如何使用Spark将文本文件拆分为多列 [英] How to split a text file into multiple columns with Spark

问题描述

我很难用定界符'|'分割文本数据文件放入数据框列.我加载的数据文件如下所示:

I'm having difficulty on splitting a text data file with delimiter '|' into data frame columns. My loaded data file looks like this:

results1.show()

+--------------------+
|                 all|
+--------------------+
|DEPT_NO|ART_GRP_N...|
|29|102|354814|SKO...|
|29|102|342677|SKO...|
|29|102|334634|DUR...|
|29|102|319337|SKO...|
|29|102|316731|DUR...|
|29|102|316728|DUR...|
|29|102|316702|DUR...|
|29|102|316702|DUR...|
|29|102|276728|I-P...|

我已经尝试了以下两种方法，这些方法可以在以前的帖子中找到:

I have tried the following 2 approaches found on previous posts:

results1.select(expr("(split(all, '|'))[1]").cast("integer").as("DEPT_NO"),expr("(split(all, '|'))[4]").cast("integer").as("ART_GRP_NO"), expr("(split(all, '|'))[8]").cast("string").as("ART_NO")).show


+-------+----------+------+
|DEPT_NO|ART_GRP_NO|ART_NO|
+-------+----------+------+
|   null|      null|     ||
|      2|         1|     3|
|      2|         1|     3|
|      2|         1|     3|
|      2|         1|     3|
|      2|         1|     3|
|      2|         1|     3|
|      2|         1|     3|
|      2|         1|     3|
|      2|         1|     2|

和

val dataframe10= sc.textFile(("D:/data/dnr10.txt")
             .toString())
             .map(_.split("|"))
             .map(c => {(c(1), c(2),c(3),c(4))})
             .toDF()
             .show()                 

+---+---+---+---+
| _1| _2| _3| _4|
+---+---+---+---+
|  D|  E|  P|  T|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  9|  ||  1|
|  2|  7|  ||  4|

由于分隔符是在每个字符之后而不是在每个'|'之后完成的，因此似乎无法识别分隔符.在这种情况下，有人可以给我提示如何进行正确的分割吗?

It looks like the delimiter is not recognized because the splitting is done after each character and not after every '|'. Can somebody give me a hint please on how to get a correct split process in this case?

推荐答案

使用RDD API :您的错误是 String.split 期望使用正则表达式，其中管道("|" >是一个特殊字符，表示或"，因此它会在任何内容上分割.另外-将数组转换为元组时，您应该从索引0开始

Using RDD API: your mistake is that String.split expects a regular expression, where pipe ("|") is a special character meaning "OR", so it splits on anything. Plus - you should start from index 0 when converting the array into a tuple

解决方法很简单-逃脱该字符:

The fix is simple - escape that character:

 sc.textFile("D:/data/dnr10.txt")
  .map(_.split("\\|"))
  .map(c => (c(0),c(1),c(2),c(3)))
  .toDF()

使用Dataframe API :此处与转义管道的问题相同.另外，您可以通过拆分一次并在选择列时多次使用该拆分列来简化代码:

Using Dataframe API: the same issue with escaping the pipe applies here. Plus you can simplify the code by splitting once and using that split column multiple times when selecting the columns:

import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.IntegerType

results1.withColumn("split", split($"all", "\\|")).select(
  $"split" getItem 0 cast IntegerType as "DEPT_NO",
  $"split" getItem 3 cast IntegerType as "ART_GRP_NO",
  $"split" getItem 7 as "ART_NO"
)

使用Spark 2.0内置的CSV支持:如果您使用的是Spark 2.0+，则可以让框架为您完成所有艰苦的工作-使用格式"csv"并设置定界符成为管道字符:

Using Spark 2.0 built-in CSV support: if you're using Spark 2.0+, you can let the framework do all the hard work for you - use format "csv" and set the delimiter to be the pipe character:

val result = sqlContext.read
  .option("header", "true")
  .option("delimiter", "|")
  .option("inferSchema", "true")
  .format("csv")
  .load("D:/data/dnr10.txt")

result.show()
// +-------+----------+------+---+
// |DEPT_NO|ART_GRP_NO|ART_NO| TT|
// +-------+----------+------+---+
// |     29|       102|354814|SKO|
// |     29|       102|342677|SKO|
// |     29|       102|334634|DUR|
// |     29|       102|276728|I-P|
// +-------+----------+------+---+

result.printSchema()
// root
//  |-- DEPT_NO: integer (nullable = true)
//  |-- ART_GRP_NO: integer (nullable = true)
//  |-- ART_NO: integer (nullable = true)
//  |-- TT: string (nullable = true)

您将获得列名，正确的类型-一切...:)

You'll get the column names, the right types - everything... :)

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