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Observability Pillars: Exploring Logs, Metrics and Traces


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Understanding Observability

Observability is the capability to gauge the internal states of a system through the examination of its outputs. A system achieves 'observability' when it becomes feasible to estimate the current state using information solely derived from outputs, particularly sensor data. Leveraging Observability data enables the identification and troubleshooting of problems, optimization of performance, and enhancement of security measures.

In the following sections, we'll delve into the three foundational pillars of Observability: Metrics, Logs, and Traces.

Exploring the Distinction: Observability vs. Monitoring

The relationship between Observability and Monitoring is intricately connected, with the latter serving as a prerequisite for the former.

Observability involves gaining insights into the internal workings of a system, enabling a profound understanding of its behavior. On the other hand, Monitoring is the process of collecting data on system performance and behavior.

Furthermore, Monitoring tends to focus on predefined metrics and thresholds to detect deviations from expected behavior. In contrast, Observability is driven by the ambition to furnish a profound comprehension of system behavior, facilitating the exploration and discovery of unexpected issues.

Perspectives and Mindsets

In terms of perspective and mindset, Monitoring adheres to a "top-down" approach, relying on predefined alerts based on known criteria. In contrast, Observability adopts a "bottom-up" approach, encouraging open-ended exploration and adaptability to changing requirements.

Observability Monitoring
Tells you why a system is at fault. Notifies that you have a system at fault.
Acts as a knowledge base to define what needs monitoring. Focuses only on monitoring systems and detecting faults across them.
Focuses on giving context to data. Data collection focused.
Give a more complete assessment of the overall environment. Keeping track of monitoring KPIs.
Observability is a traversable map. Monitoring is a single plane.
It gives you complete information. It gives you limited information.
Observability creates the potential to monitor different events. Monitoring is the process of using Observability.

Monitoring detects anomalies and alerts you to potential problems. However, Observability not only detects issues but also helps you to understand their root causes and underlying dynamics.

The Foundations of Observability

Observability, anchored in the Three Pillars—Metrics, Logs, and Traces, is built on the core concept of "Events." These events constitute the fundamental units of monitoring and telemetry, each carrying a timestamp and quantifiable attributes. What sets events apart is their context, particularly in user interactions. For instance, the act of a user clicking "Pay Now" on an eCommerce site is an event, expected within seconds.

Within monitoring tools, the spotlight falls on "Significant Events." These events serve as triggers for:

  1. Automated Alerts: Notifying SREs or operations teams promptly.
  2. Diagnostic Tools: Enabling thorough root-cause analysis.

Consider a scenario where a server's disk is nearing 99% capacity—an event of significance. Yet, understanding which applications and users contribute to this scenario is crucial for effective action.

Metrics: Numeric Insights into System Health

Metrics act as numeric indicators, providing valuable insights into a system's health. While some metrics, such as CPU, memory, and disk usage, are evident indicators of system health, numerous other critical metrics can uncover underlying issues. For instance, a gradual increase in OS handles can lead to a system slowdown, eventually necessitating a reboot for accessibility. Similar valuable metrics span the various layers of the modern IT infrastructure.

Critical to effective metric usage is careful consideration when determining which metrics to continuously collect and how to analyze them. Domain expertise plays a pivotal role in this decision-making process. While most monitoring tools can detect obvious issues, the best ones excel in providing insights into detecting and alerting complex problems. Identifying the subset of metrics that serve as proactive indicators of impending system problems is crucial. For example, an OS handle leak rarely occurs abruptly.

Tracking the gradual increase in the number of handles in use over time makes it possible to predict when the system might become unresponsive, allowing for proactive intervention.

Advantages of Metrics:

  1. Quantitative and intuitive for setting alert thresholds.
  2. Lightweight and cost-effective for storage.
  3. Excellent for tracking trends and system changes.
  4. Provides real-time component state data.
  5. Constant overhead cost; not affected by data surges.

Challenges of Metrics:

  1. Limited insight into the "why" behind issues.
  2. Lack context of individual interactions or events.
  3. Risk of data loss in case of collection/storage failure.
  4. Fixed interval collection may miss critical details.
  5. Excessive sampling can impact performance and costs.

Log Analysis for Enhanced Observability

Delving into the intricacies of log files provides a wealth of information on how an application handles requests. The detection of anomalies, such as exceptions, within these logs serves as a crucial indicator of potential issues within the application. Monitoring and analyzing these errors and exceptions in logs constitute a fundamental component of any observability solution. Additionally, parsing through logs can unveil invaluable insights into the overall performance of the application.

Unlike APIs (Application Programming Interfaces) or querying application databases, logs often harbor insights that may remain undiscovered. Unfortunately, many Independent Software Vendors (ISVs) fail to provide alternative methods for accessing the data embedded in logs. Consequently, a robust observability solution must not only facilitate log analysis but also streamline the capture of log data and its seamless correlation with metric and trace data.

Advantages of Logs:

  1. Easy Generation: Logs are easy to generate, typically consisting of timestamps and plain text, making them straightforward to create and understand.
  2. Minimal Integration: They often require minimal integration efforts from developers, allowing for quick implementation without significant coding overhead.
  3. Standardized Frameworks: Most platforms offer standardized logging frameworks, promoting consistency and ease of use across different environments.
  4. Human-Readable: Logs are human-readable, enhancing accessibility for developers and facilitating quick identification of issues or anomalies.
  5. Granular Insights: Logs provide granular insights for retrospective analysis, enabling detailed examination of the application's behavior during specific timeframes.

