Incident, Inçident, Incidënt

On 2024/03/262024/03/26 By Dan SlimmonIn incident-responseLeave a comment

When you deploy broken code, it may cause an incident. Then you’ll have to declare an incident. And don’t forget to create an incident so customers can stay informed!

“Incident” can mean a bunch of things, and these meanings are only partly distinguishable through context. When the word “incident” is used ambiguously, confusion can emerge.

The effort to fix the problem

In many organizations, the organizational formalism around responding to a problem is called an “incident.” For example,

At 08:40 UTC, engineer Geordi La Forge declared an incident after observing high API latencies in production. The incident was led by Beverly Crusher, and it was resolved after 32 minutes.

We’ll call this usage an incident-effort. An incident-effort need not correspond to an outage. You can create one for any problem on which you want multiple sets of eyes. For example, if deployment to the staging environment is borked, or if disk utilization is growing too rapidly on a database server, you might declare an incident(-effort).

The public thread of communication

When stakeholders need to be informed of a problem – often, these stakeholders are customers – we’re expected to “create an incident.” This kind of “incident” (let’s call it an incident-post) is usually a post on a publicly-facing website, describing the problem and the current state of the response to it.

An incident-effort may exist without an incident-post. An incident-post may also exist without an incident-effort (for example, if an incident is used to communicate about a planned maintenance window).

The problem itself

Finally, there is the problem itself: that which is to be fixed, or communicated about.

There are many problems that don’t merit an incident-effort, let alone an incident-post. We usually just call such problems “bugs” or “risks.”

When someone says “We’ve had too many incidents this quarter,” they’re probably talking about problems that caused downtime. But they might be talking about incident-posts themselves, depending on how they’re looking at things. It pays to clarify.

Never being one to shy away from an opportunity to make a Venn Diagram, I’ll leave you with a few common phrases around incident management:

I offer incident response training or remote or on-site. Get in touch.

Dead air on the incident call

On 2024/03/182024/03/19 By Dan SlimmonIn incident-response2 Comments

When troubleshooting a high-impact software failure in a group, you have to be ready for shifts in tenor. One moment there’s a frenzy of coordination, and the next: absolute silence.

Silence is natural and often useful. But to be an effective incident commander – whose job is to keep the problem-solving effort moving forward – you must develop a keen ear for silence.

Silence can mean different things to different people in different situations. In this post, I’ll present a few incident scenarios and explore the role of the incident commander in breaking (or simply abiding in) dead air.

“Any minute now”

Sylvain (from the [s]upport team) has spun up an incident call after getting 3 consecutive reports of broken CSS on the production app. You are the incident commander, and Oscar (from [o]perations) has immediately volunteered to be the primary investigator. Deanna and Deepak (software [d]evs) are also on the call.

There’s some ambiguity about whether the CSS issue merits a status page post. Nobody has found a “smoking gun” showing that, for example, 404 errors are happening at an elevated rate. And now Oscar announces, “I’m seeing some log entries from the web server that look a little weird. I’m gonna look at those.” This is the beginning of a 5-minute silence.

During the silence, Deanna, Deepak, and Sylvain are all waiting, hoping that these log entries that Oscar just noticed turn out to be the smoking gun. They’re putting their eggs in the basket of Oscar’s intuition. Hopefully he’s seen this issue before, and any minute now he’ll say “Okay, I’m pushing a fix.”

The problem is, it’s equally possible that Oscar has latched onto a red herring (some salient but ultimately irrelevant observation). If there were a conversation in place of silence, then Deanna could be researching the error message, or Deepak might be able to immediately rule out the error as a cause of the broken CSS, or Sylvain could provide a detail from one of the customer tickets that would narrow the scope of the investigation. But instead, everybody’s twiddling their thumbs hoping for Oscar to emerge with a fix.

An incident commander is responsible for keeping the whole problem-solving effort moving forward. So it’s incumbent on you to interrupt this silence.

Try drawing more information out of Oscar:

“Oscar, do you mind sharing your screen so Deepak and Deanna can see the weird log messages too?”
“What’s the error message, Oscar? Can you send a link to a log search?”
“Do we know when these log events started? Does that line up with when we started receiving these support tickets, Sylvain?”

The more we audit each other’s thought processes, the more effective we are at joint problem-solving. An IC must make this happen.

“LGTM”

Sylvain has spun up an incident call after getting 3 consecutive reports of broken CSS on the production website. You are the incident commander.

Oscar has checked a bunch of graph dashboards and hasn’t found any evidence of a widespread system failure. He’s said as much. Now there’s relative silence on the call for five minutes.

Deanna and Deepak are basically in agreement with Oscar: there’s no evidence of a system health issue. To them, and to Oscar, it’s not really clear how strong a signal Sylvain has. It could just be a coincidence that these three reports all arrived in a row. The engineers on the call are thinking, I guess we’ll keep poking at this, but we’re not even sure this is a real issue. We need more information.

