日期和时间列中的spark scala split timestamp列

时间:2018-10-30 17:44:31

标签: scala apache-spark timestamp apache-spark-sql

我将时间戳列拆分为日期和时间列时遇到问题。 首先,时间不考虑24h格式... 第二个日期是错误的,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|

和我的代码:

  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的Timestamp列转换为Time列,然后我想分割时间。

提前谢谢

2 个答案:

答案 0 :(得分:1)

您使用了错误的格式代码。具体来说,日期中的“ mm”代表分钟,“ hh”代表12小时值。相反,您需要“ 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"))

作为参考,以下是您可以使用的日期格式代码:SimpleDateFormat

答案 1 :(得分:1)

您可以通过简单的转换避免混淆

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|
+----------+-------------------+----------+---------+