Scala 将字符串转换为日期SparkSQL

我是Scala的新手，我有一个dataframe，我正在尝试一列dataframe的日期，从字符串开始，换言之，如下所示

1)    yyyyMMddHHmmss(20150610120256) ->yyyy-MM-dd HH:mm:ss(2015-06-10 12:02:56) 
2)    yyyyMMddHHmmss(20150611      ) ->yyyy-MM-dd(2015-06-11)

第一种情况我能成功完成，但第二种情况有问题，由于这一原因，我无法将时间转换为日期。更多详细信息，您可以在下面获得。任何帮助将不胜感激

df.printSchema
root
 |-- TYPE: string (nullable = true)
 |-- CODE: string (nullable = true)
 |-- SQ_CODE: string (nullable = true)
 |-- RE_TYPE: string (nullable = true)
 |-- VERY_ID: long (nullable = true)
 |-- IN_DATE: string (nullable = true)


df.show
Input  
+-----+-------+---------+---------+-------------------+-----------------+
| TYPE|   CODE|  SQ_CODE| RE_TYPE |            VERY_ID|  IN_DATE        |
+-----+-------+---------+---------+-------------------+-----------------+
|   F | 000544|  2017002|      OP |  95032015062763298| 20150610120256  |
|   F | 000544|  2017002|      LD |  95032015062763261| 20150611        |
|   F | 000544|  2017002|      AK |  95037854336743246| 20150611012356  |
+-----+-------+---------+--+------+-------------------+-----------------+

df=df.withColumn("IN_DATE",when(lit(length(regexp_replace(df("IN_DATE"),"\\s+",""))) === lit(8) ,
        to_date(from_unixtime(regexp_replace(df("IN_DATE"),"\\s+",""),"yyyyMMdd").cast("date")))
        .otherwise(unix_timestamp(df("IN_DATE"),"yyyyMMddHHmmss").cast("timestamp")))

Actual output
+-----+-------+---------+---------+-------------------+----------------------+
| TYPE|   CODE|  SQ_CODE| RE_TYPE |            VERY_ID|  IN_DATE             |
+-----+-------+---------+---------+-------------------+----------------------+
|   F | 000544|  2017002|      OP |  95032015062763298| 2015-06-10 12:02:56  |
|   F | 000544|  2017002|      LD |  95032015062763261| null                 |
|   F | 000544|  2017002|      AK |  95037854336743246| 2015-06-11 01:23:56  |
+-----+-------+---------+--+------+-------------------+----------------------+

df=df.withColumn("IN_DATE",when(lit(length(regexp_replace(df("IN_DATE"),"\\s+",""))) === lit(8) ,
        to_date(from_unixtime(regexp_replace(df("IN_DATE"),"\\s+",""),"yyyyMMdd").cast("timestamp")))
        .otherwise(unix_timestamp(df("IN_DATE"),"yyyyMMddHHmmss").cast("timestamp")))

Actual output
+-----+-------+---------+---------+-------------------+----------------------+
| TYPE|   CODE|  SQ_CODE| RE_TYPE |            VERY_ID|  IN_DATE             |
+-----+-------+---------+---------+-------------------+----------------------+
|   F | 000544|  2017002|      OP |  95032015062763298| 2015-06-10 12:02:56  |
|   F | 000544|  2017002|      LD |  95032015062763261| 2015-06-11 00:00:00  |
|   F | 000544|  2017002|      AK |  95037854336743246| 2015-06-11 01:23:56  |
+-----+-------+---------+--+------+-------------------+----------------------+


Expected output
+-----+-------+---------+---------+-------------------+----------------------+
| TYPE|   CODE|  SQ_CODE| RE_TYPE |            VERY_ID|  IN_DATE             |
+-----+-------+---------+---------+-------------------+----------------------+
|   F | 000544|  2017002|      OP |  95032015062763298| 2015-06-10 12:02:56  |
|   F | 000544|  2017002|      LD |  95032015062763261| 2015-06-11           |
|   F | 000544|  2017002|      AK |  95037854336743246| 2015-06-11 01:23:56  |
+-----+-------+---------+--+------+-------------------+----------------------+

请尝试此查询

df.withColumn("IN_DATE",when(lit(length(regexp_replace(df("IN_DATE"),"\\s+",""))) === lit(8) ,
to_date(from_unixtime(regexp_replace(df("IN_DATE"),"\\s+",""),"yyyyMMdd").cast(DateType)))
.otherwise(unix_timestamp(df("IN_DATE"),"yyyyMMddHHmmSS").cast(TimestampType)))

---

有几个选项可以实现日期解析器

使用内置的spark sql函数

TODATE（）

。这一执行的重要性

创建一个用户定义的函数，在该函数中，您可以根据喜欢的输入格式执行不同的日期解析，并返回字符串。了解更多有关UDF的信息

---

2015-06-11

格式为

spark.sql.types.DateType

和

2015-06-10 12:02:56

is

spark.sql.types.TimestampType

同一列上不能有两个数据类型。架构的每一列应该只有一个数据类型

我建议您创建两个新的专栏，并在其中使用您希望的格式

import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{DateType, TimestampType}
df.withColumn("IN_DATE_DateOnly",from_unixtime(unix_timestamp(df("IN_DATE"),"yyyyMMdd")).cast(DateType))
  .withColumn("IN_DATE_DateAndTime",unix_timestamp(df("IN_DATE"),"yyyyMMddHHmmSS").cast(TimestampType)) 

这将为您提供

dataframe

as

+----+------+-------+-------+-----------------+--------------+----------------+---------------------+
|TYPE|CODE  |SQ_CODE|RE_TYPE|VERY_ID          |IN_DATE       |IN_DATE_DateOnly|IN_DATE_DateAndTime  |
+----+------+-------+-------+-----------------+--------------+----------------+---------------------+
|F   |000544|2017002|OP     |95032015062763298|20150610120256|null            |2015-06-10 12:02:00.0|
|F   |000544|2017002|LD     |95032015062763261|20150611      |2015-06-11      |null                 |
|F   |000544|2017002|AK     |95037854336743246|20150611012356|null            |2015-06-11 01:23:00.0|
+----+------+-------+-------+-----------------+--------------+----------------+---------------------+

您可以看到数据类型是不同的

root
 |-- TYPE: string (nullable = true)
 |-- CODE: string (nullable = true)
 |-- SQ_CODE: string (nullable = true)
 |-- RE_TYPE: string (nullable = true)
 |-- VERY_ID: string (nullable = true)
 |-- IN_DATE: string (nullable = true)
 |-- IN_DATE_DateOnly: date (nullable = true)
 |-- IN_DATE_DateAndTime: timestamp (nullable = true)

我希望答案有帮助

我会的

• 选择更精确的数据类型-此处

  TimestampType

• 以不同的格式合并

---

这是不可能的，因为两个日期的数据类型不同。一个是TimestampType，另一个是DateType，同一列不能有两个模式？我们在spark sql中有

trim

。

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

val df = Seq("20150610120256", "20150611").toDF("IN_DATE")

df.withColumn("IN_DATE", coalesce(
  to_timestamp($"IN_DATE", "yyyyMMddHHmmss"), 
  to_timestamp($"IN_DATE", "yyyyMMdd"))).show


+-------------------+
|            IN_DATE|
+-------------------+
|2015-06-10 12:02:56|
|2015-06-11 00:00:00|
+-------------------+