The Critical Role of Logs in Diagnosing and Resolving Issues on AWS and on-prem

Introduction

Recently, I was enlisted to assist an eCommerce aggregator whose product listing service went down. Although the front end handled the issue gracefully, no sales were occurring across the dozens of customer web stores. During the investigation, we discovered that about half of the application stack emitted metrics and logs to OpenSearch. Using Kibana, we identified the root cause: the production index had been swapped with an empty one. By re-indexing the prior data into the empty index, we restored service and resumed normal operations. This incident underscored the critical role of effective logging in quickly diagnosing and resolving issues.

Understanding the Role of Logging

Definition and Purpose

Logging involves recording information about an application’s operation. It serves as a crucial diagnostic tool, providing insights into the application’s behavior, performance, and errors. Effective logging allows teams to monitor systems, troubleshoot issues, and maintain operational continuity.

Types of Logs

• Application Logs: Capture events and errors specific to the application.
• System Logs: Record operating system and hardware-related events.
• Security Logs: Track access attempts and security-related events.
• Audit Logs: Provide a trail of user actions and changes within the system.

Each type of log serves a different purpose, and together they offer a comprehensive view of the application’s state and health.

The Scenario – E-commerce Aggregator

Context

The eCommerce aggregator in question manages multiple web stores for various customers. The product listing service is a critical component, ensuring that product information is available to customers in real-time. Any disruption to this service directly impacts sales and customer satisfaction.

The Incident

One day, the product listing service went down. While the front end handled the downtime gracefully by displaying cached data, no new sales were being recorded. This lack of transactions across dozens of web stores indicated a serious back-end issue.

The Investigation Process

During the investigation, we discovered that about half of the application stack emitted metrics and logs to OpenSearch, with Kibana providing a search interface. This setup enabled us to delve into the logs and identify the root cause. We found that the production index, which stores all the product data, had been mistakenly swapped with an empty one.

The Resolution

By re-indexing the previous production index into the empty one, we restored the service. This allowed the product listing service to resume normal operations, and sales transactions quickly returned to expected levels.

This incident highlights the importance of having a robust logging and monitoring system in place. Without it, identifying and resolving the root cause would have been significantly more challenging and time-consuming.

Detecting and Diagnosing Issues with Logs

Log Collection and Centralization

Centralized log collection is essential for efficient monitoring and troubleshooting. By aggregating logs from various parts of the application stack, you can get a comprehensive view of the system’s health. In our case, the eCommerce aggregator used OpenSearch to centralize logs and metrics, enabling effective monitoring and quick diagnosis of issues.

The Investigation Process

During the incident, logs played a pivotal role in guiding our investigation. Here’s how we approached it:

1. Initial Review: We began by examining the logs in OpenSearch for any anomalies or error messages.
2. Search and Correlation: Using Kibana’s search capabilities, we correlated logs from different parts of the system to pinpoint where the issue originated.
3. Root Cause Identification: The logs revealed that the production index had been swapped with an empty one, disrupting the product listing service.

The Resolution

With the root cause identified, we proceeded to re-index the prior data into the empty production index. This step restored the product listing service, and normal operations resumed. The ability to quickly access and analyze logs was crucial in resolving the issue efficiently.

Benefits of Effective Logging

• Rapid Diagnosis: Centralized logging allows for quick identification of issues, reducing downtime.
• Comprehensive Insight: Logs provide detailed insights into application behavior, helping to understand and address underlying problems.
• Proactive Monitoring: With real-time logging and monitoring, potential issues can be detected and resolved before they impact users.

Effective logging practices are indispensable for maintaining the reliability and performance of modern application stacks. They enable teams to detect, diagnose, and resolve issues swiftly, minimizing disruption and ensuring smooth operations.

Best Practices for Effective Logging

Consistent Logging Standards

Maintaining uniform log formats across the application stack is crucial for effective logging. This involves:

• Standardized Formats: Ensuring all logs adhere to a consistent structure, making it easier to parse and analyze data.
• Timestamps: Including precise timestamps to track the sequence of events.
• Log Levels: Utilizing different log levels (INFO, WARN, ERROR, DEBUG) to categorize the severity and type of log entries.

Structured vs. Unstructured Logs

• Structured Logs: These logs follow a specific format, often JSON, allowing for easy parsing and searching. Structured logs facilitate more efficient data analysis and automated monitoring.
• Unstructured Logs: These logs lack a predefined format, making them harder to parse but still valuable for capturing free-form text data. They can be useful for logging complex error messages or detailed diagnostic information.

