Today’s post continues the trend of covering log-related topics, by answering the question: “what is log analysis?” What is this, and why is it essential for your organization?
We will discuss log analysis practices and show you a few examples of how to investigate logs for different purposes.
We have also gathered a list of log analysis solutions, elaborated on the pros and cons, and summarized when it’s recommended to choose each one.
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Logs are ubiquitous in the tech industry.
A medium-sized IT organization can generate gigabytes worth of log entries daily.
Those logs come from a large variety of sources: operating systems, network devices, web and application servers, applications, IoT devices, just to name a few.
The aggregate of all these logs has the potential to be an oracle.
They might offer a unique window to all facets of the organization.
Unfortunately, since most teams and organizations treat logging as a mere “putting out the fires” mechanism, all of this potential goes to waste. That’s where log analysis comes in helpful.
In this post, we’ll offer a guide on log analysis.
We start by defining the term.
Then we proceed to cover some of the justifications and use cases for log analysis.
After that, we show the workings of log analysis, start with normalizations, and then exploring the other phases or processes that it includes.
In the end, we gathered and covered a list of well-known log analysis tools for your convenience and recommend when it’s best to use each one.
Defining Log Analysis
You can find many different definitions of log analysis around the web, varying in their length and straightforwardness.
Here’s how I’d define it:
Log analysis is the process of reviewing and understanding logs to obtain valuable insights.
So, this process allows organizations to analyze their logs in order to obtain knowledge that they wouldn’t be able to obtain otherwise.
They can then use such knowledge to their advantage, not only by improving their decision-making process but also in a variety of different ways.
We’ll explore those in more detail next.
Log Analysis: Understanding Its Value Proposition
Why bother with log analysis? What are the benefits your organization can reap from this practice?
As you’ll see, there are many reasons why organizations do log analysis. We’ll divide our list into three main categories: Security/Compliance, Troubleshooting, and Insights.
Log Analysis for Troubleshooting
The first reason for performing log analysis is also some of the most important reasons to perform logging itself. Namely troubleshooting problems.
Software development—and IT as a whole—is terrifyingly complex. Even with huge investments into defect prevention, we can never know for sure that our project will work as intended. And when it inevitably fails, we want to be able to access as much information about the problem as possible. That way, we can assemble the puzzle, understand what went wrong and why, and fix it.
Let’s drill down with two examples from the application monitoring world:
Rules-based application monitoring
When you know what you should monitor, you can use log analysis tools to hunt download critical errors and optimize applications.
Log analysis then helps you to find the problem in real-time and fix it before it creates damages.
You create your own set of rules and get alerts to a verity of channels such as MS Teams, PagerDuty, email, more.
ML-based log analysis for application monitoring:
Searching for errors and problems can be compared to searching for a needle in a haystack.
For that reason, some log analysis tools offer automated log analysis (learn more about automated log analysis).
Basically we are talking about ML-powered engine that learns your environment and detects problems, exceptions and anomalies in the application behavior on its own. Where there is abnormal behavior, where there are events that you didn’t encounter in the past, unique errors.
Instead of creating rules, the tool scans the data and understands when problems and risks might occur.
In this case, the log analysis tool helps your department taking a proactive approach.
Security and compliance concerns are high on the list of motivations for performing log analysis.
And the reason for that is clear: security problems can have catastrophic consequences for any organization, even putting it out of business.
So, any investment you make in the security front is justified, since the costs of the lack of investment tend to infinity.
So, the first reason why organizations should care about log analysis in the context of security is to understand and respond to security incidents such as data breaches.
Organizations should be ready to act swiftly and decisively when security incidents happen since that can be the difference between staying in business or not.
Another important use case for log analysis is to help organizations to conduct forensics due to some investigation.
In our post on log forensics, we list the following as reasons for performing log forensics:
- Finding the vulnerability which was exploited to allow an invasion
- Finding proof of a crime or hack
- Enabling data recovery from disasters
- Tracking the activities of a malicious actor
Since log forensics is, in a nutshell, log analysis put to the service of computer forensics and the law, all of the above are justifications for using log analysis.
In the compliance side of things, organizations might find log analysis useful for complying with both their internal security policies and external regulations.Here is an example of how a log analysis tool visualizes and monitors users’ activity to enforce and verify security policies.
It visualizes and monitors: users’ patterns and operations, users’ access to organization assets, users’ journey, onboarded users/deleted users. for instance track past employees entrances.
