A Beginner's Guide to Log Management

In the realm of observability, the words of Arthur Conan Doyle's Sherlock Holmes resonate deeply: "You see, but you do not observe". This insightful remark aptly captures a prevalent challenge in managing log data.

IT professionals often find themselves inundated with data — seeing an overwhelming amount of information but not truly observing the critical insights hidden within.

This article discusses how effective log management is not just about collecting and centralizing data, but also about about discerning the story it tells, much like a detective piecing together clues to solve a mystery.

What is log management?

Log management refers to the practices and tools aimed at effectively managing the extensive amounts of log data produced by diverse applications and resources within an IT environment.

It is about efficiently collecting, aggregating, storing, analyzing, and eventually discarding log data once it has exceed its useful life. Its fundamental objective is to facilitate easy observation and interpretation of log data across an entire infrastructure.

By centralizing log data, the process of pinpointing and troubleshooting system issues becomes more streamlined. This eliminates the need for manual examination of separate log files, thereby greatly enhancing efficiency in issue detection and resolution.

Why is log management important?

Logs are not just records; they are a goldmine of information that, when properly managed, can offer immense value in various aspects of IT operations such as the following:

• Troubleshooting: Logs are often the first place to look when something goes wrong. They provide the information needed to trace back and understand the root causes of issues.

• Alerting: They enable the detection of anomalous or unexpected behavior in systems which you can use to trigger alerts that prompt timely interventions.

• System analysis: When log data is centralized, you can gain insights into how your systems are performing and interacting, which is necessary for both operational efficiency and strategic planning.

• Prevention of recurrence: Once a problem is identified and resolved, logs can help in understanding the issue in-depth, aiding in the development of strategies to prevent similar issues from happening again.

Neglecting proper log management leads to missing out on all the benefits that logging is intended to provide.

Unmanaged, your logs will remain a scattered collection of data, rendering them practically useless for analysis, correlation, or strategic decision-making.

If you're stuck manually combing through various log files to tackle production issues, you're in for a slow, frustrating, error-riddled, and expensive process that doesn't scale up.

This is where log management comes in.

It allows you to not just view all your log data in a centralized location but also to analyze and understand them in context, unlocking their true potential.

With a proper log management system, what seems like an endless stream of data now becomes a powerful tool for observing your environment and enhancing system reliability, security, and performance.

Sold? Next, we'll look at the log management process and what it entails.

What is the log management process?

A typical log management process is characterized by a complex pipeline that integrates various crucial aspects. This is vital for transforming raw log data into valuable, actionable insights.

To summarize, the design of an effective log management system hinges on the thoughtful consideration and integration of several key elements:

1. Collecting and centralizing the logs in a log management tool, then securing access to them.
2. Analysing the logs, continuous monitoring, and setting up alert mechanisms based on specific log patterns or anomalies.
3. Disposing and archiving logs to optimize for cost, performance, and regulatory compliance.

Next, let's dive into the log management process in greater detail.

1. Log instrumentation

The foundational step in any log management process is the generation of logs. This involves instrumenting your applications with logging statements using a robust logging framework, and also ensuring that the various tools, services, and dependencies in your environment are also writing useful logs that adequately capture their various activities.

In this early stage, logs are typically stored in local files, which can be readily accessed and examined using command-line tools like tail, grep, sed, jq, and others. Storing logs locally provides quick, though basic, access for initial review or troubleshooting purposes.

A critical concern at this stage is focusing on generating logs that are well-formatted preferably in a structured format like JSON.

Paying attention to log structure and formatting is more than a mere technical requirement; it lays a solid foundation for an efficient and effective log management strategy.

Learn more: Logging Best Practices

2. Aggregating and centralizing logs

Once your applications and dependencies are generating well-structured logs, the next step is to collect these logs from diverse sources—including web servers, cloud environments, container systems, and network devices—and centralize them in one place for unified access and analysis.

This phase is often facilitated by a logging pipeline, which encompasses several key functions:

• Collection: Log shippers like Fluentd, Filebeat, or Vector are designed to gather logs from diverse sources in your environment, including web servers, cloud services, containers, and network devices.

• Buffering: To prevent overwhelming the system, logs are often temporarily held in a queue, like Apache Kafka. This buffering ensures a smooth, controlled flow of data into the pipeline.

• Processing: At this stage, logs undergo filtering, transformation, or enrichment to prepare them for analysis. This process ensures that the logs are in the most suitable format for their intended use. Tools such as Logstash or Vector are commonly used for these tasks.

• Centralization: Finally, the processed logs are consolidated in a centralized log management system. Options vary from self-hosted solutions like Elasticsearch to fully managed services such as Better Stack, catering to different needs and scales of operation.

