spark scala在日期列和时间列中拆分时间戳列

我在将时间戳列拆分为日期列和时间列时遇到问题。 首先，时间不考虑24h格式… 第二，日期是假的，我不明白为什么

这是我的输出

+----------+----------+-------------------+---------+
|      Date| Timestamp|               Time|EventTime|
+----------+----------+-------------------+---------+
|2018-00-30|1540857600|2018-10-30 00:00:00| 12:00:00|
|2018-00-30|1540857610|2018-10-30 00:00:10| 12:00:10|
|2018-00-30|1540857620|2018-10-30 00:00:20| 12:00:20|
|2018-00-30|1540857630|2018-10-30 00:00:30| 12:00:30|
|2018-00-30|1540857640|2018-10-30 00:00:40| 12:00:40|
|2018-00-30|1540857650|2018-10-30 00:00:50| 12:00:50|
|2018-01-30|1540857660|2018-10-30 00:01:00| 12:01:00|
|2018-01-30|1540857670|2018-10-30 00:01:10| 12:01:10|
|2018-01-30|1540857680|2018-10-30 00:01:20| 12:01:20|
|2018-01-30|1540857690|2018-10-30 00:01:30| 12:01:30|
|2018-01-30|1540857700|2018-10-30 00:01:40| 12:01:40|

我的代码是：

  val df = data_input
    .withColumn("Time", to_timestamp(from_unixtime(col("Timestamp"))))
    .withColumn("Date", date_format(col("Time"), "yyyy-mm-dd"))
    .withColumn("EventTime", date_format(col("Time"), "hh:mm:ss"))

首先，我将unix时间戳列转换为时间列，然后我想拆分时间

---

提前感谢

您使用了错误的格式代码。具体来说，日期中的“mm”表示分钟，“hh”表示12小时值。相反，你想要“MM”和“HH”。像这样：

val df = data_input
    .withColumn("Time", to_timestamp(from_unixtime(col("Timestamp"))))
    .withColumn("Date", date_format(col("Time"), "yyyy-MM-dd"))
    .withColumn("EventTime", date_format(col("Time"), "HH:mm:ss"))

---

以下是您可以使用的日期格式代码供参考：

您使用的格式代码错误。具体来说，日期中的“mm”表示分钟，“hh”表示12小时值。相反，你想要“MM”和“HH”。像这样：

val df = data_input
    .withColumn("Time", to_timestamp(from_unixtime(col("Timestamp"))))
    .withColumn("Date", date_format(col("Time"), "yyyy-MM-dd"))
    .withColumn("EventTime", date_format(col("Time"), "HH:mm:ss"))

---

以下是您可以使用的日期格式代码供参考：

您可以避免与简单转换混淆

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

val df = data_input
    .withColumn("Time", $"Timestamp".cast("timestamp"))
    .withColumn("Date", $"Time".cast("date"))
    .withColumn("EventTime", date_format($"Time", "H:m:s"))

+----------+-------------------+----------+---------+
|Timestamp |               Time|      Date|EventTime|
+----------+-------------------+----------+---------+
|1540857600|2018-10-30 00:00:00|2018-10-30|    0:0:0|
|1540857610|2018-10-30 00:00:10|2018-10-30|   0:0:10|
|1540857620|2018-10-30 00:00:20|2018-10-30|   0:0:20|
|1540857630|2018-10-30 00:00:30|2018-10-30|   0:0:30|
|1540857640|2018-10-30 00:00:40|2018-10-30|   0:0:40|
|1540857650|2018-10-30 00:00:50|2018-10-30|   0:0:50|
|1540857660|2018-10-30 00:01:00|2018-10-30|    0:1:0|
|1540857670|2018-10-30 00:01:10|2018-10-30|   0:1:10|
|1540857680|2018-10-30 00:01:20|2018-10-30|   0:1:20|
|1540857690|2018-10-30 00:01:30|2018-10-30|   0:1:30|
|1540857700|2018-10-30 00:01:40|2018-10-30|   0:1:40|
+----------+-------------------+----------+---------+

---

您可以避免与简单铸造混淆

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

val df = data_input
    .withColumn("Time", $"Timestamp".cast("timestamp"))
    .withColumn("Date", $"Time".cast("date"))
    .withColumn("EventTime", date_format($"Time", "H:m:s"))

+----------+-------------------+----------+---------+
|Timestamp |               Time|      Date|EventTime|
+----------+-------------------+----------+---------+
|1540857600|2018-10-30 00:00:00|2018-10-30|    0:0:0|
|1540857610|2018-10-30 00:00:10|2018-10-30|   0:0:10|
|1540857620|2018-10-30 00:00:20|2018-10-30|   0:0:20|
|1540857630|2018-10-30 00:00:30|2018-10-30|   0:0:30|
|1540857640|2018-10-30 00:00:40|2018-10-30|   0:0:40|
|1540857650|2018-10-30 00:00:50|2018-10-30|   0:0:50|
|1540857660|2018-10-30 00:01:00|2018-10-30|    0:1:0|
|1540857670|2018-10-30 00:01:10|2018-10-30|   0:1:10|
|1540857680|2018-10-30 00:01:20|2018-10-30|   0:1:20|
|1540857690|2018-10-30 00:01:30|2018-10-30|   0:1:30|
|1540857700|2018-10-30 00:01:40|2018-10-30|   0:1:40|
+----------+-------------------+----------+---------+