spark2.x如何通过SparkSQL读取csv文件

Posted 2021-12-20 Maynor的大数据奋斗之路

package cn.itcast.spark.source

import java.util.Properties

import org.apache.spark.sql.types.DoubleType, IntegerType, LongType, StructType
import org.apache.spark.sql.DataFrame, SparkSession

object _03SparkSQLSourceTest 
	
	def main(args: Array[String]): Unit = 
		// 构建SparkSession实例对象
		val spark: SparkSession = SparkSession.builder()
			.master("local[4]")
			.appName(this.getClass.getSimpleName.stripSuffix("$"))
			.config("spark.sql.shuffle.partitions", "4")
			.getOrCreate()
		import spark.implicits._
		
		// TODO: 1. CSV 格式数据文本文件数据 -> 依据 CSV文件首行是否是列名称，决定读取数据方式不一样的
		/*
			CSV 格式数据：
				每行数据各个字段使用逗号隔开
				也可以指的是，每行数据各个字段使用 单一 分割符 隔开数据
		 */
		// 方式一：首行是列名称，数据文件u.dat
		val dataframe: DataFrame = spark.read
			.format("csv")
			.option("sep", "\\\\t")
			.option("header", "true")
			.option("inferSchema", "true")
			.load("datas/ml-100k/u.dat")
		dataframe.printSchema()
		dataframe.show(10, truncate = false)

		// 方式二：首行不是列名，需要自定义Schema信息，数据文件u.data
		// 自定义schema信息
		val schema: StructType = new StructType()
			.add("user_id", IntegerType, nullable = true)
			.add("iter_id", IntegerType, nullable = true)
			.add("rating", DoubleType, nullable = true)
			.add("timestamp", LongType, nullable = true)
		val df: DataFrame = spark.read
			.format("csv")
			.schema(schema)
			.option("sep", "\\\\t")
			.load("datas/ml-100k/u.data")
		df.printSchema()
		df.show(10, truncate = false)
		
		/* ============================================================================== */
		// TODO: 2. 读取mysql表中数据
		// 第一、简洁版格式
		/*
			def jdbc(url: String, table: String, properties: Properties): DataFrame
		 */
		val props =  new Properties()
		props.put("user", "root")
		props.put("password", "123456")
		props.put("driver", "com.mysql.cj.jdbc.Driver")
		val empDF: DataFrame = spark.read.jdbc(
			"jdbc:mysql://node1.itcast.cn:3306/?serverTimezone=UTC&characterEncoding=utf8&useUnicode=true", //
			"db_test.emp", //
			props //
		)
		println(s"Partition Number = $empDF.rdd.getNumPartitions")
		empDF.printSchema()
		empDF.show(10, truncate = false)
		
		
		// 第二、标准格式写
		/*
			WITH tmp AS (
				select * from emp e join dept d on e.deptno = d.deptno
			)
		 */
		val table: String = "(select ename,deptname,sal from db_test.emp e join db_test.dept d on e.deptno = d.deptno) AS tmp"
		val joinDF: DataFrame = spark.read
			.format("jdbc")
			.option("url", "jdbc:mysql://node1.itcast.cn:3306/?serverTimezone=UTC&characterEncoding=utf8&useUnicode=true")
			.option("driver", "com.mysql.cj.jdbc.Driver")
			.option("user", "root")
			.option("password", "123456")
			.option("dbtable", table)
			.load()
		joinDF.printSchema()
		joinDF.show(10, truncate = false)
		
		
		// 应用结束，关闭资源
		spark.stop()