Spark的基本使用入门

测试案例类

case class Player(name:String,age:Int,gender:String)

引入相关pom

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.11</artifactId>
    <version>2.3.2-mdh1.0.0-SNAPSHOT</version>
</dependency>

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.11</artifactId>
    <version>2.3.2-mdh1.0.0-SNAPSHOT</version>
</dependency>

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-hive_2.11</artifactId>
    <version>2.3.2-mdh1.0.0-SNAPSHOT</version>
</dependency>

1、自建rdd，并完成rdd-dataframe-dataset之间的相互转化

def method1():Unit={
    //master在本地设置为local，在线上集群应该设置为yarn
    val spark: SparkSession = SparkSession.builder().appName("test1").master("local[*]").getOrCreate()
    val sparkContext: SparkContext = spark.sparkContext
    //自建rdd，利用List区分行条目，利用元组区分列信息，自建rdd的api makeRDD实际调用的是parallelize
    val rdd1: RDD[(String, Int, String)] = sparkContext.makeRDD(List(("jack", 11, "male"), ("lisa", 12, "female")))
    rdd1.foreach(println)
    //隐式参数
    import spark.implicits._
    //rdd转dataframe,toDF内可以指定列名
    val df1: DataFrame = rdd1.toDF("name","age","gender")
    df1.show()
    //把rdd的元组映射为案例类，然后转为dataset
    val ds1: Dataset[Player] = rdd1.map(x => Player(x._1, x._2, x._3)).toDS()
    ds1.show()
    //把dataframe、dataset转为rdd后逐条打印
    df1.rdd.foreach(println)
    ds1.rdd.foreach(println)
    //dataframe转为dataset
    val ds2: Dataset[Player] = ds1.as[Player]
    ds2.show()
    //dataset转为dataframe，再次用toDF指定新的列名
    val df2: DataFrame = ds1.toDF("nm", "ag", "sex")
    df2.show()
  }

2、利用rdd读取本地文件，实现WordCount

def method2():Unit={
    val sparkConf: SparkConf = new SparkConf().setAppName("test2").setMaster("local[*]")
    val sparkContext: SparkContext = new SparkContext(sparkConf)
    //textFile读取文件，rdd把文件按照行区分条目
    val rddFile: RDD[String] = sparkContext.textFile("src/main/resources/myfile/goodjob.txt")
    //对rdd内每条数据split按照空格拆分为数组，对rdd数据直接flatMap拍平为单词rdd集，然后map创建元组，在对元组reduceByKey以_1为基准，相同则把_2的值相加，得到最终结果
    val rddResult: RDD[(String, Int)] = rddFile.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _)
    rddResult.foreach(println)
    //saveAsTextFile写入文件
    rddResult.saveAsTextFile("src/main/resources/myfile/result.txt")
  }

3、spark操作Hive

def method3():Unit={
    //注意spark操作hive时，需要加上enableHiveSupport
    val sparkSession: SparkSession = SparkSession.builder().appName("test3").master("local[*]").enableHiveSupport().getOrCreate()
    //先创建hive表player和player2
    sparkSession.sql("create table meta.player(name string,age int,gender string);")
    sparkSession.sql("create table meta.player2(name string,age int,gender string);")
    //新增数据到player
    sparkSession.sql("insert into table meta.player values('wangming',11,'male');")
    sparkSession.sql("insert into table meta.player values('yuki',12,'female');")
    sparkSession.sql("insert into table meta.player values('lili',13,'female');")

    //测试故意修改列名
    val df: DataFrame = sparkSession.sql("select * from meta.player where age < 12").toDF("a","b","c")
    //创建临时会话视图
    df.createOrReplaceTempView("tv1")
    sparkSession.sql("insert into meta.player2(name,age,gender) select a,b,c from tv1")
  }

4、spark操作Mysql

def method4():Unit={
    val sparkSession: SparkSession = SparkSession.builder().appName("test3").master("local[*]").enableHiveSupport().getOrCreate()
    val df1: DataFrame = sparkSession.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/my_test").option("user", "root").option("password", "123456").option("dbtable", "ui").load()
    df1.createTempView("tv1")
    val df2: DataFrame = sparkSession.sql("select * from tv1 where age <20")
    df2.write.mode("append").format("jdbc").option("url", "jdbc:mysql://localhost:3306/my_test?useUnicode=true&characterEncoding=utf8&useSSL=false&zeroDateTimeBehavior=convertToNull&jdbcCompliantTruncation=false").option("user", "root").option("password", "123456").option("dbtable","ui2").save()
  }