[jira] [Commented] (SPARK-33863) Pyspark UDF wrongly changes timestamps to UTC
[
https://issues.apache.org/jira/browse/SPARK-33863?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17260911#comment-17260911
]
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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[jira] [Commented] (SPARK-33863) Pyspark UDF wrongly changes timestamps to UTC
[
https://issues.apache.org/jira/browse/SPARK-33863?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17288802#comment-17288802
]
Nasir Ali commented on SPARK-33863:
---
[~hyukjin.kwon] and [~viirya] any update on this issue?
> 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, TimestampType
> 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):
> if datetime.time(5, 0)<=i.time() < datetime.time(12, 0):
> tmp= "Morning: " + str(i)
> elif datetime.time(12, 0)<=i.time() < datetime.time(17, 0):
> tmp= "Afternoon: " + str(i)
> elif datetime.time(17, 0)<=i.time() < datetime.time(21, 0):
> tmp= "Evening"
> elif datetime.time(21, 0)<=i.time() < datetime.time(0, 0):
> tmp= "Night"
> elif datetime.time(0, 0)<=i.time() < datetime.time(5, 0):
> tmp= "Night"
> return tmp
> 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|Morning: 2018-02-10 09:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Morning: 2018-02-11 06:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 05:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Morning: 2018-02-13 09:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Morning: 2018-02-14 06:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 05:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 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|Afternoon: 2018-02-10 15:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Afternoon: 2018-02-11 12:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 11:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Afternoon: 2018-02-13 15:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Afternoon: 2018-02-14 12:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 11:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 2018-02-22 11:27:18|
> ++-+---++{code}
>
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[jira] [Commented] (SPARK-33863) Pyspark UDF wrongly changes timestamps to UTC
[
https://issues.apache.org/jira/browse/SPARK-33863?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17341075#comment-17341075
]
Nasir Ali commented on SPARK-33863:
---
[~hyukjin.kwon] and [~viirya] This bug exist in all the 3.x.x versions of
Pyspark. Any update or suggestion?
> 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, 3.0.2, 3.1.0, 3.1.1
> 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, TimestampType
> 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):
> if datetime.time(5, 0)<=i.time() < datetime.time(12, 0):
> tmp= "Morning: " + str(i)
> elif datetime.time(12, 0)<=i.time() < datetime.time(17, 0):
> tmp= "Afternoon: " + str(i)
> elif datetime.time(17, 0)<=i.time() < datetime.time(21, 0):
> tmp= "Evening"
> elif datetime.time(21, 0)<=i.time() < datetime.time(0, 0):
> tmp= "Night"
> elif datetime.time(0, 0)<=i.time() < datetime.time(5, 0):
> tmp= "Night"
> return tmp
> 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|Morning: 2018-02-10 09:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Morning: 2018-02-11 06:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 05:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Morning: 2018-02-13 09:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Morning: 2018-02-14 06:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 05:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 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|Afternoon: 2018-02-10 15:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Afternoon: 2018-02-11 12:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 11:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Afternoon: 2018-02-13 15:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Afternoon: 2018-02-14 12:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 11:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 2018-02-22 11:27:18|
> ++-+---++{code}
>
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[jira] [Commented] (SPARK-33863) Pyspark UDF wrongly changes timestamps to UTC
[
https://issues.apache.org/jira/browse/SPARK-33863?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17357029#comment-17357029
]
dc-heros commented on SPARK-33863:
--
Have this issues resolved, I couldn't reproduce it
> 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, 3.0.2, 3.1.0, 3.1.1
> 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, TimestampType
> 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):
> if datetime.time(5, 0)<=i.time() < datetime.time(12, 0):
> tmp= "Morning: " + str(i)
> elif datetime.time(12, 0)<=i.time() < datetime.time(17, 0):
> tmp= "Afternoon: " + str(i)
> elif datetime.time(17, 0)<=i.time() < datetime.time(21, 0):
> tmp= "Evening"
> elif datetime.time(21, 0)<=i.time() < datetime.time(0, 0):
> tmp= "Night"
> elif datetime.time(0, 0)<=i.time() < datetime.time(5, 0):
> tmp= "Night"
> return tmp
> 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|Morning: 2018-02-10 09:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Morning: 2018-02-11 06:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 05:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Morning: 2018-02-13 09:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Morning: 2018-02-14 06:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 05:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 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|Afternoon: 2018-02-10 15:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Afternoon: 2018-02-11 12:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 11:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Afternoon: 2018-02-13 15:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Afternoon: 2018-02-14 12:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 11:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 2018-02-22 11:27:18|
> ++-+---++{code}
>
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[jira] [Commented] (SPARK-33863) Pyspark UDF wrongly changes timestamps to UTC
[
https://issues.apache.org/jira/browse/SPARK-33863?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17368550#comment-17368550
]
Nasir Ali commented on SPARK-33863:
---
[~dc-heros] Could you please share the output you got when you ran the above
code?
> 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, 3.0.2, 3.1.0, 3.1.1, 3.1.2
> 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, TimestampType
> 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):
> if datetime.time(5, 0)<=i.time() < datetime.time(12, 0):
> tmp= "Morning: " + str(i)
> elif datetime.time(12, 0)<=i.time() < datetime.time(17, 0):
> tmp= "Afternoon: " + str(i)
> elif datetime.time(17, 0)<=i.time() < datetime.time(21, 0):
> tmp= "Evening"
> elif datetime.time(21, 0)<=i.time() < datetime.time(0, 0):
> tmp= "Night"
> elif datetime.time(0, 0)<=i.time() < datetime.time(5, 0):
> tmp= "Night"
> return tmp
> 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|Morning: 2018-02-10 09:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Morning: 2018-02-11 06:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 05:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Morning: 2018-02-13 09:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Morning: 2018-02-14 06:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 05:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 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|Afternoon: 2018-02-10 15:27:18|
> |usr1|13.0 |2018-02-11 12:27:18|Afternoon: 2018-02-11 12:27:18|
> |usr1|25.0 |2018-02-12 11:27:18|Morning: 2018-02-12 11:27:18|
> |usr1|20.0 |2018-02-13 15:27:18|Afternoon: 2018-02-13 15:27:18|
> |usr1|17.0 |2018-02-14 12:27:18|Afternoon: 2018-02-14 12:27:18|
> |usr2|99.0 |2018-02-15 11:27:18|Morning: 2018-02-15 11:27:18|
> |usr2|156.0|2018-02-22 11:27:18|Morning: 2018-02-22 11:27:18|
> ++-+---++{code}
>
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