Today’s post continues the trend of covering log-related topics, by answering the question: “what is log analysis?” What is this, and why is it essential for your organization?
Logs are ubiquitous in the tech industry. A medium-sized IT organization can generate gigabytes worth of log entries daily. Those logs come from a large variety of sources: operating systems, network devices, web and application servers, applications, IoT devices, just to name a few. The aggregate of all these logs has the potential to be an oracle. They might offer a unique window to all facets of the organization. Unfortunately, since most teams and organizations treat logging as a mere “putting out the fires” mechanism, all of this potential goes to waste.
That’s where log analysis comes in helpful.
In this post, we’ll offer a guide on log analysis. We start by defining the term. Then we proceed to cover some of the justifications and use cases for log analysis. After that, we show the workings of log analysis, start with normalizations, and then exploring the other phases or processes that it includes.
Defining Log Analysis
You can find many different definitions of log analysis around the web, varying in their length and straightforwardness. Here’s how I’d define it:
Log analysis is the process of reviewing and understanding logs to obtain valuable insights.
So, this process allows organizations to analyze their logs in order to obtain knowledge that they wouldn’t be able to obtain otherwise. They can then use such knowledge to their advantage, not only by improving their decision-making process but also in a variety of different ways. We’ll explore those in more detail next.
Log Analysis: Understanding Its Value Proposition
Why bother with log analysis? What are the benefits your organization can reap from this practice? As you’ll see, there are many reasons why organizations do log analysis. We’ll divide our list into three main categories: Security/Compliance, Troubleshooting, and Insights.
The first reason for performing log analysis is also some of the most important reasons to perform logging itself. Namely troubleshooting problems.
Software development—and IT as a whole—is terrifyingly complex. Even with huge investments into defect prevention, we can never know for sure that our project will work as intended. And when it inevitably fails, we want to be able to access as much information about the problem as possible. That way, we can assemble the puzzle, understand what went wrong and why, and fix it.
Security and compliance concerns are high on the list of motivations for performing log analysis. And the reason for that is clear: security problems can have catastrophic consequences for any organization, even putting it out of business. So, any investment you make in the security front is justified, since the costs of the lack of investment tend to infinity.
So, the first reason why organizations should care about log analysis in the context of security is to understand and respond to security incidents such as data breaches. Organizations should be ready to act swiftly and decisively when security incidents happen since that can be the difference between staying in business or not.
Another important use case for log analysis is to help organizations to conduct forensics due to some investigation. In our post on log forensics, we list the following as reasons for performing log forensics:
- Finding the vulnerability which was exploited to allow an invasion
- Finding proof of a crime or hack
- Enabling data recovery from disasters
- Tracking the activities of a malicious actor
Since log forensics is, in a nutshell, log analysis put to the service of computer forensics and the law, all of the above are justifications for using log analysis.
In the compliance side of things, organizations might find log analysis useful for complying with both their internal security policies and external regulations.
Last but not least, the “insights” category. As already mentioned, log analysis can help organizations gain insights that wouldn’t otherwise be accessible. By having those insights, teams and organizations can improve their decision-making process, reevaluating strategies, and changing them as needed.
One typical example would be applying log analysis to understand user behavior. By doing so, the organization could, for instance, find out that users barely touch the new feature they thought would be a game-changer. Aware of this fact, the company can now make an informed decision about whether to continue supporting the feature or not.
Log Analysis: Basic Workings
As we’ve explained in our article on log collection, logs can come from a large variety of different sources. Operating systems generate logs, but so do user-facing applications, network devices, and more. A typical log file contains many log entries, sorted chronologically. Those entries are stored in a persistent medium such as a file in the disk or a database table.
In order for the logs to be processed and interpreted correctly, they need to go through some very specific changes in their content. Such changes are necessary to avoid confusion due to differences in terminology. For instance, logs that come from a certain source might use “WARN” as one of their levels, while others might employ the whole word “Warning,” or even a completely different word. It’s crucial that such divergences be found and normalized.
Keeping formats and terminology consistent across all logs will reduce the number of errors and also keep statistics accurate. As soon as you collect and process the logs, it’s time to analyze them to detect not only usual patterns but also anomalies.
Log Analysis Processes
In the last section, we’ve touched briefly on the subject of normalization, which is a process that changes the logs data in specific ways, to make the analysis easier and avoid errors.
Normalization, though, is just one of the processes log analysis includes. We’ll now cover these processes—including normalization—in more detail.
Normalization is a technique that aims for consistency. It converts messages—in our case, log entries—so all of them use the same terms and data formats. Normalization is an essential phase for every process that centralizes log data. That ensures that log entries from different types and sources express information in the same format, using the same vocabulary.
As soon as logs from all different sources are normalized, it’s time to start processing them. At the “pattern recognition” phase, log analysis software can compare incoming entries with stored patterns, allowing them to differentiate between routine, ordinary messages—that should be discarded—and extraordinary, abnormal ones, which should trigger alerts.
Classification and Tagging
Classification is precisely what its name suggests. It might be advantageous to group or categorize log entries according to their attributes. You might want to filter logs by a specific date range, or track occurrences of a given severity level across all log sources.
Correlation analysis is the process of obtaining information from a variety of sources, finding the entries from each of those sources that are relevant to a given known event. This process is valuable because when an incident occurs, it might leave pieces of evidence in log entries from many different sources.
Log Analysis: Make the Most Out of Your Logging Approach
Logs are omnipresent in IT and can come from a vast variety of sources. The primary purpose of logging is, as you’re aware, to help organizations troubleshoot problems in production. However, some techniques or processes enable organizations to use logging in exciting, novel ways. One such technique was the topic of today’s post: log analysis.
Log analysis is the process that helps you gather the raw data from your logs and discover meaning there. By analyzing your log entries, you’ll be able to find patterns you wouldn’t be able to find anywhere else. Having those insights helps you in your decision making, problem troubleshooting, and even with security and compliance.
Along with other techniques such as log analytics and log forensics, log analysis presents organizations with the opportunity of making the most out of their logging strategies. Most organizations treat logging as a mere troubleshooting facilitator. The mentioned techniques allow you to use logging more actively, as an insight generator and decision-making aid.
Now that you know the basics about log analysis, the next step for you is to roll-up your sleeves and start doing some work. Take a look at log analysis tools at your disposal—such as XpoLog’s offering, which is a tool that automates the entire log management lifecycle—and start putting log analysis to work for you ASAP.
This post was written by Carlos Schults. Carlos is a .NET software developer with experience in both desktop and web development, and he’s now trying his hand at mobile. He has a passion for writing clean and concise code, and he’s interested in practices that help you improve app health, such as code review, automated testing, and continuous build.