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Obviously not authorized to release more details than have already been made public, but when the book hits the real world sometimes you find new failure modes, software has bugs, or humans find creative mistakes. It's also very hard to build global scale systems with zero possibly of global failure. But every time a crack is found, you learn something and do what you can to eliminate the whole class of related failure modes.

Disclaimer. I don't work on the thing that broke here but I am in SRE. Speaking in broad generalities.

> Any Googlers around to explain why Canaries didn't catch configuration causing this side effect and stopping the deployment before the issue cascaded globally?

I'm not (any longer) a Googler, but according to the postmortem this was a load-related incident. Canary changes only have a small amount of traffic directed at them (by definition) and the problem probably didn't become apparent until after the change was promoted from canary.

Going further...

Googles 'Canary' system is sub-optimal and risky.

It picks a subset of the system (say 5%), and deploys the change there. It then observes this 5% of the system, and if it's okay, it rolls out to the rest of the system.

Compare that to a continual-canarying system:

* Start deploying the new code gradually (say 1 container/pod/task every second).

* While the above is in progress, continually collect stats from upgraded and non-upgraded systems.

* If the stats differ to any significant level, halt the rollout.

The continual-canarying system is much better because the sample size to see a 'significant' change depends on the size of the change and the amount of natural variation in the sample. The size of the change obviously can't be known beforehand. The variation varies widely depending on the metric of interest (cpu, memory use, request latency, etc.).

It is therefore impossible in googles canarying system to choose a suitable subset size to detect (with a sufficiently high statistical power) small but important changes in a metric while still stopping the rollout early enough for catastrophic changes in a metric.

Continual-canarying effectively solves this issue by gradually increasing the sample size as the rollout goes on.

In reality, neither continual-canarying or google-canarying work well for detecting anomalous metrics because the rollout process itself causes applications to restart, which in itself changes their performance characteristics (cold caches, empty queues, first-use delay, jit optimization, etc.).

The solution to that is to do two simultaneous continuous-canary rollouts - one of the existing version (effectively just a restart) and one of the new version, and use those two groups as input to the logic to decide if any metrics of interest have statistically significant changes.

Please go implement this stuff Google! It won't be hard to do, and will really increase the safety of your rollouts. (Even though I know it wouldn't have helped this time)

So you'd think this would be the case. I certainly did when I first joined. In fact, I actually proposed something even more extreme than your suggestion (use thompson sampling to control the rate of task restarts and rollbacks). But in practice such a continual canarying process isn't actually any better than a staged canary (like .1% -> 1% -> 10%).

Consider the types of issues you run into, there are

- Things that are painful, but not destructive (minor performance regressions)

- Things that are highly destructive (data gets deleted, major performance regressions, etc.)

The first you can handle being deployed to many people, so if you detect at 7.8% of your users instead of 10% doesn't much matter, you can run it at 10% forever without issue.

The second you'll detect on a smaller population because the changes are catastrophic.

>In reality, neither continual-canarying or google-canarying work well for detecting anomalous metrics because the rollout process itself causes applications to restart, which in itself changes their performance characteristics (cold caches, empty queues, first-use delay, jit optimization, etc.).

The problem here is that most of the time, you don't care about startup behavior, but steady-state behavior. Imagine that a new version introduces a memory leak, so the old behavior was to linearly increase to 1GB of memory over 1 hour and then level off, and the new behavior is to increase unbounded until eventually the system OOMs and restarts or whatever.

A "google style" canary would roll out to 1% of tasks or something, wait a few hours, and notice the difference and roll back. You might even experience 1% of tasks restating, but it's likely that the system can sustain that.

With the continuous canary, you'll release to 1 task per minute or whatever, and only be able to notice any change after you've pushed out to 60 tasks, and the change will likely only be detectable with any confidence once you have a notable difference in 120 or so. At that point, you've released to more than 1% of your tasks (either that, or it takes you 8 days to release a new version).

You can fix that by slowing the rate of releases, but now it takes you 8 weeks to release a new version.

Plus, even worse, you're now in a much more fail-open environment. With a 1% release, you can sustain in the kind of bad state if all of your qualification tooling fails. If you're continuously canarying though, you have to be much more careful to make sure that your tooling won't continue to push if the tooling itself is broken or getting unusual results. It's a more risky set up.

You seem to have thought about it in depth and my comments maybe missing some insight

A) Your argument is optimised for a certain type of failure which leaves you open to the others ( issues that are detected after high ratio deployment ) B) You can monitor just the jobs impacted by the canary deployment C) the progressions you discuss seem strawman like. You can have a doubling progression starting at 1 or 5, a tripling progression

D) the point about detecting at 7.5 and living at 10, what if it’s detectable at 20 and you’ve gone all out by then

Another thing to keep in mind is that you don't just want problems to be detectable: you want them to be diagnosable and fixable.