Learn more: Getting Started with Log Aggregation

3. Securing access to logs

While logs typically shouldn't hold overly sensitive data, they often contain information that needs to be safeguarded from unauthorized access. As such, implementing robust security measures for your log data is crucial.

These measures can include encrypting logs to protect their confidentiality, applying data masking or redaction techniques to obscure sensitive details, and employing hashing for data integrity.

It's also vital to restrict access to certain categories of logs based on user roles and responsibilities, coupled with regular reviews of these access privileges.

Consider implementing an audit logging system that records every instance of log access, providing a clear trail of who accessed what logs and when. This is a key aspect of ensuring accountability and tracking potential unauthorized access.

Staying updated with the latest versions of your log management tools is another critical security practice. These updates often include essential security patches that protect against vulnerabilities. The well-known Log4j vulnerability serves as a stark reminder that any component in the logging process, if left unsecured, can become an entry point for malicious actors to compromise your systems.

Learn more: Best Logging Practices to Keep Sensitive Data Out

4. Real-time log streaming

Real-time log streaming, also known as live tailing, enables you to observe log entries as they are generated and recorded across all your systems, all from a single vantage point.

This approach offers the ability to filter and highlight specific information within the continuous flow of incoming logs, making it exceptionally efficient to observe crucial system activity (such as deployments).

It stands in stark contrast to traditional monitoring techniques, which typically involved SSH-ing into multiple servers and using commands like tail -f on individual log files.

Such practices are not only time-consuming but also limited in scope and effectiveness for any sufficiently complex environment.

With the advent of log centralization and streaming, it's much easier to get a holistic view of your system's activities and respond to events in real time.

5. Searching and analysis

The moment your logs start flowing into the log management system, a new phase unfolds—one of discovery and understanding.

At its core, log management is about helping you excavate through thick layers of raw log data to uncover details that help you troubleshoot a specific problem or see broader insights that reshape your understanding of your environment.

This invites you to harness the platform's search capabilities to sift through the data mosaic and distill the information that matters to you most. Some everyday use cases for searching your logs include:

• Filtering log levels, such as WARNING or ERROR to quickly address potential issues and failures, or DEBUG to isolate logs that contain debugging information.
• Locating events that occurred within a defined time range to aid in pinpointing when an issue started or how it progressed.
• Searching for logs related to specific user activities to monitor usage patterns or investigate security incidents.

For instance, to filter logs for a specific route in your service with an error status, over the past thirty minutes, create a custom query like level=error message.request.url:"search" and set the time range to the Past 30 minutes:

If you frequently conduct a particular search, consider saving it as a preset for easy and quick access for you and your team.

Beyond simple searches, you can also correlate your application's log entries and their dependencies to construct a more holistic view of events. This will help you understand complex system interactions and identify the underlying causes of observed behaviors or issues.

6. Monitoring and alerting

Log management goes beyond manually reviewing and searching centralized logs. When you set up continuous monitoring and alerting for real-time event awareness, it transforms from a passive data store to an active, vigilant overseer of your system's health and security.

Structured logs are a game-changer in this context. Their organized format simplifies the extraction of critical fields for monitoring, enabling the swift identification of significant events, abnormal behaviors, or security incidents.

Consider, for example, the potential of crafting specific rules within your log management system:

1. Event-based alert triggers

Here, you create rules for alert notifications based on specific occurrences in your application. This could be the logging of a specific message, the completion of a notable business process, or the absence of expected logs. For instance, if a daily database backup is supposed to happen and its success log is missing, an alert can be triggered to address the issue promptly.

2. Rate-based alert triggers

Observing one or two 500 server errors may be an indication of an intermittent failure, but a flurry of such errors could indicate a more severe problem.

Setting up rate-based alerts helps in identifying these trends without overwhelming you with notifications for every minor error, which can lead to alert fatigue.

Conversely, alerts can also be set for when the occurrence of certain events falls below an expected rate, signaling potential issues.

7. Reporting and dashboards

Another vital component of log management is its capability to provide visual representations of your environment's performance and to identify trends in system behavior using the log data as a source.

Dashboards play a crucial role in this context. They simplify the tracking of key performance indicators (KPIs), presenting them in a format that is not only easy to understand but also shareable among stakeholders. These visual tools transform complex log data into intuitive, graphical representations, making it easier for teams to interpret and make informed decisions.

Such dashboards and reports can be customized to highlight the most relevant information, offering a real-time overview of system health, usage patterns, and potential bottlenecks. This enhanced visualization aids in quick identification of issues, efficient resource allocation, and strategic planning, thereby significantly improving the overall management and oversight of IT infrastructure.

