spark: How to do a dropDuplicates on a dataframe while keeping the highest timestamped row [duplicate]

Answer 1

A manual map and reduce might be needed to provide the functionality you want.

def selectRowByTimeStamp(x,y):
    if x.timestamp > y.timestamp:
        return x
    return y

dataMap = data.map(lambda x: (x.id, x))
uniqueData = dataMap.reduceByKey(selectRowByTimeStamp) 

Here we are grouping all of the data based on id. Then, when we are reducing the groupings, we do so by keeping the record with the highest timestamp. When the code is done reducing, only 1 record will be left for each id.