What Is MTTF? Mean Time to Failure Explained in Detail

“What is MTTF?” That’s the question we’ll answer with today’s post. Yep, the article’s title makes it evident that the acronym stands for “mean time to failure.” But that, on its own, doesn’t say anything. What does “mean time to failure” actually mean? Why should you care? That’s what today’s post covers in detail.

The structure of this post will mostly follow the template we’ve laid out with the mean time to detect (MTTD) article. We’ll start with the “what” of MTTF, giving a complete definition of the term. After that, we’re ready for the “why”—you’ll learn why you and your organization should care about this metric, understanding all the benefits it can provide. After that, we’ll finally be ready for some practical tips. We’ll invite you to roll-up your sleeves and learn how to calculate MTTF.

Before parting ways, we list other essential DevOps metrics you should also know. Let’s get started.

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Defining Failure

It’s common for me to start posts by offering a definition of its subject matter. This time I’m taking a different route, though: let’s begin by defining “failure.”

You might think that failure is such an obvious concept that it bears no definition. After all, if something doesn’t work at all, it has failed. What’s so complicated about it?

I beg to differ. Things aren’t black and white when it comes to failure, especially in the IT world. Indeed, there can be more granular modes of failure.

Is a car with a flat tire a failure? What about a phone whose touch screen features randomly don’t work? And a musical keyboard which doesn’t produce sound in some keys? Are these examples failures or not? The way I see it, yes. Even though you could say that they “work,” they don’t work at the level they’re supposed to.

A failure, generally speaking, means that something doesn’t meet its goals. Specifically, in the tech world, that usually means a system outage, aka downtime. But there can be scenarios in which, despite not having a full-blown system outage, you can say that there is a failure. You could have an application that performs orders of magnitude slower than it should. That’s failure. It doesn’t matter that the result was technically correct when the system takes more than 24 hours to perform a task that should have taken a few minutes, at the most.

What Is MTTF: A Definition

MTTF is a critical KPI (key performance indicator) for DevOps. As you already know, the acronym stands for mean time to failure. But what does that actually mean? In a nutshell, MTTF refers to the average lifespan of a given item. In other words, it refers to how long a piece of technology is supposed to last operating in production.

MTTF is closely related to another metric—MTBF (mean time between failures.) Well, to be fair, they’re virtually the same thing, with just one important difference. You’d use MTBF for items you can fix and put to use again. On the other hand, you’d use MTTF for items that can’t be repaired. If you find yourself in such a scenario where MTTF is used as a metric, that means repairing the problematic item isn’t an option, so you’ll have to replace them.

Mean time to failure sets an expectation. It represents the length of time you can expect an item to work in production. Keep in mind that when companies calculate the mean time for failure for their various products, they don’t usually put one unit to work continuously until it fails. Rather, this metric is often computed by running a huge number of units for a specific amount of time.

Why Should You Care About MTTF

You’ve just learned the “what” of mean time to failure. Let’s now turn our focus to the motivations behind calculating this metric. Like MTTD, one of the best reasons for calculating MTTF is to improve it. Unlike MTTD, though, this metric improves when it goes up instead of down.

MTTF measures reliability. With it, you can know how long a product typically works before it stops working. Having this piece of data, your organization is able to make informed decisions on important issues, such as inventory management (which even includes from which brands to purchase or not purchase), scheduling of maintenance sessions, and more.

MTTF also helps us, albeit indirectly, to evaluate your monitoring mechanisms. This is a quote from our post on MTTD:

“MTTD also has an additional—and arguably more important—benefit: it serves as a test of your monitoring mechanisms. Here’s the thing—your organization already adopts tools and processes to monitor incidents. If these tools and processes work as intended, it shouldn’t be that hard to keep your organization’s MTTD low. The opposite is also true. If you adopt incident management mechanisms that aren’t up to the task, you and your DevOps team will have a hard time keeping MTTD down, which can result in catastrophic consequences for your organization.”

You could say that MTTF, as a metric, relies on MTTD. In order to track how much time components work until they stop, the organization must be able to detect system outages and other problems. So, by carefully tracking MTTF, you’re also keeping an eye on the health of your monitoring procedures.

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Calculating MTTF

You calculate MTTF taking the total amount of hours of operation (aka uptime) and divide it by the number of items you’re tracking.

Suppose we have four pieces of equipment we’re testing. The first one failed after eleven hours, while the second one failed after nine hours. The third failed after seven hours, and finally, the last one failed at five hours. So, we have a total uptime of 32 hours, which divided by four equals eight hours.

This suggests this particular equipment will need to be replaced, on average, every eight hours.

Other Metrics to Be Aware Of

MTTF is a key indicator to track the reliability of your assets. Here are some other important metrics you should probably know:

  • MTTD (mean time to detect): The average amount of time it takes to detect problems in the organization.
  • MTTR (mean time to repair): The time it takes to fix an issue after its detected.
  • MTBF (mean time between failures): The time the organization goes without a system outage or other issues.

What’s Next?

In this post, we’ve answered the question, “What is MTTF?” Mean time to failure is an important metric you can use to measure the reliability of your assets. Since MTTF shows the amount of time a product, component, or other types of assets usually work until they fail, you want to keep it as high as possible.

When it comes to DevOps, MTTF is one of many important metrics we need to track. Just like MTTD, the previous metric we’ve covered, MTTF serves more than one purpose. The first and obvious one is to be a reliability measure. By tracking the mean time to failure, we understand how reliable our equipment, components, and assets are, so we can make more educated decisions. But MTTF can also help us to evaluate the effectiveness of our monitoring solutions because we have to detect outages in order to measure the time between them.

Where to go now? Well, keep searching for more knowledge. Learn about other important metrics. Learn about tools that can help you with such metrics.

For instance, take a look at the fully automated log management tool XpoLog. It collects, parses, and tags logs from many different sources.

XpoLogs’ ML-powered engine adds layers of intelligence over your searches, it automatically and proactively detects errors and allows you to prevent outages and meltdowns. XpoLog contains a leading analysis apps marketplace with thousands of ready-to-use-reports and dashboards, to extract actionable insights immediately, in real-time. Download XpoLog now and improve your monitoring mechanism.

Log management is essential for tracking metrics such as MTTD and MTTF since logs are very reliable sources of information when it comes to system outages.

That’s it for today. Thanks for reading.

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