The act of examining application or system logs to glean valuable insights regarding system behavior is known as log analysis. And, for a variety of reasons ranging from incident response to feature innovation, it is one of the most valuable processes to understand for those in the development and IT operations space.
Keep reading for a primer on log analysis, including an explanation of why log analysis is critical for all software organizations, what it can be used for, and how to make the process of analyzing logs as efficient and effective as possible.
Logs contain information that is instrumental in evaluating system behavior. In the case of a web application, for instance, a single log event may contain the IP address of the client making the request, the name of the resource being requested, the HTTP status code returned to the client, the time the request was received, and more. This type of information can be very valuable to organizations, but for them to be able to gain insights from it, the data must undergo analysis.
Effectively examining log data requires engineers to take one or more steps to contextualize it:
It is extremely difficult for developers and IT personnel to fully wrap their heads around what’s occurring within their software systems without analyzing log data. And understanding these systems as thoroughly as possible is critical for effectively performing several different processes that are crucial for organizational success.
Log analysis provides insight into system behavior. But what are these insights used to accomplish? Let’s take a look at a few processes in which log analysis provides significant value.
One of the most important post-deployment processes for any software organization is their incident response process. When problems occur within systems in production (and they inevitably will), the race for a resolution begins. The speed at which incidents are resolved is heavily dependent on the speed at which root cause is determined. And this, in turn, often hinges on effective log analysis.
Consider a situation in which users are reporting recurring issues when trying to edit their user profiles within a web application. Sure, a developer could simply begin the troubleshooting process by trying to reproduce the issue in his or her local development environment, but without specific details about the incident, the developer would likely waste time by attempting to replicate the exact scenario that resulted in the failure. Instead, the developer could accelerate root cause analysis and deliver a solution in a more timely manner by examining log entries and identifying the specific exception that’s being thrown as users attempt to edit their profiles.
Log analysis also provides significant value by enabling DevOps teams to gain insights into their applications that allow them to better identify areas for improvement. For instance, examining log entries can help teams to map patterns of user behavior.
Let’s say that you have a user registration process for your application. Through the analysis of log event data, analysts can identify the total number of visitors who begin the registration process versus the number of visitors who actually see the process through to completion. They can also identify the most common step at which visitors leave the registration process. This gives teams a potential starting point for redesigning the process as a whole, or at the very least, the particular step that is having the greatest impact on conversion rate.
Along the same lines, log analysis allows teams to determine other user behavior patterns such as which features and content are most popular with their visitors. This type of information provides DevOps teams with the direction they need to ensure that development efforts are focused on making the changes that have the greatest impact on user experience.
Log analysis is also critical for ensuring that organizations in certain industries are in compliance with required standards. For instance, organizations that manage electronic protected health information (ePHI) must be in compliance with HIPAA regulations, one of which reads as follows:
Information system activity review (Required). Implement procedures to regularly review records of information system activity, such as audit logs, access reports, and security incident tracking reports.
This requirement, defined in section 164.308(a)(1)(ii)(D), requires procedures for reviewing system activity in order to remain in compliance. Effective log analysis is likely the easiest way to do this.
In the same vein, if an issue with data security or compliance does arise, log analysis can be critical for determining the cause of the problem as well as finding a resolution that ensures the same breach or compliance shortfall doesn’t occur again.
It’s clear that regular analysis of log events is necessary for any forward-thinking development organization. With that said, reaping the benefits of log analysis is certainly a tall task without the help of a log management platform.
Modern software systems are complex and highly distributed. Gone are the days of tightly-coupled monolithic applications. Instead, a typical system consists of a series of loosely-coupled components running on a distributed infrastructure, meaning that there are many more sources of log information. Log management platforms help teams deal with such complexities by ingesting logs from various sources and centralizing them in one location for easy access and examination. Combine this with enhanced functionality for filtering and searchability, and the thought of aggregating log data to gain a holistic view of system behavior quickly becomes less intimidating to DevOps teams.