Log Files

What are log files?

Log files are software-generated files containing information about the operations, activities, and usage patterns of an application, server, or IT system.

Log files provide valuable insights into the behavior, performance, and health of systems, enabling administrators, developers, and operators to diagnose issues, track events, and analyze patterns for various purposes, including system management, security monitoring, performance optimization, and compliance auditing.

What is the typical format of an IT log file?

The format of IT log files can vary depending on the system, application, or device generating the logs. However, most log files follow a common structure that includes key components and information. A typical IT log file will usually include some of the following:

  • Timestamp: Each log entry begins with a timestamp indicating the date and time when the event occurred. This helps in chronological analysis and tracking of events.
  • Log Level or Severity: Log files often include a log level or severity indicator that categorizes the importance or severity of the event. Common levels include DEBUG, INFORMATION / INFO, WARNING, ERROR, or FATAL, among others.
  • Log Message or Description: The log message provides a brief description or summary of the event or action that occurred. It may include specific details, error messages, or relevant contextual information about the event.
  • Source or Component: Log entries usually specify the source or component that generated the log entry. This helps identify the software application, system service, or device associated with the event.
  • Event Data: Log files contain additional data related to the event, which varies based on the nature of the log entry. This data could include error codes, IP addresses, file paths, user information, response times, stack traces, or other relevant details specific to the event being logged.
  • Log ID or Sequence Number: Some log files assign a unique identifier or sequence number to each log entry. This can be useful for cross-referencing and tracking related events.
  • Contextual Information: Log files may include contextual information about the environment, system configuration, or specific parameters related to the event. This information provides additional context for troubleshooting or analysis.
  • Log File Format: Log files can be stored in various formats, such as plain text (e.g., .log, .txt), structured text (e.g., JSON, XML), or specialized formats specific to certain applications or systems.

It's important to remember that log file formats can vary significantly depending on the application or system generating the logs. Whilst some log files may follow a standardized format or use industry-standard log formats, others may have a custom format specific to the application or system. The structure and content of log files are determined by the logging mechanisms implemented within the software or system.

The capabilities of monitoring tools and parsers to access and interpret log file data can rely on the monitoring tool having implemented capabilities specifically for certain formats and how well done, up to date or deep such integrations are. As such when evaluating log parsers and monitoring tools you should carefully investigate the level of detail that they support for what you consider the key logs to be monitored.

Log Files – One of the Three Pillars of Observability

Log Files (Logs) are considered one of the “Three Pillars of Observability” along with Metrics and Traces. Understanding how log files complement metrics and traces is important to understand the limitations and pros and cons associated with log file monitoring. An overview of the “Three Pillars” and how log files can be best used alongside other data sources is given in, The Three Pillars of Observability: Metrics, Logs and Traces (eginnovations.com).

Advantages of log files

  • Logs are an extremely easy format to generate – usually a timestamp plus a payload (often plain text). Can require no explicit integration by application developers other than adding a print statement.
  • Most platforms provide a standardized well-defined framework and mechanism for logging e.g., Windows Event Logs.
  • Often plain text and human readable.
  • Can offer incredibly granular information into individual applications or components allowing retrospective replaying of support incidents.

Problems with log files

  • Logs can generate large volumes of data, on PAYG (Pay As You Go) cloud this can incur significant costs.
  • Excessive logging can also impact application performance particularly when the logging isn’t asynchronous and can block functional operations.
  • Users often use logs retrospectively rather than proactively, using tools to manually parse information after an incident has occurred and users already impacted.
  • Persistence can be a problem especially in modern architectures using auto-scaling, microservices, VMs (Virtual Machines) or containers. Log files within containers can be lost when containers are destroyed or fail.

How can log files be used for performance monitoring and optimization?

Log files can play an important role in performance monitoring and optimization by providing insights into system behavior, resource utilization, and application performance. Some of the ways log files are leveraged for performance monitoring and optimization include:

