I have some datastructures exerpiments I really want to finish, but I've been busy starting a new job (and moving) and haven't a had a chance.
So, in the meantime, lets talk about distributed system or cluster system monitoring.
The goal of "monitoring" is:
- 1) To have insight into the health of the system.
- 2) To have insight into what's impacting the health of the system.
- 3) To have insight into how health and usage numbers compare to historical values.
- 4) To receive notification when your system is, or is about to be, unhealthy.
So, how does one accomplish this? I have found through operating services myself, and watching other folks to do, that you basically always want the same things. Front-end, back-end, large or small. We have two tools at our disposal to acheive these goals. Whitebox and blackbox monitoring. Whitebox monitoring is when we get the data directly from the system, we're treating the system as an open box that we understand, and asking it to tell us what it thinks is going on. Logs are a good example of whitebox monitoring (though not one I like to use much). Whitebox monitoring is in contrast to blackbox monitoring, where you have no idea about the internals of the system. Instead you have a system completely external to your systems "probing" that system. Often this means it's acting like a user or client, pinging your servers, and watching things like latency and error rates.
So, Given our 2 tools lets go through the 4 goals above and talk about how to accomplish each.
1) Insight into the health of the system.
This is very straightforward. Your goal is to have 3 to 5 metrics that tell you if your system is working. If none of those metrics is out of threshold, then (in lieu of a known issue), you can generally assume the system is stable. I'm going to call these metrics your "key metrics". Some groups use the term "SLOs", though that term conflates this concept with client communication which I'd rather not do here.
You want these key metrics to cover as much of the system's behavior as possible, but you have a competing goal of them being be simple and easy to understand. The first goal is important because you don't want to make sure something on that front-page of graphs gets wonky when your system gets wonky, if not you'll miss problems. The second is equally important though because a single red light saying "IT'S BAD!" doesn't tell you much, and thresholds are hard to set, if you can't reason about what that metric means. When that metric is out of threshold you should be able to easily understand the impact it has, so you can decide how to proceed.
Note that getting these statistics is complex. Basically all systems have 2 relevant properties that you want to know about. 1) is it doing it's job 2) is it doing it fast enough. We can get each of these as whitebox or blackbox. Well, whitebox is better, since it reflects what the user sees right? So lets just use whitebox for both! Well, no. Here's why that's a bad idea. In general your probing is going to be a very small portion of your traffic. You usually can't afford to probe every interesting query all the time, so your users may be making different queries than your prober is. Between these two this means whitebox monitoring is usually more noisy, less granular, and likely to miss special cases. Blackbox of course suffers from not representing your users, or networking and such connecting you to your users. As a result you usually want both whitebox AND blackbox monitoring... optimally you probably want both for BOTH metrics.
For all your key metrics you want to avoid choices that cause them to change dramatically as your system scales, or as load scales. For example saying "we want to make sure users are always hitting our site" and alerting if your hits per second drops below a constant is guaranteed to alert every new years eve, and every world cup. You may need something like that, but keep these to a minimum, instead if you can use things like error rates as a fraction of total queries. Ratios are great.
Latency is weird. If you have 10 million queries per second flowing through your system, you don't want to alert because one was too slow. On the other hand you really care if some are extremely slow, or most are kindof slow. Because of this you often want to pick a couple of percentiles, maybe 99'th and 50'th (unfortunately this does depend on the scale of your system, but only very loosely), and alert on their latency being high. I'm going to cheat a bit and count those as one metric, since you can easily put them on one graph. And I'm making the rules anyway :).
Think about what your system does, and make sure your metrics are representative. If 90% of your queries are of one type that's super-cheap and fast, and 5% are expensive and incredibly slow, maybe you want to break those out into seperate metrics. You really can't do this process blind, you have to look at your system, what metrics you can get reasonably, and what they will tell you.
2) Insight into what's impacting the health of your system
Key metrics tell us whether our system is healthy, and give a 10,000 foot view of how it's unhealthy, but not a clue at all as to why or where to look. This is what the rest of your metrics are for. Here is where you go crazy, the more metrics the merrier. That said, piles and piles of metrics don't really help you if you can't find them. Think about what you want to know while debugging a given problem, and what metric you would want to help dig down and see what's wrong, or prove that something is or is not the issue.
