this post was submitted on 15 Jun 2023
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Right! The trivial organization would have been to store comments by timestamp. So in my above example, how would you appropriately index the database such that comments 5, 13, 42, and 57 are stored consecutively, even though other comments to other posts arrive in between?
If you want to optimize for loading comments in a single thread in a single community in a single server in a federation, then timestamp would be a bad choice.
A simple example of an index for this use case would be something like (ServerId, ThreadId, Timestamp). By the time you want to load comments in a thread, you know the server id and thread id.
Ok, so it is possible to do! I've always been suspicious of databases. Loading all comments in a thread is the only thing a reddit clone has to do right. For a popular thread, it may need to be done hundreds of thousands of times (ignoring caching). Everything else, like user pages, is extra. Yet with a database, if instead of a thread I wanted to display comments made every odd Tuesday that have the structure of a haiku, I could. All that power has to be paid for somewhere!
Maybe I'm just a boomer thinking in terms of spinning rust, when everything is SSDs and 128GB+ of RAM. I wonder - do you think reddit stores its entire 18 years of content in RAM, split or duplicated between shards? But I can't shake off the awe at the sheer throughput of contiguous read from disk. 10000 comments, 200 characters per comment = 2MB = done in 2ms. Don't even need 200-at-a-time pagination!
You could, but if you want to do it very efficiently and at scale, you would probably need to specialize your data access layer:
It's paid for in the logical organization that is enforced at write-time (or during a maintenance task like rebuilding indices or recomputing statistics), where millisecond responsiveness is not as important.
Lots of duplication across different layers to support different access patterns and reuse work between data retrieval tasks. You need to be able to efficiently access frequently requested data, ingest new data, synchronize data between the different layers, and provide a reasonable minimum efficiency for arbitrary requests.
Semi-related, here's a story about how Discord does it.
Great link, thanks!
Looks like Discord was using 177 nodes each with 4TB disk space running Cassandra (Java), and then in 2022 migrated to 72 nodes of 9TB disk space running ScyllaDB (C++). Switching to a C++ database and writing their services in Rust allowed them to finally end latency spikes from Java garbage collection. The messages are stored in buckets assigned by channel and time window. Buckets are replicated across 3 nodes, and are accessed using "quorum consistency". They were still having difficulties with "hot partitions" where many users at once all want to access the same bucket, leading to increased latencies. They solved it by putting a data service in front of the database that would detect multiple identical incoming queries and pool them together into a single database request. The nodes are still spending a lot of time periodically "compacting" their tables for better disk read performance.