Log Analyzer - Definition & Overview

When we say log analyzer, we’re referring to software designed for use in log management and log analysis. Log analysis tools that are leveraged to collect, parse, and analyze the data written to log files. Log analyzers provide functionality that helps developers and operations personnel monitor their applications as well as visualize log data in formats that help contextualize the data. This, in turn, enables the development team to gain insight into issues within their applications and identify opportunities for improvement.

As you will see, log analysis offers many benefits. But these benefits cannot be realized if the processes for log management and log file analysis are not optimized for the task. Development teams can achieve this level of optimization through the use of log analyzers.

One of the traditional ways to analyze logs was to export the files and open them in Excel. This time-consuming process has been abandoned as tools like Sumo Logic have entered the market. With Sumo Logic, you can integrate with several different environments using IIS web servers, NGINX, and others. With free trials available to test out their log analysis tooling at no risk, the time has never been better to see how log analyzers can help improve your strategies for log analysis and the processes described above

Effective log analysis requires the use of modern log analysis concepts, tooling, and practices.

The following tactics can increase the effectiveness of an organization’s log analysis strategy, simplify the process for incident response, and improve application quality.

Real-time log analysis refers to the process of collecting and aggregating log event information in a manner that is readable by humans, thereby providing insight into an application in real-time. With the assistance of a log aggregator and analysis software, a DevOps team will have several distinct advantages when their logs are analyzed in this way.

When log analysis is performed in real-time, development teams are alerted to potential problems within their applications at the earliest possible moment. This enables them to be as proactive as possible, thereby limiting the impact that an incident has on the end users. The types of incidents that previously went unreported and undetected by the DevOps team will now have the team’s attention in a matter of minutes. This provides the necessary framework for increasing application availability and reliability.

In addition to notifying the development team of application issues nearly instantly, real-time log file analysis provides developers with critical context that enables them to resolve incidents quickly and completely. This limits the amount of downtime experienced by the customer while also adding to the likelihood that the issue will be thoroughly resolved.

In any application built with visibility and observability in mind, log events are being generated all the time. As end users utilize the application, they are creating log events that need to be captured and evaluated in order for the DevOps team to understand how their application is being used and the state that it’s in.

To illustrate this point, imagine that you have a web app. As users navigate the app, log events are generated with each page request. Request data can provide meaningful insights, but the painstaking and tedious process of combing through massive log files on individual web servers would be too much for human beings to handle productively. Instead, these log events should be consumed by a log analyzer that centralizes all log data for all instances of the application. This enables human beings to digest the log data more efficiently and more completely, which in turn allows team members to readily evaluate the overall health of the application at any given time.

Glancing at individual requests on a single web server may not provide much insight into how the application as a whole is performing. But when thousands of requests are aggregated and utilized to create visualizations, you get a much clearer picture for evaluating the state of the application. For example, are a significant number of requests resulting in 404s? Are requests to pages that have historically responded in a reasonable time frame experiencing latency? Centralized log collection and analysis allows you to answer these questions.

In addition, it’s important to know that the analysis of log events isn’t just useful for responding to incidents that are detrimental to the health of the application. It can also help organizations keep tabs on how customers are interacting with their application. For example, you can track which sources are referring the most users and which browsers and devices are being used most frequently. This information can help organizations fine-tune their applications in order to help provide end users with the greatest value and user experience moving forward. It is much easier to gather this information when log data is contextualized through centralized log collections and intuitive visualizations – and the easiest way to do this is to use log analysis tools such as the one provided by Sumo Logic.

I would certainly be remiss if I didn’t discuss how the increased visibility provided by log analyzers allows DevOps folks to get to the root cause of application problems in the shortest time frame possible.

In the context of application troubleshooting, root cause analysis refers to the process of identifying the central cause of an application issue during incident response. When dealing with application issues of any complexity, log files are almost always a focal point. But, as is often the case, raw logs also contain a plethora of information that has no relevance to the issue at hand. This sort of information (or noise) in log files can make it difficult to isolate information related to a particular incident.

In the realm of root cause analysis, log analyzers provide critical tooling designed to empower development and operations personnel to sift through the noise and dig into the data that’s relevant to the issue at hand. This includes:

• Alert functionality - Notifying the correct staff of an issue at the earliest possible moment in time. In addition to leading to a faster resolution simply by starting the process of analysis sooner, alerting often helps incident response personnel connect the dots between the problem and its cause by providing an exact time frame for when the issue surfaced.
• Visualizations - Representing log entries in a manner that provides context for the data being collected. In the process of root cause analysis, it is not uncommon for an alarming trend to accompany the incident. Visualizations that depict such trends can prove extremely useful in helping staff develop hypotheses that bring them closer to identifying the root cause of the problem.

Search and filter functionality for centralized log data - As mentioned earlier, sifting through log entries can be extremely tedious. Advanced search and filter functionality for centralized log data can help cut down the amount of time it takes to isolate instances of a particular incident in order to begin deciphering its underlying cause.

The single biggest data set that IT can use for monitoring, planning, and optimization is log data. After all, logs are what the IT infrastructure generates while it is going about its business. Log data is generally the most detailed data available for analyzing the state of the business systems, whether it be for operations, application management, or security. Best of all, the log data is being generated whether it is being collected or not. But in order to use it, some non-trivial additional infrastructure has to be put in place. And with that still, first generation log management tools did run into problems scaling to the required amount of data, even before the data explosion we have seen over the last couple of years really took off.

Log data does not fall into the convenient schemas required by relational databases. Log data is, at its core, unstructured, or, in fact, semi-structured, which leads to a deafening cacophony of formats; the sheer variety in which logs are being generated is presenting a major problem in how they are analyzed. The emergence of Big Data has not only been driven by the increasing amount of unstructured data to be processed in near real-time, but also by the availability of new toolsets to deal with these challenges.

Classic relational data management solutions simply are not built for this data, as every single legacy vendor in the SIEM and Log Management category has painfully experienced. Web-scale properties such as Google, Yahoo, Amazon, LinkedIn, Facebook and many others have faced the challenges embodied in the 3Vs first. At the same time, some of these companies have decided to turn what they learned in building large scale infrastructures to run their own business into a strategic product asset itself. The need to solve planetary-scale problems has led to the invention of Big Data tools, such as Hadoop, Cassandra, HBase, Hive, and the lot. And so today it is possible to leverage offerings such as Amazon AWS in combination with the aforementioned Big Data tools to build platforms that can address the challenges – and opportunities – of Big Data head on and without requiring a broader IT footprint.

In this article, I have discussed the many ways in which log analyzers simplify the processes of log file analysis and root cause analysis while enabling DevOps teams to react more quickly to problems that threaten application quality and reliability.

While tools that provide this type of invaluable and in-depth functionality would be difficult to build and manage in-house, Sumo Logic offers platform-specific integrations for many popular servers and applications in use today. For instance, with the Apache App for Sumo Logic, development teams can monitor and analyze all of their Apache server logs in one centralized location.

With Apache log analysis tools from Sumo Logic, development teams will no longer have to jump from server to server in order to analyze activity on their site. In addition, log data from all sources can be used to construct visualizations that help development teams understand their visitors in more depth than ever before. Finally, with the support of real-time analytics and alert functionality, DevOps folks will have the insights they need to respond to problems as efficiently and proactively as possible.

Sumo Logic also has log analyzer app integrations for use with environments using IIS web servers, NGINX, and others. With free trials available to test out their log analysis tooling at no risk, the time has never been better to see how log analyzers can help improve your strategies for log analysis and the processes described above.