If you detect a problem with FooBar frontend at 1pm, the first question anyone's going to ask is, "What changed in FooBar frontend or any of its dependencies at or shortly before 1pm?"

If the answer is, "Bazz backend deployed at 12:50pm" then someone with no understanding of the innards of FooBar or Bazz can roll back Bazz and there's a pretty darn good chance they'll have fixed the problem quickly. This is a scalable approach to fixing things that works well in practice.

If the answer is, "Nothing just changed. Every layer of the system has been gradually changing all day" then your oncall person will need an in-depth understanding of every service involved in the system as well as creativity in guessing where to start looking. It can work, but it's going to be slower and more difficult and fewer people will have the system-specific knowledge and debugging skills do it well and quickly.

A) like I say, in practice issues that are only detected after large deployment are small. If they weren't small, you'd have been able to detect them earlier.

B) absolutely, but if a large subset of the jobs recently restarted you get noise and nonsense

C) indeed you can do exponential continuous rollouts, but then you are getting pretty close to a 3 or 4 stage where each stage is a 5x increase in size or whatever.

All excellent points. I'll just add that often times step functions observed in obscure dashboards have been major signposts in identifying arcane root causes... (hey, look what ELSE happened suddenly at about 4.34am!)

Those gradual rollouts exist, but sometimes you have changes that aren't so easy to deploy a fraction of a percent at a time.

So Google don't test for load? They should ;)

Most peoples load tests involve taking a system to breaking point, then turning the load off and going to lunch.

People need to gradually reduce the load after the breaking point to check the system recovers.

Load Tests are also pretty hard to do in distributed systems. If you test the application alone, you probably won't find most of the issues. You'll need to test the application complete with all it's dependencies (databases, load balancers, failover mechanisms, external servers, etc.). You'll also probably want to test it with representative user requests, and all databases filled with representative data. That in turn typically means your loadtest system will need to be as big and expensive as your production system. Have fun explaining to the boss why you need to double the infrastructure costs. If you do it in the cloud, you can just do a loadtest for 10 mins per day and save a bunch of $$$, but you still need tooling to be able to deploy a complete replica of your system, fill it with realistic data and send it realistic requests, all automatically.

Using real user data and logs of user interactions and real user requests is best for loadtesting, but comes with it's own risks. You need to make sure the loadtest systems doesn't send out emails to users, or accidentally communicate with the real production systems in any way. It also means you have to secure your loadtest infrastructure as well as your production infrastructure. GDPR data deletion requests need to apply there too, etc.

Googler here, but haven't looked at any of the details of this incident.

One of the tricky things about canaried releases and staggered rollouts is that to implement them, you need another layer of configuration to manage that. And then that's another potential source of outages - and scary ones too, because your configuration is so closely tied to prod itself, . It's kind of the whole "my test needs a test, and the test of my test needs a test, and ..." issue.

Plus, you can only spend so much time testing out disaster scenarios before other priorities take over - if your customers are asking for features A, B, C, well, at some point it becomes a judgment call whether you spend time on the new features or testing the resiliency of your service. Even when you do test the hell out it, it's hard to guarantee you've tested literally every edge case.

The other thing is that, as you point out, as you add more and more mitigations for failure scenarios that you have experience with, the fraction of the failures that you've never seen before starts to increase. It's somewhat obvious, because by definition a regression test safeguards against failures that you've experienced, but it's an important thing to keep in mind when thinking about exactly what the tests you do write will keep you safe from.

> into what they call "cells"

Google literally invented this concept, and it has been spread to other companies by Xooglers.

https://pdos.csail.mit.edu/6.824/papers/borg.pdf

> Google literally invented this concept

Did they?

Here's a summary of James Hamilton's paper (then a Microsoft employee) on building large-scale internet applications published in 2007, 8 years (?) before Borg was made public knowledge: https://blog.acolyer.org/2016/09/12/on-designing-and-deployi...

   Partition the system in such a way that
   partitions are infinitely adjustable
   and fine-grained, and not bounded by any
   real world entity (person, collection,
   customer, etc.).

> it has been spread to other companies by Xooglers.

https://twitter.com/bcantrill/status/1092849514229059585 I guess the notion that Google is an all-superior tech giant that faces unique problems and solves them in the best way possible is wide spread misplaced notion among Googlers?

