You can write a UDAF in which the buffer contains the top K and manage it. This means you don’t need to sort at all. Furthermore, in your example you don’t even need a window function, you can simply use groupby and explode. Of course, this is only relevant if k is small…
From: Andy Dang [mailto:nam...@gmail.com] Sent: Tuesday, January 03, 2017 3:07 PM To: user Subject: top-k function for Window Hi all, What's the best way to do top-k with Windowing in Dataset world? I have a snippet of code that filters the data to the top-k, but with skewed keys: val windowSpec = Window.parititionBy(skewedKeys).orderBy(dateTime) val rank = row_number().over(windowSpec) input.withColumn("rank", rank).filter("rank <= 10").drop("rank") The problem with this code is that Spark doesn't know that it can sort the data locally, get the local rank first. What it ends up doing is performing a sort by key using the skewed keys, and this blew up the cluster since the keys are heavily skewed. In the RDD world we can do something like: rdd.mapPartitioins(iterator -> topK(iterator)) but I can't really think of an obvious to do this in the Dataset API, especially with Window function. I guess some UserAggregateFunction would do, but I wonder if there's obvious way that I missed. ------- Regards, Andy
