What is Log Management? Benefits, Challenges & Best Practices

Let's face it: Managing logs is not exactly the most exhilarating aspect of monitoring and observability workflows. Even if you're a seasoned IT geek, you probably don't wake up in the morning excited to collect, analyze, and rotate your log files. In fact, you may not even have asked yourself the question, "What is log management?" because you don't think all that much about logs.

Nonetheless, effective log management is essential work for any organization seeking to take full advantage of log data to optimize application performance. That's why learning the ins and outs of working with logs – such as how to collect them, where to store them, how to analyze and report on them, and what to do with log data after you've analyzed it – is so important for almost anyone whose job includes application performance management, monitoring, and observability.

Keep reading for guidance on how best to approach log management today as we dive into what log management means, why it's important, and how to navigate the challenges of managing logs.

What is log management?

Log management is the process of collecting, analyzing, and storing logs – meaning files that applications, operating systems, and other IT resources generate to record events and status changes. Typically, organizations deploy a log management system to support these tasks.

The purpose of log management is to help ensure that organizations can use logs as effectively as possible. Alongside other sources of visibility, like metrics and traces, logs provide critical insight into what's happening within containers, applications, servers, and so on. But without a comprehensive, systematic approach to gathering, parsing, and storing that information, it's challenging to take full advantage of log data to help identify and correct performance issues. Nor will you be able to follow methodologies like the 4 Golden Signals, which hinge in part on collecting and analyzing log data.

Note, too, that log management is especially important today because modern systems often produce many different types of logs using various formats and store them in different locations. Having a log management strategy helps ensure that you can efficiently collect logs from across all of your software resources, and then analyze and correlate log data effectively.

Types of logs

Logs come in many types and forms – so many that we can't exhaustively describe them here. But we can cover the main types of logs you'll encounter in modern software environments, which include:

• Operating system logs: These logs typically record events that occur within a server's operating system or with resources controlled by the operating system – such as issues with hardware devices or kernel modules.
• Application logs: Applications often generate logs that record events or actions taken by applications, such as serving client requests.
• Error logs: While both operating system logs and application logs sometimes record errors alongside other events, in other cases they generate separate logs, called error logs, that track errors exclusively.
• Access and authentication logs: Some systems and applications also generate separate logs to record access and authentication events, such as user login events.
• Audit logs: Audit logs are similar to access and authentication logs (and in some cases these terms are synonymous), but audit logs are sometimes broader in that they record any type of security-related event, including but not limited to access and authentication requests. For example, as part of Kubernetes logging, you can enable audit logs to track all requests made to the Kubernetes API.

In case it's not clear, these categories of log file types overlap, and the exact types of logs that a given application or system generates can vary.

Some applications might generate a single log file that records everything that happens within the app, for instance, while others produce separate status, error, and audit logs. Some applications may also generate logs on a service-by-service basis instead of recording events from across all services in a single log file, too. And some apps don't produce logs at all. The types of logs you get, the way they are generated and the places they are stored depend on how developers decide to approach log implementation.

As for logs at the operating system level, they tend to be more uniform. Although Linux, Windows, and macOS each have their own approaches to generating logs, the way each type of OS produces and manages logs is consistent across different versions and flavors of that OS.

The 5 steps in the log management process

Working with logs effectively boils down to five key steps, which form the log management process.

1. Collection

First, you need to collect log data. As we mentioned, this can be challenging in today's complex, distributed systems because logs are often strewn across varying locations, which means teams must find a way of pulling log data in from disparate sources.

2. Centralized storage

Typically, logs are stored in a central location after collection is complete. Centralized storage is valuable because it allows log data to be analyzed from a central vantage point.

In addition, storing logs in a central location is important because some logs, like those stored in containers, are ephemeral, meaning they disappear when the resources that generate them shut down. Copying logs to central storage ensures that the log data will persist even if the original logs disappear.

3. Analysis

Analysis is the process of analyzing logs to find relevant events, patterns, and anomalies. Today, this process is usually automated with the help of log monitoring and observability tools that can parse logs and detect insights.

The most effective log management solutions are also able to correlate data from multiple logs, as well as contextualize logs with metrics and traces, to maximize visibility and actionability. For example, if you detect an error event inside a server log, being able to measure the server's CPU and memory metrics at the time the error occurred would help you determine whether a lack of sufficient CPU or memory might have caused the error.

4. Reporting and alerting

In the context of log management and log management tools, reporting is the practice of generating reports that summarize insights or trends revealed by log data. Reports help IT teams gain a big-picture view of what's happening inside their systems. They might reveal, for instance, that a certain type of error occurs on a routine basis, prompting engineers to look into ways to prevent that type of error.

In addition to generating reports, logs can also trigger alerts about potential issues that the IT team should investigate immediately. Alerts don't summarize overarching trends, but they do help guide an effective response to performance issues.

5. Action

IT teams should respond to alerts and reports by taking action. This means investigating the issue, determining its root cause, and then remediating it. In addition, action may include taking steps to prevent similar issues from recurring in the future.

Note, too, that action in response to issues identified by logs can sometimes be automated. As so-called AIOps tools – meaning those that use AI to assess and fix issues automatically – become more sophisticated, automated response is becoming an increasingly important component of log management.

