Not sure if kinesis have such flexibility. What else possibilities are there
at transformations level?
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Any example for this please
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You can also group by the key in the transformation on each batch. But yes
that's faster/easier if it's already partitioned that way.
On Tue, Mar 9, 2021 at 7:30 AM Ali Gouta wrote:
> Do not know Kenesis, but it looks like it works like kafka. Your producer
> should implement a paritionner that
Do not know Kenesis, but it looks like it works like kafka. Your producer
should implement a paritionner that makes it possible to send your data
with the same key to the same partition. Though, each task in your spark
streaming app will load data from the same partition in the same executor.
I
We are doing batch processing using Spark Streaming with Kinesis with a batch
size of 5 mins. We want to send all events with same eventId to same
executor for a batch so that we can do multiple events based grouping
operations based on eventId. No previous batch or future batch data is
concerned.
We are doing batch processing using Spark Streaming with Kinesis with a batch
size of 5 mins. We want to send all events with same eventId to same
executor for a batch so that we can do multiple events based grouping
operations based on eventId. No previous batch or future batch data is
concerned.
