1、Spark On Hive的配置

1）、在Spark客户端配置Hive On Spark

在Spark客户端安装包下spark-2.3.1/conf中创建文件hive-site.xml：

配置hive的metastore路径

<configuration>
   <property>
        <name>hive.metastore.uris</name>
        <value>thrift://mynode1:9083</value>
   </property>
</configuration>

2）、启动Hive的metastore服务

hive --service metastore

3）、启动zookeeper集群，启动HDFS集群

4）、启动SparkShell读取Hive中的表总数，对比hive中查询同一表查询总数测试时间

./spark-shell 
--master spark://node1:7077,node2:7077 
 --executor-cores 1 
--executor-memory 1g 
--total-executor-cores 1
import org.apache.spark.sql.hive.HiveContext
val hc = new HiveContext(sc)
hc.sql("show databases").show
hc.sql("user default").show
hc.sql("select count(*) from jizhan").show

• 注意：

如果使用Spark on Hive  查询数据时，出现错误：

找不到HDFS集群路径，要在客户端机器conf/spark-env.sh中设置HDFS的路径：

2、读取Hive中的数据加载成DataFrame

• 在Spark1.6版本中HiveContext是SQLContext的子类，连接Hive使用HiveContext。

在Spark2.0+版本中之后，建议使用SparkSession对象，读取Hive中的数据需要开启Hive支持。

• 由于本地没有Hive环境，要提交到集群运行，提交命令：

./spark-submit 
--master spark://node1:7077,node2:7077 
--executor-cores 1 
--executor-memory 2G 
--total-executor-cores 1
--class com.lw.sparksql.dataframe.CreateDFFromHive 
/root/test/HiveTest.jar

java:

SparkConf conf = new SparkConf();
conf.setAppName("hive");
JavaSparkContext sc = new JavaSparkContext(conf);
//HiveContext是SQLContext的子类。
HiveContext hiveContext = new HiveContext(sc);
hiveContext.sql("USE spark");
hiveContext.sql("DROP TABLE IF EXISTS student_infos");
//在hive中创建student_infos表
hiveContext.sql("CREATE TABLE IF NOT EXISTS student_infos (name STRING,age INT) row format delimited fields terminated by '\t' ");
hiveContext.sql("load data local inpath '/root/test/student_infos' into table student_infos");

hiveContext.sql("DROP TABLE IF EXISTS student_scores"); 
hiveContext.sql("CREATE TABLE IF NOT EXISTS student_scores (name STRING, score INT) row format delimited fields terminated by '\t'");  
hiveContext.sql("LOAD DATA "
+ "LOCAL INPATH '/root/test/student_scores'"
+ "INTO TABLE student_scores");
/**
 * 查询表生成DataFrame
 */
DataFrame goodStudentsDF = hiveContext.sql("SELECT si.name, si.age, ss.score "
+ "FROM student_infos si "
+ "JOIN student_scores ss "
+ "ON si.name=ss.name "
+ "WHERE ss.score>=80");

hiveContext.sql("DROP TABLE IF EXISTS good_student_infos");

goodStudentsDF.registerTempTable("goodstudent");
DataFrame result = hiveContext.sql("select * from goodstudent");
result.show();

/**
 * 将结果保存到hive表 good_student_infos
 */
goodStudentsDF.write().mode(SaveMode.Overwrite).saveAsTable("good_student_infos");

Row[] goodStudentRows = hiveContext.table("good_student_infos").collect();  
for(Row goodStudentRow : goodStudentRows) {
	System.out.println(goodStudentRow);  
}
sc.stop();

scala:

1.val spark = SparkSession.builder().appName("CreateDataFrameFromHive").enableHiveSupport().getOrCreate()
2.spark.sql("use spark")
3.spark.sql("drop table if exists student_infos")
4.spark.sql("create table if not exists student_infos (name string,age int) row format delimited fields terminated by '\t'")
5.spark.sql("load data local inpath '/root/test/student_infos' into table student_infos")
6.
7.spark.sql("drop table if exists student_scores")
8.spark.sql("create table if not exists student_scores (name string,score int) row format delimited fields terminated by '\t'")
9.spark.sql("load data local inpath '/root/test/student_scores' into table student_scores")
10.// val frame: DataFrame = spark.table("student_infos")
11.// frame.show(100)
12.
13.val df = spark.sql("select si.name,si.age,ss.score from student_infos si,student_scores ss where si.name = ss.name")
14.df.show(100)
15.spark.sql("drop table if exists good_student_infos")
16./**
17.* 将结果写入到hive表中
18.*/
19.df.write.mode(SaveMode.Overwrite).saveAsTable("good_student_infos")