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This post was updated on .
Hello,
I'm trying to build a stream event correlation engine with Flink and I have some questions regarding the execution of jobs. In my architecture I need to have different sources of data, lets say for instance: firewallStream= environment.addSource([FirewalLogsSource]); proxyStream = environment.addSource([ProxyLogsSource]); and for each of these sources, I need to apply a set of rules. So lets say I have a job that has as a source the proxy stream data with the following rules: //Abnormal Request Method stream.[RuleLogic].addSink([output]) //Web Service on Non-Typical Port stream.[RuleLogic].addSink([output]) //Possible Brute Force stream.[RuleLogic].addSink([output]) These rules will probably scale to be in the order of 15 to 20 rules. What is the best approach in this case: 1. Should I create 2 jobs one for each source and each job would have the 15-20 rules? 2. Should I split the rules into several jobs? 3. Other options? Thank you and Regards, Pedro Chaves.
Best Regards,
Pedro Chaves |
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Hi Pedro, I think it would be better to have two jobs and keep all the rules in one place. If it's not too many sources you might even consider having everything in one job so you don't have to duplicate the rules. There's a tradeoff, though, if it becomes too much stuff then splitting up will be beneficial because it makes the jobs easier to maintain/monitor. Cheers, Aljoscha On Wed, 2 Nov 2016 at 10:26 PedroMrChaves <[hidden email]> wrote: Hello, |
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Thank you.
Best Regards,
Pedro Chaves |
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