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0 pointsfinnh1y ago0 comments

> These numbers are a bit better than Twitter’s numbers. Because of how unbalanced the social graph is, getting performance this good and this reliable is not easy. For example, when someone with 20M followers posts a status, that creates a huge burst of load which could delay other statuses from fanning out. How we handled this is described more below.

Huh. I haven't written myself a twitter, but I've always assumed that this problem is solved by a mix of fan-on-write and fan-on-read. Most people are fan-on-write, but those with a follower count > $THRESHOLD are fan-on-read.

The fan-on-read set is small enough to be easily cacheable, so the extra reads required to service a follower's feed are more than compensated for by the fact that the content is in RAM in a cache.

Sounds like RedPanda went exclusively with fan-on-write. I'd be surprised if Twitter does the same - but it sounds like OP has more knowledge about this than me, so I'm happy to be educated!

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nathanmarz1y ago

The chronological timeline at Twitter fans out on write. This makes sense when you consider that the most important application metric is the latency to load the timeline. That latency is a lot lower when you only need one query on the materialized timeline rather than a ton of queries for everyone you follow.

finnhOP1y ago

Good to learn, thanks. "Everyone you follow" isn't quite what I was saying, but I also don't use twitter so I'm doubly in the dark here.

I'm surprised that someone with, say, a Twitter problem and tens of millions of followers - the two seem to go hand-in-hand - drives tens of millions of writes every time they post. But there you go, learned something today.

fizx1y ago

I was tech lead on that subsystem for a little while in 2010. A lot of smart people thought about the hybrid approach, either by using the search index to drive the timeline or building a custom ring-buffer-based index of all tweets. Ultimately two systems are harder to maintain than one, custom indices are hard, and the low-complexity approach dominated a higher-performance approach.

Also, contrary to popular opinion, we didn't go down when Justin Bieber tweeted, but we did have elevated error rates when large quantities of Justin Bieber followings put pressure on the MySQL row lock of his following count. In retrospect, lock striping would have helped, but the migration would have been horrific.

nathanmarz1y ago

Back in the days of the fail whale, Twitter would go down when Justin Bieber tweeted because their system couldn't handle the load.

j / k navigate · click thread line to collapse

0 pointsfinnh1y ago0 comments

The fan-on-read set is small enough to be easily cacheable, so the extra reads required to service a follower's feed are more than compensated for by the fact that the content is in RAM in a cache.

Sounds like RedPanda went exclusively with fan-on-write. I'd be surprised if Twitter does the same - but it sounds like OP has more knowledge about this than me, so I'm happy to be educated!

0 comments

nathanmarz1y ago

finnhOP1y ago

Good to learn, thanks. "Everyone you follow" isn't quite what I was saying, but I also don't use twitter so I'm doubly in the dark here.

fizx1y ago

nathanmarz1y ago

Back in the days of the fail whale, Twitter would go down when Justin Bieber tweeted because their system couldn't handle the load.

j / k navigate · click thread line to collapse