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From "Tzu-Li (Gordon) Tai" <tzuli...@apache.org>
Subject Re: Efficient grouping and parallelism on skewed data
Date Fri, 18 Aug 2017 04:38:02 GMT
Hi John,

Do you need to do any sort of grouping on the keys and aggregation? Or are you simply using
Flink to route the Kafka messages to different Elasticsearch indices?

For the following I’m assuming the latter:
If there’s no need for aggregate computation per key, what you can do is simply do is pipeline
the input stream directly to the Elasticsearch sink.
The Flink Elasticsearch Sink API allows you to request each individual incoming record to
a different index.
If you want to have more Elasticsearch sink instances for a specific id, what you can do is
split the stream, splitting out ids that you know to have higher throughput, and pipeline
that split stream to an Elasticsearch Sink with higher parallelism.

Gordon

On 18 August 2017 at 11:06:02 AM, Jakes John (jakesjohn12345@gmail.com) wrote:

Can some one help me in figuring out how to implement in flink. 

I have to create a pipeline Kafka->flink->elasticsearch. I have high throughput data
coming into Kafka. All messages in Kafka have a key called 'id' and value is a integer that
ranges 1 to N. N is dynamic with max value as 100.  The number of messages across different
ID's are drastically different. For eg. Number of incoming messages with id 10 can be 500
times the number of incoming messages with id 11.   
One requirement is that messages with a particular id has to be written to a corresponding
elasticsearch index. Eg. Messages with id 1 is written to elasticsearch index 1, Messages
with id 2 is written to elasticsearch index 2 and so on. ... In other words, there will be
100 elasticsearch indices at most.

I have the control over Kafka. I can make sure that messages are written to a single topic
or messages are separately written to different topics based on their ids.  The only requirement
is that messages are written to indices that correspond to the ids.

1. What are the possible ways that I can achieve this in Flink? 
2. If I use a single kafka topic and a single flink job,  what is the best way to group ids
in this case and set parallelism according to the distribution of data.? The parallelism required
to write into ES is going to be different for different ids(as i said earlier, distribution
of data across ids are drastically different).
3. If i have a Kafka topic per id and a topology per id looks ugly and too resource intensive.
There are some ids that have very very few data. What is the best way to do this if we were
to choose this option ?


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