Simple Practices for Easier Log Management and Troubleshooting
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Managing logs can quickly become overwhelming—especially when you're dealing with massive amounts of data coming from multiple microservices. When you’re troubleshooting a production issue, sifting through noisy, unstructured logs can feel like trying to find a needle in a haystack.
But it doesn’t have to be this hard. By implementing a few practical strategies, you can turn log management into an efficient, streamlined process that actually helps you solve problems faster.
In this guide, we’ll explore key practices for easier log management, from defining clear logging objectives to using structured formats and sampling logs strategically. Whether you're a developer, DevOps engineer, or tech lead, these steps will help you simplify log handling and make troubleshooting less of a headache.
Why Simplifying Log Management Matters
When your logs are cluttered and hard to navigate, troubleshooting becomes an uphill battle. Clear and organised logs allow you to quickly identify issues, trace problems across services, and minimise downtime. In contrast, poor log management slows everything down, leaving you sifting through irrelevant data when time is of the essence.
One of the biggest challenges in achieving easier log management is the sheer volume of information. Modern applications generate vast amounts of logs, and if you don’t have a streamlined process, you’ll end up drowning in data. From misconfigured log levels to unstructured formats, these issues compound, making it difficult to find what you need when you need it.
With these challenges in mind, let's dive into how defining clear logging objectives can reduce noise and help you focus on what really matters.
Establish Clear Logging Objectives
Without clear logging objectives, you end up with a mess of noisy, irrelevant data that slows down troubleshooting. To achieve easier log management, it’s essential to define specific goals for your logs, ensuring that they effectively serve your operational needs.
Here are some key objectives to aim for:
- Reduce Noise: Collect only the most relevant information to avoid overwhelming logs with unnecessary data.
- Align Logs with Business Goals: Ensure your logs capture data supporting key operational and business objectives, like tracking critical events or monitoring system health.
- Perform Regular Reviews: Continuously evaluate your logging strategy to trim unnecessary entries and adjust verbosity, keeping logs relevant as your system evolves.
With these objectives in place, you can keep your logs organised and actionable.
Next we’ll explore how proper log levels can further refine your logging approach for maximum efficiency.
Use Proper Log Levels
Log levels are your first line of defence in avoiding noisy logs and ensuring easier log management. By properly categorising log entries based on their severity, you can make troubleshooting more efficient and focus on the right data at the right time.
Let’s break down the key log levels and their use in real-world scenarios.
INFO: The Routine Data
INFO logs capture standard operational information. These entries track routine events that are important for understanding the system’s normal behavior but aren’t necessarily errors or warnings. For example, logging user logins, API requests, or service starts would fall under this category.
Example:
A web application logs every user login as an INFO log. This allows the team to track user activity without cluttering the logs with unnecessary details unless something goes wrong.
WARN: Something Needs Attention
WARN logs indicate something abnormal happened, but the system is still functioning. These logs point to potential problems that require attention but don’t immediately disrupt operations. Warnings are useful for flagging issues that could lead to bigger problems if left unchecked.
Example:
A database connection might take longer than expected, but the request still completes. A WARN log entry is generated, allowing you to investigate why the latency occurred before it causes a serious outage.
ERROR: System Disruptions
ERROR logs are generated when something goes wrong and impacts the normal operation of your system. These logs should signal a failure or malfunction that requires immediate attention. Unlike WARN logs, ERROR entries point to events that have disrupted functionality, such as failed API calls, service crashes, or broken database connections.
Example:
A failed API request due to an authentication error would trigger an ERROR log. This lets your team quickly zero in on why a service isn’t functioning as expected.
FATAL: Critical Failures
FATAL logs indicate a critical failure that causes the system to crash or become unusable. These are the highest-priority logs and often mean the system needs to be restarted or a major issue needs to be resolved to restore functionality.
Example:
A FATAL log would be generated if your core service crashes due to an unhandled exception. The team would know to prioritize this issue immediately since the system is now offline.
Adjusting Log Verbosity for Troubleshooting
During routine operation, you want to keep logs at an appropriate verbosity level—too many logs, and you’re drowning in noise; too few, and you might miss important information. However, when you're troubleshooting a specific issue, increasing log verbosity can be helpful. Temporarily setting your logs to capture more detailed entries (like DEBUG logs) can provide deeper insights into the issue at hand.
Case Study:
A company had set all log entries to ERROR level during routine operations, meaning only errors were captured. During a sudden drop in application performance, the team struggled to identify the issue because all relevant INFO and WARN logs had been filtered out. They switched to capturing more granular INFO and DEBUG logs, allowing them to pinpoint the source of the problem: a misconfigured load balancer.
