Observability (2/3)
Swaminathan Nagarajan
Digital Consulting | Teaching | Career Counselling & Coaching
In Part 1, we explored the concept of observability and its significance in modern IT systems. Now, let's delve into the core components that make observability actionable: logs, metrics, and traces. Understanding these pillars is crucial for building resilient, efficient, and user-centric systems.
The Three Pillars Explained
1. Logs: The Detailed Records
What They Are: Logs are timestamped records of discrete events within a system. They provide detailed insights into what happened, when, and under what context.
Use Cases:
Example: When a user encounters a 500 Internal Server Error, logs can reveal the exact exception thrown and the stack trace leading up to it.
2. Metrics: The Quantitative Indicators
What They Are: Metrics are numerical representations of system performance over time. They are aggregated data points that help in monitoring the health and efficiency of systems.
Use Cases:
Example: A sudden spike in CPU usage metrics might indicate a runaway process or an inefficient algorithm.
3. Traces: The Journey Maps
What They Are: Traces follow the path of a single request as it traverses through various services and components in a distributed system.
Use Cases:
Example: In a microservices architecture, a trace can show how a user's request moves from the frontend to the backend services, highlighting delays at each step.
How They Work Together
While each pillar provides valuable insights individually, their true power lies in their integration:
This synergy enables teams to move from detection to resolution swiftly, enhancing system reliability and user satisfaction.
Imagine an e-commerce platform experiencing intermittent slowdowns during the checkout process. Here's how each observability pillar contributes to identifying and resolving the issue:
1. Metrics: Detecting the Anomaly
Observation: The operations team notices a spike in the average response time for checkout transactions, increasing from 1.5 seconds to 4 seconds over the past hour.
Action: An alert is triggered based on predefined thresholds, prompting immediate investigation.
Insight: Metrics provide a high-level view, indicating that there's a performance degradation in the checkout service. However, they don't pinpoint the root cause.
2. Traces: Pinpointing the Bottleneck
Observation: Using distributed tracing tools, engineers examine individual checkout transactions. They discover that the delay consistently occurs during the payment processing step.
Action: Traces reveal that the payment service is taking significantly longer to respond, especially when interacting with a third-party payment gateway.
Insight: Traces help identify that the latency is not within the internal systems but is caused by external API calls to the payment gateway.
3. Logs: Uncovering the Root Cause
Observation: By analyzing logs from the payment service, engineers find repeated timeout errors and warning messages indicating failed attempts to connect to the third-party API.
Action: Logs show that the payment gateway's API started returning errors due to a recent configuration change on their end.
Insight: Logs provide the granular details necessary to understand the exact failure, including error codes and stack traces.
4. Resolution and Business Impact
Resolution: Armed with insights from metrics, traces, and logs, the engineering team contacts the third-party provider, who acknowledges the issue and rolls back the problematic configuration.
Business Outcome: The checkout process returns to normal performance levels, reducing cart abandonment rates and preserving customer satisfaction.
By integrating all three pillars, organizations can swiftly detect, diagnose, and resolve issues, minimizing downtime and maintaining a seamless user experience.
Top Observability Tools in 2025
Selecting the right tools is pivotal for effective observability. Here are some leading platforms:
1. Datadog
Overview: Datadog is a SaaS-based monitoring and security platform that provides a unified view of infrastructure, applications, and logs. It offers real-time observability of the entire technology stack.
Key Features:
Organizations Using Datadog:
2. New Relic
Overview: New Relic offers a comprehensive observability platform that combines metrics, events, logs, and traces in one place. It's designed to help engineers monitor, debug, and improve their entire stack.
Key Features:
Organizations Using New Relic:
3. Splunk
Overview: Splunk provides a data platform that enables organizations to search, monitor, and analyze machine-generated data. Its observability solutions offer insights across applications and infrastructure.
Key Features:
Organizations Using Splunk:
4. Dynatrace
Overview: Dynatrace offers a software intelligence platform that uses AI to provide full-stack observability, automate operations, and deliver digital experiences.
Key Features:
Organizations Using Dynatrace:
5. Grafana
Overview: Grafana is an open-source platform for monitoring and observability. It allows users to query, visualize, and alert on metrics and logs from various data sources.
Key Features:
Organizations Using Grafana:
These tools help in collecting, analyzing, and visualizing observability data, enabling proactive system management.
To Sum up
Understanding and implementing the three pillars of observability—logs, metrics, and traces—is essential for modern IT operations. They provide a comprehensive view of system behavior, facilitating rapid issue detection and resolution.
In Part 3, we'll explore best practices for implementing observability, selecting the right tools for your organization, and fostering a culture that embraces observability for continuous improvement.