Application Performance Monitoring (APM) is great for tracking the health and performance of your software tool. APM helps you understand what’s happening inside your application by monitoring various parameters such as CPU/memory stress, internal network throughput, and more. However, mixing in log analytics can take your APM game up a notch.
Almost all software tools generate logs when they run. Most APM tools are designed to either process an app’s logs to determine its state or to generate logs from inside an app after being instrumented. This article will focus on making the best use of logs using advanced log analytics.
Feel free to navigate the guide using these links:
- What is Log Analytics
- How do Log Management and APM Work Together?
- How was Advanced Log Analytics Previously Defined?
- Conclusion
What is Log Analytics?
In simple terms, log analytics is a well-described process of analyzing the logs generated from an application. Log Analytics is also the name of a service by Microsoft that is offered as part of their Azure offerings and helps to collect, process, and analyze the logs generated in their Azure services. However, our discussion will focus on this term’s broader meaning.
The logs generated by your app’s internal components might seem like an unnecessary overhead at a glance, but they may hold valuable secrets to the inner workings of those components. Moreover, many complex systems are quite hard to instrument, so log analysis automatically becomes a favored choice for performance monitoring.
If you already have a real-time solid application performance monitoring setup, you can use log analysis to reinforce your proactive monitoring efforts by analyzing event data in a non-real-time environment and identifying trends and patterns that occur in your app’s performance or usage. We’ll discuss this and a few more log analytics use cases in a later subsection.
Components of Log Analytics
Let’s take a quick look at the various components or steps involved in log analytics.
Preparation
The first step in the log analysis process is to prepare the log data. It is essential to understand that a component’s logs can be pretty verbose, and when it comes to modern applications, multiple components generate such verbose logs simultaneously.
Therefore filtering based on log severity, aggregating based on time or events, and removing inaccurate or incomplete logs is the first step to making your log analysis results accurate. You could also consider structuring/restructuring your log data to ensure that the log data collected from multiple data sources are in the same format. You can even go a step further by normalization and more complex aggregation.
Analysis
The analysis is the main component of a log analytics strategy. This is where you review and evaluate the cleaned logs data and generate results that will help you better understand your app’s performance. The log analysis step is often reinforced with machine learning or artificial intelligence systems to pick up trends in the data and perform predictive analysis to estimate when a trend will be noticed again.
By far, this is the most crucial step of log analytics. You can get the most out of your log analytics setup if you do this right. Some standard techniques and best practices used in this step are:
- Pattern Identification: Identifying patterns in your logs can help you understand how they would re-occur, thereby improving your chances of preventing events and issues before they occur again.
- Correlation Analysis: Correlating data from multiple sources can help you paint the bigger picture of the health of your system.
- Predictive Analysis: Based on the identified patterns, you can generate predictions as to when a metric will plummet or peak.
- Artificial Ignorance: Noise can often creep in when collating data from a considerable number of sources. Artificial ignorance can be applied to the log analytics process to ignore routine updates.
Visualizations
Visualization is the next step in log analytics that helps you quickly understand the analysis results. Simply generating results and identifying trends is not enough; you need the generated report to be easily understandable. It would be best to have a dashboard to help you quickly glance through information and determine the best possible course of action to enhance your app’s performance.
Why Do You Need Log Analytics?
There are several benefits of implementing log analytics. Here are a few of those:
Reinforce Your Real-time Monitoring Efforts
Many people often assume that real-time monitoring is about picking up incidents and acting on them, and there’s not enough time to analyze logs to aid the process. However, logs can come in handy if you know how to use them well.
There are multiple logging analytics tools that can help you reinforce your real-time monitoring efforts by analyzing the past logs data. You can use these to develop intelligent, proactive systems that react to downtimes better.
Uncover User Behavior Patterns
Besides ensuring uptime, monitoring also aims to help you better understand your users. By analyzing the user behavior patterns, you can understand the importance of each component of your system. You can better understand the impact of downtime on each component and scale them appropriately.
In these situations, logs can be helpful and used to track user behavior across various system components. You can run multiple kinds of analysis on the data to understand how your users use your app.
Identify Suspicious Behavior
When you have an idea of how your users typically use your app, it becomes easy to spot anomalies in app performance and user behavior. Regular and active log analysis can churn out data on suspicious system performance and user activity to help you understand when a downtime or security breach might be incoming.
Locate and Fix Security Breaches
The initial signs of security breaches often get documented in logs. Still, due to a lack of active log monitoring, they remain hidden until the breach becomes big enough to deal real damage. Actively processing and analyzing your logs can help you stop such issues before they become big enough to take your system down.
How do Log Management and APM work together?
While APM and Log Management are two very different monitoring segments, APM helps you understand your app’s performance, while log data provides insights about historical log data. However, they are closely related as logs are among the most critical data sources in application performance monitoring.
Pairing up log analytics with an APM tool like Scout can help you streamline your monitoring efforts. Apart from achieving other log analytics goals, such as securing sensitive data and identifying long-term trends, you can use logs as a fast-paced, real-time monitoring aide.
How was Advanced Log Analytics Previously Defined?
Log Analysis started as a straightforward service in the early days of Unix. The operating system offers CLI tools like grep, head, and tail to search and filter through its text outputs, which was one of the first methods of examining logs. There were no intricate processes to filter out unnecessary noise from the logs, and text manipulation was the only way to generate any results.
A few years later, tools like syslog-ng came up that supported log collection and management over the network. This was particularly useful in modern operating systems that started generating separate logs for each process. However, these tools only developed and improved log collection; they did not significantly improve how logs were processed and analyzed.
With the introduction of modern DevOps, this began to change. Since DevOps asks for a faster, automated process of software delivery, old log analytics methods that relied on manual techniques like text manipulation wouldn’t suffice. The need for quicker and automated log analytics gave birth to the process of log analytics as we see it today. Aggregation and filtering were added to smoothen data, and machine learning and AI were employed to get the most out of the log data. Many tools now offer all of these features under the same hood, making the lives of DevOps engineers easier.
Conclusion
Logs play a crucial role while debugging an application. Without access to logs, you will have no clue why an app behaves erratically or goes down. However, there’s so much more you can do with logs than just debugging; log analytics helps you look into this.
In this article, we saw how you could leverage various log analytics components to improve your logs collection, data, and results to help you understand and uncover patterns in your app’s performance and usage history. You also saw a brief history of log analytics and how it has strengthened over time. If you are looking for an application performance monitoring solution that can help you before you dive into the log analytics phase, check out Scout APM. Feel free to take a 14-day free trial of the tool before making a decision!