使用ReduceByKey对值列表进行分组

本文介绍了使用ReduceByKey对值列表进行分组的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

我想对每个键的值列表进行分组，并且正在执行以下操作:

I want to group list of values per key and was doing something like this:

sc.parallelize(Array(("red", "zero"), ("yellow", "one"), ("red", "two"))).groupByKey().collect.foreach(println)

(red,CompactBuffer(zero, two))
(yellow,CompactBuffer(one))

但是我注意到Databricks的一篇博客文章，建议不要对大型数据集使用groupByKey.

But I noticed a blog post from Databricks and it's recommending not to use groupByKey for large dataset.

避免使用GroupByKey

是否有一种方法可以使用reduceByKey达到相同的结果?

Is there a way to achieve the same result using reduceByKey?

我尝试过此操作，但它是将所有值连接在一起.顺便说一下，就我而言，键和值都是字符串类型.

I tried this but it's concatenating all values. By the way, for my case, both key and value are string type.

sc.parallelize(Array(("red", "zero"), ("yellow", "one"), ("red", "two"))).reduceByKey(_ ++ _).collect.foreach(println)

(red,zerotwo)
(yellow,one)

推荐答案

使用aggregateByKey:

 sc.parallelize(Array(("red", "zero"), ("yellow", "one"), ("red", "two")))
.aggregateByKey(ListBuffer.empty[String])(
        (numList, num) => {numList += num; numList},
         (numList1, numList2) => {numList1.appendAll(numList2); numList1})
.mapValues(_.toList)
.collect()

scala> Array[(String, List[String])] = Array((yellow,List(one)), (red,List(zero, two)))

有关aggregateByKey的详细信息，请参见此答案，此链接，以了解使用可变数据集ListBuffer的背后原理.

See this answer for the details on aggregateByKey, this link for the rationale behind using a mutable dataset ListBuffer.

Is there a way to achieve the same result using reduceByKey?

以上实际上是更糟糕的性能，请查看@ zero323的评论以获取详细信息.

The above is actually worse in performance, please see comments by @zero323 for the details.

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AggregateByKey