Logs are one of the most valuable assets when it comes to IT system management and monitoring. As they record every action that took place on your network, logs provide the insight you need to spot issues that might impact performance, compliance, and security. That’s why log management should be part of any monitoring infrastructure.
The first challenge is to aggregate your logs in a single and accessible location which you can easily do as part of your logging solution setup. However, merely centralizing logs is not enough – to gain insights from the aggregated logs you need to follow up with log analysis, which is what we’ll cover in this post.
By the end of this tutorial, you’ll find out what log analysis is, why it is important, and how to perform it to make sure your whole infrastructure is up to par.
Definition: What Is Log Analysis?
Log analysis is the process of analyzing computer-generated log events to get insight into the health and performance of IT environments and applications.
By looking at logs, companies can proactively and reactively mitigate risks, comply with security policies, audits, and regulations, and understand online user behavior.
Why Is Log Analysis Important: Purpose and Benefits
Most businesses are required to archive and analyze logs as part of their compliance regulations. They must regularly perform system log monitoring and analysis to search for errors, anomalies, or suspicious or unauthorized activity that deviates from the norm. Log analysis allows them to re-create the chain of events that led up to a problem and effectively troubleshoot it.
Moreover, while at first glance data log analysis may seem to affect only the IT aspect of your business, in fact, it impacts all its aspects, from legal to finance, sales and marketing, human resources, security, and operations. When leveraging log analysis, you can detect issues before or as they happen and avoid time waste, unnecessary delays, and additional costs, as we’ll explain shortly.
But let’s dive into the specifics – here’s why log analysis is necessary for your business:
Reduce Problem Diagnosis and Resolution Time
Hunting down issues can be a tedious and time-wasting task, especially when it’s not clear if the problem is on the application layer or infrastructure.
Whether it’s one or the other, or a combination of the two, they prolong the time your app delivers a poor user experience. Log file analysis allows you to take a proactive approach by pointing out issues and their root causes before or as they happen. This avoids time loss and reduces MTTR.
Also, DevOps can intervene and solve problems faster. This is what Sematext Logs is all about: shortening the time a business needs to detect and solve production problems and allow teams to focus more on improving existing and adding new functionalities to products and services they are creating instead of spending time troubleshooting. This, in turn, increases the value of software they are building, leads to more frequent releases, and increases the overall value for the business.
If you want to learn more about Sematext Logs, then check out the short video below:
Reduce Customer Churn
Customers are more selective than ever when it comes to the applications they use. With such a large pool of alternatives at their disposal, they can easily turn to one of your competitors if they’re not satisfied with your product or service. You must deliver an excellent user experience that, looking beyond functionalities, boils down to a stable and performant application with regular updates.
Frequent downtimes and poor product quality are among the top reasons for high customer churn. By analyzing your log files, you can detect the root cause of performance and stability issues faster, thus improving your users’ experience and reducing customer churn
For example, you’ll be able to search for HTTP errors and understand where and why they occurred; or detect when users don’t receive the information they searched for or if their requests are taking too long to load, or if some microservices are experiencing issues, and so on.
Improve Resource Usage & Production Infrastructure Costs
One of the most challenging aspects of any organization is resource management – from network bandwidth to CPU cycles or storage capacity and beyond.
You can speculate resource sizing, but you either end with not enough resources – which leads to poor performance, frustrated customers, and, ultimately, lost sales – or too many – which increase expenses, thus affecting your bottom line. Instead of guessing your resource requirements, log file analysis – along with metrics-based resource usage and planning – allows you to easily and more accurately understand your current resource utilization and your future resource requirements.
Also, when it comes to system performance, more often than not, it’s not the software at fault, but rather users’ requests that overload your system to the point where it has trouble handling the demand. Log analysis allows you to track resource usage and detect where your system is struggling so that you can add extra capacity.
On the other hand, you can also see underutilized or dead assets so that you can restructure and optimize your infrastructure to improve productivity and proficiency. You can also use server-sprawl data to optimize your on-premise or cloud infrastructure costs.
How Does Log Analysis Work and How Do You Do It?
