There is no way to solve this within spark.
One option you could do is break up your application into multiple application.
First application can filter and write the filtered results into a kafka queue.
Second application can read from queue and sum. Third application can read from
queue and do count.
From: Shu Li Zheng
Date: Tuesday, December 26, 2017 at 5:32 AM
To: "user@spark.apache.org"
Subject: [Structured Streaming] Reuse computation result
Hi all,
I have a scenario like this:
val df = dataframe.map().filter()
// agg 1
val query1 = df.sum.writeStream.start
// agg 2
val query2 = df.count.writeStream.start
With spark streaming, we can apply persist() on rdd to reuse the df computation
result, when we call persist() after filter() map().filter() operator only run
once.
With SS, we can’t apply persist() direct on dataframe. query1 and query2 will
not reuse result after filter. map/filter run twice. So is there a way to solve
this.
Regards,
Shu li Zheng
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