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https://issues.apache.org/jira/browse/SPARK-33863?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17260911#comment-17260911
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Nasir Ali commented on SPARK-33863:
-----------------------------------
[~hyukjin.kwon] and [~viirya] I have simplified example code, added/revised
details, and also added output and expected output. Please let me know if you
need more information.
> Pyspark UDF wrongly changes timestamps to UTC
> ---------------------------------------------
>
> Key: SPARK-33863
> URL: https://issues.apache.org/jira/browse/SPARK-33863
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.0.1
> Environment: MAC/Linux
> Standalone cluster / local machine
> Reporter: Nasir Ali
> Priority: Major
>
> *Problem*:
> I have a dataframe with a ts (timestamp) column in UTC. If I create a new
> column using udf, pyspark udf wrongly changes timestamps into UTC time. ts
> (timestamp) column is already in UTC time. Therefore, pyspark udf should not
> convert ts (timestamp) column into UTC timestamp.
> I have used following configs to let spark know the timestamps are in UTC:
>
> {code:java}
> --conf spark.driver.extraJavaOptions=-Duser.timezone=UTC
> --conf spark.executor.extraJavaOptions=-Duser.timezone=UTC
> --conf spark.sql.session.timeZone=UTC
> {code}
> Below is a code snippet to reproduce the error:
>
> {code:java}
> from pyspark.sql import SparkSession
> from pyspark.sql import functions as F
> from pyspark.sql.types import StringType
> import datetime
> spark = SparkSession.builder.config("spark.sql.session.timeZone",
> "UTC").getOrCreate()df = spark.createDataFrame([("usr1",17.00,
> "2018-02-10T15:27:18+00:00"),
> ("usr1",13.00, "2018-02-11T12:27:18+00:00"),
> ("usr1",25.00, "2018-02-12T11:27:18+00:00"),
> ("usr1",20.00, "2018-02-13T15:27:18+00:00"),
> ("usr1",17.00, "2018-02-14T12:27:18+00:00"),
> ("usr2",99.00, "2018-02-15T11:27:18+00:00"),
> ("usr2",156.00, "2018-02-22T11:27:18+00:00")
> ],
> ["user","id", "ts"])
> df = df.withColumn('ts', df.ts.cast('timestamp'))
> df.show(truncate=False)def some_time_udf(i):
> return str(i)udf = F.udf(some_time_udf,StringType())
> df.withColumn("day_part", udf(df.ts)).show(truncate=False)
> {code}
>
> Below is the output of the above code:
> {code:java}
> +----+-----+-------------------+-------------------+
> |user|id |ts |day_part |
> +----+-----+-------------------+-------------------+
> |usr1|17.0 |2018-02-10 15:27:18|2018-02-10 09:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|2018-02-11 06:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|2018-02-12 05:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|2018-02-13 09:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|2018-02-14 06:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|2018-02-15 05:27:18|
> |usr2|156.0|2018-02-22 11:27:18|2018-02-22 05:27:18|
> +----+-----+-------------------+-------------------+
> {code}
> Above output is incorrect. You can see ts and day_part columns don't have
> same timestamps. Below is the output I would expect:
> {code:java}
> +----+-----+-------------------+-------------------+
> |user|id |ts |day_part |
> +----+-----+-------------------+-------------------+
> |usr1|17.0 |2018-02-10 15:27:18|2018-02-10 15:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|2018-02-11 12:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|2018-02-12 11:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|2018-02-13 15:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|2018-02-14 12:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|2018-02-15 11:27:18|
> |usr2|156.0|2018-02-22 11:27:18|2018-02-22 11:27:18|
> +----+-----+-------------------+-------------------+
> {code}
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