当前位置：首页 > article >正文

FlinkSql读取kafka数据流的方法(scala)

article 2025/2/21 3:48:29

我的scala版本为2.12

<scala.binary.version>2.12</scala.binary.version>

我的Flink版本为1.13.6

<flink.version>1.13.6</flink.version>

FlinkSql读取kafka数据流需要如下依赖：

    <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
    </dependency>

我们首先建一个kafka主题person_test。然后建立一个scala类作为kafka的生产者，示例内容如下：

package cha01

import org.apache.kafka.clients.producer.{KafkaProducer, ProducerConfig, ProducerRecord}

import java.util.Properties
import Util._

import scala.util.Random

object FlinkSqlKafkaConnectorProducer {
  def main(args: Array[String]): Unit = {
    val producerConf = new Properties()
    producerConf.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"single01:9092")
    producerConf.setProperty(ProducerConfig.BATCH_SIZE_CONFIG,"10")
    producerConf.setProperty(ProducerConfig.LINGER_MS_CONFIG,"50")
    producerConf.setProperty(ProducerConfig.RETRIES_CONFIG,"2")
    producerConf.setProperty(ProducerConfig.ACKS_CONFIG,"1")
    producerConf.setProperty(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
      "org.apache.kafka.common.serialization.IntegerSerializer")
    producerConf.setProperty(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
      "org.apache.kafka.common.serialization.StringSerializer")

    val topic = "person_test"
    val producer:KafkaProducer[Integer,String] = new KafkaProducer(producerConf);
    val rand = new Random()

    for(i <- 1 to 2000){
      val line: String = s"$i,Origami$i,${rand.nextInt(30)+18},${if (rand.nextInt(10) >=8) "Male" else "Female"}"
      val record: ProducerRecord[Integer, String] =
        new ProducerRecord[Integer, String](topic, 0, System.currentTimeMillis(), i, line)
      producer.send(record)
      Thread.sleep(50+rand.nextInt(500))
    }

    producer.flush()
    producer.close()

  }
}

此kafka生产者会随机生产出一系列类似以下内容的数据，类型为csv：

id,name,age,gender
1,Origami1,25,Female
2,Origami2,30,Male
3,Origami3,22,Female

随后再在工程中建立一个scala类，内容示例如下：

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.EnvironmentSettings
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

object CSVKafkaSystem {
  def main(args: Array[String]): Unit = {
    val settings = EnvironmentSettings.newInstance()
      .inStreamingMode()
      .build()
    val see: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val tabEnv: StreamTableEnvironment = StreamTableEnvironment.create(see)

    tabEnv.executeSql(
      """
         |CREATE TABLE person(
         |id INT,
         |name STRING,
         |age INT,
         |gender STRING
         |) WITH (
         |'connector' = 'kafka',
         |'topic'= 'person_test',
         |'properties.bootstrap.servers' = 'single01:9092',
         |'properties.group.id' = 'person_test_group',
         |'scan.startup.mode' = 'earliest-offset',
         |'format' = 'csv',
         |'csv.ignore-parse-errors' = 'true',
         |'csv.field-delimiter' = ','
         |)
         |""".stripMargin)

    tabEnv.sqlQuery("SELECT * FROM person").execute().print()
  }
}

其中的一些参数解释如下：'

connector' = 'kafka'

指定连接器类型为kafka

'topic'= 'person_test'

指定要读取的kafka主题为person_test

'properties.bootstrap.servers' = 'single01:9092'

指定kafka所在的服务器的ip地址以及端口号

'properties.group.id' = 'person_test_group'

指定 Kafka 消费者组的 ID为person_test_group

'scan.startup.mode' = 'earliest-offset'

指定了控制 Flink 从 Kafka 中读取数据时的起始位置