SparkSQL读取Json文件并执行SQL的三种方法

• 最终输出

+---+--------+
|age|    name|
+---+--------+
| 30|zhangsan|
| 31|    lisi|
| 32|  wangwu|
| 32|     sid|
+---+--------+

1、Scala程序
从SparkSession入手
SparkSession是旧的版本中SQLContext和HiveContext的组合封装。
import spark.implicits._用来隐式地将DataFrames转化为RDD，当DataFrames的变量调用RDD的方法的时候，DataFrames中的隐式转化方法会将DataFrames转化为RDD。

import org.apache.spark.sql.SparkSession

object Hive_Json {
  def main(args: Array[String]): Unit = {
    val path = "C:/java/spark_practise/src/main/resources/input/people.json"
    val spark = SparkSession.builder().appName("SparkSessionTest").master("local[2]").getOrCreate()
    import spark.implicits._
    val people = spark.read.json(path)
    people.show()
    people.createOrReplaceTempView("people")
    spark.sql("select * from people").show()
    spark.stop()
  }
}

2、 json文件

{"name":"zhangsan","age":30}
{"name":"lisi","age":31}
{"name":"wangwu","age":32}
{"name":"sid","age":32}

3、pom文件设置

<?xml version="1.0" encoding="UTF-8"?>
<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>org.example</groupId>
    <artifactId>spark_practise</artifactId>
    <version>1.0-SNAPSHOT</version>


    <properties>
        <scala.version>2.11.8</scala.version>
        <spark.version>2.2.0</spark.version>
    </properties>

    <dependencies>
        <!-- scala依赖 -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <!-- spark依赖 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!-- spark依赖 -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!-- hivecontext要用这个依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <!-- 该插件用于将 Scala 代码编译成 class 文件 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
                <executions>
                <execution>
                    <!-- 声明绑定到 maven 的 compile 阶段 -->
                    <goals>
                        <goal>testCompile</goal>
                    </goals>
                </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.1.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
        </build>
</project>

• 使用Spark-Shell读取本地文件
  hdfs和本地文件分别的前缀是hdfs和file，默认的文件系统根据hadoop/etc/core-site.xml文件中的fs.defaultFS配置。
  使用hadoop dfs -put 本地文件 远程文件的方式将文件放到HDFS文件系统中。

  
  
  

  或者把路径改成本地路径就可以读取本地的文件

参考资料：
Spark系列--SparkSQL(三)执行SparkSQL查询