spark流计算平均值

56lgkhnf  于 2021-06-07  发布在  Kafka
关注(0)|答案(2)|浏览(560)

我收到Kafka的数据格式,其中null是键。

null,val1,val2,val3,val4,val5,val6,val7,...val23
null,val1,val2,val3,val4,val5,val6,val7,...val23
null,val1,val2,val3,val4,val5,val6,val7,...val23

现在,我已经Map了这些值,以删除空键,并使用以下代码形成新的键和值对。

val topics = Array("kafka-topic")
    val stream = KafkaUtils.createDirectStream[String, String](
    streamingContext,
    PreferConsistent,
    Subscribe[String, String](topics, kafkaParams)
    )
    streamingContext.checkpoint("hdfs:///hdfs/location")
    val record= stream.map(record=>record.value().toString)

    val rdds=record.transform
    {
    pps=>pps.flatMap(_.split(","))
    }

    val ppds= rdds.transform
`  `{
    pair=>pair.map(vals=>
(vals(2).toString(),Set(vals(1).toLong,vals(2),vals(3),vals(4),val(5),val(6),val(7)....val(23)
 }

其中vals(2)一个字符串将是键,其余22个值将是值。
我现在正试图在20秒的时间窗口内获得每个键的所有值的平均值,并不断地将计算出的每个键的平均值推送到数据存储(hbase)中。在批处理模式中,我知道有aggregatebykey()方法允许您这样做。
在流媒体模式下,如何实现这一点?
还有一种可能是某些值是字符串。如何跳过字符串值,只计算数值类型的平均值,同时不断地将更新推送到hbase?

q43xntqr

q43xntqr1#

使用reducebykeyandwindow,

// Reduce last 30 seconds of data, every 10 seconds

val aggregateFunction = (a:Int,b:Int) => (a + b)
val pairDStream = // DStream contains (word,1)
val windowedWordCounts = pairDStream.reduceByKeyAndWindow(aggregateFunction, Seconds(30), Seconds(10))

上面的示例将用于计算窗口期间的字数,而不是像上面那样使用简单的加法函数,您可以编写更复杂的聚合函数,并将其与reducebykeyandwindow一起使用
了解更多信息
https://docs.cloud.databricks.com/docs/latest/databricks_guide/07%20spark%20streaming/10%20window%20aggregations.html

jbose2ul

jbose2ul2#

你可以这样使用:

// Map each hashtag to a key/value pair of (hashtag, 1) so we can count them up by adding up the values
    val hashtagKeyValues = hashtags.map(hashtag => (hashtag, 1))

    // Now count them up over a 5 minute window sliding every one second
    val hashtagCounts = hashtagKeyValues.reduceByKeyAndWindow( (x,y) => x + y, (x,y) => x - y, Seconds(300), Seconds(1))
    //  You will often see this written in the following shorthand:
    //val hashtagCounts = hashtagKeyValues.reduceByKeyAndWindow( _ + _, _ -_, Seconds(300), Seconds(1))

    // Sort the results by the count values
    val sortedResults = hashtagCounts.transform(rdd => rdd.sortBy(x => x._2, false))

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