spark中reduceByKey的意外行为（使用scala）？是"；rev"；spark或scala中的保留字？

我有一个档案，里面有一些记录

1,1,957,1,299.98,299.98
2,2,1073,1,199.99,199.99
3,2,502,5,250.0,50.0
4,2,403,1,129.99,129.99
5,4,897,2,49.98,24.99
6,4,365,5,299.95,59.99
7,4,502,3,150.0,50.0
8,4,1014,4,199.92,49.98
9,5,957,1,299.98,299.98
10,5,365,5,299.95,59.99

我创建了一对RDD，如下所示

val orderItemsRDD=sc.textFile（“/shiva/spark/input/data/retail\u db/order\u items”）

val orderitemspaireddd=orderItemsRDD.map（order=>{val x=order.split（“，”）；（x（1）.toInt，x（4）.toFloat）}

输出配对RDD的类型为RDD[（Int，Float）]，如下所示

orderitemspaireddd:org.apache.spark.rdd.rdd[（Int，Float）]=MapPartitionsRDD[120]位于地图26处

现在我使用reduceByKey来获得sum

val orderItemsRBKRDD=orderitemspaireddd.reduceByKey（（总计，修订）=>total+rev）

但令人惊讶的是，输出的类型是RDD[（Int，String）]

orderItemsRBKRDD:org.apache.spark.rdd.rdd[（Int，String）]=ShuffledRDD[121]位于reduceByKey的地址：34

如果我检查输出数据，它看起来是这样的

scala> orderItemsRBKRDD.take(10).foreach(println)
(41234,102921,41234,249,2,109.94,54.97)
(65722,164249,65722,365,2,119.98,59.99164250,65722,730,5,400.0,80.0164251,65722,1004,1,399.98,399.98164252,65722,627,5,199.95,39.99164253,65722,191,2,199.98,99.99)
(28730,71921,28730,365,5,299.95,59.9971922,28730,502,1,50.0,50.0)
(68522,171323,68522,127,1,329.99,329.99)
(23776,59498,23776,1073,1,199.99,199.9959499,23776,403,1,129.99,129.99)
(32676,81749,32676,365,1,59.99,59.9981750,32676,627,4,159.96,39.9981751,32676,191,3,299.97,99.9981752,32676,1073,1,199.99,199.99)
(53926,134834,53926,365,2,119.98,59.99134835,53926,191,1,99.99,99.99)
(4926,12324,4926,1014,4,199.92,49.9812325,4926,1073,1,199.99,199.9912326,4926,365,4,239.96,59.9912327,4926,957,1,299.98,299.98)
(38926,97183,38926,191,5,499.95,99.9997184,38926,502,5,250.0,50.097185,38926,365,5,299.95,59.99)
(29270,73214,29270,365,5,299.95,59.9973215,29270,365,2,119.98,59.9973216,29270,1004,1,399.98,399.9873217,29270,627,4,159.96,39.9973218,29270,1004,1,399.98,399.98)

我改变了我的代码如下

scala>val orderItemsRBKRDD=orderitemspaireddd.reduceByKey（（总计，收入）=>total+revenue）

现在输出如下图所示，与预期一致（正确）

orderItemsRBKRDD:org.apache.spark.rdd.rdd[（Int，Float）]=shuffleddd[123]位于reduceByKey的地址：28

数据是

scala> orderItemsRBKRDD.take(10).foreach(println)
(41234,109.94)
(65722,1319.8899)
(28730,349.95)
(68522,329.99)
(23776,329.98)
(32676,719.91003)
(53926,219.97)
(4926,939.85)
(38926,1049.9)
(29270,1379.8501)

唯一的区别或变化是变量名从“rev”更改为“revenue”

“rev”在spark或scala中是保留字吗？

正如Balaji所说，变量“rev”在spark或scala中不是保留关键字。

---

也许您可以在Jira中创建一个票证。

rev不是保留字。我想问题在代码的其他地方。