Using the correct log levels for everyday operations and troubleshooting can significantly enhance easier log management. Now that you understand the importance of proper log levels, let’s move on to how structuring your logs can further streamline your troubleshooting efforts.
Structure Your Logs
Unstructured logs might work for small systems, but when your application scales, sifting through massive, messy logs becomes a nightmare. That’s where structured log formats come in, offering a clear, consistent way to organize your logs and make easier log management a reality.
Why Structured Log Formats Are Superior
In the early days, logging often meant dumping plain-text messages into files with little to no format. While this worked for small applications, it quickly becomes inefficient when your system scales. With unstructured logs, it’s hard to filter, search, or parse the data without manual effort, wasting valuable time.
Structured logs, on the other hand, organise data into a consistent format—typically key-value pairs—making them far easier to query and analyse. For example, using JSON as a structured format allows logs to be machine-readable, making them compatible with various tools and analytics platforms.
Example:
Unstructured Log:
|
Error: Service failed at 10:00am. Check connection. |
Structured Log (JSON):
|
{ |
With structured logs, you can easily search for specific fields, like the error level or timestamp, without needing to manually parse through raw text.
For teams looking to manage large-scale structured logs efficiently, tools like OpenObserve (O2) are invaluable. O2 enables seamless ingestion, storage, and analysis of structured log formats like JSON, helping you get actionable insights quickly, with 140x lower storage costs than traditional solutions. Sign up to get started!
With structured logging in place, you’ve already made a big step toward easier log management. Now, let's dive into the top tools and frameworks that can take your log management and troubleshooting to the next level.
Top Tools and Frameworks
Effective log management requires the right tools to organise, store, and analyse data at scale. Below are some of the best tools and frameworks to simplify log management and streamline your troubleshooting process.
OpenObserve (O2)
If you're looking for a cost-effective, scalable solution, OpenObserve (O2) is an excellent choice. O2 allows you to manage large-scale logs, traces, and metrics all in one unified platform. With up to 140x lower storage costs compared to other solutions, OpenObserve offers efficient log ingestion and analysis without draining your resources. It’s ideal for teams needing a simplified approach to log management and troubleshooting.
Sign Up | Website | GitHub
ELK Stack (Elasticsearch, Logstash, Kibana)
The ELK Stack—Elasticsearch, Logstash, and Kibana—offers a complete solution for collecting, processing, and visualising logs. Elasticsearch handles the search and analytics, Logstash processes and transforms logs, and Kibana provides an intuitive dashboard for visualising the data. It’s a powerful combination that helps you stay on top of large-scale log management.
Fluentd
Fluentd is a robust tool for collecting, transforming, and delivering logs. It supports structured logging and can easily integrate with various backends, such as Elasticsearch, Prometheus, and many others. Fluentd's flexibility makes it a great choice for managing logs in distributed systems.
Read more on Filter logs at source in otel collector
Prometheus
Primarily known for metrics collection, Prometheus can also be configured for log aggregation and monitoring. It’s particularly useful for teams managing microservices architectures, where both logs and metrics must be handled at scale.
Read more about Using Prometheus APM Tools for Asset Performance Management
With the right tools in place, easier log management becomes much more achievable.
Now, let’s explore how creating meaningful log entries can further enhance your troubleshooting capabilities.
Create Meaningful Log Entries
One of the biggest obstacles to easier log management is dealing with cryptic, context-free log messages that tell you next to nothing. Logs should provide clarity, not confusion. A well-written log entry can save you hours of troubleshooting by pointing directly to the problem and suggesting potential solutions.
Let’s explore how to make your log entries more meaningful and useful.
Avoid Cryptic Messages—Be Specific and Contextual
Logs need to speak clearly. Cryptic messages like "Error 42 occurred" offer no context, leaving you digging through code to figure out what went wrong. Instead, always include relevant information, such as what action was being performed, the conditions leading up to the issue, and any variables that could affect the outcome. The goal is to ensure that someone reading the log knows exactly what happened without needing to investigate further.
Example of a Bad Log Entry:
|
Error: Failed at 10:15 AM |
Example of a Good Log Entry:
|
Error: Database connection failed at 10:15 AM. Host: db-server1, Port: 5432. Retry attempt 3/5. |
Add Remediation Information
Whenever possible, logs should not only tell you what went wrong but also suggest what to do next. Adding remediation information, such as potential fixes or next steps, makes logs more actionable and speeds up problem resolution.
