What is Root Cause Analysis and How it Can Help You Solve Your Problems

What is Root Cause Analysis (RCA)?

Imagine your application as 100 haystacks, each representing a tier, and every haystack containing a needle that’s causing your user experience to suffer. As an administrator, your job is to search for it and acquire eliminate it as soon as possible. The difficulty is that every haystack contains over half 1,000,000 pieces of hay, each of which represents a line of code in your application. It’s no surprise, then, that in today’s complex, distributed environments, finding the foundation reason for performance issues can take days or weeks.

That’s why identifying unhappy users (EUM), slow business transactions (application mapping), and problematic haystacks (tiers) in your application is no longer enough — you wish to seek out the needles, which necessitates code-level visibility across the stack, from the appliance, business, and user experience all the way down to the infrastructure and network. EUM and application mapping can facilitate your isolating a performance issue, but they cannot tell you what is causing it so you’ll fix it. You would like to understand not only what happened, but why it happened yet.

Root cause analysis (RCA) is the solution, which was first developed by Sakichi Toyoda in 1958 as a part of Toyota’s manufacturing process and has since been adopted by nearly every industry, from publishing to engineering. It is a step within the APM process designed to scale back the time unit to resolution (MTTR) for application performance problems within the case of application performance management. Within the process of triaging and resolving performance issues, RCA uses anomaly detection. Stakeholders can begin root cause analysis in one among two ways after detecting the problem:

By establishing a room to analyze the present historical system, reconstruct the timeline of when the anomaly first occurred and what happened next, and type through multiple errors to work out what underlying defect presumably caused this event,

We can quickly pinpoint the cause by using computing (AI) and machine learning (ML) to automatically create an entire anomaly timeline, monitor data streams in real-time, and use historical and contextual correlation to quickly pinpoint the cause so we will go on to identifying the desired fix or reconfiguration to resolve the difficulty.

What is the Procedure for Conducting a Root Cause Analysis?

Identify issues

It’s all well and good to resolve problems, but you want to first understand what constitutes an issue and strain any false positive alerts to problems that do not meet those criteria. Is that the slow reaction time therein critical business transactions thanks to a real problem, like an unexpected spike in traffic, or a known problem, like a spike in traffic during peak season?

As a result, anomaly detection is prioritized. Machine learning algorithms are utilized in anomaly detection to automatically define and learn what constitutes “normal” application behavior over time. That way, you’ll avoid alert storms by removing the strain of manual threshold-setting and automatically filtering out the noise associated with false positives.

Once an anomaly has been accepted as real, it is time to urge right down to business.

Engage RCA

Machine learning is additionally utilized in root cause analysis to work out the basis explanation for performance issues discovered by anomaly detection. RCA focuses on the cause, whereas anomaly detection focuses on the symptoms.

This is when machine learning starts digging deeper and presenting you with the possible causes of an anomaly. Perhaps the slow interval was thanks to sluggish third-party code. This is often discovered by Root cause analysis in an exceeding two-step process:

• Fault domain isolation: Machine learning can isolate the fault domain to pinpoint the precise location of the problem without requiring you to sift through logs and determine which components were affected.
• Analysis of logs, snapshots, traces, infrastructure, and other data to work out which components are affected.

To more accurately diagnose the behavior and reduce repair time, your APM solution should clearly expose the offending anomalies, still because of the top suspected causes and any contributing tiers, exit calls, or inter-tier network issues.

Determine what actions should be taken

The goal of using machine learning rather than manual methods is to assign issues to the acceptable teams so they’ll take action at an acceptable time. When it involves CI/CD validation, cloud right-sizing, network optimization, or security enforcement, good APM tools display these insights in a very way that produces it easy to drill down into the matter to higher understand where it came from and either negate or take action, whether it’s CI/CD validation, cloud right-sizing, network optimization, or security enforcement.

How to Begin using Automated Root Cause Analysis

AI alone won’t be ready to complete all of the tasks. To make sure that the method is both efficient and meaningful, follow these steps:

Get started straight away

When the incident remains fresh in everyone’s mind, RCA should be done as soon as possible. The correct data and metrics are important — you wish enough information about the system to maneuver forward — but so are human intelligence and different perspectives — because, in the end, finding the basis cause (which can vary in severity) necessitates methodical, organizational diligence and also the right mindset.

Approach matters with an open mind

Root cause analysis (RCA) tests our assumptions about how an application works, how the network of dependencies looks, and what the foremost likely reason for an occasion is — and rightly so. Assumptions get within the way — what you’re thinking that you recognize about the appliance can lead you to disregard any evidence that contradicts the speculation, making finding the basis cause impossible or time-consuming. Instead, target gathering the info you’ll have to quickly form and test a hypothesis. Keep an open mind and be inquisitive about the basis cause and you may be more likely to approach it pragmatically with evidence to duplicate your hypotheses. It is also critical for teams to know that processes, not people, cause problems, and assigning blame accomplish nothing.

Make an outsized and deep net

You’ll want to use machine learning to uncover as many possible factors as possible, like not just the kind of change but also a broad timeframe just in case the basis cause occurred before the incident. It can then drill right down to a finer level. The more granular your data, the simpler it’ll be to spot and properly solve the problem.

Recognize things

It’s crucial to grasp the context. Not only do RCA tools must capture and present data on how individual components of the system work, but they also must surface meaningful insights into how they interact. Create a map of those dependencies to know why a change in performance occurred and the way to avoid it in the future by tracing those correlations to seek out the basis cause, and connections between seemingly unrelated events, and creating a map of those dependencies. Modern applications have complicated and dynamic dependencies, and technologists, especially in larger organizations, know less about the application than they think.

Look for long-term solutions

Knowing what the matter is and what caused it’s not enough; finding solutions is a very important part of RCA (whether corrective or preventative). It is also not nearly fixing the first problem. It’s about arising with strategies to correct/prevent problems within the future, recouping, and taking a 30,000-foot view of the matter.

Silos of information should be avoided

Over-reliance on knowledge silos is perhaps the foremost common blunder. This happens after you haven’t got reliable observability tools in situ to illuminate the large picture while focusing on the precise problem so the suitable teams can respond. However, if you do not share your work with key stakeholders, it’s pointless. It is the equivalent of gathering evidence at a criminal offense scene but never turning it over to the acceptable authorities to create an arrest. Your APM solution should make it simple to report the acceptable data to varied audiences.

Close the loop and keep improving

When it’s all said and done, it isn’t the top. When done correctly, RCA is an iterative process. A quarterly or annual review of RCAs, actionable items, and results adds to the worth of the work. You ought to also review your RCA process on an everyday basis to work out if there are any ways to enhance it. A data-driven approach will improve the team’s understanding of how the app works and make sure that each new mystery is solved in a way that strengthens the app over time.

About Enteros

Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.