使用 Spark/scala 將字符串轉換為數據幀

Question

我想將字符串列轉換為時間戳列，但它始終返回空值。

  val t = unix_timestamp(col("tracking_time"),"MM/dd/yyyy").cast("timestamp")
   val df=   df2.withColumn("ts", t)

任何的想法 ？

謝謝你 。

Answer 1

確保您的String column與指定的格式MM/dd/yyyy匹配。

• 如果不匹配，則返回null 。

Example:

val df2=Seq(("12/12/2020")).toDF("tracking_time")
val t = unix_timestamp(col("tracking_time"),"MM/dd/yyyy").cast("timestamp")

df2.withColumn("ts", t).show()
//+-------------+-------------------+
//|tracking_time|                 ts|
//+-------------+-------------------+
//|   12/12/2020|2020-12-12 00:00:00|
//+-------------+-------------------+

df2.withColumn("ts",unix_timestamp(col("tracking_time"),"MM/dd/yyyy").cast("timestamp")).show()
//+-------------+-------------------+
//|tracking_time|                 ts|
//+-------------+-------------------+
//|   12/12/2020|2020-12-12 00:00:00|
//+-------------+-------------------+
//(or)  by using to_timestamp function.

df2.withColumn("ts",to_timestamp(col("tracking_time"),"MM/dd/yyyy")).show()
//+-------------+-------------------+
//|tracking_time|                 ts|
//+-------------+-------------------+
//|   12/12/2020|2020-12-12 00:00:00|
//+-------------+-------------------+

Answer 2

正如@Shu 提到的，原因可能是tracking_time列的格式無效。 不過值得一提的是，Spark 正在尋找模式作為列值的前綴。 研究這些例子以獲得更好的直覺

Seq(
  "03/29/2020 00:00",
  "03/29/2020",
  "00:00 03/29/2020",
  "03/29/2020somethingsomething"
).toDF("tracking_time")
  .withColumn("ts", unix_timestamp(col("tracking_time"), "MM/dd/yyyy").cast("timestamp"))
  .show()
//+--------------------+-------------------+
//|       tracking_time|                 ts|
//+--------------------+-------------------+
//|    03/29/2020 00:00|2020-03-29 00:00:00|
//|          03/29/2020|2020-03-29 00:00:00|
//|    00:00 03/29/2020|               null|
//|03/29/2020somethi...|2020-03-29 00:00:00|