ARE LOGS USEFUL?

The more you think about it, the less useful logs become, because:

  • They are big, so a lot of data must be analyzed.
  • They are somewhat cryptic, as you do not know what to expect.
  • They are not structured as you would like.

All in all, they are not what you long for and so, if not required, you would not use them.

But they contain the information that you need to detect the root causes for your problems. They are the only source of truth delivered by the systems. So, the reason you need logs is because they hold information that can't be found anywhere else. They are these magical files that track errors we don't even think of.

So, you need to do something with them, even if it is not clear what. The question that thus arises is, what am I going to do with my logs?

THE NEED FOR LOG MANAGEMENT?

First, logging is a good thing. Logging such that you maximize the value you get out of them is a better thing.

Log management addresses every aspect of log data. A log management system empowers businesses to centrally collect, parse, analyze, store, and archive their log data.

Log management allows teams to monitor and improve system performance, quickly identify issues and bugs and bolster security across an enterprise’s entire system. Logs and log data are the foundation of effective troubleshooting for application and service reliability and security.

So, from the perspective of the user, a log management system helps to manage the logs and makes the log useful for troubleshooting and long-time storage if required. This system becomes more and more critical as you will generate more logs from different sources. A robust log management system is the best way to store and analyze logs, ensuring optimal network and application observability and security.

LOG ANALYTICS

With the logs in a system, we still need to do something with the information. Here log analytics comes in handy. The log management system helps you with the analysis of large amounts of log files, through automatic parsing, timelines, easy to use query languages and so on. Most effective log management tools all provide these features out-of-the box.

However, log analytics do not work autonomously, the root cause still must be found by the engineer.

LOGS AND AI

Today, we have the technology at hand to optimize the logging process and to get to root causes quickly.

Using streaming parsing of logs and anomaly detection algorithms will bring you to a solution that is capable of automatically detecting root causes in your logs. These techniques are more and more refined and will deliver you the correct output.

However, these kinds of techniques do not come out-of-the box. Simple algorithms included in tools might today already bring added value, as they are based on anomaly detection rules.

For complex use cases you will either need to do the programming yourself or work with a partner with the necessary expertise to analyze your use case and find the correct solution.

USE CASE: APPLICATION LOG MONITORING

Logs are useful, very useful for application monitoring of all kinds. From application logs we can learn a lot of things, like the structure of the application, the communication between the different parts of the application, what goes wrong with the application both from technical standpoint and from application standpoint and above all, we can also capture the user experience of the application.

Ok, so far, the theory. How do you approach this and what do you need to do?

Here comes the specialist work. First, the logs need to be correctly parsed. This can be done through specialized algorithms but always requires insight into the data. What is the structure of the logs, the fields, does a log message contain more than one line, does it give stack traces and so on. All these aspects must be analyzed to create a good parsing algorithm.

Once the data is parsed, the log needs to be analyzed to understand the application structure and how the application works. The goal is to find the different components of the application and their relationships. After the structure is known, the application can be visualized with all its components and their interaction.

The next step is to find the anomalies in the messages. Not all messages contain the keyword “error”, and an error might be the result of previous messages. To find the anomalies, message chains must be analyzed through AI algorithms that pinpoint the point in time where it went wrong.

Once this is done, the algorithm requires a few retraining and some classification to tune the anomaly mechanism.

Of course, you do not want to do this for every application, only for your most critical ones.

CONCLUSION

Logs are a powerful tool for monitoring, analyzing, and managing systems, applications, and networks. They offer a wealth of information that can be harnessed to improve system reliability, security, performance, and compliance while facilitating efficient troubleshooting and decision-making.

Analytic tools are the norm in today’s modern log management tools, but still need human intervention to find the root causes.

The power of logs is really unleashed when AI models are used to help you find the root cause rather than trying to sift through loads of data yourself. Imagine the profit of application log monitoring when you have an application that does not meet the everyday needs of the end-user.

HOW CAN WE HELP YOU WITH LOG MONITORING?

MonitorNow introduces the means to manage your logs from collection to reporting using tools and services to maintain, report and interpret your logs.

We deliver log management and provide you open source (OpenSearch) or commercial tools (Elasticsearch) to interpret your logs, analyze the data and report on the items in your log files.

We deliver log management as a project or as a service.