Last but not least, the “insights” category. As already mentioned, log analysis can help organizations gain insights that wouldn’t otherwise be accessible.
By having those insights, teams and organizations can improve their decision-making process, reevaluating strategies, and changing them as needed.
One typical example would be applying log analysis to understand user behavior as mentioned.
By doing so, the organization could, for instance, find out that users barely touch the new feature they thought would be a game-changer.
Aware of this fact, the company can now make an informed decision about whether to continue supporting the feature or not.
Log Analysis: Basic Workings
As we’ve explained in our article on log collection, logs can come from a large variety of different sources. Operating systems generate logs, but so do user-facing applications, network devices, and more. A typical log file contains many log entries, sorted chronologically. Those entries are stored in a persistent medium such as a file in the disk or a database table.
In order for the logs to be processed and interpreted correctly, they need to go through some very specific changes in their content. Such changes are necessary to avoid confusion due to differences in terminology. For instance, logs that come from a certain source might use “WARN” as one of their levels, while others might employ the whole word “Warning,” or even a completely different word. It’s crucial that such divergences be found and normalized.
Keeping formats and terminology consistent across all logs will reduce the number of errors and also keep statistics accurate. As soon as you collect and process the logs, it’s time to analyze them to detect not only usual patterns but also anomalies.
Log Analysis Processes
In the last section, we’ve touched briefly on the subject of normalization, which is a process that changes the logs data in specific ways, to make the analysis easier and avoid errors.
Normalization, though, is just one of the processes log analysis includes. We’ll now cover these processes—including normalization—in more detail.
Normalization is a technique that aims for consistency. It converts messages—in our case, log entries—so all of them use the same terms and data formats. Normalization is an essential phase for every process that centralizes log data. That ensures that log entries from different types and sources express information in the same format, using the same vocabulary.
As soon as logs from all different sources are normalized, it’s time to start processing them. At the “pattern recognition” phase, log analysis software can compare incoming entries with stored patterns, allowing them to differentiate between routine, ordinary messages—that should be discarded—and extraordinary, abnormal ones, which should trigger alerts.
Classification and Tagging
Classification is precisely what its name suggests. It might be advantageous to group or categorize log entries according to their attributes. You might want to filter logs by a specific date range, or track occurrences of a given severity level across all log sources.
Correlation analysis is the process of obtaining information from a variety of sources, finding the entries from each of those sources that are relevant to a given known event. This process is valuable because when an incident occurs, it might leave pieces of evidence in log entries from many different sources.
Log Analysis: Make the Most Out of Your Logging Approach
Logs are omnipresent in IT and can come from a vast variety of sources. The primary purpose of logging is, as you’re aware, to help organizations troubleshoot problems in production. However, some techniques or processes enable organizations to use logging in exciting, novel ways. One such technique was the topic of today’s post: log analysis.
Log analysis is the process that helps you gather the raw data from your logs and discover meaning there. By analyzing your log entries, you’ll be able to find patterns you wouldn’t be able to find anywhere else. Having those insights helps you in your decision making, problem troubleshooting, and even with security and compliance.
Along with other techniques such as log analytics and log forensics, log analysis presents organizations with the opportunity of making the most out of their logging strategies. Most organizations treat logging as a mere troubleshooting facilitator. The mentioned techniques allow you to use logging more actively, as an insight generator and decision-making aid.
Log Analysis Tools – Comparison List
Log Analysis With ELK Stack
ELK is an acronym that stands for Elasticsearch, Logstash, and Kibana.
Elasticsearch is a search and analysis tool. Logstash is a “data processing pipeline.”
It’s used to ingest data from many different sources, such as databases, CSV files, and logs.
Kibana is known as the charting tool for the ELK stack. It provides search and data visualization functionalities for data indexed on Elasticsearch.
As letters in the acronym refer to open-source solutions, you can deploy your own ELK stack without having to pay for it, which may prove a good alternative for organizations on tighter budgets.
ELK’s setup is labor-intensive, and it presents high storage and computation requirements. Also, the open-source version doesn’t offer some desirable features, such as alerting and monitoring capabilities, which would require the Gold tier in their subscription model.
Who should choose ELK?
This solution might be better suited to organizations who like the flexibility of open-source and can afford in a more DIY approach.
ELK might also be the natural choice for small development teams who already use Elasticsearch for other needs.
