如何使用数组类型列从CSV加载数据到spark数据帧

Question

我有CSV文件，如图所示：

name,age,languages,experience
'Alice',31,['C++', 'Java'],2
'Bob',34,['Java', 'Python'],2
'Smith',35,['Ruby', 'Java'],3
'David',36,['C', 'Java', 'R']4

在加载数据时，默认情况下，所有列都以字符串形式加载。

scala> val df = spark.read.format("csv").option("header",true).load("data.csv")
df: org.apache.spark.sql.DataFrame = [name: string, age: string ... 2 more fields]
scala> df.show()
+-------+---+------------------+----------+
|   name|age|         languages|experience|
+-------+---+------------------+----------+
|'Alice'| 31|   ['C++', 'Java']|         2|
|  'Bob'| 34|['Java', 'Python']|         2|
|'Smith'| 35|  ['Ruby', 'Java']|         3|
|'David'| 36|['C', 'Java', 'R']|         4|
+-------+---+------------------+----------+
scala> df.printSchema()
root
|-- name: string (nullable = true)
|-- age: string (nullable = true)
|-- languages: string (nullable = true)
|-- experience: string (nullable = true)

因此，我将自定义模式定义为String、Integer、Array、Integer数据类型：

import org.apache.spark.sql.types.{StructField, StructType, StringType, ArrayType, IntegerType}
val custom_schema = new StructType(Array(StructField("name", StringType), StructField("age", IntegerType), StructField("languages", ArrayType(StringType)), StructField("experience", IntegerType)))

当我使用自定义模式加载数据时，它抛出错误

运行命令后的终端屏幕截图

scala> val df = spark.read.format("csv").option("header",true).schema(custom_schema).load("data.csv")
org.apache.spark.sql.AnalysisException: CSV data source does not support array<string> data type.
at org.apache.spark.sql.execution.datasources.DataSourceUtils$.$anonfun$verifySchema$1(DataSourceUtils.scala:67)
at org.apache.spark.sql.execution.datasources.DataSourceUtils$.$anonfun$verifySchema$1$adapted(DataSourceUtils.scala:65)
at scala.collection.Iterator.foreach(Iterator.scala:941)
at scala.collection.Iterator.foreach$(Iterator.scala:941)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1429)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at org.apache.spark.sql.types.StructType.foreach(StructType.scala:102)
at org.apache.spark.sql.execution.datasources.DataSourceUtils$.verifySchema(DataSourceUtils.scala:65)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:445)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:326)
at org.apache.spark.sql.DataFrameReader.$anonfun$load$3(DataFrameReader.scala:308)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:308)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:240)
... 47 elided

如何通过将列作为数组来加载数据以引发数据帧？

Answer 1

从文件中读取后，您可以将其转换为数组，方法是使用regexp_replace删除方括号([，](，并使用split用逗号(,(分割剩余字符串，例如.

val df = spark.read.format("csv").option("header",true).load("data.csv")
val transformedDf = df.withColumn("languages",
split(
regexp_replace(col("languages"),"\[|\]",""),
","
)
)