The post-mortem is specific that they won't turn it back on without resolving this but I feel like the default assumption for any halfway competent entity would be that they fix the known issue that they have disabled something because.
Before the active incident is “resolved” theres an evaluation of probable/plausible reoccurrence. Usually we/they would have potential mitigations and recovery runbooks prepared as well to quickly react to any reoccurance. Any likely open risks are actively worked to mitigate before the immediate issue is considered resolved. That includes around-the-clock dev team work if its the best known path to mitigation.
Next any plausible paths to “risk of reoccurance” would be top dev team priority (business hours) until those action items are completed and in deployment. That might include other teams with similar DIY DNS management, other teams who had less impactful queue depth problems, or other similar “near miss” findings. Service team tech & business owners (PE, Sr PE, GM, VP) would be tracking progress daily until resolved.
Then in the next few weeks at org & AWS level “ops meetings” there are going to be the in depth discussions of the incident, response, underlying problems, etc. the goal there being organizational learning and broader dissemination of lessons learned, action items, best practice etc.
for a large system, it's in practice very nice to split up things like that - you have one bit of software that just reads a bunch of data and then emits a plan, and then another thing that just gets given a plan and executes it.
this is easier to test (you're just dealing with producing one data structure and consuming one data structure, the planner doesn't even try to mutate anything), it's easier to restrict permissions (one side only needs read access to the world!), it's easier to do upgrades (neither side depends on the other existing or even being in the same language), it's safer to operate (the planner is disposable, it can crash or be killed at any time with no problem except update latency), it's easier to comprehend (humans can examine the planner output which contains the entire state of the plan), it's easier to recover from weird states (you can in extremis hack the plan) etc etc. these are all things you appreciate more and more and your system gets bigger and more complicated.
> If it was one thing, wouldn't this race condition have been much more clear to the people working on it?
no
> Is this caused by the explosion of complexity due to the over use of the microservice architecture?
no
it's extremely easy to second-guess the way other people decompose their services since randoms online can't see any of the actual complexity or any of the details and so can easily suggest it would be better if it was different, without having to worry about any of the downsides of the imagined alternative solution.
interesting take, in light of all the brain drain that AWS has experienced over the last few years. some outside opinions might be useful - but perhaps the brain drain is so extreme that those remaining don't realize it's occurring?
The Oxide and Friends folks covered an update system they built that is similarly split and they cite a number of the same benefits as you: https://oxide-and-friends.transistor.fm/episodes/systems-sof...
Distributed systems with files as a communication medium are much more complex than programmers think with far more failure modes than they can imagine.
Like… this one, that took out a cloud for hours!
I think the communications piece depends on what other systems you have around you to build on, its unlikely this planner/executor is completely freestanding. Some companies have large distributed filesystems with well known/tested semantics, schedulers that launch jobs when files appear, they might have ~free access to a database with strict serializability where they can store a serialized version of the plan, etc.
The two DNS components comprise a monolith: neither is useful without the other and there is one arrow on the design coupling them together.
If they were a single component then none of this would have happened.
Also, version checks? Really?
Why not compare the current state against the desired state and take the necessary actions to bring them inline?
Last but not least, deleting old config files so aggressively is a “penny wise pound foolish” design. I would keep these forever or at least a month! Certainly much, much longer than any possible time taken through the sequence of provisioning steps.
Often network engineers are unaware of some of the tricky problems that DS research has addressed/solved in the last 50 years because the algorithms are arcane and heuristics often work pretty well, until they don’t. But my guess is that AWS will invest in some serious redesign of the system, hopefully with some rigorous algorithms underpinning the updates.
Consider this a nudge for all you engineers that are designing fault tolerant distributed systems at scale to investigate the problem spaces and know which algorithms solve what problems.
Reading these words makes me break out in cold sweat :-) I really hope they don't
- "Rapidly updatable" depends on the specific implementation, but the design allows for 2 billion changesets in flight before mirrors fall irreparably out of sync with the master database, and the DNS specs include all components necessary for rapid updates: push-based notifications and incremental transfers.
- DNS is designed to be eventually consistent, and each replica is expected to always offer internally consistent data. It's certainly possible for two mirrors to respond with different responses to the same query, but eventual consistency does not preclude that.
- Distributed: the DNS system certainly is a distributed database, if fact it was specifically designed to allow for replication across organization boundaries -- something that very few other distributed systems offer. What DNS does not offer is multi-master operation, but neither do e.g. Postgres or MSSQL.
Can't speak for the current incident but a similar "slow machine" issue once bit our BigCloud service (not as big an incident, thankfully) due to loooong JVM GC pauses on failing hardware.
>[...] Right before this event started, one DNS Enactor experienced unusually high delays needing to retry its update on several of the DNS endpoints. As it was slowly working through the endpoints, several other things were also happening. First, the DNS Planner continued to run and produced many newer generations of plans. Second, one of the other DNS Enactors then began applying one of the newer plans and rapidly progressed through all of the endpoints. The timing of these events triggered the latent race condition. When the second Enactor (applying the newest plan) completed its endpoint updates, it then invoked the plan clean-up process, which identifies plans that are significantly older than the one it just applied and deletes them. At the same time that this clean-up process was invoked, the first Enactor (which had been unusually delayed) applied its much older plan to the regional DDB endpoint, overwriting the newer plan. The check that was made at the start of the plan application process, which ensures that the plan is newer than the previously applied plan, was stale by this time due to the unusually high delays in Enactor processing. [...]
It outlines some of the mechanics but some might think it still isn't a "Root Cause Analysis" because there's no satisfying explanation of _why_ there were "unusually high delays in Enactor processing". Hardware problem?!? Human error misconfiguration causing unintended delays in Enactor behavior?!? Either the previous sequence of events is considered unimportant, or Amazon is still investigating what made Enactor behave in an unpredictable way.