Parse Date Format

Question

I have the following DataFrame containing the date format - yyyyMMddTHH:mm:ss+UTC

Data Preparation

sparkDF = sql.createDataFrame([("20201021T00:00:00+0530",),
                               ("20211011T00:00:00+0530",),
                               ("20200212T00:00:00+0300",),
                               ("20211021T00:00:00+0530",),
                               ("20211021T00:00:00+0900",),
                               ("20211021T00:00:00-0500",)
                              ]
                              ,['timestamp'])


sparkDF.show(truncate=False)

+----------------------+
|timestamp             |
+----------------------+
|20201021T00:00:00+0530|
|20211011T00:00:00+0530|
|20200212T00:00:00+0300|
|20211021T00:00:00+0530|
|20211021T00:00:00+0900|
|20211021T00:00:00-0500|
+----------------------+

I m aware of the date format to parse and convert the values to DateType

Timestamp Parsed

sparkDF.select(F.to_date(F.col('timestamp'),"yyyyMMdd'T'HH:mm:ss+0530").alias('timestamp_parsed')).show()

+----------------+
|timestamp_parsed|
+----------------+
|      2020-10-21|
|      2021-10-11|
|            null|
|      2021-10-21|
|            null|
|            null|
+----------------+

As you can see , its specific to +0530 strings , I m aware of the fact that I can use multiple patterns and coalesce the first non-null values

Multiple Patterns & Coalesce

sparkDF.withColumn('p1',F.to_date(F.col('timestamp'),"yyyyMMdd'T'HH:mm:ss+0530"))\
       .withColumn('p2',F.to_date(F.col('timestamp'),"yyyyMMdd'T'HH:mm:ss+0900"))\
       .withColumn('p3',F.to_date(F.col('timestamp'),"yyyyMMdd'T'HH:mm:ss-0500"))\
       .withColumn('p4',F.to_date(F.col('timestamp'),"yyyyMMdd'T'HH:mm:ss+0300"))\
       .withColumn('timestamp_parsed',F.coalesce(F.col('p1'),F.col('p2'),F.col('p3'),F.col('p4')))\
       .drop(*['p1','p2','p3','p4'])\
       .show(truncate=False)

+----------------------+----------------+
|timestamp             |timestamp_parsed|
+----------------------+----------------+
|20201021T00:00:00+0530|2020-10-21      |
|20211011T00:00:00+0530|2021-10-11      |
|20200212T00:00:00+0300|2020-02-12      |
|20211021T00:00:00+0530|2021-10-21      |
|20211021T00:00:00+0900|2021-10-21      |
|20211021T00:00:00-0500|2021-10-21      |
+----------------------+----------------+

Is there a better way to accomplish this, as there might be a bunch of other UTC within the data source, is there a standard UTC TZ available within Spark to parse all the cases

Vincent Doba · Answer 1 · 2021-09-15T08:15:03.800

You can usually use x, X or Z for offset pattern as you can find on Spark date pattern documentation page. You can then parse your date with the following complete pattern: yyyyMMdd'T'HH:mm:ssxx

However, if you use those kind of offset patterns, your date will be first converted in UTC format, meaning all timestamp with a positive offset will be matched to the previous day. For instance "20201021T00:00:00+0530" will be matched to 2020-10-20 using to_date with the previous pattern.

If you want to get displayed date as a date, ignoring offset, you should first extract date string from complete timestamp string using regexp_extract function, then perform to_date.

If you take your example "20201021T00:00:00+0530", what you want to extract with a regexp is 20201021 part and apply to_date on it. You can do it with the following pattern: ^(\\d+). If you're interested, you can find how to build other patterns in java's Pattern documentation.