Benefits of log monitoring and management

Effective log monitoring and management provides several key benefits to IT teams and the business as a whole:

• Troubleshooting: As one key source of visibility into performance issues, logs enable effective troubleshooting. When something goes wrong, logs often record the issue. They also provide contextual information that can help teams assess and fix it.
• Performance: Along similar lines, logs help organizations optimize the performance of applications and services. That's because logs provide insight into potential issues and help teams research and remediate them, leading to fewer disruptions and a better end-user experience.
• Security: Although security events are only one type of information that logs can record, logs are essential for detecting security issues, like malicious requests or anomalous activity associated with a breach.
• Compliance: Some compliance frameworks require organizations to maintain log files for security and auditing purposes. Even if there is not a specific mandate related to logging, the information stored in logs can help organizations demonstrate to regulators and auditors that they're in compliance with the standards they need to meet.

Common log management challenges

While log monitoring and management are important, no one said they're easy. Expect to face challenges like the following.

Lack of standardization

As we mentioned, developers can design applications to generate logs in whichever formats they wish. There are some popular formats – such as the Common Event Format (CEF) and the Common Log Format (CLF) – but not all applications and operating systems follow them. And even if they do, there are still multiple formats to deal with.

This lack of standardization can make it challenging to correlate and compare log data quickly because each type of log records and structures data a little differently. As a result, it's often necessary to convert logs from one format to another, or extract data and restructure it in a standardized way, before you can analyze logs.

High log volume

Modern systems can generate a lot of logs and log data. This is especially true in the context of microservices-based, cloud-native apps, which often generate a different log file for each microservice or container – meaning a single app could produce dozens of logs. On top of this, you have at least one operating system log file for each node running in your cluster, with other types of logs – like load balancer logs and orchestrator logs – tossed in the mix.

Contending with this high volume of log files and data requires a highly efficient approach to log management. The ability to automate processes like log collection, transformation, and analysis is critical.

Limited context

Individual log files typically offer limited context. They record certain types of events, but they don't provide the complete set of information you need to know what else was happening at the time those events occurred.

This is why it's important to be able to correlate log files with each other and with other sources of visibility – such as, again, metrics and traces. Correlation allows you to determine, for instance, whether multiple microservices experienced the same type of performance issue at the same time, or if the problem was limited to just one service.

Storage limitations

Although log files only contain text, and individual lines of text don't take up a lot of space, log files can add up to clog the media you use to store log data. This can lead to situations where you run out of space to store new logs. It can also bloat your storage costs.

To avoid running out of storage for logs, most organizations rotate log files. Log rotation means deleting older logs or moving them to lower-cost storage (like a "cold" cloud object storage tier). Just remember to avoid deleting any logs that you may need to retain for a certain period due to compliance mandates, or that you simply want to have on hand so you can research long-term trends.

7 log management best practices

The following best practices can help make log management more efficient and effective:

1. Automate using log management tools

It's virtually impossible to manage logs effectively at scale without the help of automation tools. To keep up with the large volume and complexity of log data, it's critical to automate the process of collecting, storing, processing and, in most cases, rotating log files.

2. Consider the cloud

Cloud storage offers a convenient and scalable place to consolidate logs in a central location. A potential downside is that cloud storage bills may become high if you’re storing very large volumes of log data. But in many cases, the total cost of storage is lower in the cloud, especially if you rotate out the log data you no longer need.

3. Standardize log formats

To the extent possible, generate all of your logs in the same format. You may not have control over log formats in the case of applications or platforms developed by third parties. But if you build apps in-house, or if you have third-party apps that give you options about how logs are formatted, generating all of them in a consistent format will make it easier to work with the data efficiently.

4. Convert logs

In cases where you can't generate logs by default in a consistent format, converting logs to a standard format is the next best option. Various log management solutions are available that can automatically convert logs from one format to another.

5. Contextualize log data

We said it before and we'll say it again, because it's so important: Data from individual logs is often not all that useful. To drive real visibility and action, your logs should be correlated with each other, as well as with other observability insights.

6. Configure effective log levels

Some applications and platforms support multiple log levels. Log levels are labels that identify the severity or urgency of events. Using levels, it becomes easier to distinguish high-priority issues from less severe ones. If log levels are available, it's a best practice to turn them on.

7. Collect log data in real time

Collecting log data as soon as it's produced is important for two reasons. First, it ensures that you can identify and react to issues as soon as possible. Second, it protects you against losing log data in the event that the logs are ephemeral and the resource that hosts them (like a container) shuts down unexpectedly.

Types of log management tools

Several types of tools are available to help teams work with logs efficiently:

• Log collection tools, which automate the process of collecting logs from disparate locations and storing them in a central place.
• Log analysis tools, which automatically parse log files and identify relevant events or anomalies.
• Log visualization tools, which can represent log events or patterns using graphs or charts, making it easier for humans to recognize insights.
• Log rotation tools, which automate the process of deleting or archiving log data that you no longer need.

In some cases, standalone tools exist that focus on each of these needs. You can also find log management systems or platforms that provide access to multiple types of log management functionality through a single product.

Log management with groundcover

When it comes to log management for the cloud-native world, groundcover has you covered. The groundcover log management system can efficiently collect, query and store logs from any source, at any scale. You can also configure custom log analysis and alerting, helping to ensure that your team knows right away about issues that matter – and that they're not distracted by irrelevant or redundant log data.

Learning to love logging

As we mentioned, it's not exactly easy for most people to get excited about log management. But when you realize just how important log management is for optimizing performance – and how modern log management solutions can help to automate tedious processes like log collection and analysis – it becomes easier to embrace log management as one of the key steps in a modern observability strategy, and to recognize the value of implementing a log management system.