Logs are streams of chronologically arranged messages generated by applications, network devices, operating systems, and any programmable or smart device.
When analyzed, they can provide a wealth of information about the components in your stack and how they interact. Here’s how you can perform log analysis the proper way:
- Collect – set up a log collector to gather all the logs across your infrastructure
- Centralize and index – ship the logs to a centralized logging platform. It’s easier to query and analyze logs from a single pane of glass. When collected to the central location, logs are also normalized to a common format to avoid confusion and ensure uniformity. Along with indexing, it makes data readily available and searchable for efficient log analysis. Read more about log aggregation.
- Search and analyze – you can search for logs matching various patterns, and structures. For example, logs with a specific exception or severity level. Analyzing logs via reports and dashboards make information available for everyone including people outside the IT department. Not to mention, of course, that it’s easier to spot trends or anomalies by looking at graphs or other visual representations of your data.
- Monitor and alert – set up alerts to notify you in real-time when certain conditions are not met. This helps detect what happened, when, where, why, and how it impacted performance, enabling you to build appropriate countermeasures and models to avoid risks. Alerts may also have triggers, such as calling a webhook to restart a service.
Log Analysis Use Cases & Applications
From handling security issues to troubleshooting app performance anomalies or compliance with regulations, there is a wide range of situations where analyzing log data provides invaluable insights. Here are the most common use cases for log analysis:
Better System Troubleshooting
One of the most obvious use cases for log analysis is probably in troubleshooting servers, networks, or systems, from application crashes to configuration issues and hardware failure. Fast troubleshooting helps avoid downtime and performance issues that can increase customer churn.
Troubleshooting with log analysis is often used in production monitoring as it enables DevOps to detect and solve critical system errors faster, improving operational efficiency. They have more time to invest in production, thus reducing production downtime.
Respond Better to Data Breaches and Other Cyber Security Incidents
When it comes to cyber-security, logs provide a fountain of information about your attackers, such as IP addresses, client/server requests, HTTP status codes, and more. However, they remain under-appreciated. Many companies fail to understand the value of logging analysis still relying only on basic firewalls or other security software to protect their data against DNS attacks. However, without log analysis, you can’t understand security risks and respond accordingly.
Logs act as a red flag. With security log analysis, you can track down suspicious activities and set up thresholds, rules, and parameters to protect your system from similar threats in the future. With log analysis, you’re even able to assist in blocking your attackers by their IP address.
Log data analysis systems can alert you whenever they detect anomalies so that you can quickly intervene and eliminate the threat. They use artificial intelligence and machine learning to spot patterns and behaviors that would have otherwise flown under the radar.
Furthermore, logs are extremely useful in cyber forensics. In case of an investigation, forensic log analysis can provide the time and place of every event that happened in your network or system.
Ensure Compliance with Security Policies, Regulations & Audits
Most organizations are subject to government-set standards and industry requirements they need to adhere to guarantee safety and functionality. As such, many are required to log data and analyze it on a daily basis. Doing so, not only helps to defend against insider and outsider threats but also to demonstrate a willingness to comply with ISO, General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Sarbanes-Oxley, PCI DSS, and many others.
Besides being critical to cyber security, log files analysis can also help with audit requirements, litigation needs, subpoena requests, and forensic investigations.
In short, considering the ever-growing complexity of systems and software solutions, log analysis is the only way to ensure policies are followed, and regulations are met.
Understand Online User Behavior
Log analysis is one of the best ways to understand your app or webapp’s visitors’ behavior. It shows not only how many visitors you had but also allows you to re-trace their exact journey and understand on what pages they spent the most time, what were they doing on your website, why are there changes in the number of visitors, and so on.
With trends and patterns in plain view, it’s easy to spot opportunities like when is the best time to send a newsletter, when to release a new version or launch a product, when to close down your site for maintenance or tests, and much more.
Furthermore, log analysis can be used to impact marketing efforts as well. By collecting data such as referring sites, page accessed, and conversion rates, you can determine how well your marketing campaign does and take measures to improve it if needed.
Similarly, as logs contain information about conversion errors, customer navigation, and traffic loads, logging analysis can provide meaningful insights about how to improve website performance to better support the sales process.