Example:
|
Warning: Disk space running low on server xyz. Only 5GB remaining. Please clear unnecessary files or increase disk space. |
This kind of entry doesn't just flag a potential issue but also helps direct the person responding to a specific course of action.
Well-Contextualized Log Entries in Action
Well-contextualized logs can make all the difference when tracking down issues. Here’s an example that includes everything you need for easier troubleshooting:
Example:
|
Fatal: Service API crashed at 3:00 PM. Endpoint: /users/register, User ID: 54321, Reason: Null pointer exception. Immediate action required to restart the service. |
This entry tells you what service failed, which endpoint was involved, and what needs to be done, allowing you to act quickly on the problem.
With these best practices, creating meaningful log entries becomes a crucial part of easier log management. Now that your logs are rich with context, let’s discuss the importance of balancing machine-readability with human-readability in the next section.
Log in Machine Parseable Format
If you want easier log management, logging in a machine-parseable format like JSON is a game-changer. It’s not just about storing data—it's about making that data actionable. When your logs are structured so that both humans and machines can understand, you open up a world of possibilities for automation, faster analysis, and smarter insights.
Use JSON or Other Structured Formats
One of the simplest ways to make logs machine-readable is by using JSON or similar structured formats. JSON allows logs to be consistent, easily searchable, and compatible with a wide range of tools that can automatically parse and analyse them. This ensures that your logs are not only readable by a person but can also be quickly processed by machines for faster troubleshooting or deeper analysis.
Example:
|
{ |
In this structured format, tools can easily search for specific fields, like all "error" logs or logs for a particular "user_id." This precision makes it incredibly efficient for both immediate analysis and long-term insights.
Balance Machine-Readability and Human-Readability
While machine-readability is crucial, you can't ignore the fact that humans still need to make sense of the logs. Striking a balance between machine readability and human readability is key. Ensure that your logs contain enough structure for tools to parse, but don’t sacrifice clarity for those who need to read and act on the logs.
For example, while JSON logs are highly structured and machine-friendly, they should still be easy for developers and engineers to scan through during real-time troubleshooting. Using readable keys (e.g., "user_id" instead of "uid") and including clear messages makes it easier for humans to understand without having to rely solely on tools.
By balancing these two elements, you create versatile logs that are ready for automation yet still easy to interpret on the fly.
Now that you understand the importance of machine-readable logs, let's look at another powerful technique for managing logs efficiently: log sampling.
Sample Your Logs
When you're dealing with a flood of logs, capturing every single event can be overkill—and expensive. That’s where log sampling comes in. By selectively capturing logs, you can reduce storage costs and still retain the critical data you need for effective easier log management.
Selective Log Capture as a Cost-Control Strategy
Log sampling allows you to capture a percentage of events rather than every single occurrence. This strategy is especially valuable when monitoring high-traffic systems where logging everything would generate unnecessary bulk and drive up costs. The key is to strike the right balance between too much and too little information.
If you're concerned about storage costs while sampling your logs, consider a solution like OpenObserve (O2), which offers a scalable, low-cost platform for log ingestion. O2’s architecture allows you to implement sampling strategies effectively, keeping your log volume manageable while still providing critical insights.
Basic and Advanced Sampling Methods
Basic Sampling: A simple method of log sampling is to capture a fixed percentage of all logs. For example, you might decide to capture 10% of all "INFO" level logs while capturing 100% of "ERROR" logs. This approach helps you manage log volume without losing important error data.
Example:
|
sampling: |
This setup ensures you prioritise critical error information while reducing noise from less important logs.
Advanced Sampling: For more complex systems, advanced sampling techniques might be necessary. One such method is dynamic sampling, where logs are captured based on certain conditions. For instance, you might increase the sample rate when an error rate spikes or when certain thresholds are met in system performance metrics.
Example: In an e-commerce platform, you might log all checkout events at a higher rate than product page views, as checkout logs provide more actionable insights for troubleshooting issues that directly affect revenue.
By using both basic and advanced sampling strategies, you can optimise easier log management while keeping costs under control.
With log sampling in place, you can fine-tune your log collection without overwhelming your storage or processing capabilities.
Conclusion
Implementing these strategies for easier log management—from structured logging to effective log sampling—can significantly improve your troubleshooting processes and reduce operational costs. By using tools like OpenObserve (O2), you not only streamline log ingestion but also manage large-scale logs, traces, and metrics at 140x lower storage costs compared to traditional solutions.
Ready to take your log management to the next level? Sign up for OpenObserve today, visit the website to explore more features, or check out the GitHub repository to dive into the code.
