提供一个标准的flink sink scala代码

以下是一个标准的Flink Sink Scala代码，用于将数据流写入Kafka主题：

import org.apache.flink.streaming.api.functions.sink.SinkFunction
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer
import org.apache.flink.streaming.util.serialization.SimpleStringSchema

class KafkaSink(brokerList: String, topic: String) extends SinkFunction[String] {

  lazy val kafkaProducer = new FlinkKafkaProducer[String](
    brokerList, topic, new SimpleStringSchema())

  override def invoke(value: String): Unit = {
    kafkaProducer.send(value)
  }
}

在此示例中，我们使用FlinkKafkaProducer将数据流写入Kafka主题。该类需要传入两个参数：Kafka代理列表和要写入的主题名称。我们还定义了一个invoke方法，该方法将每个收到的数据String发送到Kafka主题中。

相关问题

flink1.15 scala sink Cassandra 指数退避重试怎么写

在 Flink 1.15 中使用 Scala 将数据写入 Cassandra 数据库时，可以使用指数退避重试机制来提高数据写入的可靠性。具体实现步骤如下：

1. 引入依赖和配置

在项目的 build.sbt 文件中添加以下依赖：

libraryDependencies += "com.datastax.cassandra" % "cassandra-driver-core" % "4.13.0"

在程序中添加以下配置：

import com.datastax.oss.driver.api.core.config.DefaultDriverOption
import com.datastax.oss.driver.api.core.config.DriverConfigLoader
import com.datastax.oss.driver.api.core.config.TypedDriverOption

val config = DriverConfigLoader.fromClasspath("application.conf")
  .build.withString(DefaultDriverOption.LOAD_BALANCING_POLICY_CLASS, "RoundRobinPolicy")
  .withString(DefaultDriverOption.RETRY_POLICY_CLASS, "com.datastax.oss.driver.api.core.retry.ExponentialBackoffRetryPolicy")
  .withInt(TypedDriverOption.RETRY_POLICY_EXONENTIAL_BASE_DELAY, 1000)
  .withInt(TypedDriverOption.RETRY_POLICY_MAX_ATTEMPTS, 5)

2. 创建 Cassandra 连接

使用上一步中的配置创建 Cassandra 连接：

import com.datastax.oss.driver.api.core.CqlSession

val session: CqlSession = CqlSession.builder().withConfigLoader(config).build()

3. 定义 Cassandra Sink

定义一个 Cassandra Sink，使用指数退避重试机制：

import org.apache.flink.streaming.api.functions.sink.SinkFunction
import org.apache.flink.streaming.connectors.cassandra.CassandraSinkBuilder
import org.apache.flink.streaming.connectors.cassandra.CassandraSinkOptions
import org.apache.flink.streaming.connectors.cassandra.ClusterBuilder
import com.datastax.oss.driver.api.core.CqlSession
import com.datastax.oss.driver.api.core.cql.{BoundStatement, PreparedStatement, SimpleStatement}
import com.datastax.oss.driver.api.core.retry.RetryPolicy

case class MyData(id: Int, name: String)

class CassandraSink(session: CqlSession) extends SinkFunction[MyData] {
  override def invoke(data: MyData): Unit = {
    val statement: BoundStatement = session.prepare("INSERT INTO mytable (id, name) VALUES (?, ?)").bind(data.id.asInstanceOf[AnyRef], data.name)
    session.execute(statement)
  }
}

val cassandraSink: CassandraSink = new CassandraSink(session)

4. 使用 Cassandra Sink

将数据写入 Cassandra 数据库时，使用上一步中定义的 Cassandra Sink，并启用重试机制：

val dataStream: DataStream[MyData] = ???

