As your tech stack increases, every new device (network devices, servers, applications) creates a large amount of distributed log data. This forms part of what is called “machine data”, which is growing 50x faster than traditional business data. In fact, everything in your stack is continuously writing new events to your log files, including error logs that contain a record of critical errors encountered by a running application, operating system, or server. All of this contributes to log overload.
The good news is that log data can be incredibly valuable for fast-moving organizations because it contains the behavior patterns of your applications and infrastructure. However, discerning which data is relevant can often be too big of a challenge for even the most experienced teams.
Traditionally, log management tools created a centralized repository for all of your log data, but with an abundance of data, the logs were only sifted through manually after a problem had already occurred. With machine learning, the more logs that are gathered in a log analysis tool, the more information can be used to create algorithms. Those log intelligence algorithms can be used to detect patterns and anomalies proactively, keeping the time spent sifting through logs to a minimum.
What Is a Log Analysis Tool?
A log analysis tool is essential for effective monitoring, gathering, and assessing your logs in one centralized location for users to gain app- or system-level insights from gathered log data. Analyzing logs allows you to rapidly troubleshoot and fix issues that have arisen by surfacing the most meaningful behavior patterns out of a mountain of log data. However, traditional log analysis tools require a lot of upfront work, including manual query-level matching or rule-based policies. This can help to mitigate business risks and troubleshoot applications currently running in your system, but does not account for new or distributed log data that comes with a growing environment.
What Is Log Intelligence?
Log intelligence can be defined as a method of log analysis that is powered by AI and automation. Intelligence platforms learn what’s “normal” behavior in your systems, and surface performance impacting issues in the context of alerts and metrics in the same timeframe. This extra layer of intelligence analyzes logs automatically, finding the root cause of issues and surfacing anomalies that exist within log data, sometimes even pre-empting trouble before it occurs.
How Do You Apply Machine Learning to a Log Analysis Tool?
Step 1 – Gather Data and Learn
When manually searching through log data, the fewer logs, the less you have to sort through. With machine learning, the more data you have, the more algorithms can be set to see what works best under a plethora of different conditions. Gathering as much information from as many data sources as possible will allow machines to predict future issues.
Step 2 – Define Normal Ranges From Learned Data
With enough log data necessary to see trends over time, the next step in applying machine learning is detecting what would fall in a normal range from log data. This can be done either manually or with the use of detection algorithms that return differences in log data.
Step 3 – Create Algorithms
Once the log data is gathered, and a normal range is set, these can be used to deploy algorithms that can alert you when log data leaves the normal, defined range of whatever metric you’re tracking. Or more likely, whatever series of hundreds or even thousands of metrics being tracked.
Difficulties With the Volume and Variety of Logs
When looking at one log over a length of time, the volume of those logs is easy to understand and see anomalies. When looking at logs for different metrics and data sources, the variety of those logs are easy to discern from one another.
Combining the volume of logs with a variety of hundreds of thousands of data sources is extraordinarily difficult, and finding correlations between different logs from different data sources is even harder to comprehend.
As logs continue to expand, artificial intelligence is necessary to create algorithms that automatically adapt and find anomalies.
Benefits of AI for Log Analysis
Having artificial intelligence with log analysis tools provides a variety of benefits, allowing you to:
–Sort through data faster. AI can group similar logs together and keep your logs more organized, allowing you to get to where you need to look faster.
–Detect issues automatically. With manual log analysis, you need to set datapoints that happen outside of a normal range. With machine learning, this can be done for you, which comes in handy when there are hundreds of thousands of datapoints and logs. When an issue with one is found, it can be automatically detected.
–Only be alerted to important information. Alerts from logs, like many alerts in IT, are prone to “boy who cried wolf syndrome.” When a log analysis tool creates too many alerts, no single alert stands out as the cause of an issue, if there even is an issue at all. With AI, you can move towards only being alerted when something worth your attention is happening, clearing the clutter and skipping the noise.
–Detect anomalies before they create issues. One of the most powerful benefits of AI for log analysis is detecting anomalies early. In most catastrophic events, there’s typically a chain reaction that occurs because an initial anomaly wasn’t addressed. AI allows you to remove the cause, not the symptom.
–Allocate resources faster and more efficiently. When you’re not spending so much time analyzing log data, you can more quickly and accurately allocate your resources where they’re most needed.
With the many different log analysis platforms available, it can be difficult to know what to look for. LogicMonitor believes in taking an algorithmic approach to understanding log signals through platforms that provide log intelligence. If you are interested in learning more, reach out to your Customer Success Manager or read more about our LM Logs platform here.
James Yaria is an employee at LogicMonitor.
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