Challenges of Logs:

  1. Data Volume: Logs can generate large data volumes, leading to increased storage costs and potentially overwhelming system resources.
  2. Performance Impact: Logging, especially without asynchronous mechanisms, can impact application performance, introducing delays or bottlenecks in processing.
  3. Retrospective Nature: Logs are primarily used retrospectively, making it challenging to identify and address issues proactively before they impact the system.
  4. Persistence Challenges: Modern architectures, such as microservices and serverless, pose challenges in persisting logs effectively, potentially resulting in data loss.
  5. Risk of Log Loss: In containerized and auto-scaling environments, there's a risk of log loss, as instances may scale up or down rapidly, leading to potential gaps in the log data.


Tracing is a relatively recent development, especially suited to the complex nature of contemporary applications.  It works by collecting information from different parts of the application and putting it together to show how a request moves through the system.

The primary advantage of tracing lies in its ability to deconstruct end-to-end latency and attribute it to specific tiers or components. While it can't tell you exactly why there's a problem, it's great for figuring out where to look.

Advantages of Traces:

  1. Pinpointing Issues: Traces are ideal for pinpointing issues within a service, providing a detailed map of the journey a request takes through the system.
  2. End-to-End Visibility: They offer end-to-end visibility across multiple services, allowing for a comprehensive understanding of the entire transaction flow.
  3. Effective Bottleneck Identification: Traces are effective in identifying performance bottlenecks, enabling targeted optimizations to enhance overall system efficiency.
  4. Debugging Aid: Traces aid debugging by recording request/response flows, facilitating the identification of specific steps where issues or errors occur.
  5. Contextual Insights: Traces provide contextual insights into system behavior, allowing developers to understand how different components interact during the processing of a request.

Challenges of Traces:

  1. Limited Long-Term Trends: Traces have a limited ability to reveal long-term trends, making it challenging to identify gradual changes in system behavior over extended periods.
  2. Diverse Trace Paths: In complex systems, diverse trace paths may emerge, making it more challenging to analyze and comprehend the overall flow of transactions.
  3. Lack of Explanation for Issues: Traces alone may not explain the cause of slow or failing spans (steps), requiring additional context or integration with other monitoring tools for a comprehensive understanding.
  4. Performance Overhead: Implementing tracing mechanisms adds overhead, potentially impacting system performance, especially in high-throughput or resource-constrained environments.

Tracing Integration Made Effortless

In the past, integrating tracing posed challenges, but the advent of service meshes has transformed the process into a seamless endeavor. Service meshes now manage tracing and stats collection at the proxy level, ensuring effortless observability throughout the entire mesh. This eliminates the need for additional instrumentation from applications within the mesh, simplifying the implementation process.

While each discussed component has its own set of pros and cons, there's often a desire to leverage them collectively for comprehensive observability. 🧑‍💻

Observability Tools Tools dedicated to observability play a crucial role in collecting and analyzing data pertaining to user experience, infrastructure, and network telemetry. This proactive approach allows for the early identification of potential issues, preemptively addressing them to prevent any adverse impact on critical business key performance indicators (KPIs).

Discover a range of popular observability tools that cater to diverse monitoring needs:

  1. Prometheus: Renowned for its scalability and support for multi-dimensional data collection, Prometheus stands out as a leading open-source monitoring and alerting toolkit.
  2. Grafana: Often paired with Prometheus, Grafana serves as a visualization and dashboarding platform, offering comprehensive insights into system performance.
  3. Jaeger: Addressing the complexities of microservices-based architectures, Jaeger is an open-source distributed tracing system dedicated to monitoring and troubleshooting.
  4. Elasticsearch: When combined with Kibana and Beats, Elasticsearch forms the ELK Stack, specializing in search and analytics for log management and analysis.
  5. Honeycomb: Positioned as an event-driven observability tool, Honeycomb provides real-time insights into application behavior and performance.
  6. Datadog: As a cloud-based observability platform, Datadog seamlessly integrates logs, metrics, and traces, delivering end-to-end visibility.
  7. New Relic: Offering solutions for application performance monitoring (APM) and infrastructure monitoring, New Relic aids in tracking and optimizing application performance.
  8. Sysdig: Focused on container monitoring and security, Sysdig provides deep visibility into containerized applications.
  9. Zipkin: As an open-source distributed tracing system, Zipkin excels in monitoring request flows and identifying latency bottlenecks.
  10. Squadcast: Positioned as an incident management platform, Squadcast streamlines incident response and resolution by integrating with various observability tools.

Conclusion: In the realm of observability, the synergy of logs, metrics, and traces forms the foundation for a comprehensive view of distributed systems. Strategic incorporation, such as placing counters and logs at entry and exit points, and utilizing traces at decision junctures, enhances the effectiveness of debugging. Combining observability with incident management creates an efficient response mechanism for incidents, minimizing their impact on business operations and improving overall system reliability.

Squadcast Integration: Squadcast proves instrumental in this ecosystem by seamlessly integrating with a wide array of observability tools, including Honeycomb, Datadog, New Relic, Prometheus, and Grafana. Start a free trial of Squadcast's incident platform today, and explore its ability to minimize incident impact and enhance system reliability. Whether using the pre-built integrations or leveraging Squadcast's public API, the platform ensures adaptability to various observability tools. Book a demo today to witness the power of Squadcast in action.

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Squadcast Inc

Squadcast is a cloud-based software designed around Site Reliability Engineering (SRE) practices with best-of-breed Incident Management & On-call Scheduling capabilities.
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