Sylvain, on the other hand, is positive that something is wrong. Getting 3 support tickets in a row about the same behavior is very strong evidence to him. He’s presented his information to the investigators, and now he’s thinking, Okay, they say it’s not a widespread issue. But I’m sure Oscar is getting to the bottom of it.

There’s been a common ground breakdown, and as a result, a silence that becomes more and more frustrating.

As incident commander, you should focus the group’s attention on observable symptoms by asking questions like:

“Has anybody been able to reproduce these broken page-loads in a browser? Preferably with Dev Tools turned on?”
“Sylvain, I don’t have an intuition for support ticket frequencies. How unusual is it to get 3 reports of the same thing right in a row like this?”
“Can we find, in the access logs, just one example of a stylesheet request that returned a non-200 response?”

“Let’s see here…”

Sylvain has spun up an incident call after getting 3 consecutive reports of broken CSS on the production website. You are the incident commander. The investigation has been going along, and Oscar is chasing down a hunch that a particular error message from the web server is related to the stylesheet failures. Deanna is digging into some code to help validate Oscar’s hunch.

Deepak joins the call. There’s no chatter, as everyone is waiting for Oscar and Deanna to come up with their findings. So Deepak reads the chat scrollback, which takes him about 5 minutes. It’s not until the end of those 5 minutes that Deepak understands what Oscar and Deanna are working on.

As it happens, Deepak has seen the web server error message in question before. He knows what it means, and he can explain why it’s a red herring. But for the 5 minutes it takes him to get up to speed by reading the chat scrollback, silence persists.

In order to keep a problem-solving effort moving forward, an incident commander should ensure that every new participant gets up-to-date knowledge of what the group is doing and why. At small scale (less than, say, 10 people on the call), you can do this verbally. For example, you could say to Deepak when he joins the call, “Hi Deepak. Right now, Oscar and Deanna are investigating a web server error message that might be related to failed stylesheet loads. You can see the error message in the chat.”

When there are more than 10 people, the verbal approach stops working. It becomes necessary to have a shared document of some sort, continuously updated by a “scribe.” It’s not sufficient for this document to be merely a timeline of events: it must highlight the current state of the joint diagnostic effort. I recommend clinical troubleshooting for this.

“I need 5 minutes”

When incident response is going right, everybody understands what’s being done by whom, and why. As information comes to light and our strategies evolve, it takes more or less constant communication to maintain this state. That’s why silence on an incident call is so often an indicator of trouble: when there’s silence, communication isn’t happening.

There is, however, a healthy kind of dead air.

Sometimes an investigator needs to go silent for a while to chase down a hunch, or collect some data, or research some question. As long as such a silence is negotiated in advance, with a specific time to reconvene, it can serve a crucial purpose. I call this functional dead air.

It’s the job of the incident commander to ensure that every nontrivial silence is functional. First, communicate what’s being done by whom, and why. Only then, do it.

I offer incident response training for engineers. Get in touch!

Clinical troubleshooting: diagnose any production issue, fast.

On 2024/03/082024/03/08 By Dan SlimmonIn Problem-Solving, Workflow6 Comments

Over my career as an SRE, I’ve diagnosed thousands of production issues. When I’m lucky, I have enough information at hand, and enough expertise in the systems involved, to get to the bottom of the problem on my own. But very often I need to bring together a team.

Troubleshooting with a team unleashes enormous power. Problems that would have taken me days to solve as an individual might take only hours or minutes, thanks to the benefit of pooled knowledge.

However, collaborative troubleshooting also comes with dangers. Time and again, I’ve seen groups struggle to make progress on an investigation due to miscommunication, misalignment, and confusion. Among other difficulties, the fundamental common ground breakdown can have especially heinous consequences in this context.

Over the years, I’ve developed a reliable method for harnessing the diagnostic power of groups. My approach is derived from a different field in which groups of experts with various levels of context need to reason together about problems in a complex, dynamic system: medicine.

I call this method clinical troubleshooting.

The clinical troubleshooting process

Although clinical troubleshooting can be useful in solo troubleshooting, it really shines as a group activity. It’s a lightweight structure that always adds value. I recommend reaching for clinical troubleshooting as soon as you need to involve another person in any “why” question about an unexpected behavior of your system.

Step 0: Get a group together

Before you start, gather the people you’ll be troubleshooting with. Any synchronous communication channel can work for this: Slack, Zoom, a meeting room; you name it.

You don’t need a big group. In fact, a small group is best. What matters most is that you bring together diverse perspectives. If you’re a backend engineer, try to pull in a network engineer and a frontend engineer, or a support agent and a sysadmin. Cast a wide net.

Once you have an initial group, share a blank Google doc with everyone.

