Optimize > Log Indexing and Analysis
Why
Logs provide visibility into the health, security, and performance of an application and infrastructure stack. It enables developer teams and system administrators to more easily diagnose incidents and rectify production issues. Uncovering this intelligence can guide your business decisions, investigations, and security.
Log files are incredibly useful in tracing a data breach or a security incident. With proper log analysis techniques, it’s easier to frame where the incident occurred, discover patterns, and diagnose the root cause. This allows product teams to promptly respond to data breaches and other security incidents.
Using machine learning to analyze logs allows you to predict user behavior patterns on the application. This helps product managers to focus on the most used features and to optimize ROI. Also, logs serve as audit trails for product compliance or certifications.
How
Use the following steps to better manage the log indexing and analysis process:
- Determine what types of logs you should collect.
- Determine how to collect and process log data.
- Use a collector to collect logs as a part of your stack.
- Centralized log aggregation: Integrate data from all log sources into a centralized platform to streamline the search and analysis process.
- Sanitize, parse, and transform log data: Clean, pre-process, and transform log data. (e.g. filter out unnecessary log records, apply defaults to missing values in incomplete log records, convert unstructured log data to a structured format, transform log data into a unified output format, etc.).
- Determine log storage and retention strategy.
- Estimate log storage requirement.
- Determine the required level of data replication and log rotation strategy.
- Ensure compliance with regulations on data protection and privacy in storing logs.
- Create an effective log indexing scheme: Indexing makes logs searchable, so security and IT personnel can quickly find the information they need. The following are a few best practices of log indexing:
- Refresh indices at larger time intervals.
- Buffer and index a collection of log files together.
- Search and analyze your log data: Techniques such as pattern recognition, normalization, tagging, and correlation analysis can be implemented either manually or using native machine learning.
- Set up tools to monitor and alert: Automation with machine learning and analytics can enable the continuous monitoring of large volumes of logs that cover a variety of systems and applications.
- Combine log analytics with visual exploration: Streamlined reports and dashboarding provide better visibility on matrices for decision making.