Retention and Rotation Policies

Effective logging practices include managing log storage through retention and rotation policies:

• Retention Policies: Define how long logs are kept before they are archived or deleted. This ensures that logs are available for forensic analysis when needed without consuming excessive storage.
• Rotation Policies: Implement log rotation to handle large volumes of log data by periodically archiving and compressing old logs. This practice helps in maintaining system performance and managing storage efficiently.

Logging Context

Logs should provide meaningful context to aid in diagnosing issues. Compare the following examples:

Poor Logging Context:

_logger.LogDebug("Validating search request state.");

This message provides no information about what is being validated or the state of the request.

Good Logging Context:

Logger?.LogDebug($"Searching for {productSearchRequest?.ToJson()}");

This message includes specific details about the search request, making it more useful for diagnosing issues.

Incorporating context into logs ensures that each log entry provides valuable information, reducing the time needed to understand and resolve issues.

Example Practices

• Set Log Retention Based on Need: For critical systems, retain logs for a longer period to facilitate thorough investigations if issues arise.
• Implement Log Rotation: Rotate logs daily or based on log size to prevent storage bloat and maintain performance.
• Use Structured Logging: Opt for structured logging formats like JSON for easier parsing and analysis with tools like OpenSearch and Kibana.
• Provide Context in Logs: Ensure log messages contain relevant details to aid in troubleshooting.

Implementing these best practices ensures that your logging system is robust, scalable, and capable of providing valuable insights into your application’s performance and issues.

Tools and Technologies

OpenSearch

Overview: OpenSearch is an open-source search and analytics suite derived from Elasticsearch. It provides powerful search capabilities and is designed for scalability and performance.

Benefits:

• Centralized Logging: Aggregates logs from various sources into a single searchable database.
• Scalability: Handles large volumes of log data efficiently.
• Search and Analytics: Offers advanced search capabilities, enabling quick identification of issues and patterns.

Kibana

• Overview: Kibana is an open-source data visualization and exploration tool used for log and time-series analytics. It integrates seamlessly with OpenSearch, providing a user-friendly interface for searching and visualizing log data.

Capabilities:

• Visualization: Creates real-time visualizations such as graphs, charts, and dashboards to monitor system performance and trends.
• Search Interface: Allows users to perform detailed searches on log data, making it easier to pinpoint issues.
• Alerting: Configures alerts based on specific log patterns or metrics, enabling proactive monitoring and quick responses to potential issues.

Amazon CloudWatch

Overview: Amazon CloudWatch is a monitoring and observability service designed for DevOps engineers, developers, site reliability engineers (SREs), and IT managers. It provides data and actionable insights to monitor applications, respond to system-wide performance changes, and optimize resource utilization.

Benefits:

• Comprehensive Monitoring: Collects and tracks metrics, logs, and events for AWS resources and applications.
• Real-Time Insights: Provides real-time visibility into application performance and operational health.
• Seamless Integration: Easily integrates with other AWS services for enhanced monitoring and automation.

Sending Logs to CloudWatch and Subscribing OpenSearch

Log Collection and Centralization: Set up your application to send logs to Amazon CloudWatch. This provides a centralized location for all your logs, making it easier to manage and analyze log data.

Steps to Send Logs to CloudWatch:

• Configure Application Logs: Use AWS SDKs or CloudWatch Agent to configure your application to send logs to CloudWatch.
• Set Up Log Groups: Create log groups and log streams in CloudWatch to organize and manage your logs effectively.
• Custom Metrics: Create custom metrics in CloudWatch for specific log events or patterns.

Subscribing OpenSearch to CloudWatch Logs:

• CloudWatch Log Subscriptions: Set up CloudWatch Log Subscriptions to send log data to OpenSearch for enhanced search and analysis capabilities.
• Lambda Function: Use AWS Lambda to process log data from CloudWatch and send it to OpenSearch.
• Integration: Ensure seamless integration between CloudWatch and OpenSearch to maintain real-time log monitoring and analytics.

Integration with Application Stacks

• Logging Libraries and Frameworks: Use libraries and frameworks (e.g., Log4j, Winston, Fluentd) that support structured logging and can easily integrate with CloudWatch and OpenSearch.
• Log Shippers: Utilize log shippers (e.g., Filebeat, Logstash) to collect, parse, and forward logs to CloudWatch and then to OpenSearch.
• Cloud Services: Leverage cloud-native logging services (e.g., AWS CloudWatch) that integrate with OpenSearch for seamless log collection and monitoring.

