https://www.cnblogs.com/bajiaotai/p/15381309.html

2.1 pom.xml 示例 (spark版本: 3.0.0  scala版本: 2.12)

http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
4.0.0

<groupId>com.dxm.sparksql</groupId>  
<artifactId>sparksql</artifactId>  
<version>1.0-SNAPSHOT</version>

<!-- 指定变量 spark的版本信息 scala的版本信息-->  
<properties>  
    <spark.version>3.0.0</spark.version>  
    <scala.version>2.12</scala.version>  
</properties>

<dependencies>

    <dependency>  
        <groupId>org.apache.spark</groupId>  
        <artifactId>spark-core\_${scala.version}</artifactId>  
        <version>${spark.version}</version>  
    </dependency>

    <dependency>  
        <groupId>org.apache.spark</groupId>  
        <artifactId>spark-yarn\_${scala.version}</artifactId>  
        <version>${spark.version}</version>  
    </dependency>  
    <dependency>  
        <groupId>org.apache.spark</groupId>  
        <artifactId>spark-sql\_${scala.version}</artifactId>  
        <version>${spark.version}</version>  
    </dependency>

    <dependency>  
        <groupId>mysql</groupId>  
        <artifactId>mysql-connector-java</artifactId>  
        <version>5.1.27</version>  
    </dependency>

    <dependency>  
        <groupId>org.apache.spark</groupId>  
        <artifactId>spark-hive\_${scala.version}</artifactId>  
        <version>${spark.version}</version>  
    </dependency>

    <dependency>  
        <groupId>org.apache.hive</groupId>  
        <artifactId>hive-exec</artifactId>  
        <version>1.2.1</version>  
    </dependency>

</dependencies>

2.2 spark版本与scala版本对应关系的问题

#根据下面链接 即可查询 spark版本和scala版本的对应关系及依赖配置
https://www.cnblogs.com/bajiaotai/p/16270971.html

2.3 在scala代码中查看运行时的scala版本

println(util.Properties.versionString)

2.4 FAQ 因Spark版本和Scala版本不一致导致的报错

待补充

3. 代码测试

object TestSparkSQLEnv extends App {

//1.初始化 SparkSession 对象
val spark = SparkSession
.builder
.master("local")
//.appName("SparkSql Entrance Class SparkSession")
//.config("spark.some.config.option", "some-value")
.getOrCreate()

//2.通过 SparkSession 获取 SparkContext
private val sc: SparkContext = spark.sparkContext

//3.设置日志级别
// Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN
// This overrides any user-defined log settings //会覆盖掉 用户设置的日志级别 比如 log4j.properties
sc.setLogLevel("ERROR")

import spark.implicits._

//4.创建DataFream
private val rdd2DfByCaseClass: DataFrame = spark.sparkContext
.makeRDD(Array(Person("疫情", "何时"), Person("结束", "呢")))
.toDF("名称", "行动")
rdd2DfByCaseClass.show()
// +----+----+
// |名称|行动|
// +----+----+
// |疫情|何时|
// |结束| 呢|
// +----+----+

//5.关闭资源
spark.stop()

}

如果能正常执行,恭喜你环境搭建没问题,如果遇到问题请留言共同探讨