Splunk Log Analysis Solution
The first log management tool in our list is Splunk, which is a comprehensive utility very well-known by sysadmins. It’s available as a downloadable tool for Linux, Windows, and macOS X. A cloud version also exists, as well as a free version with limited capabilities.
Splunk is a complete solution with an extensive list of features, which includes machine data indexing, real-time and historical searching, advanced reporting functionalities, and more.
As already mentioned, Splunk is a popular tool among system administrators. Since the community is so large, you have many other fellow users who ask for help. This might also make the onboarding of new team members easier: it’s probable that they already know the solution.
Splunk is probably a better fit for organizations with larger budgets. Despite offering a free version, most of the more desirable features are only available for users of the Enterprise edition. Splunk is also somewhat harder to learn than its competitors, which is relevant when thinking about TCO.
Who should choose Splunk?
Splunk might be an excellent fit for organizations that are searching for reliable technology and a consolidated brand and have the budget for it.
Loggly is another cloud-based solution. It’s a log aggregation and analytics service that allows you to analyze all your log data in real-time from a single place.
Loggly comes with good search capabilities, combined with the capacity to collect and analyze logs from many different sources from a centralized place. In the visualization department, Loggly comes with pre-configured dashboards covering popular technologies but allows you to combine its advanced charts into customized dashboards.
Despite having good search capabilities and great visualization tools, Loggly might not be as feature-rich as some of the other tools on this list.
Who should choose Loggly?
Loggly might be the best fit for organizations looking to deploy primarily to cloud instead of on-prem and also can do without more advanced features.
SumoLogic Log Analysis Tool
SumoLogic is a cloud-based platform which provides centralized log analytics service.
It uses machine learning to detect patterns from your logs in real-time, allowing you to gain insights into your application’s behavior.
SumoLogic doesn’t require a labor-intensive install processing. It’s easy to set up and start using and doesn’t require a lot of upfront costs.
SumoLogic is a unified platform for all your logs and metrics. It presents an extensive list of features, which includes great search capabilities and the use of advanced analytics by leveraging machine learning and predictive algorithms.
Probably the major con of SumoLogic is its pricing model around log data retention.
With the free and trial accounts, you get seven and three days, respectively. To retain data for longer periods, you’d need a professional or enterprise account, which can get prohibitive depending on your organization’s budget.
Who should choose SumoLogic?
SumoLogic is a feature-rich and convenient—due to being SaaS—solution.
That, combined with the way its pricing model works in regards to log data retention, makes it an interesting choice for small, cloud-only organizations starting with a small number of logs.
XPLG Log Analysis Platform
XpoLog is a fully automated log management platform, which makes use of AI to learn your environment and warns you about potential problems.
XpoLog is a feature-rich platform that is easy to maintain and deploy. It contains a marketplace featuring apps for a wide array of platforms.
The tool offers algorithms that automate analysis. Its AI-powered analysis layer allows teams to discover issues quicker.
When it comes to the pricing model, XpoLog is an affordable tool with great ROI and TCO.
XpoLog has a smaller community than other solutions on this list, so finding help might be slightly harder. Also, its focus is on IT and security and less on the developer’s community like some of the competitors.
In short, XpoLog is a great solution, it is not as known as some of the more high-profile items on this list.
Who should choose XpoLog?
XpoLog is a great fit for enterprises and SMEs that look for an affordable solution with quick deployment, but make a point of having great monitoring technology for their apps and IT infrastructure.
Log Analysis Uses Cases Examples
Log analysis solutions allow you to get crucial insights in order to optimize resources, support continuous delivery of services and troubleshoot errors, detect possible risks and get to the source, system health, and more.
Here are some guides on how you can perform the investigation manually and also how you can get the insights out-of-the-box with XpoLog:
- Linux security – How to Investigate Suspected Break-in Attempts In LINUX
- Amazon Monitoring and Analysis
- Windows server security: How to Look for Suspicious Activities In Windows Servers
- Monitoring, Analyzing, and Troubleshooting Your NGINX Logs.
- AWS S3 Security: How to Secure and Audit AWS S3 Buckets?
- Apache Error Log and Access Log Analyzing and Troubleshooting
Now that you know the basics about log analysis and understand the different offers of each tool, the next step for you is to roll-up your sleeves and start doing some work.
Take a look at a log analysis tool such as XpoLog’s, and start putting log analysis to work for you ASAP.