What is a log file?

Log files are data files (typically text-based) generated by devices, networks, applications, and operating systems containing recorded information about their activities, operations, and usage patterns. They serve as a primary historical record of everything that happens within an environment, providing crucial data on events, processes, security, performance metrics, and user activity.

Log files typically include descriptive contextual data like timestamps, recording exactly what happened within a system and when. Easily accessible and ubiquitous, log data is critical for troubleshooting system issues, resolving security incidents, and gaining insight into user behavior. Depending on the software or operating system, log files can appear in structured, semi-structured, and unstructured formats.

Log management is the continuous process of collecting, storing, and processing log data for future analysis. Effective log management is the first step in gaining actionable insights from log data, allowing for optimized troubleshooting and better system performance. This leads to deeper forensic analysis and more efficient resource management.

A centralized log management approach relies on tools that aggregate data from operating systems and other sources into a single, unified platform. Log management lets organizations sort and store their log data. From there, IT, DevOps, and SecOps teams can use log monitoring to track activity while log analysis helps proactively discover potential threats.

In practice, log management provides centralized access to log data in a sortable and searchable framework. Together with metrics, traces, and profiling, log management is a fundamental signal for both observability and security teams.

There's a log file for every digital event under the sun. Depending on the type of log source, that includes event logs, application logs, server logs, authorization logs, access logs, change logs, availability logs, resource logs, threat logs, audit logs, and performance logs—to list just a few.

Log files are produced at every level of IT infrastructure, across networks, web services, and servers, operating systems and apps, databases and firewalls, and containers and endpoints. Nearly every component of a system or network generates a different type of data. It then records and collects that information in a log. Typically, there are different levels of logging, so more verbose, data-rich logs can be generated for specific troubleshooting cases.

Here are some of the most common log file types:

System logs

System logs, or syslogs, record events within an operating system. These activities can include everything from system changes and startup messages to unexpected shutdowns, errors, and warnings. Nearly every operating system generates system logs, including Windows, MacOS, and Linux.

Web server logs

Web server logs keep a record of traffic patterns and errors. They can be used to diagnose technical problems if certain sections are not being indexed properly or visited often. They are useful for search engine optimization (SEO), recording how often a page is visited, how quickly new pages are likely to be indexed, or when on-page optimization changes are likely to be reflected within the SERPs.

Application logs

Application logs record events, errors, and operations that occur within software applications during runtime. They're used by developers, operations teams, and security analysts to monitor application health, troubleshoot issues, and track user activity. Application logs contain critical information about application behavior, including error messages, stack traces, performance metrics, user actions, and system states that help maintain reliable and secure software operations.

Security logs

Security logs track and record security-related events when they occur — like successful and unsuccessful login attempts, password and access-control changes, file deletions, and intrusion detection alerts. Security logs are often configurable by event type, allowing administrators to predefine the events they need to track and flag for security.

Network logs

Network logs record data from events that take place on a network or device, including network traffic, application events, and user activity. Monitoring network logs can surface network and device issues before they cause downtime, as well as offer visibility into overall network health and performance.

Error logs

Error logs record errors, warnings, unhandled error messages, and custom error messages. Software, systems, and applications all have error logs, which often contain information about the severity level of the error, along with the relevant context to debug and diagnose them.

The location of any given log file or log type will be dictated by the operating system, application, server, or service generating them.

• In Linux, most log files can be found in the "/var/log" directory.
• In Windows, you can access logs through the Event Viewer application.
• In MacOS, you can use the Console app.
• In applications, you can often specify log file locations in settings.
• Consult application-specific documentation for information on log file paths.
• Use command line tools like grep to search within log files.
• Use dedicated log management tools to centralize and analyze logs from multiple applications.

Reading, analyzing, and interpreting logs

Most .log files are similar to .txt files that any human can simply open up and read. At scale, however, attempting to work with terabytes of log data manually is unrealistic at best. On a general level, log analysis techniques like correlation, pattern recognition, and system performance analysis are used to detect anomalies and pinpoint root causes within your log data.

At an operational level, SREs, IT teams, DevOps engineers, and IT architects rely on log analytics tools to help them resolve application and system issues quickly and get ahead of future problems. Visualization tools and reporting dashboards can also help aggregate data for non-technical users, making it easier to see trends and anomalies.

Log file management and storage

Many analytics tools struggle with the sheer volume and diversity of logging data in today's exponentially expanding systems. Centralized log management and storage provide a foundation for an effective log analytics strategy.

