HI Palle,
I am a beginner in Flink.
However, I can say something about your other questions:
1. It is better to use Spark to create aggregate views. It is a lot
faster than MR. You could use either batch or streaming mode in spark based on
your needs.
2. If your aggregate data is in tabular format, you could store it in
Hive.
3. For your live model, you could use either spark streaming (micro
batches) or use Storm (process individual tuples). It is easy to put business
logic in storm bolts to work on each tuple.
4. But please take care of latency (and other issues) when accessing
aggregate data from live model. Your model should be able to handle latencies
(from aggregate data access) and not create a backlog of streaming data that
may lead to Storm failing the tuple.
Hope this helps.
Best regards / Mit freundlichen Grüßen / Sincères salutations
M. Lohith Samaga
-----Original Message-----
From: pa...@sport.dk [mailto:pa...@sport.dk
Sent: Friday, May 06, 2016 16.23
To: user@flink.apache.org
Subject: Where to put live model and business logic in Hadoop/Flink BigData
system
Hi there.
We are putting together some BigData components for handling a large amount of
incoming data from different log files and perform some analysis on the data.
All data being fed into the system will go into HDFS. We plan on using
Logstash, Kafka and Flink for bringing data from the log files and into HDFS.
All our data located in HDFS we will designate as our historic data and we will
use MapReduce (probably Flink, but could also be Hadoop) to create some
aggregate views of the historic data. These views we will locate probably in
HBase or MongoDB.
These views of the historic data (also called batch views in the Lambda
Architecture if any of you are familiar with that) we will use from the live
model in the system. The live model is also being fed with the same data
(through Kafka) and when the live model detects a certain value in the incoming
data, it will perform some analysis using the views in HBase/MongoDB of the
historic data.
Now, could anyone share some knowledge regarding where it would be possible to
implement such a live model given the components we plan on using? Apart from
the business logic that will perform the analysis, our live model will at all
times also contain a java object structure of maybe 5-10 java collections
(maps, lists) containing approx 5 mio objects.
So, where is it possible to implement our live model? Can we do this in Flink?
Can we do this with another component within the Hadoop Big Data ecosystem?
Thanks.
/Palle
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