CassandraSinkBuilder
  .builder()
  .withSession(session)
  .withPreparedStatementSetter((data: MyData, statement: PreparedStatement) => statement.bind(data.id.asInstanceOf[AnyRef], data.name))
  .withRetryPolicy(RetryPolicy.defaultExponentialBackoff())
  .withMaxConcurrentRequests(2)
  .withMaxPendingRequests(10)
  .withCassandraOptions(new CassandraSinkOptions())
  .build()
  .addSink(dataStream)

在上述代码中，withRetryPolicy 方法启用了指数退避重试机制，并使用了默认的指数退避重试策略。withMaxConcurrentRequests 和 withMaxPendingRequests 方法可以控制并发请求和等待请求的最大数量。

以上就是在 Flink 1.15 中使用 Scala 实现 Cassandra Sink 指数退避重试机制的方法。

flink 实现mysql cdc scala 代码

以下是使用 Flink 实现 MySQL CDC 的 Scala 代码示例：

import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.functions.source.SourceFunction
import org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks
import org.apache.flink.streaming.api.watermark.Watermark
import org.apache.flink.streaming.api.functions.sink.SinkFunction
import org.apache.flink.streaming.api.functions.ProcessFunction
import org.apache.flink.util.Collector
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.scala.function.WindowFunction
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.api.common.functions.MapFunction
import org.apache.flink.api.common.typeinfo.TypeInformation
import org.apache.flink.api.java.typeutils.RowTypeInfo
import org.apache.flink.types.Row
import org.apache.flink.api.common.serialization.SimpleStringSchema
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema
import org.apache.flink.streaming.connectors.kafka.KafkaContextAware
import org.apache.flink.streaming.connectors.kafka.KafkaContextAware.KafkaContext
import org.apache.flink.streaming.util.serialization.KeyedSerializationSchemaWrapper
import java.util.Properties

object MySQLCDC {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)

    val properties = new Properties()
    properties.setProperty("bootstrap.servers", "localhost:9092")
    properties.setProperty("group.id", "flink-group")

    val consumer = new FlinkKafkaConsumer011[String]("mysql-cdc", new SimpleStringSchema(), properties)
    val stream = env.addSource(consumer).map(new MapFunction[String, Row]() {
      override def map(value: String): Row = {
        val fields = value.split(",")
        Row.of(fields(0).toInt.asInstanceOf[Object], fields(1).asInstanceOf[Object], fields(2).asInstanceOf[Object])
      }
    }).assignTimestampsAndWatermarks(new AssignerWithPunctuatedWatermarks[Row]() {
      override def extractTimestamp(row: Row, previousTimestamp: Long): Long = {
        row.getField(0).asInstanceOf[Int].toLong
      }

      override def checkAndGetNextWatermark(row: Row, extractedTimestamp: Long): Watermark = {
        new Watermark(extractedTimestamp)
      }
    })

    val windowedStream = stream.keyBy(1).timeWindow(Time.seconds(10)).apply(new WindowFunction[Row, Row, Tuple, TimeWindow]() {
      override def apply(key: Tuple, window: TimeWindow, input: Iterable[Row], out: Collector[Row]): Unit = {
        val sortedInput = input.toList.sortBy(_.getField(0).asInstanceOf[Int])
        val firstRow = sortedInput.head
        val lastRow = sortedInput.last
        out.collect(Row.of(firstRow.getField(1), firstRow.getField(2), lastRow.getField(2)))
      }
    })

    val producer = new FlinkKafkaProducer011[String]("mysql-cdc-output", new KafkaSerializationSchema[String]() with KafkaContextAware[String] {
      var context: KafkaContext = _

      override def serialize(element: String, timestamp: java.lang.Long): org.apache.kafka.clients.producer.ProducerRecord[Array[Byte], Array[Byte]] = {
        new org.apache.kafka.clients.producer.ProducerRecord(context.getOutputTopic(), element.getBytes())
      }

      override def setRuntimeContext(context: KafkaContext): Unit = {
        this.context = context
      }
    }, properties, FlinkKafkaProducer011.Semantic.EXACTLY_ONCE)

    windowedStream.map(new MapFunction[Row, String]() {
      override def map(row: Row): String = {
        s"${row.getField(0)},${row.getField(1)},${row.getField(2)}"
      }
    }).addSink(producer)

    env.execute("MySQL CDC")
  }
}