Spark Streaming应用程序在连续批量失败后停止是一种容错机制,可防止应用程序无限期地运行并消耗资源
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
object StreamingApp {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("StreamingApp").setMaster("local[*]")
val ssc = new StreamingContext(conf, Seconds(1))
// 创建DStream
val lines = ssc.socketTextStream("localhost", 9999)
// 处理DStream
val words = lines.flatMap(_.split(" "))
val wordCounts = words.map(word => (word, 1)).reduceByKey(_ + _)
// 输出结果
wordCounts.print()
// 设置检查点目录
ssc.checkpoint("checkpoint")
// 启动StreamingContext
ssc.start()
// 监听StreamingContext的终止信号
sys.addShutdownHook {
ssc.stop(true, true)
}
}
}
为了实现连续批量失败后停止的功能,我们需要捕获异常并在一定数量的连续失败后停止StreamingContext。以下是修改后的代码:
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
object StreamingApp {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("StreamingApp").setMaster("local[*]")
val ssc = new StreamingContext(conf, Seconds(1))
// 创建D打印我似乎没有在您发入的链接中找到与Spark Streaming应用连续失败后停止相关的内容。
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