What is log parsing?

Log parsing is a process that converts structured or unstructured log file data into a common format so a computer can analyze it. It's a foundational tool for proactive IT management, giving an organization valuable insights into an application's behavior and performance.

The goal of log parsing is to identify and group log entries into relevant fields and relational datasets using a structured format, such as a database, JSON, or CSV, so information can be easily searched and analyzed. With log data structured, DevOps and SRE teams can simplify log analytics to improve performance, protect system health and security, meet service-level agreements, and comply with regulatory requirements. Log parsing enables organizations to make data-driven decisions that support growth and spur innovation.

A log file is a record of events, activities, and hardware states that occur within an IT system. Logs include time stamps, IP addresses, error codes, and usernames—typically in plain text format, either structured or unstructured.

Log parsing involves using specialized software tools or scripts to automate log data extraction and analysis, especially in big data environments where large volumes of logs are generated regularly.

Log parsing typically consists of the following activities.

• Collecting log files. Log files generated by web servers, application servers, databases, and network devices are stored in various formats within predefined directories or locations.

• Ingesting log files. Log parsing tools ingest log files from their specified locations.

• Recognizing patterns. Log parsing tools use predefined patterns or regular expressions to recognize and extract relevant data from the log entries.

• Tokenizing entries. Log parsing tokenizes log entries into key-value pairs or structured data to make it easier to analyze. With tokenizing, analysts can search and filter logs based on specific criteria.

• Normalizing data. The log parsing process normalizes data to ensure consistency. Normalizing might involve converting time stamps to a standardized format or resolving abbreviations.

• Storing data. Extracted and parsed log data is stored in a central repository or database for analysis and retention.

• Querying and analyzing data. Once the log data is parsed and stored, it can be queried and analyzed to gain insights into system performance, security incidents, or other events of interest. This can involve creating dashboards, generating reports, setting up alerts, or performing ad-hoc queries.

Log parsing is used to manage and analyze logs that various systems, applications, and devices generate across different domains. These include IT infrastructure (logs from web servers, application servers, database servers), networking (logs from routers, switches, firewalls), and operating systems (logs from Windows events, Syslog on Unix/Linux systems).

Additional IT systems and environments where log parsing is essential include the following:

• Virtualization, such as logs from hypervisors (VMware, Hyper-V)

• DevOps, such as logs from build servers (Jenkins, GitLab CI)

• Container orchestration, such as logs from Docker containers and Kubernetes pods

• Cloud platforms, such as logs from services like AWS CloudTrail, Azure Monitor, and Google Cloud Logging

• Web applications, such as logs from JavaScript errors, HTTP requests, application errors, and database queries

• Enterprise applications, such as logs from customer relationship management systems (Salesforce), enterprise resource planning systems (SAP, Oracle), and custom-built applications

• Mobile applications, such as logs from Android (Logcat) and iOS applications

• Internet-of-Things devices, such as logs from sensors capturing telemetry data, device status, and environmental conditions

• Security systems, such as logs from intrusion detection and prevention systems (IDPS) and centralized log management systems

• Compliance audits, such as logs used to demonstrate adherence to regulatory requirements (such as Payment Card Industry Data Security Standard, HIPAA, and General Data Protection Regulation)

Log file analysis helps organizations ensure all their applications and services are fully and continuously operational and secure. By consistently and proactively reviewing log events, teams can quickly identify IT system disruptions and prevent issues for fast mean time to repair and continuous, optimized performance. As a result, they can improve customer and employee satisfaction and retention.

Additional reasons log parsing is critical to DevOps, ITOps, and others charged with maintaining IT systems include the following:

Log parsing helps identify and diagnose issues within systems and applications. It provides detailed information about errors, warnings, and abnormal behaviors crucial for fast mean time to detect that minimizes downtime.

Log parsing helps determine the sequence of events leading up to an issue, isolate contributing factors, and implement corrective actions to prevent recurrence.

Log parsing supports continuous integration and continuous delivery practices. It provides visibility into application deployments, performance metrics, and operational feedback loops for iterative improvement.

Automated log parsing systems can generate real-time alerts and notifications based on predefined thresholds, anomalies, or critical events. This proactive approach to network monitoring allows teams to respond swiftly to emerging issues before they escalate.

By analyzing logs, organizations can monitor the performance of their systems, applications, and networks. They can track response times, resource usage, and throughput and identify bottlenecks or inefficiencies.

Log parsing, as part of an automated data ingestion solution, can pre-parse logs for their various attributes. As a result, analysts can just search or filter on those attributes, such as JSON attributes, without having to write complicated queries to parse them first.

Logs contain valuable information for detecting security incidents, unauthorized access attempts, malware infections, and other suspicious activities. Log parsing enables security teams to analyze patterns, anomalies, and compromise indicators to respond promptly to threats.

Log parsing provides the audit trails required to verify adherence to security policies and demonstrates due diligence in protecting sensitive data.

By analyzing historical log data, organizations can forecast future capacity needs, plan infrastructure upgrades, and optimize resource allocation based on trends and usage patterns identified through log parsing.

Log parsing contributes to business intelligence by providing teams with insights about user behavior, application usage trends, customer interactions, and other data-driven decisions that can improve operational efficiency and customer satisfaction.

Log parsing is a fundamental practice that forms the backbone of proactive IT management and decision-making processes across industries. It enables organizations to maintain operational resilience, enhance security posture, comply with regulations, optimize performance, and derive actionable insights from their IT infrastructure and applications. But distributed cloud environments and monitoring tool sprawl can make log management and analytics derived from log parsing difficult.

Dynatrace Log Management and Analytics simplifies log parsing and analysis by automating data extraction, centralizing log management, and aligning log data with performance metrics.