Driving down the highway, you usually glance intermittently at your speedometer to ensure that you stay within the speed limit, or whatever window above the speed limit you’re willing to drive. While monitoring your speed mitigates the risk of a ticket, you still need to look out for various threats on the road, like cars going through stop signs. By observing your surroundings, you take in real-time information that can help prevent a crash.
Similarly, organizations need to monitor for anomalies using static metrics, but they also need real time insights into previously unknown issues. In distributed architectures that contain microservers, cloud-native applications, and serverless computing, traditional monitoring can offer benefits but comes with limitations. Increasingly, organizations are shifting towards observability. While the terms observability and monitoring are used interchangeably, they have nuanced differences that impact organizational resilience.
By understanding the differences between observability and monitoring, organizations can manage system health in a more holistic way.
What Is Observability?
Observability measures how well the organization understands a system’s internal state based on the external data it produces. While derived from control theory, software engineers have adapted the concept to address the challenges of complex systems. While monitoring identifies an issue, observability empowers IT teams to ask why the issue exists based on exploring the collected data to answer questions they might not have realized they need to ask.
In an observable system, IT operations have the data necessary to debug issues and understand failures. Observability encourages an exploratory, investigative mindset by correlating different data types so people navigate from a symptom to the issue’s root cause.
What Is Monitoring?
Monitoring is the collecting, processing, and analyzing of system data to track its performance and health over time. It focuses on reviewing predefined metrics and logs to determine whether the system operates as expected to detect known issues and alerts teams to problems.
Monitoring tools track key performance indicators (KPIs) and trigger an alert when the metric crosses a pre-configured threshold, prompting an investigation. Generally, monitoring is reactive and used to anticipate known problems.
What Is Telemetry Data?
Telemetry is the draw, high-cardinality event data automatically collected from the system then sent to a centralized location for analysis. Typically, telemetry encompasses the logs, metrics, and traces that applications and infrastructure generate.
What Is the Difference Between Monitoring and Observation?
Although both disciples provide insight into system health, they take fundamentally different approaches. While monitoring is about observing important, predetermined signals, observability is about using data to understand any signals.
Depth
While monitoring provides a high-level surface view of system health, observability offers the granular context that enables teams to perform root cause analysis in complex, distributed systems. The fundamental difference here is:
- Monitoring: Aggregated data that indicates an issue exists.
- Observability: Detailed logs and end-to-end request traces to ask deep, exploratory questions.
Scope
While monitoring has a narrowly, predefined scope, observability is broader and more dynamic. The fundamental difference here is:
- Monitoring: Specific metrics and health checks identified as important during system design or after a previous incident.
- Observability: Investigate known and unknown issues by capturing individual requests as they travel across services, databases, and infrastructure components.
Data Use
While monitoring uses data for alerting and dashboarding, observability treats data as an exploration and discovery resource. The fundamental difference here is:
- Monitoring: Comparing collected and aggregated data against current values to identify trends based on static thresholds, often through dashboards that display the KPIs.
- Observability: Preserving raw telemetry for correlation and analysis, encouraging ad-hoc querying and interactive hypothesis testing.
What Are the Pillars of Observability?
Metrics, logs, and traces provide complementary views into system behavior for a comprehensive understanding of its performance and health.
Metrics
Metrics are numerical representations of data measured over time. Dashboards and alerting systems often use them because they are lightweight and optimized for storage, retrieval, and aggregation. Some common examples of metrics include:
- CPU utilization
- Request count
- Error rate
- Application latency
Metrics help IT operations identify trends and understand a system’s overall health.
Logs
Logs are immutable, timestamped records of discrete events that provide granular, event-level detail about system and environment activities. A log entry typically contains:
- Timestamp to identify when an event occurred.
- Severity level to provide insight into potential impact.
- Contextual information about the technology or system.
According to an article in Big Data Wire, distributed systems can generate astronomical volumes with tens of millions of log lines streaming daily.
Traces
Traces, also known as distributed traces, represent a single request’s end-to-end journey as it moves through a distributed system’s services. A trace contains multiple spans defined as units of work within a service, like an API call or a database query. IT operations uses traces to:
- Understand relationships and dependencies between microservices.
- Identify performance bottlenecks.
- Pinpoint the source of errors in a complex architecture.
How Observability and Monitoring Can Work Together
Effective system management strategies combine monitoring and observability:
- Monitoring: Efficient, low-overhead metrics to detect abnormal activity and trigger alerts when signals cross the defined threshold.
- Observability: Investigation of the alert using the telemetry data and drilling down into traces to see the request’s failed path and examine detailed logs from the services involved.
Monitoring provides the detection while observability enables diagnosis and resolution. By combining the two, organizations can significantly reduce downtime.
Steps for Effectively Implementing Observability and Monitoring
Many organizations have monitoring capabilities. However, by building observability into their IT operations and security processes, they can more effectively and efficiently identify, investigate, and respond to issues.
Start With Comprehensive Monitoring
Comprehensive monitoring enables organizations to identify normal activity and define anomalies that trigger alerts. The monitoring should have clear dashboards and alerts for key system-level and business-level KPIs to serve as the signal generator for observability.
Aggregate Logs to Gain Granular Visibility
By centralizing the system’s logs, organizations can more effectively correlate and search the data, especially if the platform can normalize the data into a standardized schema. Structuring logs using key-value pairs enables easier queries.
Incorporate Tracing to Connect Metrics and Logs
Configuring applications to generate distributed traces makes correlating between services easier. Propagating a trace ID across service calls and including it in logs streamlines telemetry correlation for a single request.
Leverage Machine Learning for Enhanced Anomaly Detection
Modern observability platforms use machine learning (ML) and AIOps to automatically detect anomalies that static thresholds would miss. This helps proactively identify emerging issues before they impact users.
Build Dashboards for Monitoring and Observability
Dashboards are critical for monitoring high-level metrics. When the dashboards allow users to click through and drill down into the associated traces and logs, IT operations has a platform that enables both monitoring and observability in a single location.
Implement Automation to Streamline Incident Response
Automated workflows enrich monitoring alerts with observability data. For example, if an alert automatically triggers a query that pulls the relevant traces and logs, the incident response team can remediate an issue faster.
Create a Feedback Loop to Improve Monitoring with Observability Insights
Insights from observability-driven investigation can help fine-tune the thresholds for alerts. For example, after discovering a new failure mode, creating a new metric and alert enables proactive detection.
Communicate Observability’s Impact on Business Outcomes
Since monitoring and observability both rely on metrics and data, organizations can review trends over time that translate into business value. As a CyberSecurity Magazine article notes, low level alerts can mask high risk threats. Observability combined with monitoring improves mean time to detect (MTTD) and mean time to respond (MTTR). In turn, this reduces service outages improve the user experience and reduce costs.
Graylog: A Single Hub of Observability and Monitoring
Graylog provides a cost-efficient solution for IT OPS so that organizations can implement robust infrastructure monitoring while staying within budget. With our solution, IT ops can analyze historical data regularly to identify potential slowdown or system failures while creating alerts that help anticipate issues. By sending alerts via text message, email, Slack, or Jira ticket, you reduce response time and efficiently collaborate with others.
You can use Graylog Operations to leverage observability for managing complex, distributed environments because it offers the flexibility and scalability IT ops needs.
