我在 Scala 和 Python 中编写了以下代码,但是返回的 DataFrame 似乎没有应用我正在应用的架构中的非空字段。italianVotes.csv
是一个 csv 文件,以“~”作为分隔符和四个字段。我正在使用火花2.1.0.
意大利投票.csv
2657~135~2~2013-11-22 00:00:00.0
2658~142~2~2013-11-22 00:00:00.0
2659~142~1~2013-11-22 00:00:00.0
2660~140~2~2013-11-22 00:00:00.0
2661~140~1~2013-11-22 00:00:00.0
2662~1354~2~2013-11-22 00:00:00.0
2663~1356~2~2013-11-22 00:00:00.0
2664~1353~2~2013-11-22 00:00:00.0
2665~1351~2~2013-11-22 00:00:00.0
2667~1357~2~2013-11-22 00:00:00.0
Scala
import org.apache.spark.sql.types._
val schema = StructType(
StructField("id", IntegerType, false) ::
StructField("postId", IntegerType, false) ::
StructField("voteType", IntegerType, true) ::
StructField("time", TimestampType, true) :: Nil)
val fileName = "italianVotes.csv"
val italianDF = spark.read.schema(schema).option("sep", "~").csv(fileName)
italianDF.printSchema()
// output
root
|-- id: integer (nullable = true)
|-- postId: integer (nullable = true)
|-- voteType: integer (nullable = true)
|-- time: timestamp (nullable = true)
Python
from pyspark.sql.types import *
schema = StructType([
StructField("id", IntegerType(), False),
StructField("postId", IntegerType(), False),
StructField("voteType", IntegerType(), True),
StructField("time", TimestampType(), True),
])
file_name = "italianVotes.csv"
italian_df = spark.read.csv(file_name, schema = schema, sep = "~")
# print schema
italian_df.printSchema()
root
|-- id: integer (nullable = true)
|-- postId: integer (nullable = true)
|-- voteType: integer (nullable = true)
|-- time: timestamp (nullable = true)
我的主要问题是,当我在架构中将前两个字段设置为不可为空时,为什么它们可以为空?