The Scalable Log Monitoring System is a sophisticated distributed architecture that seamlessly integrates Elasticsearch and Kibana, centralizing and visualizing logs from multiple microservices running across various instances. This design not only streamlines logging but also enhances system insights, promoting proactive issue detection and resolution.

• Overview
• Architecture
• Monitoring Dashboards in Kibana
• Results
• Impact
• Note
• Contact
• Acknowledgements

This project employs a distributed architecture, meticulously designed to handle the complexities of several microservices running across diverse instances. With each server equipped with its unique Filebeat instance, the system ensures efficient log crawling and fetching. Filebeat's configuration on each server meticulously points to specific log paths that need to be monitored, along with the respective indexes to be watched. As logs emerge and get processed, they are channeled to a centralized Logstash server. This pivotal component filters and processes the log messages dispatched from the Filebeat instances across each server. From here, events are intelligently sorted and curated based on their metric or index type, and then these curated log events are ready to be indexed in Elasticsearch.

1. Distributed Architecture with Microservices: The codebase is structured with numerous microservices, each operating on distinct instances. This decentralization ensures modularity, scalability, and resilience.

2. Filebeat Integration at Server Level: Each server in the architecture hosts its dedicated Filebeat instance. These instances are fine-tuned to crawl specific log paths and monitor designated indexes. It ensures real-time and efficient log data capture.

3. Centralized Logging with Logstash: Logstash stands as the linchpin, receiving and sifting through the myriad log messages transmitted from Filebeat across every server. Its primary role involves processing, enriching, and indexing the logs, priming them for Elasticsearch.

4. Dynamic Event Creation for Elasticsearch: Once Logstash processes the logs, they are categorized based on their metric or index type. Subsequent to this categorization, log events are created, ensuring they are aptly structured for indexing and querying within Elasticsearch.

To ensure that the data is not just stored but also effectively used for insights:

1. Comprehensive Dashboards: Designed dashboards in Kibana that offer a granular view of each metric. Whether it's system health, error rates, or any other KPI, the dashboards provide a detailed visualization.

2. Data-Driven Insights: With the help of these dashboards, there's a data-driven approach to understanding system behavior. It's not just about collecting logs but also deriving actionable insights from them. Impact

Our efforts in designing and implementing the Scalable Log Monitoring System have culminated in tangible, impactful outcomes. To better illustrate the system's capabilities and provide a hands-on demonstration, we've created a video walkthrough.

🎥 Watch the Video Demonstration

This video showcases the intricate functionalities and the results derived from our system. It offers a detailed look into the monitoring dashboards in Kibana, the granular visualizations for each metric, and the enhanced insights that have significantly improved our operational efficiency.

We believe that seeing is believing. This demonstration underscores the value and potential of our system, and we encourage everyone to view it for a comprehensive understanding.

One of the most significant achievements of this system was a 15% reduction in on-call ticket incidents. This reduction was a direct result of the enhanced system insights provided by the comprehensive monitoring dashboards. By being proactive and spotting issues before they escalate, the system ensures smoother operations and lesser disruptions.

Please note that this project was executed during my tenure at a previous organization, and due to confidentiality agreements, it remains non-public. Nonetheless, discourse around its architecture, design principles, and best practices is always encouraged.

For further inquiries or to delve deeper into project-related discussions, please don't hesitate to reach out.

I'd like to extend my sincere gratitude to the entire team and contributors who played an instrumental role in bringing this project to fruition. Your expertise, dedication, and collaborative spirit were pivotal in achieving the goals set out for this system. Additionally, a special thanks to the open-source community for providing tools and platforms that served as the foundation for this project. Your relentless pursuit of innovation continues to inspire and drive projects like ours.