For example. I wake up at 2am to a page saying that the 99'th percentile latency is high on my webservice. This webservice is backed by OpenTSDB sitting on HSpace on HFS and the whole thing is running on EBS backed EC2 instances. What do I do? If I've set up measurement of latency and compute percentiles *per component*. I click on a link by my latency graph and it takes me to a breakdown of latency per component and per query type. I look and I see that operations with *'s on the first parameter are crazy slow, that is whole-table scans at the HSPace layer. Every layer is saying things are stupid slow, so the other breakdown is useless today. Well, I think maybe it's HSpace so I look click on a link for that and I see that one tablet is slow. Huh, now I log into the EC2 instance backing that tablet and find that the EC2 instance takes 30 seconds to authenticate my ssh connection... well shit, my EC2 instance is probably getting hosed by a competing workload, I can dig around on the machine and maybe I'll find the competing workload from someone else is blowing all my cachelines... so I go buy a larger nicer machine, and tomorrow I'll see if I can get dedicated machines or something. I file a bug to get on that and go back to bed. (To be clear I've never run OpenTSDB, HSpace, or HFS, I just wanted an example with a relatively deep stack behind it)
That's ideally how you want debugging to look. It takes a LOT of work and a LOT of metrics to make things that smooth - and most of the time it won't be nearly that nice, but the closer you get the nicer it'll be.
3) To have insight into how these compare to historical values.
It's third quarter. My manager comes to me and asks what I need for budget next year. I ask around and find out that we're taking on a new large client that's half again the size of our largest client. After getting numbers it turns out that it's actually about half the size of our largest client... but that still means their query load is 20% of our total on average. Additionally I dig into their use-case and find out that it matches that of another client... okay.
So, I dig up the client that their use-case matches and look at their historical query load on various parts of our system. I take the peaks over the last 2 months and compare that to the average to get an idea of spikiness. Peaks are about 300% of normal, and occur at 12 noon. I compare that to the system as a whole and find that the spike is the same as that in the system overall. Damn, that sucks.
So I take our system as a whole, match it to a growth function, and based on that predict our traffic 4'th quarter next year due to organic growth. Then I check that function against our representative client and find that it matches. So I scale the represenative client up to the scale of our new client, add that to the other curve, and I've got our required capacity. From there we backsolve to how much of each resource we need, maybe adding a few percent slop here and there for systems that don't scale linearly, a little extra headroom, and the like. In short, you spitball it, but not until you get some backed numbers.
This sort of solving requires history, and it requires being able to query and analyze that history. I've repeatedly tried to build a generalized tool for capacity planning and have yet to succeed, in fact I've yet to succeed in even building a specialized tool for a specific task. If anyone knows of any I'd be very interested, for now I do it with ad-hoc queries similar to the process described above. Again the above is not a real scenario, but it maps closely to the process I have used, and will use again, when capacity planning.
4) To receive notification if your system is, or is about to be, unhealthy.
And now to the last bullet point. The one that all operations engineers hold near and dear to their hearts, and yet hate with a passion... Alerting.
We have out key metrics, so obviously we want to get notified when those are out of whack. One could argue that the key metrics are all we need for monitoring, and ideally this is the case. That said, the world we live in is never ideal, and the reality is that our key metrics are almost certainly going to miss some cases. Also, key metrics tend to be designed to tell you about user impacting issues. What about issues that you know are going to be user impacting? You can surface these sooner if you alert on them directly, rather than waiting for them to impact the key metrics enough. Examples of these are, part or all of the service is simply absent to our monitoring, our monitoring itself is noticably broken somehow, we are missing capacity that we're supposed to have and are just lucky that we're not in a peak load. All of these are clearly interesting pagable events, even though the key metrics are looking healthy.
There's another interesting set of cases as well. Our key metrics going out of whack are almost certainly pagable events. What about little niggling things? Things that are wrong, but we really don't want to get paged for. Things that may not show up in the key metrics until several things go wrong at once. For example, lets say that once we lose over 10% of our machines things start going south because we'll be out of capacity if we lose a few more. Or our system is supposed to be N+2 but we're at 95% of capacity before we become only N+1. Systems are constantly segfaulting, but never enough to actually cause a user-impacting problem. These are *interesting* events, and we want to hear about them, but don't want to get woken up in the middle of the night. For these events you want some sort of notification, or some kind.
So, in my view, that's what monitoring is for. That's what we're trying to accomplish. With that in mind my next post is going to be about tools. Since starting at Meteor I've been researching all of the tools available outside of Google, and I have to say that I'm a bit disappointed. I expected awkward kludgy tools, but I expected the tools to be able to do the things I needed. I'll go into first describing what we need to accomplish the goals listed here, and then talk about some of the extant systems and how they do or do not fall short.