Step 1: Identify symptoms

Add a Symptoms header to the doc.

You’re the one bringing the issue, so you must have some observations already. Write those down in a numbered list.

It’s important that it be a numbered list rather than a bulleted list. As the troubleshooting process goes on, you’re going to want to refer to individual symptoms (and, later, hypotheses and actions). If each symptom has a number and the number never changes, this is a lot easier.

Ask your collaborators to list symptoms, too. They may have observed some other facet of the problem, or they may think to look at a graph that you don’t know about.

Here’s what an initial symptom list might look like:

Symptoms

About 5 times a day, the Storage API sends a spike of 503 responses. Each spike lasts about 500 milliseconds and includes between 200 and 1000 responses (about 0.1 to 0.5% of all responses sent during the interval)
Outside of these spikes, the Storage API has not sent any 503 responses at all in the last 14 days.
The failing requests have short durations, around the same as those of normal requests (mostly under 100 milliseconds).

(In this and subsequent examples, don’t worry about understanding the exact technical situation. Clinical troubleshooting can be used on problems in any part of any tech stack.)

All the symptoms on the list should be precise and objective. In other words, if a statement is quantifiable, quantify it. Don’t make suppositions yet about why these phenomena have been observed. That comes next.

Once you’re all on the same page about what problem you’re investigating, the initial symptom list is done.

Step 2: Brainstorm hypotheses

Add a Hypotheses header to the doc. Invite your colleagues to join you in suggesting hypotheses that might explain the symptoms.

Let the ideas flow, and write them all down. This is where having a diverse set of perspectives in the room really pays off. Your co-investigators will think of hypotheses that would never have occurred to you, and vice versa. The more of these you come up with, the more likely the actual explanation will be on the list.

A hypothesis can’t be just anything, though. A hypothesis must

explain (at least some of) the symptoms,
accord with all known facts, and
be falsifiable (that is: if it were false, we’d be able somehow to prove it false).

For example, given the symptoms above, “requests to the storage API are getting queued up behind a long-running query” would not be a sound hypothesis, since it’s inconsistent with Symptom 3. If requests were queued up, we’d expect them to take longer before failing.

After some discussion, your hypothesis list might look like this:

Hypotheses

A particular request causes an out-of-memory (OOM) event on a storage server, and all in-flight requests to that server get killed.
A network hiccup causes connections between the load balancer and a storage server to be severed.
~~Requests to the storage API are getting queued up behind a long-running query~~
1. ~~Discarded because inconsistent with Symptom 3~~
A network hiccup causes connections between storage API servers and a persistence layer node to be severed.

Again, use a numbered list. If a hypothesis is ruled out or deemed irrelevant, don’t delete it: you don’t want the list numbering to change. Instead, mark it in some with formatting. I use strikethrough. Gray works too.

Step 3: Select actions

Add an Actions header.

In a new numbered list, choose one or two actions that will advance the troubleshooting effort. Usually, you should pick actions that will rule out, or “falsify,” one or more of the hypotheses on the table.

To rule out Hypothesis 2 above, for instance, you could review the logs for one of the error spikes and check whether all the affected requests were associated with the same load balancer or the same storage server. If the requests are distributed across your infrastructure, then Hypothesis 2 is ruled out (and Hypothesis 1 as well, for that matter!).

When you agree upon actions, it’s best to assign them to individuals. Sometimes an action can be taken right away, and other times it’ll take a while and the group will have to reconvene later. But ownership should never be unclear.

Ruling out hypotheses the only purpose of actions in clinical troubleshooting. You can also assign actions that expand the group’s understanding of the problem, in order to generate new symptoms and new hypotheses. These actions can be things like, “Read the documentation on system X‘s network configuration,” or “Search for blog posts about error message E.” As long as there’s at least one hypothesis in the running, though, there ought to be at least one action in flight that could falsify it. That’s one of the ways clinical troubleshooting ensures constant progress.

Steps 4 through N: Cycle back through

When actions are completed, you get more information for the symptom list. More symptoms suggest new hypotheses. New hypotheses imply further actions. Just keep going through the cycle until you’re satisfied.

Sometimes you won’t be satisfied until you have a definitive diagnosis: a solid explanation for all the symptoms that’s been proven right. Other times, you’ll be satisfied as soon as you take an action that makes the problem go away, even if there’s still uncertainty about what exactly was going on.

In any case, clinical troubleshooting will reliably get you to the solution.

Keep things moving forward

In the absence of structure, collaborative diagnosis can stall out. Or worse, go backward.

With clinical troubleshooting, there’s always a next step forward. Teams that practice this method will consistently get to the bottom of technical mysteries, even when strapped for data or under intense pressure. And over time, as this scientific way of thinking becomes a habit, and then a culture, we come to understand the behavior of our system that much better.

I can teach your team how to do this. Get in touch.