Rather than open a connection per record, if you do a DStream foreachRDD at the
end of a 5 minute batch window
http://spark.apache.org/docs/latest/streaming-programming-guide.html#output-operations-on-dstreams
then you can do a rdd.foreachPartition to get the RDD partitions. Open a
connection to vertica (or a pool of them) inside that mapPartitions, then do a
partition.foreach to write each element from that partition to vertica, before
finally closing the pool of connections.
Hope this helps,
Ewan
From: Nikhil Goyal [mailto:nownik...@gmail.com]
Sent: 23 May 2016 21:55
To: Ofir Kerker <ofir.ker...@gmail.com>
Cc: user@spark.apache.org
Subject: Re: Timed aggregation in Spark
I don't think this is solving the problem. So here are the issues:
1) How do we push entire data to vertica. Opening a connection per record will
be too costly
2) If a key doesn't come again, how do we push this to vertica
3) How do we schedule the dumping of data to avoid loading too much data in
state.
On Mon, May 23, 2016 at 1:33 PM, Ofir Kerker
<ofir.ker...@gmail.com<mailto:ofir.ker...@gmail.com>> wrote:
Yes, check out mapWithState:
https://databricks.com/blog/2016/02/01/faster-stateful-stream-processing-in-apache-spark-streaming.html
_
From: Nikhil Goyal <nownik...@gmail.com<mailto:nownik...@gmail.com>>
Sent: Monday, May 23, 2016 23:28
Subject: Timed aggregation in Spark
To: <user@spark.apache.org<mailto:user@spark.apache.org>>
Hi all,
I want to aggregate my data for 5-10 min and then flush the aggregated data to
some database like vertica. updateStateByKey is not exactly helpful in this
scenario as I can't flush all the records at once, neither can I clear the
state. I wanted to know if anyone else has faced a similar issue and how did
they handle it.
Thanks
Nikhil