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Spark SQL+Hive历险记

阅读更多


基础依赖环境
Apache Hadoop2.7.1
Apache Spark1.6.0
Apache Hive1.2.1
Apache Hbase0.98.12

(1)提前安装好scala的版本,我这里是2.11.7

(2)下载spark-1.6.0源码,解压进入根目录编译

(3)dev/change-scala-version.sh 2.11
修改pom文件,修改对应的hadoop,hbase,hive的版本

执行编译支持hive功能的spark
(4)mvn -Pyarn -Phive  -Phive-thriftserver -Phadoop-2.7.1 -Dscala-2.11 -DskipTests clean package
三种测试方式:

bin/spark-submit  --class org.apache.spark.examples.SparkPi --master spark://h1:7077 examples/target/spark-examples_2.11-1.6.0.jar  100

bin/spark-submit  --class org.apache.spark.examples.SparkPi --master yarn-cluster examples/target/spark-examples_2.11-1.6.0.jar  10

bin/spark-submit  --class org.apache.spark.examples.SparkPi --master yarn-client  examples/target/spark-examples_2.11-1.6.0.jar  10



(一):命令行Spark SQL接口调试
编译成功后,将提前安装好的hive/conf/hive-site.xml拷贝到spark的conf/目录下,
执行,spark-sql的启动命令,同时使用--jars 标签把mysql驱动包,hadoop支持的压缩包,以及通过hive读取hbase相关的jar包加入进来,启动

bin/spark-sql --jars
 lib/mysql-connector-java-5.1.31.jar,
lib/hadoop-lzo-0.4.20-SNAPSHOT.jar,
/ROOT/server/hive/lib/hive-hbase-handler-1.2.1.jar,
/ROOT/server/hbase/lib/hbase-client-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-common-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-server-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-hadoop2-compat-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/guava-12.0.1.jar,
/ROOT/server/hbase/lib/hbase-protocol-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/htrace-core-2.04.jar 


(二):Intellj IDEA15.0里面代码调试:
sbt的依赖:

//下面不需要使用的依赖,大家可根据情况去舍
name := "scala-spark"

version := "1.0"

scalaVersion := "2.11.7"

//使用公司的私服,去掉此行则使用默认私服
resolvers += "Local Maven Repository" at "http://xxxx:8080/nexus/content/groups/public/"

//使用内部仓储
externalResolvers := Resolver.withDefaultResolvers(resolvers.value, mavenCentral = false)

//Hadoop的依赖
libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "2.7.1" //% "provided"

//Habse的依赖
libraryDependencies += "org.apache.hbase" % "hbase-client" % "0.98.12-hadoop2" // % "provided"

libraryDependencies += "org.apache.hbase" % "hbase-common" % "0.98.12-hadoop2"  //% "provided"

libraryDependencies += "org.apache.hbase" % "hbase-server" % "0.98.12-hadoop2" //% "provided"

//Spark的依赖
libraryDependencies += "org.apache.spark" % "spark-core_2.11" % "1.6.0" //% "provided"

//Spark SQL 依赖
libraryDependencies += "org.apache.spark" % "spark-sql_2.11" % "1.6.0" //% "provided"

//Spark For Hive 依赖
libraryDependencies += "org.apache.spark" % "spark-hive_2.11" % "1.6.0"

//java servlet 依赖
libraryDependencies += "javax.servlet" % "javax.servlet-api" % "3.0.1" //% "provided"




scala主体代码

  def main(args: Array[String]) {
    //设置用户名
    System.setProperty("user.name", "username");
    System.setProperty("HADOOP_USER_NAME", "username");
    //此处不需要设置master,方便到集群上,能测试yarn-client , yarn-cluster,spark 各种模式
    val sc=new SparkConf().setAppName("spark sql hive");
    val sct=new SparkContext(sc);
    //得到hive上下文
    val hive = new org.apache.spark.sql.hive.HiveContext(sct);
    //执行sql,并打印输入信息
    hive.sql("show tables ").collect().foreach(println);
    //关闭资源
    sct.stop();
  }



写好代码,在win上运行,有bug,/tmp/hive没有执行权限https://issues.apache.org/jira/browse/SPARK-10528
所以建议还是拿到linux上执行,而且win上只能调standalone模式,不能调yarn-cluster和yarn-client模式。

记住一个血的bug,在代码里的SparkConf()一定不要setMaster("")的值,否则你粗心了,在集群上执行各种模式时候会
出现莫名其妙的bug
//写代码方式,查询


//yarn集群模式
bin/spark-submit
 --class com.tools.hive.SparkHive   
 --master yarn-cluster --files conf/hive-site.xml 
 --jars lib/datanucleus-api-jdo-3.2.6.jar,
lib/datanucleus-rdbms-3.2.9.jar,
lib/datanucleus-core-3.2.10.jar,
lib/mysql-connector-java-5.1.31.jar  
scala-spark_2.11-1.0.jar //这是主体的jar,不用跟--jars放在一起,否则会有问题