  • Resource Utilization: Log files capture information about CPU usage, memory consumption, disk I/O, network activity, and other resource metrics. By analyzing these metrics over time, you can identify bottlenecks, resource constraints, or inefficiencies that may be impacting system performance. This information helps optimize resource allocation and capacity planning.
  • Application Performance: Log files contain valuable information about application behavior, response times, error messages, and exceptions. By analyzing application logs, you can identify performance issues, pinpoint specific components or processes causing slowdowns, and optimize code, database queries, or configurations to improve overall application performance.
  • Latency Analysis: Log files can help identify latency issues within a system or application. By analyzing timestamps and log entries related to request/response cycles, you can measure and identify areas where response times are slower than expected. This allows you to optimize code, network configurations, or database queries to reduce latency and improve system responsiveness.
  • Error and Exception Analysis: Log files provide detailed information about errors, exceptions, warnings, and other issues encountered by the system or application. Analyzing error logs can help identify recurring errors or patterns, allowing you to address them proactively and optimize code or configurations to prevent or mitigate these errors.
  • Threshold Monitoring: Log files can be used to set and monitor performance thresholds. By defining specific thresholds for critical metrics such as CPU utilization, memory usage, or response times, you can generate alerts or notifications when these thresholds are breached. This enables proactive monitoring and allows you to take timely actions to optimize performance and prevent performance degradation.
  • Trend Analysis: By analyzing log files over a period of time, you can identify performance trends and patterns. This helps in detecting gradual performance degradation, anticipating capacity needs, and planning for scaling or optimization efforts. Trend analysis can also help identify seasonal or periodic patterns that may impact performance.
  • Integration with Monitoring Tools: Log files can be integrated with monitoring and analysis tools, such as log management solutions or AIOps platforms such as eG Enterprise. These tools can aggregate and correlate log data from multiple sources, provide advanced analytics, visualizations, and reporting capabilities, enabling deeper insights into performance issues and optimization opportunities.

By leveraging log files for performance monitoring and optimization, organizations can gain valuable insights into system behavior, identify performance bottlenecks, and make informed decisions to optimize resource utilization, enhance application performance, and ensure a smooth user experience.

What are the most common log files used to monitor IT systems?

There are many common log files used to monitor IT systems across different operating systems and components. Here are some commonly monitored log files in IT systems:

  • System Logs: System logs, such as the Windows Event Log (Windows) or syslog (Unix/Linux), capture various system-level events, including startup/shutdown events, hardware errors, software installations, authentication events, and system service status. These logs provide insights into the overall health and behavior of the operating system.
  • Application Logs: Application-specific logs contain information about the behavior, events, errors, and performance of individual applications or services running on the IT system. These logs are essential for troubleshooting, identifying application-specific issues, and monitoring performance. Examples include IIS logs for web servers or application-specific logs for databases or messaging systems.
  • Security Logs: Security logs track security-related events and activities on an IT system, providing valuable insights into potential security breaches, unauthorized access attempts, or system vulnerabilities. Examples include Windows Security Event Logs, audit logs, or logs generated by intrusion detection systems (IDS) or firewalls.
  • Network Logs: Network logs capture network-related events, traffic, and connection information. They help monitor network health, identify network disruptions or anomalies, and track network performance metrics. Examples include firewall logs, router or switch logs, and DNS logs.
  • Database Logs: Database logs record database activities, transactions, and errors. They are critical for monitoring database performance, identifying potential bottlenecks, tracking database modifications, and diagnosing issues. Examples include transaction logs in relational databases like SQL Server or MySQL.
  • Web Server Logs: Web server logs capture information about HTTP requests, responses, access attempts, and errors for web applications. These logs are useful for monitoring website availability, analyzing user traffic patterns, detecting anomalies or security threats, and optimizing web server performance. Examples include Apache access logs or IIS logs.
  • Virtualization Logs: Virtualization platforms generate logs that provide insights into virtual machine (VM) management, resource allocation, and hypervisor performance. Monitoring virtualization logs helps ensure efficient resource utilization, track VM performance, and identify virtualization-related issues.
  • Middleware Logs: Middleware logs contain information about the behavior and performance of middleware components such as message queues, application servers, or integration platforms. These logs are crucial for monitoring the performance and reliability of middleware services and detecting issues that may affect overall system functionality.
  • Operating System Logs: Operating system logs provide detailed information about system events, resource utilization, hardware status, and software errors. They are valuable for diagnosing operating system-related issues, identifying performance bottlenecks, and monitoring system health. Examples include Windows Event Logs or Linux system logs (syslog).

The specific log files monitored will of course vary based on the IT infrastructure, operating systems, applications, and components used in a particular system.

How can log file monitoring be automated?

Often log files are used retrospectively to analyze service incidents and problems, administrators often parse and manually examine logs to figure out what has occurred and look for records that might identify the cause of an issue. Sometimes the level of verbosity in logging will be insufficient to gain full insights, usually because logging has been set at levels to avoid a performance impact on the system.

AIOps powered monitoring tools such as eG Enterprise are able to automatically and continually monitor logs at scales beyond human capabilities. AIOps systems baseline and learn the normal behavior of systems including logging, setting dynamic alert thresholds and allowing anomaly detection. When AIOps platforms detect anomalies, additional data can be collected. Often, this type of proactive monitoring will detect problems before employees or customers are impacted by application or service problems.

AIOps systems can correlate log data with metric, event and trace data from the entire application and infrastructure landscape and differentiate root-cause issues from secondary issues and avoid alert storms when problems occur.

The ability to auto-deploy monitoring is important in modern IT systems to ensure log file data is always captured, especially in the context of modern auto-scaling systems that may involve ephemeral resources such as containers or virtual servers / machines. Real time monitoring and capture of log files is now essential as log file data can be lost when systems scale back.