Log Analysis Best Practices: Functionalities You Should Know About
Log analysis is a complex process that should follow the following functions:
Pattern Detection and Recognition refers to filtering incoming messages based on a pattern book. Detecting patterns is an integral part of log analysis as it helps spot anomalies.
Log Normalization is the function of converting log elements such as IP addresses or timestamps, to a common format.
Classification and Tagging is the process of tagging messages with keywords and categorizing them into classes. This enables you to filter and customize the way you visualize data.
Correlation Analysis refers to collecting data from different sources and finding messages that belong to a specific event. It helps to make connections between logs since multiple systems record an incident. For example, in the case of malicious activity, it allows you to filter and correlate logs coming from your network devices, firewalls, servers, and other sources. Correlation analysis is usually associated with alerting systems – based on the pattern you identified, you can create alerts for when your log analyzer spots similar activity in your logs.
Artificial Ignorance is a machine learning process that recognizes and discards log entries that are not useful and is used to detect anomalies. When it comes to logging analysis, it means to ignore routine messages generated from the normal operation of the system like regular system updates, thus labeling them as uninteresting. Artificial ignorance alerts your about new and unusual events, even about common events that should have occurred but did not – for example, if a weekly update has failed. These should be investigated.
Log Analysis Tools
Businesses generate huge amounts of log data which makes log analysis a tedious process unless you’re using a log analysis tool.
Log analysis tools are essential for effective monitoring, enabling you to extract meaningful data from logs and troubleshoot app- or system-level errors. They allow you to detect trends and patterns and use these insights to anticipate and mitigate risks and even guide your business decisions.
From developers to DevOps, SecOps, and more, every IT professional would benefit from log analysis software.
When choosing your log analysis solution, we recommend looking beyond budget and functionalities and also take into consideration how much time you have to invest in analyzing your logs. Do you want to spend time building a log analysis tool, or you’d rather use a service that does it out of the box so that you can focus on your core business? Discover why you’d benefit more from a fully-featured log analysis solution such as our Sematext Logs.
Why Use Sematext Logs for Log Analysis
Sematext Logs is a log management platform that can be used as a service or installed on-premises. The log analysis functionalities extend that of the ELK stack, meaning you can collect logs from a large number of log shippers, logging libraries, platforms, and frameworks. With Sematext, you get powerful searching, filtering, and tagging capabilities that enable easy and fast anomaly detection. Further, combined with Sematext Monitoring, it makes for a unified solution that enables efficient monitoring by allowing you to correlate logs with infrastructure and application metrics. You can then set up alerts on both to be notified whenever alert rules are met so that you can quickly jump in and start tracking down the issues.
Give it a try! There’s a free 14-day trial you could use to explore all its capabilities.
If you want to see how Sematext Logs stacks up against similar solutions, check out our reviews that cover the entire log management landscape, from log analysis tools to log management software, and cloud logging services.
Thanks to its multiple purposes, log analysis is a critical part of log management. It helps with monitoring and alerting, measuring productivity, security incident response, governmental compliance, and it’s useful even in cyber-forensics.
There are plenty of log analysis tools to help you make sense of your log data, both free and paid. They help streamline your DevOps workflow and saves time by avoiding going through massive amounts of unstructured data. With such tools, you are better equipped to detect issues and threats before they impact your business, find the root cause, and proactively and reactively mitigate risks. Such tools will also make it easier for you to implement logging best practices that will help you get the most out of your application logs.
If you’re not already using one, it’s best to get it soon and not wait for a serious incident to come up. If you need help deciding or any support regarding logging, at Sematext, we offer Logging Consulting so feel free to reach us out!
- Log Management Guide gives you a refresher on logging basic concepts
- Kubernetes Logging Guide is a tutorial about how to centralize and analyze Kubernetes logs
- Docker Logging Guide offers an introduction to how to log and analyze in Docker
- Linux Logging Guide is a tutorial about how to view, search, and centralize Linux logs.
- Java Logging Guide offers an overview of the basic concepts you need to know to start centralizing Java logs