So your code should be:

from pyspark.sql import functions as F

sparkDF.select(
  F.to_date(
    F.regexp_extract(F.col('timestamp'), '^(\\d+)', 0), 'yyyyMMdd'
  ).alias('timestamp_parsed')
).show()

And with your input you will get:

+----------------+
|timestamp_parsed|
+----------------+
|2020-10-21      |
|2021-10-11      |
|2020-02-12      |
|2021-10-21      |
|2021-10-21      |
|2021-10-21      |
+----------------+

The SQL Ref link was helpful , I had seen that link , but couldn't remember where it was buried in the Spark API docs , Thanks for your help — Vaebhav, Sep 15 '21 at 08:25

score 2 · Accepted Answer · answered Sep 15 '21 at 06:54

i think you have got the 2nd argument of your to_date function wrong which is causing null values in your output

the +530 in your timestamp is the Zulu value which just denotes how many hours and mins ahead (for +) or behind (for -) the current timestamp is withrespect to UTC. Please refer to the response by Basil here Java / convert ISO-8601 (2010-12-16T13:33:50.513852Z) to Date object This link has full details available for the same.

To answer your question if you replace +0530 by Z it should solve your problem.

Here is the spark code in scala that I tried and worked:

val data = Seq("20201021T00:00:00+0530",
                               "20211011T00:00:00+0530",
                               "20200212T00:00:00+0300",
                               "20211021T00:00:00+0530",
                               "20211021T00:00:00+0900",
                               "20211021T00:00:00-0500")

import spark.implicits._
val sparkDF = data.toDF("custom_time")

import org.apache.spark.sql.functions._
val spark_DF2 = sparkDF.withColumn("new_timestamp", to_date($"custom_time", "yyyyMMdd'T'HH:mm:ssZ"))

spark_DF2.show(false)

here is the snapshot of the output. As you can see there are no null values.

This is helpful Vikas , also the explanation around the Zulu thing — Vaebhav, Sep 15 '21 at 08:23

Amit Kumar · Answer 3 · 2021-09-15T07:05:00.730

You can create "udf" in spark and use it. Below is the code in scala.

import spark.implicits._

//just to create the dataset for the example you have given
val data = Seq(
  ("20201021T00:00:00+0530"),
  ("20211011T00:00:00+0530"),
  ("20200212T00:00:00+0300"),
  ("20211021T00:00:00+0530"),
  ("20211021T00:00:00+0900"),
  ("20211021T00:00:00-0500"))

val dataset = data.toDF("timestamp")
val udfToDateUTC = functions.udf((epochMilliUTC: String) => {
  val formatter = DateTimeFormatter.ofPattern("yyyyMMdd'T'HH:mm:ssZ")
  val res = OffsetDateTime.parse(epochMilliUTC, formatter).withOffsetSameInstant(ZoneOffset.UTC)
  res.toString()
})

dataset.select(dataset.col("timestamp"),udfToDateUTC(dataset.col("timestamp")).alias("timestamp_parsed")).show(false)

//output
+----------------------+-----------------+
|timestamp             |timestamp_parsed |
+----------------------+-----------------+
|20201021T00:00:00+0530|2020-10-20T18:30Z|
|20211011T00:00:00+0530|2021-10-10T18:30Z|
|20200212T00:00:00+0300|2020-02-11T21:00Z|
|20211021T00:00:00+0530|2021-10-20T18:30Z|
|20211021T00:00:00+0900|2021-10-20T15:00Z|
|20211021T00:00:00-0500|2021-10-21T05:00Z|
+----------------------+-----------------+

UDF's can get difficult to control in terms of speed , especially if the data volume is enormous — Vaebhav, Sep 15 '21 at 08:24

Azmat Siddique · Answer 4 · 2022-07-29T04:38:22.083

-1

from pyspark.sql.functions import date_format

customer_data = select("<column_name>",date_format("<column_name>",'yyyyMMdd').cast('customer')

edited Jul 29 '22 at 04:38

answered Jul 29 '22 at 04:27

Azmat Siddique

21
2

Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer). – Community Aug 01 '22 at 15:08

Parse Date Format

Data Preparation

Timestamp Parsed

Multiple Patterns & Coalesce

4 Answers4

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