8. Log retention and archiving

Log retention is the process of determining how long logs should be stored. This decision is crucial, especially since many log management systems base their pricing on storage duration. For self-hosted systems, regularly purging or archiving older logs could be necessary for maintaining scalability and efficiency.

Legal and regulatory mandates, internal policies, and the specific utility of the logs typically guide the retention period Often, different types of logs have varied retention policies. For instance, at the University of Buffalo, the standard retention period is 30 days, but logs related to IDM audits or UNIX auth/login activities are kept for six months.

When the predefined retention period ends, there are two primary options for handling the expired logs:

1. Purging: This involves permanently deleting the logs. It's crucial to be certain that these logs will no longer be needed before choosing this option, as it eliminates any possibility of future retrieval or analysis.

2. Archiving: This approach transfers expired logs to a less expensive, cold storage medium (such as Amazon S3). While this still incurs some cost, it's significantly lower than keeping the logs active in the primary management system. Archived logs can be retrieved and re-ingested into the system if needed for further analysis.

Both strategies have their place in a comprehensive log management plan, allowing for an effective balance between cost, compliance, and the practical need for historical data analysis.

Log management challenges

The rapid increase in log data volume, fueled by the adoption of microservices, cloud computing, and containerization has significantly complicated the log management process for modern application environments.

To be effective in the current landscape, your log management pipeline must effectively address these fundamental challenges:

• High volume of log data: Modern applications generate a substantial and ever-increasing amount of log data. Collecting, storing, and querying this data is not only challenging but also often quite costly.

• Managing self-hosted systems: When opting for a self-hosted log management system, dedicated engineering resources are often necessary to manage and scale the environment. This commitment needs careful consideration before deciding on a self-hosted solution.

• Scalability and performance issues: Some log management tools may struggle to scale effectively with the growing volume of data, leading to slow data ingestion and delaying the analysis and response to the critical events captured in the logs. Carefully consider the performance characteristics of each tool you're adopting to ensure that it meets your current requirements with room to scale further.

• Lack of standardization: The absence of logging standards limits the ability to correlate data effectively across different systems and formats.

• Data security and privacy concerns: Preventing unauthorized access to log data at rest and in transit is a major challenge, especially when logs contain sensitive information. Ensure to select logging tools that can protect your data with encryption and access control.

• Preventing alert fatigue: Minimizing false positives and alert fatigue, while still ensuring critical issues are promptly addressed, is another challenging aspect of log management.

• Long-term data management: Managing the lifecycle of log data from creation to archival or deletion, especially in environments where data accumulates rapidly, requires careful planning and resource allocation.

While it's beyond the scope of this article to delve into each of these challenges in detail, you need to keep them in mind when designing and implementing a log management pipeline for your business.

Choosing the right log management solution

An important aspect of formulating your log management strategy is choosing an appropriate log management tool. You'll likely decide between going for a self-hosted tool, or using a fully-managed service.

A self-hosted solution might be more suitable if you require extensive customization or if you have stringent data security and privacy needs that necessitate storing all your data on-premise.

These systems typically offer more control over data storage and processing but demand a higher level of resource investment in terms of hardware, software, and skilled personnel for maintenance and management.

On the other hand, a fully managed service is ideal if you're seeking ease of use, scalability, and reduced operational overhead. Such services typically provide robust out-of-the-box features, regular updates, and seamless scalability to accommodate growing data volumes.

The key is to balance your needs with the capabilities and limitations of each type of solution to ensure that your log management strategy is effective and sustainable.

Better Stack provides a robust log and incident management solution that goes beyond centralized log storage. It includes advanced search functionality, monitoring, and alerting features designed to help you swiftly identify and rectify issues in real-time.

Sign up for a free account, and see how easy log management can be.

Learn more: Top Log Management and Aggregation tools

Final thoughts and next steps

It's clear that implementing a comprehensive log management strategy in modern software environments demands careful research and planning.

While it's certainly feasible, success hinges on careful strategizing and making informed choices.

I hope the tips in this article have helped steer you in the right direction. For further exploration, please see our detailed logging guides.

Got any further questions or comments on log management? Write me on X (Twitter).

Thanks for reading!

Article by

Ayooluwa Isaiah

Ayo is the Head of Content at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including LWN.net, Digital Ocean, and CSS-Tricks. When he’s not writing or coding, he loves to travel, bike, and play tennis.

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Log aggregation is a key aspect of log management that involves consolidating log data from multiple sources and centralizing them in one place for easy searching and analysis

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