Well ... it could take out nodes 1 and 4. So
is having a bad experience now. Amazing devops teams are on it, etc , but it's still not great for them. Not much we can do about that. But what about everyone else?pic.twitter.com/1J6oorA510
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Well ... it could take out nodes 1 and 4. So
is having a bad experience now. Amazing devops teams are on it, etc , but it's still not great for them. Not much we can do about that. But what about everyone else?pic.twitter.com/1J6oorA510
Well, if we look at
's neighbors. They're still fine! As long as their client is fault tolerant, which can be as simple as using retries, they can still get service.
gets service from node 2 for example.pic.twitter.com/xJRpdK6Fgn
O.k. let's PAUSE for a second and appreciate that. Same number of nodes. Same number of nodes for each customer. Same number of customers. Just by using MATH, we've reduced the blast radius to 1 customer! That's INSANE.
The blast radius ends up getting really small. It's roughly proportionate to the factorial of the shard size (small) divided by the factorial of the number of nodes (which is big) ... so it can get really really small.pic.twitter.com/LmaffLA3tR
Surely this should be shardsize! / nodes^shardsize ? Trivial example, with 10 nodes and a shard size of 1, the blast radius is 1/10, not 1/10! .
That's a better approximation, and the real math is later in the thread, but the main take-away for people to remember is that the bigger the count of nodes relative to the size of the shard .. the better, exponentially so.
Yes and no. Your formula makes it look like increasing the shard size increases the blast radius, but (up to reasonable limits) the exact opposite is true -- as long as shardsize = o(nodes) the blast radius *decreases* exponentially as the shard size increases.
I still haven't even found a great to visualize or graph it that can help people get a feel for the sensitivities. It asymptotes so quickly, but I'd really love to nail a few paragraphs that could help people reason it out.pic.twitter.com/bZ1NXEd7AM
I think the best way to look at it is in terms of logarithms (or for non-mathematicians, "how many 9s"). Your blast radius is bounded from above by (S/N)^S where S = the shard size and N = the total number of nodes.
So the "number of 9s of unaffected clients" > S * log(N/S); if you want 99.99% of clients to be unaffected you could have each client try 4 out of 40 nodes, or 2 out of 200, or 8 out of 8*sqrt(10), for example.
Does the log come from Stirling's Approximation?
Yes. If I'm remembering the formula right, for k = o(sqrt(n)), log choose(n, k) ~ k * log (n e / k) + O(log k). I dropped the k log(e) so that I could get rid of the O() term while having a strict inequality.
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