Source URL: https://cmtops.dev/posts/building-observability-with-clickhouse/
Source: Hacker News
Title: Building Observability with ClickHouse
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text outlines the author’s journey in building an observability project using ClickHouse for data warehousing alongside Grafana for visualization and alerting. It highlights the limitations of various tech stacks considered, particularly focusing on Elasticsearch and related technologies, and ultimately discusses the strengths of ClickHouse for modern infrastructure observability.
Detailed Description: The author provides a detailed account of the process taken to determine an effective tech stack for observability within their infrastructure. The insights are particularly relevant for professionals focusing on data observability, performance bottlenecks, and integrating multiple systems efficiently.
– **Initial Approach**:
– Started with the Elastic Stack (Elasticsearch, Fluentd, Kibana) for logging and visualization.
– Encountered performance issues and resource usage challenges with Elasticsearch.
– **Limitations of Initial Tech Stack**:
– **Elasticsearch**: While it supports horizontal scaling, it is resource-intensive and not always efficient in log data processing.
– **Loki with Grafana**: Found to be lacking in documentation and stability, leading to high maintenance and scalability concerns.
– **Timescale/InfluxDB**: While considered viable, they were lacking certain clustering features necessary for the integration.
– **Selection of ClickHouse**:
– Chose ClickHouse for its cost-effectiveness, powerful SQL-like query language, and strong community support.
– Discussed the need for replication and high availability given the infrastructure’s geographical distribution.
– **Log Collection and Transformation**:
– Utilized Fluent Bit and Vector for data ingestion into ClickHouse.
– Vector’s programming language, VRL, facilitates complex data transformation tasks.
– **Visualization with Grafana**:
– The integration of Grafana with ClickHouse proved effective, allowing detailed log analysis and metric visualization.
– Examples of performed queries show the flexibility and customizability of the visualizations.
– **Iterative Learning**:
– Emphasized the need for regular updates and refinements to the visualization dashboards based on evolving data needs.
– Identified challenges with performance during large data selections and adjusted strategies to manage database load effectively.
– **Future Enhancements**:
– Plans for evolving the project to include features like a message queue, alerting mechanisms for significant events, and the ingestion of various other log types.
Professionals in AI, cloud, and infrastructure security will find the author’s insights on scalability, observability, and performance optimization particularly applicable, as they reflect on the processes of evaluating and deploying robust tech stacks in their own environments. The text is a rich resource for understanding practical challenges in infrastructure observability and the evolving solutions in the realm of data management technologies.