I don't know about this case, it wouldn't surprise me either way. Borg is certainly old, probably pre 2008. The same story as Mr/Hadoop wouldn't surprise me.

It sounds like this public report may just be using the word "configuration" to mean anything other than the software itself. I imagine that what really happened is they got alerted to high disk usage and the operator issued a major compaction which took out the bigtable. That's caused numerous Google outages and is consistent with the report.

Compactions do NOT reduce disk usage. On the contrary, they rewrite the data, so they generate new and presumably smaller SSTables. Which means more disk usage. It's the garbage collector on the master that removes files. You can combine the two, but that requires some tooling that compacts a section of the table, then triggers (or waits for, I can't recall) the garbage collector.

Well: in many cases, compactions can make more files eligible to be garbage collected. So if you want to accelerate the freeing up of space, running a compaction may serve that goal. It will temporarily increase usage, but once the garbage collector catches up the net effect is to reduce disk usage. Unless I'm mis-remembering how things work. (Caveat: I left Google almost 9 years ago.) And so "run a compaction to free up space" is a reasonable shorthand most of the time... unless you're so close to the edge that you don't have room for the temporary increase.

You're not misremembering. It's just that timing matters. And GC always got less attention than compactions.

My rule of thumb was: if I have a change that will shrink tablets by 10% through e.g. compression and I apply it wholesale with a manual compaction of the entire table, I should assume that in the worst case the table will temporarily take up 190% of current space, until the next GC run or two. If all or most of your data is in one table, this can be a problem. Organic compactions are friendlier because they occur over the course of (typically) days, leaving the GC with plenty of time to clean up.

The real issue is that Bigtable's used to be in one big shared cell which could handle temporary 190% increases in resources.

Now, to increase isolation between services, everyone is off in a partition, and each individual partition is much smaller and therefore can't withstand resource spikes without falling over.

And I bet the "emergency loan" functionality for various resources still isn't automated and still has arcane requirements to meet and a delay before it kicks in. Yay - a 15 minute delay. Thats exactly what I need when my entire service is down! /s

This will not be of general interest to HN, but I disagree. If compaction is behind then commanding a higher compaction rate can free space, and it can also bring down your service. You can also call for higher rates of splits and merges with the same effect. I have no idea of those actions contributed to this outage.

Compactions happen on the tablet server. They only write data. By definition, they increase disk usage, right away. Only the garbage collector, which needs global state and thus runs on the master, can actually delete files in Colossus and bring down disk usage. It doesn't always run as frequently as one would think or hope. It definitely doesn't run immediately after one tablet has been compacted.

I remember one incident where a team got an alert that they were at 90% of quota or so. They compacted data too aggressively and they actually hit 100% before the GC could do its job. That's why I mentioned that you should compact a fraction of the table, then let GC run. Google being Google, I'm sure such a tool has been written, in several variants, by N different teams.

As long as we're splitting hairs, only the Colossus curator can really delete the file and if the quota manager is behind you can still run out of space.

The quota manager can be behind in the opposite direction and let you go way over quota too. Yay - free unlimited disk space for all! (for ~5 minutes!)

I dunno what AWS cells are and if the terminology matches, but Google's Borg already has a concept of cells internally, a cluster can hold cells, and each cell can represents a kind of job.

From what you describe the 'AWS cells' are very different than Borg's.

My view is that at a high level aws cells are isolated, fuzzy-partitioned, migratable, heterogeneous instances of a complete service stack that are managed such that they always fail independently of each other; contact points among these cell instances, if required, are established via highly available intermediate services (like ReplicatedStateMachinesOrStorage); the clients are routed to assigned cells by a highly available routing layer (like NetworkTrafficShapers/DNS), which can also help facilitate migration of clients from one/more cell to another.

I encourage you to view the re:invent presentation on it [0] if resiliency in distributed systems is something that interests you.

[0] https://youtube.com/watch?v=swQbA4zub20

That description matches how I think of borg cells as well. (Googler)

Thanks. I read the paper now and I kind of get why you guys think they're similar.

I find them similar too in what they're trying to achieve [blast-radius minimization], but I think AWS' concept of cells [going by re:invent presentation] is more of an architectural pattern with strong inclination towards certain non-negotiable reqs ["thinnest possible routing layer", "workload migration among cells is a first-class citizen", "avoiding critical cross-cell tasks/dependencies other than migration", "creating/removing cells are zero-downtime events"] for different flavours of services [stateful vs stateless (dynamodb vs lambda?), zonal versus regional versus global (ebs vs kinesis vs route53), for example] than a concrete implementation like Borg.

I might be wrong on all accounts, though.

In short; QA forget to ask the bartender where the bathroom was.