《深入理解Spark》之Transform、foreachRDD、updateStateByKey以及reduceByKeyAndWindow

最新推荐文章于 2022-05-22 10:10:02 发布

lyzx_in_csdn

最新推荐文章于 2022-05-22 10:10:02 发布

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分类专栏： Spark

本文链接：https://blog.csdn.net/lyzx_in_csdn/article/details/79153044

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本文详细探讨了Spark中的关键操作，包括Transform函数的使用，如何通过foreachRDD处理每个RDD，以及状态管理的updateStateByKey方法。此外，还深入解析了用于时间窗口聚合的reduceByKeyAndWindow，阐述了这些操作在大数据处理中的应用和优势。

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package com.lyzx.day32

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}

class T1 {
  /**
    * Transform Operation
    *
    * The transform operation (along with its variations like transformWith) allows arbitrary
    * RDD-to-RDD functions to be applied on a DStream.
    * It can be used to apply any RDD operation that is not exposed in the DStream API. For example,
    * the functionality of joining every batch in a data stream with another dataset is not directly exposed
    * in the DStream API. However, you can easily use transform to do this.
    * This enables very powerful possibilities.
    * For example, one can do real-time data cleaning by joining the input data stream with
    * precomputed spam information (maybe generated with Spark as well) and then filtering based on it.
    *
    * Transform 操作允许任意的RDD到RDD的函数被应用于DStream
    * 它可以应用任何能在RDD上应用的函数而不暴露DStream的API
    *