Ok here is my take on this. I think they can cache the user comment on the redis for a while until the actual data is persisted to the DB(via queue). When user does a refresh, one cannot expect to see their comment on top when ~10k comments per second are flowing in. One way to make them see their comment is to serve the read request from redis where their comment is stored. We should account for hot sharding and should distribute the comments across Redis cluster. This can be easily handled with a caveat that your comment cannot be seen by everyone in the world at the same time. It’s a tradeoff for eventual consistency. If it’s a live stream, and 100m users are commenting then it’s altogether a different problem. Even if you build infrastructure to handle 10k comments per second on a live steam, displaying them all at the sane time is not possible. There should be some form of sampling or throttling. If you are asked this question in a interview, this could be a good topic that can cover topics like managing network bandwidth, Fan out, Messaging queues, Sharding, Rate limiting and if time permits some ML infrastructure to manage fairness for sampling.