//yarn客户端模式
bin/spark-submit 
--class com.tools.hive.SparkHive    
--master yarn-client 
--files conf/hive-site.xml  
--jars lib/datanucleus-api-jdo-3.2.6.jar,
lib/datanucleus-rdbms-3.2.9.jar,
lib/datanucleus-core-3.2.10.jar,
lib/mysql-connector-java-5.1.31.jar 
 scala-spark_2.11-1.0.jar //这是主体的jar,不用跟--jars放在一起,否则会有问题

//spark alone模式
bin/spark-submit 
--class com.tools.hive.SparkHive   
 --master spark://h1:7077
 --files conf/hive-site.xml 
 --jars lib/datanucleus-api-jdo-3.2.6.jar,
lib/datanucleus-rdbms-3.2.9.jar,
lib/datanucleus-core-3.2.10.jar,
lib/mysql-connector-java-5.1.31.jar 
 scala-spark_2.11-1.0.jar //这是主体的jar,不用跟--jars放在一起,否则会有问题


以Spark SQL 方式查询,不一定非得让你写代码,这就是sql的魅力,spark sql也能使用sql通过hive的元数据,查询hdfs数据或者hbase表等

//yarn-cluster集群模式不支持spark sql
Error: Cluster deploy mode is not applicable to Spark SQL shell.


//yarn客户端模式
bin/spark-sql    
--master yarn-client 
--files conf/hive-site.xml  
--jars lib/datanucleus-api-jdo-3.2.6.jar,
lib/datanucleus-rdbms-3.2.9.jar,
lib/datanucleus-core-3.2.10.jar,
lib/mysql-connector-java-5.1.31.jar  
  -e "select name ,  count(1) as c from info group by name order by c desc ;"

//spark alone模式
bin/spark-sql    
--master spark://h1:7077 
--files conf/hive-site.xml 
 --jars lib/datanucleus-api-jdo-3.2.6.jar,
lib/datanucleus-rdbms-3.2.9.jar,
lib/datanucleus-core-3.2.10.jar,
lib/mysql-connector-java-5.1.31.jar   
 -e "select name ,  count(1) as c from info group by name order by c desc ;"



Spark SQL + Hive + Hbase方式集成

//yarn客户端模式
bin/spark-sql    --master yarn-client --files conf/hive-site.xml  --jars lib/
datanucleus-api-jdo-3.2.6.jar,
lib/datanucleus-rdbms-3.2.9.jar,
lib/datanucleus-core-3.2.10.jar,
lib/mysql-connector-java-5.1.31.jar,
lib/hadoop-lzo-0.4.20-SNAPSHOT.jar,
/ROOT/server/hive/lib/hive-hbase-handler-1.2.1.jar,
/ROOT/server/hbase/lib/hbase-client-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-common-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-server-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-hadoop2-compat-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/guava-12.0.1.jar,
/ROOT/server/hbase/lib/hbase-protocol-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/htrace-core-2.04.jar    
-e "select * from dong limit 2 ;"



//spark alone模式
bin/spark-sql    --master spark://h1:7077 --files conf/hive-site.xml  --jars lib/
datanucleus-api-jdo-3.2.6.jar,
lib/datanucleus-rdbms-3.2.9.jar,
lib/datanucleus-core-3.2.10.jar,
lib/mysql-connector-java-5.1.31.jar,
lib/hadoop-lzo-0.4.20-SNAPSHOT.jar,
/ROOT/server/hive/lib/hive-hbase-handler-1.2.1.jar,
/ROOT/server/hbase/lib/hbase-client-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-common-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-server-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/hbase-hadoop2-compat-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/guava-12.0.1.jar,
/ROOT/server/hbase/lib/hbase-protocol-0.98.12-hadoop2.jar,
/ROOT/server/hbase/lib/htrace-core-2.04.jar  
  -e "select count(*) from dong  ;"






总结:
使用某个spark命令提交任务时,如果对参数比较模糊,可以使用
bin/spark-xxx  -h命令查看,参数介绍

另外spark 整合 hive关联hbase的时候或者spark整合hive 的时候,会出现很多问题,最常见的就是 :
(1)mysql驱动包找不到
(2)datanucleus相关的类找不到
(3)运行成功,而没有结果
(4).....

Spark SQL整合Hive时,一定要把相关的jar包和hive-site.xml文件,提交到 集群上,否则会出现各种莫名其妙的小问题,
经过在网上查资料,大多数的解决办法在Spark的spark-env.sh里面设置类路径,经测试没有生效,所以,还是通过--jars 这个参数来提交依赖的jar包比较靠谱。



参考链接:
winuitls.exe下载地址,如果再win上想远程连接spark alone集群提交任务,可能要用到:
http://teknosrc.com/spark-error-java-io-ioexception-could-not-locate-executable-null-bin-winutils-exe-hadoop-binaries/
http://zengzhaozheng.blog.51cto.com/8219051/1597902


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