What are Logs?

Log files are automatically generated records detailing events within a software system. They are one of the most imperative tools for troubleshooting problems, monitoring system performance, and detecting security threats.

Log files can contain a wide variety of information, such as:

• Error messages
• Warnings
• Debug statements
• System metrics
• User Activity
• Security events

By analyzing log files, you can answer the following questions:

• How is your system performing?
• What errors are occurring and why?
• How are users interacting with your system?
• Is your system under attack?

Log files are essential for any organization to maintain a reliable and secure software system.

• A web developer can use log files to identify errors preventing users from accessing their website.
• A system administrator can use log files to monitor the performance of their servers and identify any potential problems.
• A security analyst can use log files to detect suspicious activity and investigate potential security threats.

Log files can also be used to improve the performance and reliability of software systems. For example, developers can use log files to identify bottlenecks in their code and make changes to improve performance.

How do Log Types and Categories work?

Log Types

Log files can be classified into different types based on content and purpose. Some of the most common log types include:

• Event logs: Event logs keep track of specific system activities like logins, file changes, and app issues. They're mainly used for fixing problems and checking system actions.
• Metric logs: Metric logs measure system performance numbers, like how much CPU or memory is used. They help in checking system health. However, it's good to note that using logs to capture these metrics can be costly.
• Unstructured logs: Unstructured logs are more free-flowing text logs that don't have a set structure or format.

Log Categories

Log files can also be classified into different categories based on their purpose. Some of the most common log categories include:

• System logs: System logs record events related to the operating system and hardware, such as startup and shutdown events, errors, and warnings.
• Application logs: Application logs record events related to specific applications, such as startup and shutdown events, errors, and warnings.
• Security logs: Security logs record security-related events, such as login attempts, failed authentication attempts, and intrusion detection alerts.
• Audit logs: Audit logs record system changes and user activity events.

How can you Understand Log Levels?

Log levels indicate the severity of a log event. The most common log levels are:

• Debug: Debug logs contain detailed information about the system's internal state. They are typically used for development and debugging purposes.
• Info: Info logs contain informational messages about the system. They are typically used for monitoring and troubleshooting purposes.
• Warning: Warning logs contain warnings about potential problems with the system. They are typically used for monitoring and troubleshooting purposes.
• Error: Error logs contain messages about errors in the system. They are typically used for troubleshooting purposes.
• Fatal: Fatal logs contain messages about errors that have caused the system to crash. They are typically used for troubleshooting purposes.

Log data is one of the most valuable assets for organizations of all sizes. Through log data analysis, organizations can gain key insights into system performance.

1. High-fidelity data

   Logs are generated by the system rather than manually entered by users. This makes log data an ideal source of truth for troubleshooting and analysis.

2. Logging for Metric

   While logs can be transformed into metrics (numerical indicators of system health), doing so is often avoided due to performance concerns. Yet, in the absence of a dedicated metric system, logs become a convenient choice for many developers.

3. Ease of Integration

   Logs can be easily added to any software system and stored and managed in various ways.

4. Detailed system insights

   By studying log data, organizations can discover detailed information about system behavior, errors, and user interactions.

5. Debug production systems

   Logs can be used to debug production systems without changing the code. This is important for existing systems that have been running for years.

6. Searching and Correlating Logs

   With tools such as grep and sed, logs can be swiftly searched and linked, simplifying the process of pinpointing the root cause of an issue by tracing back to the originating code. Conversely, metrics may present more challenges in terms of correlation and tracing to their source code.

Overall, logs are an essential tool for observability and troubleshooting. Organizations can improve their systems' reliability, performance, and security by understanding the importance of logs and implementing a comprehensive log management strategy.

Here are some specific examples of how logs can be used:

• Troubleshooting: Logs can be used to troubleshoot problems that occur in systems. For example, if an application is crashing, logs can be used to identify the root cause of the problem.
• Security: Logs can improve security by identifying suspicious activity. For example, logs can detect unauthorized access to systems or malicious activity.
• Compliance: Logs can be used to comply with industry regulations and standards. For example, organizations can use logs to demonstrate that they meet security requirements or track user activity.
• Performance optimization: Logs can be used to optimize system performance. For example, logs can be used to identify performance bottlenecks and to track the impact of changes to the system.

For a deeper dive into observability and log challenges, start with the basics in our guide to Observability Needs A Reboot .

Logs vs. Metrics

Metrics are numerical representations of system state or performance. They are typically collected at regular intervals and used to monitor the health and performance of a system. Metrics can also be used to generate alerts and identify trends.

Key differences between logs and metrics

• Logs are qualitative, while metrics are quantitative. Logs contain text-based descriptions of events, while metrics are numerical values.
• Logs are fine-grained, while metrics are aggregated at some intervals at the origination and lose accuracy.
• Logs are typically used for troubleshooting and debugging, while metrics are typically used for monitoring and performance analysis.
• Logs can contain a wide variety of information, while metrics are typically focused on a specific aspect of the system, such as CPU usage or memory utilization.

Examples of logs

• Error messages
• User activity logs
• System status logs
• Application logs
• Security logs

Examples of metrics

• CPU usage
• Memory utilization
• Disk I/O
• Network traffic
• Response times

Logs vs. Traces

While logs offer a diverse range of details, traces primarily concentrate on a request's movement within a system. They are records of the flow of a request through a distributed system. They track the request's path through different components and services. Traces can be used to identify performance bottlenecks and troubleshoot problems.

Key differences between logs and events

• Logs can contain a wide variety of information, while traces are typically focused on the flow of a request through a system.
• Logs are typically stored for a longer period of time than traces.
• Generally, 100% of the logs produced are stored, while trace employs a principal of sampling. Usually, the sampling is no more than 1%.

Examples of traces

• A trace of a web request as it flows through a web server, application server, and database.
• A trace of a message as it flows through a messaging queue and then to a microservice.

Logs vs. Events

Events are discrete occurrences that happen in a system. They can be generated by the system, applications, or users. Events have the potential to initiate alerts, produce log entries, or set off other operations.

Key differences between logs and events

• Logs are typically records of events, while events are the occurrences themselves.
• Logs can contain a wide variety of information, while events typically focus on a specific occurrence.
• Logs are typically stored for a longer period of time than events.
• Logs are typically large in number, while events are usually less.

Examples of events

• A user logging into a system.
• An application sending a message to a queue.
• A system generating an error message.
• Completion of a designated task.

Comparison of Logs v/s Other Observability Signals

Logs provide a detailed record of system activity that can be used to troubleshoot problems, identify performance bottlenecks, and track security threats.

Organizations can improve their systems' reliability, performance, and security by understanding the importance of logs and implementing a comprehensive log management strategy from the very start In most software projects, logs are an afterthought. However, by giving logs the attention they deserve from the start and setting clear guidelines, you lay the foundation for greater success as your project evolves.

Here are some key takeaways from our blog post:

• Logs are essential for observability.
• Logs can be used for various purposes, including troubleshooting, security, compliance, and performance optimization.
• There are different types and categories of logs.
• Logs can be used with other observability data, such as metrics, traces, and events.
• There are challenges associated with managing logs, but there are also strategies to mitigate these challenges.
• Implement log strategy and discipline from the get-go.

Whether you're a seasoned sysadmin, a developer, or someone just beginning to explore the world of logs and observability, I encourage you to test the power of logs. They can be a valuable tool for improving your software systems' reliability, performance, and security.