Spark -- StructuredStreaming第三章与其他技术整合 kafka 生产数据写入MySQL表

整合Kafka

官网介绍

http://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html

●Creating a Kafka Source for Streaming Queries

// Subscribe to 1 topic
val df = spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribe", "topic1")
  .load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]
// Subscribe to multiple topics(多个topic)
val df = spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribe", "topic1,topic2")
  .load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]
// Subscribe to a pattern（订阅通配符topic）
val df = spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribePattern", "topic.*")
  .load()
df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

●Creating a Kafka Source for Batch Queries（kafka批处理查询）

// Subscribe to 1 topic 
//defaults to the earliest and latest offsets(默认为最早和最新偏移)
val df = spark
  .read
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribe", "topic1")
  .load()df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]
// Subscribe to multiple topics, (多个topic)
//specifying explicit Kafka offsets(指定明确的偏移量)
val df = spark
  .read
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribe", "topic1,topic2")
  .option("startingOffsets", """{"topic1":{"0":23,"1":-2},"topic2":{"0":-2}}""")
  .option("endingOffsets", """{"topic1":{"0":50,"1":-1},"topic2":{"0":-1}}""")
  .load()df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]
// Subscribe to a pattern, (订阅通配符topic)at the earliest and latest offsets
val df = spark
  .read
  .format("kafka")
  .option("kafka.bootstrap.servers", "host1:port1,host2:port2")
  .option("subscribePattern", "topic.*")
  .option("startingOffsets", "earliest")
  .option("endingOffsets", "latest")
  .load()df.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]

●注意：读取后的数据的Schema是固定的，包含的列如下：

Column	Type	说明
key	binary	消息的key
value	binary	消息的value
topic	string	主题
partition	int	分区
offset	long	偏移量
timestamp	long	时间戳
timestampType	int	类型

●注意：下面的参数是不能被设置的，否则kafka会抛出异常：

group.id:kafka的source会在每次query的时候自定创建唯一的group id
auto.offset.reset :为了避免每次手动设置startingoffsets的值，structured streaming在内部消费时会自动管理offset。这样就能保证订阅动态的topic时不会丢失数据。startingOffsets在流处理时，只会作用于第一次启动时，之后的处理都会自动的读取保存的offset。
key.deserializer，value.deserializer，key.serializer，value.serializer 序列化与反序列化，都是ByteArraySerializer
enable.auto.commit:Kafka源不支持提交任何偏移量

上代码演示！！！

package cn.itcast.structedstreaming

import org.apache.spark.SparkContext
import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}

object KafkaStructuredStreamingDemo {
  def main(args: Array[String]): Unit = {
    //1.创建SparkSession
    val spark: SparkSession = 
SparkSession.builder().master("local[*]").appName("SparkSQL").getOrCreate()
    val sc: SparkContext = spark.sparkContext
    sc.setLogLevel("WARN")
    import spark.implicits._
    //2.连接Kafka消费数据
    val dataDF: DataFrame = spark.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", "node01:9092")
      .option("subscribe", "spark_kafka")
      .load()
    //3.处理数据
    //注意:StructuredStreaming整合Kafka获取到的数据都是字节类型,所以需要按照官网要求,
//转成自己的实际类型
    val dataDS: Dataset[String] = dataDF.selectExpr("CAST(value AS STRING)").as[String]
    val wordDS: Dataset[String] = dataDS.flatMap(_.split(" "))
    val result: Dataset[Row] = wordDS.groupBy("value").count().sort($"count".desc)
    result.writeStream
      .format("console")
      .outputMode("complete")
      .trigger(Trigger.ProcessingTime(0))
      .option("truncate",false)//超过长度的列不截断显示,即完全显示
      .start()
      .awaitTermination()
  }
}

整合MySQL

简介

●需求

我们开发中经常需要将流的运算结果输出到外部数据库，例如MySQL中，但是比较遗憾Structured Streaming API不支持外部数据库作为接收器

如果将来加入支持的话，它的API将会非常的简单比如：

format("jdbc").option("url","jdbc:mysql://...").start()

