2.1.1</version></dependency><!-- https://mvnrepository.com/artifact/org.apache.hive/hive-exec --><dependency><groupId>org.apache.hive</groupId><artifactId>hive-exec</artifactId><version>2.1.1</version></dependency></dependencies><build><plugins><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><executions><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><shadedArtifactAttached>true</shadedArtifactAttached><shadedClassifierName>allinone</shadedClassifierName><artifactSet><includes><include>*:*</include></includes></artifactSet><filters><filter><artifact>*:*</artifact><excludes><exclude>META-INF/*.SF</exclude><exclude>META-INF/*.DSA</exclude><exclude>META-INF/*.RSA</exclude></excludes></filter></filters><transformers><transformerimplementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"><resource>reference.conf</resource></transformer><transformerimplementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"><resource>META-INF/spring.handlers</resource></transformer><transformerimplementation="org.apache.maven.plugins.shade.resource.AppendingTransformer"><resource>META-INF/spring.schemas</resource></transformer><transformerimplementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer"><manifestEntries><Main-Class></Main-Class></manifestEntries></transformer></transformers></configuration></execution></executions></plugin></plugins></build></project>

三. 项目部署

1 maven项目编译成jar包

本文编译成 SparkHive-0.0.1-SNAPSHOT-allinone.jar

2 上传jar包至linux服务器

通过rz命令上传

3 通过spark-submit命令运行jar包

命令：

sh bin/spark-submit --class com.lm.hive.SparkHive.App --master yarn  --files /home/winit/spark-1.6.0/conf/hive-site.xml  java_jar/SparkHive-0.0.1-SNAPSHOT-allinone.jar

结果：

运行结果

四.异常问题

在网上搜索，很多是通过SQLContext实例去查询Hive数据，当本人使用时，出现以下异常

1. Exception in thread "main" org.apache.spark.sql.AnalysisException: Table not found: `bi_ods`.`owms_m_locator`;

SQLContext查询异常

解决：

SQLContext sqlContext = new SQLContext(sc);

替换为：

HiveContext hiveContext = new HiveContext(sc);

2. Exception in thread "main" java.lang.SecurityException: class "javax.servlet.FilterRegistration"'s signer information does not match signer information of other classes in the same package

解决：

javax.servlet相关包重复导入引起的

hadoop-client或hadoop-common排除java.servelt等包

<dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-client</artifactId><version>2.6.4</version><exclusions><exclusion><groupId>javax.servlet</groupId><artifactId>*</artifactId></exclusion></exclusions></dependency>

五. SQLContext和HiveContext区别和联系

Spark SQL程序的主入口是SQLContext类或它的子类。创建一个基本的SQLContext，你只需要SparkContext

除了基本的SQLContext，也可以创建HiveContext。SQLContext和HiveContext区别与联系为：

SQLContext现在只支持SQL语法解析器（SQL-92语法）

HiveContext现在支持SQL语法解析器和HiveSQL语法解析器，默认为HiveSQL语法解析器，用户可以通过配置切换成SQL语法解析器，来运行HiveSQL不支持的语法。

使用HiveContext可以使用Hive的UDF，读写Hive表数据等Hive操作。SQLContext不可以对Hive进行操作。

Spark SQL未来的版本会不断丰富SQLContext的功能，做到SQLContext和HiveContext的功能容和，最终可能两者会统一成一个Context

HiveContext包装了Hive的依赖包，把HiveContext单独拿出来，可以在部署基本的Spark的时候就不需要Hive的依赖包，需要使用HiveContext时再把Hive的各种依赖包加进来。

SQL的解析器可以通过配置spark.sql.dialect参数进行配置。在SQLContext中只能使用Spark SQL提供的”sql“解析器。在HiveContext中默认解析器为”hiveql“，也支持”sql“解析器。

详情见：SQLContext和HiveContext区别与联系

代码下载地址：Java + Spark SQL + Hive + Maven简单实例代码