With log data gathered in one location from all your sources, it's easier to manage and analyze them. Because logs collect so much data from distributed, often siloed, systems — all with different naming conventions, formats, and schema — log categorization helps standardize them for efficient analysis.

Log data also needs to be readily retrievable to teams when they need it. Individuals can use plain text searches like the grep command — a line tool that lets them reduce the size of logfiles to filter by date or IP address and more. But often, organizations need a cost-effective, secure storage solution that supports high availability, data integrity, and scalability.

Depending on your organization's needs, storage might be on-premises, cloud-based, distributed, or hybrid. The quality of your indexing and compression approaches will always have a direct impact on both the speed of accessing your log data and costs. Secure log retention is also a fundamental component of regulatory compliance.

Common log formats

At the most general level, log files come in structured, semi-structured, or unstructured formats. A log's specific format defines how the contents of that log file will be interpreted. Log formats can also define the fields and data types contained in a log file. Across today's increasingly complex infrastructures, log formats can vary widely, but some commonly used log formats include:

• Windows Event logs contain data from events that occur on the Windows operating system, including security, system, application, and DNS events. They are often used by administrators to troubleshoot application and system errors, track events like user logins, and get to the bottom of security incidents.
• JSON, or JavaScript Object Notation logs, are semi-structured logs that contain multiple key-value pairs. JSON logs allow data to be nested in different layers and also offer a way of maintaining data types like string, boolean, number, array, and object.
• CEF, or Common Event Format, is a standardized text-based format used by security-related devices and applications designed to make it easier to gather, aggregate, and integrate different log data in log management systems and SIEM.
• CLF, or NCSA Common Log Format, is among the oldest standardized log formats used by web servers. Because the text-based log format is fixed, you can't customize fields.
• W3C Extended Log File Format is used by Windows IIS servers. The highly customizable format allows you to configure what fields to include, which can help minimize the size of the files.
• ELF, or Extended Log Format, is used by web applications and contains data corresponding to a single HTTP transaction. They contain more information and field flexibility than similar CLF files.

Syslog is the standard logging format for Unix and Linux systems. The format is widely supported and can be centrally collected across networks. It follows a structured format with facility codes, severity levels, and timestamps. Syslog can be used for system logging, security auditing, and general messaging between system components.

OpenTelemetry (OTel) seeks a way forward to standardize log files and log formats for a richer correlation between logs and traces. This would make logs significantly more valuable in distributed and heterogeneous systems, producing greater observability.

With support for existing legacy logging libraries, log collection, and processing solutions, the OpenTelemetry log data model attempts to provide common ground on what logs should be, including what data needs to be recorded and interpreted by a logging system. All newly designed logging systems are expected to emit logs according to OpenTelemetry's log data model.

Common log use cases

From real-time application and performance monitoring to compliance, user behavior, root cause analysis, and SIEM, Elastic log analytics empower organizations to leverage their log data for maximum results and multiple use cases.

Whether you use log analytics for observability or security, machine learning capabilities can help you remove the noise from your observability environment. LLMs and AI assistants offer a wealth of diagnostic and domain experience to help teams thrive. Log analytics can also help you gain visibility into third-party services and applications and take a more proactive approach to application management using SLOs with logging.

With an avalanche of data to ingest every day, enterprise log management platforms help organizations manage and process the vast quantities of log data generated across organizations and systems. Effective log management is vital for maintaining the health, security, and performance of IT systems, ensuring compliance, diagnosing and debugging issues, reducing bottlenecks, and optimizing resources.

Complex, dynamic, distributed cloud systems and large-scale cloud applications are often tricky to observe. Comprehensive enterprise log management increases the availability and searchability of logs so DevOps, SecOps, and IT teams can do their jobs more efficiently.

Elastic Observability is one of the most widely deployed log analytics and management tools for a reason. A leader in log monitoring, Elastic offers scalable, centralized log monitoring for hybrid cloud. Built on Elasticsearch, it provides powerful and flexible log management and search functionality. Whether on-premises or in the Elastic Cloud, Elastic can easily scale to handle petabytes of your organization's log data for troubleshooting, insights, observability, or security initiatives.

For enterprise log management solutions, the LogsDB index mode reduces the storage footprint of log data by up to 65%, enabling teams to expand visibility without exceeding their budget.

• The 3 pillars of observability: Unified logs, metrics, and traces
• What is Log Analytics?
• What is Log Monitoring?
• Log monitoring with Elastic
• Log analytics with Elastic Stack
• Stream any log file with Elastic Observability