#StackBounty: #docker #tcp #parallel-processing #kubernetes #distributed-computing Scaling chat log workers horizontally

Bounty: 100

I’ve thought about this a lot but can’t come up with a solution I’m happy with.

Basicly this is the problem: Log 100k+ Chats (some slower, some faster) into cassandra. So save userId, channelId, timestamp and the message.

Cassandra already supports horizontal scaling out of the box, I have no issue here.

Now my software that reads these chats does it over TCP (IRC). Something like 300 messages / sec are usual for the top 1k channels and 1 single IRC connection can’t handle that from my experiments.

What I now want to build is multiple instances (with Docker/Kubernetes) of the logger and share the load between those. So ideally if I have maybe 4 workers and 1k chats (example). They would each join atleast 250 channels. I say atleast because I would want optional redundancy so I can have 2 loggers in the same chat to make sure no messages get lost.
There is no issue with duplicates, because all messages have a unique ID.

Now how would I best and dynamically share the current channels joined between the workers. I wanna avoid having a master or controlling point. Should also be easy to add more workers that then reduce the load on other workers.

Are there any good articles about this kind of behaviour? Maybe good concepts or protocols already defined? Like I said i wanna avoid another central control point so no rabbitmq, redis or whatever.

Edit: I’ve looked into something like the Raft Consensus Algorithm, but it doesn’t make sense I think, since I don’t want my clients to agree on a shared state instead divide the state between them “equally”.


Get this bounty!!!

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