This was written by Alexis Lê-Quôc
As engineers, we pride ourselves in understanding our infrastructure. As we spend time designing, building, and replacing its parts, we learn its quirks. We discover coupling between areas we thought independent of each other. We are often reminded that these beasts are complex systems and observing reality often shatters our assumptions about how they should behave.
To tame this complexity and still keep operations running, we build a web of sensors and checks to get humans notified when things go wrong. Alerts become an integral part of our lives. They interrupt our work and our conversations. They wake us up from deep sleep. We diligently respond, engage in investigations, fix what need to be fixed, and move on with our lives until the next check fails, etc.
Sound familiar? Let's do a little exercise. Out of the following alerts, which are the ones you are most likely to fix? Alerts that:
- occurred the most often over the past 6 months?
- occurred the most often last month?
- were the most visible to the rest of the organization?
- woke us up yesterday at 3AM for the second night in a row?
Clearly, the last one is going to get your attention, even if only happened twice during an entire year. It’s also the one that will be on top of your mind when asked about the most pressing work that has to get done. The bias of hindsight is very strong and loss of sleep justifies the work priorities, right?
Are all biases that obvious? How can we protect ourselves from drifting away into a continuous string of “low-hanging fruits” and top-of-mind fixes? The answer is easy to find and not that hard to get. It’s in the alerting stream itself. It’s “in the computer”.
Diving into the Alert Stream
To illustrate my point I have chosen to dive into our alerting stream from Nagios. It’s a tool we (and a number of our customers) happen to use internally. It is an objective source, it is reliable and it does not care if it must wake us up in the middle of the night. It’s not a perfect view into what’s working or not in our infrastructure, but it’s an early warning system as shown in the following example.
Nagios is also easy to analyze. All that we need is in /var/log/nagios3/nagios.log.
In this particular case, the data is parsed on the nagios server with a simple bit of python code, sent over to a SQL database for storage, and rolled up into a set of csv files.
nagios.log ---> a few sql tables ---> csv files ---> analysis
CSV are an ideal format for data that simple: easy-to-write, easy-to-read, well-supported by tools like numpy, pandas and R.
R provides me with a nice platform to explore my Nagios data set and answer a few basic questions.
Asking Questions of the Data
What are the trends?
Having consolidated alerts by day of the year, split between notifying alerts and non-notifying failing checks in by_day.csv according the the following structure:
day_of_year,notifying,daily
1,1,12
1,0,426
2,0,1158
2,1,2
3,0,630
3,1,48
4,0,1398
4,1,76
5,1,8
5,0,834R provides an easy way to plot data fairly cleanly with the ggplot2 package. You can get similar results via d3.js (which we use to produce similar reports on the web).
alerts_by_day <- read.csv(‘by_day.csv’)
ggplot(alerts_by_day, aes(day_of_year, daily, color=factor(notifying))) + geom_line()
+ xlab("Day of year")
+ ylab("Service Alerts")
+ ggtitle("Notifying v. silent alerts per day")
+ geom_smooth()
We can clearly see the trend in non-notifying failures. It could be anything from machines not getting package updates (something for which I don’t want to be waken up in the middle of the night) to checks that have been disabled (i.e. technical debt).
Now focusing on notifying alerts (in that greenish tint), I can easy get a read of the situation as the year progresses. Here is the graph with just the notifying alerts.
There was obviously a big spike 3-4 months into 2012 but overall things are stabilizing. I may still consider that the number of notifications is too high but I know at least where I’m starting from.
A Case of the Fridays
Is there a worst day to be on-call? That’s another easy one to answer once the data is properly extracted. Fridays are not the best day to go to the movies, while Tuesdays see a lot of variability (is it because we release more code on Tuesdays?)
ggplot(dd_by_hod, aes(occurrence_dow, daily, group=occurrence_dow))
+ geom_boxplot()
+ scale_x_discrete(breaks=seq(0, 6),
labels=c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"))
+ xlab("Day of the week")
+ ylab("Number of alerts")
+ ggtitle("Daily distribution")
The More (Data) The Merrier
Once you start digging, it’s damn hard to stop. The company I work for consumes and analyzes this kind of data for a living, so it was tempting to run a few queries against a larger data set of independent Nagios streams to ask more questions, to name a (tiny) few:
- Does the size of the infrastructure have an effect on the ratio between notifying and silent alerts? Little, the ratio remains between 1 and 3%.
- Does the size of the infrastructure have an effect on the distribution of alerts across hosts? Surprisingly little, the distribution is close to a power law. Regardless of the size, a few number of hosts will concentrate most of the alerts.
Lessons Learned
Having spent a number of hours looking into Nagios streams I walked away with a few conclusions.
First, presentation matters. The data is there. With a bit of love, we can make data more usable. The real leap comes with turning them into digestible-at-a-glance bits. Tools like ggplot2 and d3.js are your friends.
Second, it’s easy to visualize trends. Visuals are a great conversation starter, and having hard data anchors the conversation. The harder part is to explain and ultimately control the trends. At least our brains are freed from looking at tedious logs!
Finally, it’s a lot of fun. Tools like R, pandas, d3.js, matplotlib, to name a few, make this kind of exercise a great weekend project. It won’t necessarily scale (otherwise I’d be out of a job), but you’ll come out with a fresh perspective on your monitoring chatter.
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