IDEA, SparkSql read data in HIve

Traditional Hive calculation engine as MapReduce, after Spark1.3 version, SparkSql released, and SparkSql fully compatible with apache hive basic, based on Spark powerful computing capabilities, the Spark hive processing data processing speed is far faster than traditional Hive .
SparkSql step using the read data in the following table HIve idea in
1, first, a test environment, the core-site.xml conf directory under hadoop cluster, hdfs-site.xml Hive and the conf directory hive-site. xml copy the resources directory

2, pom dependent

 <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.0.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>2.0.1</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.25</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.0.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.4.11</version>
        </dependency>

        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.41</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.4.11</version>
        </dependency>

        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.11</artifactId>
            <version>2.2.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>2.1.0</version>
        </dependency>

        <dependency>
            <groupId>com.101tec</groupId>
            <artifactId>zkclient</artifactId>
            <version>0.3</version>
        </dependency>

        <dependency>
            <groupId>com.github.sgroschupf</groupId>
            <artifactId>zkclient</artifactId>
            <version>0.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
            <version>2.3.0</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.0.1</version>
        </dependency>

3, code development

import org.apache.spark.sql.{DataFrame, SparkSession}

object SparkSql_Hive {
  def main(args: Array[String]): Unit = {
    //创建SparkSession对象
    val spark = SparkSession.builder()
      .appName(this.getClass.getSimpleName)
      .master("local[*]")
      .config("dfs.client.use.datanode.hostname", "true")
      .enableHiveSupport()
      .getOrCreate()
   //指定库名
    val sql1 = "use mydb"
    spark.sql(sql1)
    //查看该库下的表结构
    val sql2 = "show tables"
    spark.sql(sql2).show()
    //读取hivemydb库下per表
    val sql3 = "select * from mydb.per"
    spark.sql(sql3).show()
  }
}

4, see the printed results
show all tables in the database mydb

can see SparkSql has read data Hive of
5, problems encountered in the test
(1), can not find the relevant class HIve

Exception in thread "main" java.lang.IllegalArgumentException: Unable to instantiate SparkSession with Hive support because Hive classes are not found.
    at org.apache.spark.sql.SparkSession$Builder.enableHiveSupport(SparkSession.scala:778)
    at com.yangshou.SparkSql_Hive$.main(SparkSql_Hive.scala:12)
    at com.yangshou.SparkSql_Hive.main(SparkSql_Hive.scala)

Through access to relevant data, concluded that the Spark is the wrong version, the pom file Spark2.1.0 version 2.0.1 instead, and ultimately solve the problem