无法将“ordId”从字符串向上转换为 int,因为它可能会截断

Cannot up cast `ordId` from string to int as it may truncate

我正在尝试将一个小文件作为数据集读取,但出现错误

"Cannot up cast ordId from string to int as it may truncate".

代码如下:

object Main {
  case class Orders(ordId: Int, custId: Int, amount: Float, date: String)

  def main(args : Array[String]): Unit ={

    val schema = Encoders.product[Orders].schema

      val spark = SparkSession.builder
        .master ("local[*]")
        .appName ("")
        .getOrCreate ()


    val df = spark.read.option("header",true).csv("/mnt/data/orders.txt")


    import spark.implicits._
    val ds = df.as[Orders]

  }
}

orders.txt

ordId,custId,amount,date
1234,123,400,20190112
2345,456,600,20190122
1345,123,500,20190123
3456,345,800,20190202
5678,123,600,20190203
6578,455,900,20190301

我该如何解决这个错误?。另外我想知道我是否需要先将文件作为 Dataframe 读取,然后再转换为 Dataset?

尝试传递 schema(使用.schema),同时阅读DataFrame

import org.apache.spark.sql.Encoders
val schema = Encoders.product[Orders].schema
val ds=spark.read.option("header",true).schema(schema).csv("/mnt/data/orders.txt").as[Orders]
ds.show()

结果:

+-----+------+------+--------+
|ordId|custId|amount|    date|
+-----+------+------+--------+
| 1234|   123| 400.0|20190112|
| 2345|   456| 600.0|20190122|
| 1345|   123| 500.0|20190123|
| 3456|   345| 800.0|20190202|
| 5678|   123| 600.0|20190203|
| 6578|   455| 900.0|20190301|
+-----+------+------+--------+

架构:

ds.printSchema()

root
 |-- ordId: integer (nullable = true)
 |-- custId: integer (nullable = true)
 |-- amount: float (nullable = true)
 |-- date: string (nullable = true)

更新:

有多种方法可以从日期列中提取月份信息

  1. 使用unix_timestampfrom_unixtime函数:
ds.withColumn("mnth",from_unixtime(unix_timestamp($"date","yyyyMMdd"),"MMM")).show()

(或)

  1. 使用to_datedate_format函数:
ds.withColumn("mnth",date_format(to_date($"date","yyyyMMdd"),"MMM")).show()

结果:

+-----+------+------+--------+----+
|ordId|custId|amount|    date|mnth|
+-----+------+------+--------+----+
| 1234|   123| 400.0|20190112| Jan|
| 2345|   456| 600.0|20190122| Jan|
| 1345|   123| 500.0|20190123| Jan|
| 3456|   345| 800.0|20190202| Feb|
| 5678|   123| 600.0|20190203| Feb|
| 6578|   455| 900.0|20190301| Mar|
+-----+------+------+--------+----+