Real-World Application

In our ecommerce aggregator scenario, the integration of CloudWatch and OpenSearch was pivotal. By centralizing logs in CloudWatch and using OpenSearch for advanced search capabilities, we quickly identified and resolved the issue. This integration ensured that we had real-time insights into the system’s health, enabling prompt and effective troubleshooting.

Benefits of Using CloudWatch and OpenSearch

• Improved Visibility: Gain comprehensive visibility into your application and infrastructure logs.
• Enhanced Troubleshooting: Quickly diagnose and resolve issues using advanced search and visualization tools.
• Proactive Monitoring: Set up alerts and dashboards to monitor system health and detect anomalies before they impact users.

Using tools like Amazon CloudWatch and OpenSearch can significantly enhance your logging strategy, providing the necessary infrastructure to collect, analyze, and act on log data effectively.

Proactive Measures and Continuous Improvement

Monitoring and Alerts

Setting Up Alerts: Configure alerts to monitor for unusual log patterns or specific error conditions. This proactive approach ensures that potential issues are flagged immediately, allowing for swift intervention.

• Threshold-Based Alerts: Trigger alerts when metrics exceed defined thresholds (e.g., high error rates, slow response times).
• Pattern Recognition: Use machine learning to detect anomalies and unexpected log patterns that may indicate underlying problems.

Automating Responses: Implement automated responses for common issues to minimize downtime and manual intervention.

• Self-Healing Scripts: Automate recovery processes such as restarting services or rolling back deployments when specific conditions are met.
• Notification Systems: Integrate with notification systems (e.g., Slack, email) to ensure that the right teams are informed immediately.

Regular Audits and Drills

Periodic Review of Logging Practices: Regularly audit your logging configuration and practices to ensure they remain effective and aligned with current needs.

• Configuration Review: Check that logging configurations are up-to-date and consistent across all components.
• Data Integrity: Verify that logs are accurate, complete, and tamper-proof.

Simulation of Incidents: Conduct drills to test the logging and monitoring system’s ability to detect and respond to issues.

• Incident Simulation: Simulate common failure scenarios to evaluate the effectiveness of your logging and alerting setup.
• Post Mortem or Root Cause Analysis: After drills or actual incidents, conduct reviews to identify strengths and weaknesses in your logging strategy.

Continuous Improvement

Feedback Loops: Establish feedback loops to continuously refine logging practices based on real-world experiences and evolving requirements.

• User Feedback: Gather input from developers and operators to identify areas for improvement.
• Incident Analysis: Use insights from incident investigations to enhance logging configurations and alerting rules.

Training and Awareness: Ensure that all team members understand the importance of good logging practices and know how to use the tools available.

• Regular Training: Provide training sessions on logging tools, best practices, and new features.
• Documentation: Maintain comprehensive documentation on logging standards, configurations, and troubleshooting procedures.

By adopting these proactive measures and committing to continuous improvement, you can ensure that your logging system remains robust, efficient, and capable of supporting the ongoing health and performance of your applications. This proactive approach not only helps in resolving issues faster but also in preventing them, ultimately leading to a more resilient and reliable system.

Conclusion

The incident with the eCommerce aggregator underscored the critical role that good logging practices play in modern application stacks. By leveraging centralized logging and powerful tools like OpenSearch and Kibana, we were able to quickly identify and resolve a potentially costly issue. Effective logging not only aids in rapid diagnosis and troubleshooting but also enhances overall system reliability and performance.

To ensure the robustness of your logging strategy, it is essential to implement consistent logging standards, utilize structured logging, and enforce retention and rotation policies. Additionally, integrating advanced tools and maintaining a proactive approach with regular audits, monitoring, and continuous improvement will help you stay ahead of potential issues.

Good logging practices are not just about fixing problems – they are about creating a resilient system that can withstand and quickly recover from unexpected disruptions. By prioritizing effective logging, you can maintain operational continuity, improve user experience, and safeguard your business operations.

Encourage your teams to review and enhance their logging practices regularly. If you need assistance in setting up or optimizing your logging infrastructure, our consultancy is here to help. Reach out to us to ensure your systems are well-prepared to handle any challenges that come their way.