我需要将在线 Kafka 流式传输期间保存到 HDFS 的事件注入回 DStream PySpark 以进行相同的算法处理。我发现了 Holden Karau 的代码示例，它“等同于像 Kafka 这样的可检查点、可重放、可靠的消息队列”。我想知道是否可以在 PySpark 中实现它：
package com.holdenkarau.spark.testing
import org.apache.spark.streaming._
import org.apache.spark._
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import scala.language.implicitConversions
import scala.reflect.ClassTag
import org.apache.spark.streaming.dstream.FriendlyInputDStream
/**
* This is a input stream just for the testsuites. This is equivalent to a
* checkpointable, replayable, reliable message queue like Kafka.
* It requires a sequence as input, and returns the i_th element at the i_th batch
* under manual clock.
*
* Based on TestInputStream class from TestSuiteBase in the Apache Spark project.
*/
class TestInputStream[T: ClassTag](@transient var sc: SparkContext,
ssc_ : StreamingContext, input: Seq[Seq[T]], numPartitions: Int)
extends FriendlyInputDStream[T](ssc_) {
def start() {}
def stop() {}
def compute(validTime: Time): Option[RDD[T]] = {
logInfo("Computing RDD for time " + validTime)
val index = ((validTime - ourZeroTime) / slideDuration - 1).toInt
val selectedInput = if (index < input.size) input(index) else Seq[T]()
// lets us test cases where RDDs are not created
Option(selectedInput).map{si =>
val rdd = sc.makeRDD(si, numPartitions)
logInfo("Created RDD " + rdd.id + " with " + selectedInput)
rdd
}
}
}