Posts tagged Mission Control
Ok, after a series of posts extolling the virtues of my current project, it’s time to take a more critical look at some of its current limitations, and what we might do about them. In my introductory post, I talked about how Mission Control can let us know how “crashy” a new release is, within a short interval of it being released. I also alluded to the fact that things appear considerably worse when something first goes out, though I didn’t go into a lot of detail about how and why that happens.
It just so happens that a new point release (56.0.2) just went out, so it’s a perfect opportunity to revisit this issue. Let’s take a look at what the graphs are saying (each of the images is also a link to the dashboard where they were generated):
ZOMG! It looks like 56.0.2 is off the charts relative to the two previous releases (56.0 and 56.0.1). Is it time to sound the alarm? Mission control abort? Well, let’s see what happens the last time we rolled something new out, say 56.0.1:
We see the exact same pattern. Hmm. How about 56.0?
Yep, same pattern here too (actually slightly worse).
What could be going on? Let’s start by reviewing what these time series graphs are based on. Each point on the graph represents the number of crashes reported by telemetry “main” pings corresponding to that channel/version/platform within a five minute interval, divided by the number of usage hours (how long users have had Firefox open) also reported in that interval. A main ping is submitted under a few circumstances:
- The user shuts down Firefox
- It’s been about 24 hours since the last time we sent a main ping.
- The user starts Firefox after Firefox failed to start properly
- The user changes something about Firefox’s environment (adds an addon, flips a user preference)
A high crash rate either means a larger number of crashes over the same number of usage hours, or a lower number of usage hours over the same number of crashes. There are several likely explanations for why we might see this type of crashy behaviour immediately after a new release:
- A Firefox update is applied after the user restarts their browser for any reason, including their browser crash. Thus a user whose browser crashes a lot (for any reason), is more prone to update to the latest version sooner than a user that doesn’t crash as much.
- Inherently, any crash data submitted to telemetry after a new version is released will have a low number of usage hours attached, because the client would not have had a chance to use it much (because it’s so new).
Assuming that we’re reasonably satisfied with the above explanation, there’s a few things we could try to do to correct for this situation when implementing an “alerting” system for mission control (the next item on my todo list for this project):
- Set “error” thresholds for each crash measure sufficiently high that we don’t consider these high initial values an error (i.e. only alert if there is are 500 crashes per 1k hours).
- Only trigger an error threshold when some kind of minimum quantity of usage hours has been observed (this has the disadvantage of potentially obscuring a serious problem until a large percentage of the user population is affected by it).
- Come up with some expected range of what we expect a value to be for when a new version of firefox is first released and ratchet that down as time goes on (according to some kind of model of our previous expectations).
The initial specification for this project called for just using raw thresholds for these measures (discounting usage hours), but I’m becoming increasingly convinced that won’t cut it. I’m not a quality control expert, but 500 crashes for 1k hours of use sounds completely unacceptable if we’re measuring things at all accurately (which I believe we are given a sufficient period of time). At the same time, generating 20–30 “alerts” every time a new release went out wouldn’t particularly helpful either. Once again, we’re going to have to do this the hard way…
If this sounds interesting and you have some react/d3/data visualization skills (or would like to gain some), learn about contributing to mission control.
Shout out to chutten for reviewing this post and providing feedback and additions.
One of the great design decisions that was made for Treeherder was a strict seperation of the client and server portions of the codebase. While its backend was moderately complicated to get up and running (especially into a state that looked at all like what we were running in production), you could get its web frontend running (pointed against the production data) just by starting up a simple node.js server. This dramatically lowered the barrier to entry, for Mozilla employees and casual contributors alike.
I knew right from the beginning that I wanted to take the same approach with Mission Control. While the full source of the project is available, unfortunately it isn’t presently possible to bring up the full stack with real data, as that requires privileged access to the athena/parquet error aggregates table. But since the UI is self-contained, it’s quite easy to bring up a development environment that allows you to freely browse the cached data which is stored server-side (essentially:
git clone https://github.com/mozilla/missioncontrol.git && yarn install && yarn start).
In my experience, the most interesting problems when it comes to projects like these center around the question of how to present extremely complex data in a way that is intuitive but not misleading. Probably 90% of that work happens in the frontend. In the past, I’ve had pretty good luck finding contributors for my projects (especially Perfherder) by doing call-outs on this blog. So let it be known: If Mission Control sounds like an interesting project and you know React/Redux/D3/MetricsGraphics (or want to learn), let’s work together!
I’ve created some good first bugs to tackle in the github issue tracker. From there, I have a galaxy of other work in mind to improve and enhance the usefulness of this project. Please get in touch with me (wlach) on irc.mozilla.org #missioncontrol if you want to discuss further.
Time for an overdue post on the mission control project that I’ve been working on for the past few quarters, since I transitioned to the data platform team.
One of the gaps in our data story when it comes to Firefox is being able to see how a new release is doing in the immediate hours after release. Tools like crashstats and the telemetry evolution dashboard are great, but it can take many hours (if not days) before you can reliably see that there is an issue in a metric that we care about (number of crashes, say). This is just too long — such delays unnecessarily retard rolling out a release when it is safe (because there is a paranoia that there might be some lingering problem which we we’re waiting to see reported). And if, somehow, despite our abundant caution a problem did slip through it would take us some time to recognize it and roll out a fix.
Enter mission control. By hooking up a high-performance spark streaming job directly to our ingestion pipeline, we can now be able to detect within moments whether firefox is performing unacceptably within the field according to a particular measure.
To make the volume of data manageable, we create a grouped data set with the raw count of the various measures (e.g. main crashes, content crashes, slow script dialog counts) along with each unique combination of dimensions (e.g. platform, channel, release).
Of course, all this data is not so useful without a tool to visualize it, which is what I’ve been spending the majority of my time on. The idea is to be able to go from a top level description of what’s going on a particular channel (release for example) all the way down to a detailed view of how a measure has been performing over a time interval:
This particular screenshot shows the volume of content crashes (sampled every 5 minutes) over the last 48 hours on windows release. You’ll note that the later version (56.0) seems to be much crashier than earlier versions (55.0.3) which would seem to be a problem except that the populations are not directly comparable (since the profile of a user still on an older version of Firefox is rather different from that of one who has already upgraded). This is one of the still unsolved problems of this project: finding a reliable, automatable baseline of what an “acceptable result” for any particular measure might be.
Even still, the tool can still be useful for exploring a bunch of data quickly and it has been progressing rapidly over the last few weeks. And like almost everything Mozilla does, both the source and dashboard are open to the public. I’m planning on flagging some easier bugs for newer contributors to work on in the next couple weeks, but in the meantime if you’re interested in this project and want to get involved, feel free to look us up on irc.mozilla.org #missioncontrol (I’m there as ‘wlach’).