但是目前我们只能自己自定义一个JdbcSink，继承ForeachWriter并实现其方法

上代码演示！！！

package cn.itcast.structedstreaming

import java.sql.{Connection, DriverManager, PreparedStatement}

import org.apache.spark.SparkContext
import org.apache.spark.sql._
import org.apache.spark.sql.streaming.Trigger


object JDBCSinkDemo {
  def main(args: Array[String]): Unit = {
    //1.创建SparkSession
    val spark: SparkSession = 
SparkSession.builder().master("local[*]").appName("SparkSQL").getOrCreate()
    val sc: SparkContext = spark.sparkContext
    sc.setLogLevel("WARN")
    import spark.implicits._
    //2.连接Kafka消费数据
    val dataDF: DataFrame = spark.readStream
      .format("kafka")
      .option("kafka.bootstrap.servers", "node01:9092")
      .option("subscribe", "spark_kafka")
      .load()
    //3.处理数据
    //注意:StructuredStreaming整合Kafka获取到的数据都是字节类型,所以需要按照官网要求,转成自己的实际类型
    val dataDS: Dataset[String] = dataDF.selectExpr("CAST(value AS STRING)").as[String]
    val wordDS: Dataset[String] = dataDS.flatMap(_.split(" "))
    val result: Dataset[Row] = wordDS.groupBy("value").count().sort($"count".desc)
    val writer = new JDBCSink("jdbc:mysql://localhost:3306/bigdata?characterEncoding=UTF-8", "root", "root")
    result.writeStream
      .foreach(writer)
      .outputMode("complete")
      .trigger(Trigger.ProcessingTime(0))
      .start()
      .awaitTermination()
  }

  class JDBCSink(url:String,username:String,password:String) extends ForeachWriter[Row] with Serializable{
    var connection:Connection = _ //_表示占位符,后面会给变量赋值
    var preparedStatement: PreparedStatement = _
    //开启连接
    override def open(partitionId: Long, version: Long): Boolean = {
      connection = DriverManager.getConnection(url, username, password)
      true
    }

    /*
    CREATE TABLE `t_word` (
        `id` int(11) NOT NULL AUTO_INCREMENT,
        `word` varchar(255) NOT NULL,
        `count` int(11) DEFAULT NULL,
        PRIMARY KEY (`id`),
        UNIQUE KEY `word` (`word`)
      ) ENGINE=InnoDB AUTO_INCREMENT=26 DEFAULT CHARSET=utf8;
     */
    //replace INTO `bigdata`.`t_word` (`id`, `word`, `count`) VALUES (NULL, NULL, NULL);
    //处理数据--存到MySQL
    override def process(row: Row): Unit = {
      val word: String = row.get(0).toString
      val count: String = row.get(1).toString
      println(word+":"+count)
      //REPLACE INTO:表示如果表中没有数据这插入,如果有数据则替换
      //注意:REPLACE INTO要求表有主键或唯一索引
      val sql = "REPLACE INTO `t_word` (`id`, `word`, `count`) VALUES (NULL, ?, ?);"
      preparedStatement = connection.prepareStatement(sql)
      preparedStatement.setString(1,word)
      preparedStatement.setInt(2,Integer.parseInt(count))
      preparedStatement.executeUpdate()
    }

    //关闭资源
    override def close(errorOrNull: Throwable): Unit = {
      if (connection != null){
        connection.close()
      }
      if(preparedStatement != null){
        preparedStatement.close()
      }
    }
  }
}

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Spark -- StructuredStreaming第三章 与其他技术整合 kafka 生产数据写入MySQL表

整合Kafka

官网介绍

上代码演示！！！

整合MySQL

简介

上代码演示！！！

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Spark -- StructuredStreaming第三章与其他技术整合 kafka 生产数据写入MySQL表