apache spark - SparkSQL timeseries data: how to apply custom aggregation function on window in DataFrame -


i have incoming data frame, in following format (timestamp, data1, data2):

2015-09-25t11:00:00.000z "test" "value1" 2015-09-25t12:00:00.000z "test" "value2" 2015-09-25t13:00:00.000z "test" "value3"

i need "look back" based on window size , perform aggregation on third column, if window size 1 hour, output should be:

2015-09-25t11:00:00.000z "test" "value1" 2015-09-25t12:00:00.000z "test" "value1, value2" 2015-09-25t13:00:00.000z "test" "value2, value3"

for 2 hour window:

2015-09-25t11:00:00.000z "test" "value1" 2015-09-25t12:00:00.000z "test" "value1, value2" 2015-09-25t13:00:00.000z "test" "value1, value2, value3"

i thinking writing custom aggregation function can group , using spark sql windowed operation, not supported in spark 1.6. maybe working on tasks before , can help?


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