Hi Navneeth If you need the redis cache to be fault tolerant, I am afraid you have to choose redis cluster since Flink might deploy task on another node which is different from previous node after job failover.
If you don't care about the fault tolerance, you could implement a customized operator which launch redis. By the way, there existed a way to combine objects on heap in memory with checkpoint mechanism to ensure fault tolerance, you could refer to [1] and [2]. The basic idea is to cac [1] https://github.com/apache/flink/blob/9df5c80e7e729f49595ef6814462165831fd1307/flink-table/flink-table-runtime-blink/src/main/java/org/apache/flink/table/runtime/operators/bundle/AbstractMapBundleOperator.java#L147 [2] https://github.com/apache/flink/blob/9df5c80e7e729f49595ef6814462165831fd1307/flink-table/flink-table-runtime-blink/src/main/java/org/apache/flink/table/runtime/operators/aggregate/MiniBatchLocalGroupAggFunction.java#L89 ________________________________ From: Navneeth Krishnan <reachnavnee...@gmail.com> Sent: Wednesday, January 8, 2020 15:36 To: Yun Tang <myas...@live.com> Cc: user <user@flink.apache.org> Subject: Re: Using redis cache in flink Hi Yun, Thanks, the way I want to use redis is like a cache not as state backend. I would still have rocksdb state backend for other states. The reason to use cache instead of managed state is because I’d get around 10k msgs per task slot and I don’t have to get the state from rocksdb for each lookup. In memory cache would be fine but to rebuild the state I want to use redis. Regards On Tue, Jan 7, 2020 at 11:21 PM Yun Tang <myas...@live.com<mailto:myas...@live.com>> wrote: Hi Navneeth If you wrap redis as a state backend, you cannot easily share data across slots as Flink construct state backend per operator with local thread only. If you use a redis cluster as a externalized service to store your data, you can share data across slots easily. However, compared with the reduced cost of serialization, the introduce of network communicate cannot be ignored. There exists trade-off here, and we cannot ensure there would be a performance gain. Actually, I prefer the time used in CPU serialization is much less than the time consumed through the network. Best Yun Tang ________________________________ From: Navneeth Krishnan <reachnavnee...@gmail.com<mailto:reachnavnee...@gmail.com>> Sent: Wednesday, January 8, 2020 12:33 To: user <user@flink.apache.org<mailto:user@flink.apache.org>> Subject: Using redis cache in flink Hi All, I want to use redis as near far cache to store data which are common across slots i.e. share data across slots. This data is required for processing every single message and it's better to store in a in memory cache backed by redis rather than rocksdb since it has to be serialized for every single get call. Do you guys think this is good solution or is there any other better solution? Also, Is there any reference on how I can create a centralized near far cache since the job and operators are distributed by the job manager. Thanks
