This is using python with Spark 1.6.1 and dataframes.
I have timestamps in UTC that I want to convert to local time, but a given
row could be in any of several timezones. I have an 'offset' value (or
alternately, the local timezone abbreviation. I can adjust all the
timestamps to a single zone or with a single offset easily enough, but I
can't figure out how to make the adjustment dependent on the 'offset' or
'tz' column.
There appear to be 2 main ways of adjusting a timestamp: using the
'INTERVAL' method, or using pyspark.sql.from_utc_timestamp.
Here's an example:
---
data = [ ("2015-01-01 23:59:59", "2015-01-02 00:01:02", 1, 300,"MST"),
("2015-01-02 23:00:00", "2015-01-02 23:59:59", 2, 60,"EST"),
("2015-01-02 22:59:58", "2015-01-02 23:59:59", 3, 120,"EST"),
("2015-03-02 15:59:58", "2015-01-02 23:59:59", 4, 120,"PST"),
("2015-03-16 15:15:58", "2015-01-02 23:59:59", 5, 120,"PST"),
("2015-10-02 18:59:58", "2015-01-02 23:59:59", 4, 120,"PST"),
("2015-11-16 18:58:58", "2015-01-02 23:59:59", 5, 120,"PST"),
("2015-03-02 15:59:58", "2015-01-02 23:59:59", 4, 120,"MST"),
("2015-03-16 15:15:58", "2015-01-02 23:59:59", 5, 120,"MST"),
("2015-10-02 18:59:58", "2015-01-02 23:59:59", 4, 120,"MST"),
("2015-11-16 18:58:58", "2015-01-02 23:59:59", 5, 120,"MST"),]
df = sqlCtx.createDataFrame(data, ["start_time", "end_time",
"id","offset","tz"])
from pyspark.sql import functions as F
df.withColumn('testthis', F.from_utc_timestamp(df.start_time, "PST")).show()
df.withColumn('testThat', df.start_time.cast("timestamp") - F.expr("INTERVAL
50 MINUTES")).show()
----
those last 2 lines work as expected, but I want to replace "PST" with the
df.tz column or use the df.offset column with INTERVAL
Here's the error I get. Is there a workaround to this?
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-14-fe409c16a012> in <module>()
----> 1 df.withColumn('testthis', F.from_utc_timestamp(df.start_time,
df.tz)).show()
/opt/spark-1.6.1/python/pyspark/sql/functions.py in
from_utc_timestamp(timestamp, tz)
967 """
968 sc = SparkContext._active_spark_context
--> 969 return
Column(sc._jvm.functions.from_utc_timestamp(_to_java_column(timestamp), tz))
970
971
/opt/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in
__call__(self, *args)
796 def __call__(self, *args):
797 if self.converters is not None and len(self.converters) > 0:
--> 798 (new_args, temp_args) = self._get_args(args)
799 else:
800 new_args = args
/opt/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in
_get_args(self, args)
783 for converter in self.gateway_client.converters:
784 if converter.can_convert(arg):
--> 785 temp_arg = converter.convert(arg,
self.gateway_client)
786 temp_args.append(temp_arg)
787 new_args.append(temp_arg)
/opt/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/java_collections.py in
convert(self, object, gateway_client)
510 HashMap = JavaClass("java.util.HashMap", gateway_client)
511 java_map = HashMap()
--> 512 for key in object.keys():
513 java_map[key] = object[key]
514 return java_map
TypeError: 'Column' object is not callable
--
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