Gordon,
Thanks for the pointer. I did some searches for usages of isEndOfStream and it’s a little confusing. I see that all implementors of DeserializationSchema must
implement this method, but it’s not called from anyone central in the Flink streaming engine, but rather each source can decide to use this in it’s own implementation – for example Kafka stops processing the topic when isEndOfStream returns true. This is nice,
but localizes the treatment just to that Operator, and, even though it goers a long way in ensuring that I get just my bounded data, it still does not give me the ability to stop my job when I have finished consuming the elements.
Also, in my case I need to ensure that I have reached a certain offset for each of the Kafka partitions that are assigned to the instance of source function.
It seems from the code that I need a different implementation of KafkaFetcher.runFetchLoop that has slightly different logic for changing running to be false.
What would you recommend in this case?
From: Tzu-Li (Gordon) Tai [mailto:[hidden email]]
Sent: Thursday, February 08, 2018 12:24 PM
To: [hidden email]; Marchant, Hayden [ICG-IT] <[hidden email]>
Subject: Re: Kafka as source for batch job
Hi Hayden,
Have you tried looking into `KeyedDeserializationSchema#isEndOfStream` [1]?
I think that could be what you are looking for. It signals the end of the stream when consuming from Kafka.
Cheers,
Gordon
On 8 February 2018 at 10:44:59 AM, Marchant, Hayden ([hidden email]) wrote:
I know that traditionally Kafka is used as a source for a streaming job. In our particular case, we are looking at extracting records from a Kafka topic from a particular well-defined offset range (per partition) - i.e. from offset X to offset Y. In this case, we'd somehow want the application to know that it has finished when it gets to offset Y. This is basically changes Kafka stream to be bounded data as opposed to unbounded in the usual Stream paradigm.
What would be the best approach to do this in Flink? I see a few options, though there might be more:
1. Use a regular streaming job, and have some external service that monitors the current offsets of the consumer group of the topic and manually stops job when the consumer group of the topic has finished
Pros - simple wrt Flink, Cons - hacky
2. Create a batch job, and a new InputFormat based on Kafka that reads the specified subset of Kafka topic into the source.
Pros - represent bounded data from Kafka topic as batch source, Cons - requires implementation of source.
3. Dump the subset of Kafka into a file and then trigger a more 'traditional' Flink batch job that reads from a file.
Pros - simple, cons - unnecessary I/O.
I personally prefer 1 and 3 for simplicity. Has anyone done anything like this before?
Thanks,
Hayden Marchant
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