Structured Streaming入门实例

Structured Streaming入门实例

Structured Streaming是Spark2.4版本推出的新的实时流处理计算。相比Spark Streaming具有更低的延迟。

具体信息请看：Structured Streaming官方介绍

示例一：words count

• Scala代码

package com.test

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession


object WordCount {

  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)

    val spark = SparkSession.builder()
      .master("local")
      .appName("WordCount")
      .getOrCreate()

    // 读取socket流数据，监听端口9998
    val lines = spark.readStream
      .format("socket")
      .option("host", "localhost")
      .option("port", "9998")
      .load()

    // 隐式转换
    import spark.implicits._
    
    // 将一行数据进行空格分割后打平
    val words = lines.as[String].flatMap(_.split(" "))

    // 根据value进行groupby，计算出count
    val wordCounts = words.groupBy("value").count()

    // 将流数据写入console控制台
    val query = wordCounts
      .writeStream
      .format("console")
      .outputMode("complete")
      .start()

    // 将进程阻塞
    query.awaitTermination()
  }
}

• nc -lk 9998进行传输数据

 

 

这里解释下：outputMode

outputMode输出模式分为3种：append，complete，update。

• append：只有流数据中的新行将写入sink
• complete：每次有更新时,流数据中的所有行都将写入sink
• update：每次有更新时,只有流数据中更新的行要写入sink。如果查询没有包含聚合，相当于“append”模式。

示例二：WordCount Append模式

object WordCountAppend {

  def main(args: Array[String]): Unit = {

    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)

    val spark = SparkSession
      .builder()
      .master("local")
      .appName("WordCountAppend")
      .getOrCreate()


    val lines = spark.readStream
      .format("socket")
      .option("host", "localhost")
      .option("port", "9998")
      .load()


    val query = lines.writeStream
      .format("console")
      .outputMode("append")
      .start()

    query.awaitTermination()
  }
}

自己体验一把就行

示例三：读取csv/json文件

package com.test

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}

object CSVFormat {
  def main(args: Array[String]): Unit = {

    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)

    val spark = SparkSession
      .builder()
      .master("local")
      .appName("CSVFormat")
      .getOrCreate()

    // 定义schema
    val schema = StructType(
      List(
        StructField("name", StringType),
        StructField("age", StringType),
        StructField("sex", StringType)
      )
    )

    val lines = spark.readStream
      .format("csv")
      .schema(schema)
      .load("/data")

    val query = lines.writeStream
      .format("console")
      .outputMode("append")
      .start()

    query.awaitTermination()
  }
}

在/data目录下存放一些csv文件，或者逐步放入csv文件，可以看到界面实时的输出csv文件的内容。

 

 

示例四：kafka流数据读取

package com.test

import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession


object KafkaFormat {

  def main(args: Array[String]): Unit = {
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.apache.kafka").setLevel(Level.WARN)

    val spark = SparkSession
      .builder()
      .master("local")
      .appName("KafkaFormat")
      .getOrCreate()


    // 读取kafka的数据
    val df = spark.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", "localhost:9092")
      .option("subscribe", "test")
      .load()

    // 隐式转换
    import spark.implicits._

    // 截取 key，value 2个字段
    val lines = df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
      .as[(String, String)]

    
    val res = lines.map { line =>
      // 解析value的值
      val columns = line._2.split(",")
      (columns(0), columns(1), columns(2))
    }.toDF()

    res.createOrReplaceTempView("tmp")

    val result = spark.sql("select _1 as name, _2 as age , _3 as sex from tmp")

    val query = result.writeStream
      .format("console")
      .outputMode("append")
      .start()

    query.awaitTermination()
  }
}

启动kafka生成器命令行，sh kafka-console-producer.sh --broker-list localhost:9092 --topic test，向命令行写入caocao,32,male。

 

 

示例五：解析kafka的json数据

package com.test

import com.google.gson.Gson
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession

object KafkaFormatJson {
  def main(args: Array[String]): Unit = {

    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.apache.kafka").setLevel(Level.WARN)

    val spark = SparkSession.builder()
      .master("local")
      .appName("KafkaFormatJson")
      .getOrCreate()

    // 读取kafka流数据
    val lines = spark.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", "localhost:9092")
      .option("subscribe", "test")
      .load()

    // 隐式转换
    import spark.implicits._

    val values = lines.selectExpr("CAST(value AS STRING)").as[String]

    val res = values.map { value =>
      // 解析json逻辑
      val gson = new Gson
      val people = gson.fromJson(value, classOf[People])
      (people.name, people.age, people.sex)
    }

    res.createOrReplaceTempView("tmp")

    // spark sql
    val result = spark.sql("select _1 as name, _2 as age, _3 as sex from tmp")

    // 写入
    val query = result.writeStream
      .format("console")
      .outputMode("append")
      .start()

    query.awaitTermination()
  }
}

People类

package com.test

case class People(name: String, age: String, sex: String) extends Serializable

启动程序，在kafka生成器命令行，输入{"name":"caocao","age":"32","sex":"male"}数据。可以看到console的信息