Prometheus and Open-Source Observability with Eric Schabell
Apr 15, 2025
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Eric Schabell, Director of Community and Developer at Chronosphere and a CNCF Ambassador, shares his insights on enhancing observability in cloud-native systems. He dives into how Prometheus, while popular, struggles with large data volumes and cost optimization. Schabell discusses the challenges of scaling observability, balancing self-hosted versus managed solutions, and the importance of managing metrics effectively. Listeners will also learn about the critical role of efficient data collection and visualization in supporting agile monitoring and issue resolution.
Modern cloud-native systems require specialized observability tools like Prometheus due to the limitations of traditional monitoring tools in dynamic environments.
Prometheus utilizes a pull-based data collection model that efficiently gathers metrics while presenting challenges in managing large data volumes and cost optimization.
Organizations may need to transition from open-source observability tools to managed solutions like Chronosphere as demands increase, ensuring better resource allocation and operational efficiency.
Deep dives
Challenges of Monitoring Cloud-Native Systems
Modern cloud-native systems present unique challenges for monitoring due to their dynamic and distributed nature, which traditional monitoring tools are ill-equipped to handle. This has led to the rise of specialized observability platforms, such as Prometheus, designed specifically for these environments. Prometheus employs a pull-based data collection model that allows it to efficiently gather metrics without requiring intrusive instrumentation, making it an attractive option for DevOps teams. However, the tool faces difficulties managing large data volumes and achieving cost optimization, prompting discussions on best practices for deploying Prometheus at scale.
Understanding Prometheus and Its Features
Prometheus serves as an advanced metrics collection tool tailored for cloud-native observability. It is designed for high performance and scalability while allowing developers to customize data collection through features like auto instrumentation. The tool collects metrics by scraping defined endpoints and offers a robust query language, PromQL, facilitating easy data retrieval and visualization. Although Prometheus provides built-in alerting mechanisms, organizations often integrate additional visualization tools like Grafana for more comprehensive dashboarding capabilities.
The Importance of Service Discovery
Service discovery plays a critical role in effectively monitoring dynamic environments like Kubernetes, where the existence of services constantly changes. Prometheus initially requires static endpoint definitions for scraping, but it can be configured to utilize various service discovery tools to dynamically track services. This capability automates the detection of new pods or containers, reducing the manual overhead of managing lists of endpoints as the system scales. As a result, organizations can maintain visibility into their cloud-native architectures without being burdened by static configurations.
Distinguishing Between Metrics, Logs, and Traces
In observability, metrics, logs, and traces serve distinct purposes and are uniquely structured for different insights. Metrics are real-time quantitative measurements, while logs are textual records detailing the running state of applications, and traces provide a timeline of service requests. Understanding when to use each type of data is crucial; for instance, metrics serve operational monitoring needs, while logs are useful for debugging. The choice between these observability signals depends on the specific requirements organizations have for monitoring their systems efficiently.
Navigating Migration to Managed Observability Solutions
As organizations grow, the limitations of open-source observability tools like Prometheus often become evident, requiring consideration of managed solutions like Chronosphere. Signs indicating the need for a transition include increased incident frequency, resource exhaustion, or a spike in service demand, all of which strain self-managed systems. Managed platforms streamline the self-monitoring process while providing additional functionalities such as advanced analytics and financial oversight over data usage and associated costs. Consequently, organizations can refocus resources on development and innovation instead of being mired in operational overhead.
Modern cloud-native systems are highly dynamic and distributed, which makes it difficult to monitor cloud infrastructure using traditional tools designed for static environments. This has motivated the development and widespread adoption of dedicated observability platforms.
Prometheus is an open-source observability tool designed for cloud-native environments. Its strong integration with Kubernetes and pull-based data collection model have driven its popularization in DevOps. However, a common challenge with Prometheus is that it struggles with large data volumes and has limited cost-optimization capabilities. This raises the question of how best to handle Prometheus deployments at large scale.
Eric Schabell works in DevRel at Chronosphere where he’s the Director of Community and Developer. He is also a CNCF Ambassador. Eric joins the show with Kevin Ball to talk about metrics collection, time series data, managing Prometheus at scale, tradeoffs between self-hosted vs. managed observability, and more.
Full Disclosure: This episode is sponsored by Chronosphere.
Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space.
