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What is Observability?

Jubril Oyetunji
Jubril Oyetunji

Table of Contents

As businesses grow and offer more complex services and features, gaining deeper visibility into application behavior becomes critical. Effectively tracking application activity and understanding the underlying causes of their behavior empowers businesses to improve performance, troubleshoot issues efficiently, and ensure optimal functionality across all services.

The concept of observability refers to the practice of instrumenting applications to measure internal runtime states and behaviors. By capturing detailed metrics, logs, and traces, organizations gain actionable visibility into critical aspects like service health, resource usage, application errors and more. This inside-out understanding of systems enables proactively optimizing experiences, preventing downtimes, and quickly diagnosing problems when they do occur.

In this article will explore what observability entails, examining its key pillars, implementation best practices, and use cases across industries.

Observability vs. Monitoring: What's the Difference?

While related, observability and monitoring represent distinct approaches for gaining insight into an application’s internal state.

Monitoring refers to tracking overall system health metrics and Service Level Agreements(SLAs) along with setting alerts around defined thresholds. It delivers a high-level view focused on functional output.

Observability encompasses detailed telemetry data like traces, logs, and granular metrics within the application code and infrastructure. It aims to give engineers an understanding of interrelated components and the ability to ask different questions about the system.

The distinction is subtle but important. Traditional Monitoring tools tells you that CPU utilization spiked or your website is slow. Observability allows you to reconstruct why it happened by tracing the journey of a failed transaction across individual components that make up your application.

The Three Pillars of Observability + One New Addition

Observability: Logs vs Traces vs Metrics! | by umang goel | Medium

Modern observability is held together by three major pillars and a relatively new addition in recent times; each one plays a key role providing the big picture of a system’s behavior.

Metrics refer to measurements on application and infrastructure behavior, captured as quantitative time-series data. Metrics include indicators like request latency, error rates, CPU usage, and more. By tracking metrics, teams can set performance baselines and thresholds to monitor overall system health. Metrics can also enable teams to correlate issues raised in other pillars—for example, investigating logs for errors that occur when latency spikes, or monitoring the performance of a new software release in real time.

Logs record event data on discrete actions within an application, capturing info related to significant occurrences. Logs enable auditability and provide clues during troubleshooting. Log data can likewise be combined with traces and metrics to uncover the root cause behind problems highlighted by those observability signals.

Traces follow the path of a request as it propagates across distributed services, tracking each step along the way. X-ray views into request traces become especially critical given modern service-oriented architectures, where a single user action can span across hundreds of interconnected microservices. In these complex environments, trace data delivers insight into the impact each service has on end-user experience. Traces also allow teams to spot and isolate the root cause of issues surfaced in metrics or logs.

Profiling is a more recent addition to the pillars of observability. Profiling provides deep insights into an application's resource consumption, particularly around CPU and memory usage. Continuous profiling helps teams understand precisely where software applications are spending resources, enabling them to identify potential bottlenecks or performance issues

Tools like pprof from Google capture profile data and provide visualizations that are essential for making sense of complex profiling information.

No single pillar provides the complete observability picture—each has blind spots that can be mitigated by cross-referencing with the other pillars. Combining metrics, logs, and traces establishes integrated observability into complex, modern applications.

Why is Observability Important?

Whereas monitoring provides high-level visibility into overall system health and external outputs, an observability solution offers granular internal insights to manage "unknowns" - issues that emerge unpredictably in complex environments.

The unknown could refer to overloaded resources under peak traffic and downstream dependencies suddenly failing. Without observability, reliability engineers would only detect these problems indirectly via blunt monitoring alerts or unhappy customers.

Observability tooling lights up system internals - traces help track process flows, metrics reveal abnormal resource usage across application and infrastructure components, and logs capture errors and relevant system data. This instrumentation empowers teams to quickly isolate and diagnose application performance issues and address anomalies or emerging failures before they become critical.

Common Challenges Observability Can Help Solve

Complex applications like large-scale e-commerce platforms inevitably run into tricky issues that observability tooling can help overcome.

Take for example an outage on Amazon's website during Prime Day sales. Suddenly customer checkouts start failing at the final transaction confirmation step. Without observability, engineers would be blind to the underlying cause and scrambling to
With observability tooling in place, Amazon can rapidly trace where the transaction process is breaking.

  • Granular traces reveal the final API call from the checkout service to the payment processor is spiking resource contention on backend databases, sometimes timing out.
  • Metrics confirm the database cluster is overloaded.
  • Logs further signal certain payment confirmation transactions are now erroring out.

Observability data indicates the root cause lies with a surge in orders overwhelming the databases. Engineers can quickly scale up database capacity until service returns to normal. Customers may have experienced some slowness but avoided a prolonged outage.

Components of Better Observability

Realizing the full benefits of observability involves integrating the right mix of tooling across metrics, traces, and logs. While numerous proprietary solutions exist, open source alternatives are rapidly gaining popularity offering compelling capabilities at lower costs. These components work together to deliver comprehensive system visibility and actionable insights for root cause analysis and optimization.

Metrics and Outlier Detection
Time series data platforms like Prometheus deliver powerful capabilities around capturing, querying, graphing, and alerting based on metrics data emitted from a variety of sources. Its sophisticated PromQL supports analytics like forecasting, quantiles, and anomaly detection to uncover outliers.

Distributed Tracing
Micro-service frameworks like OpenTracing and OpenTelemetry implement common open standards for instrumenting and propagating distributed traces to illuminate request journeys through interconnected services. This helps provide a vendor neutral way of collecting distributed traces. Open source tracing analysis and storage solutions like Jaegerprovide intuitive UI's to analyze and make sense of traces.

Log Management
Modern systems may produce a high volume of server and application logs, capturing everything from access to event data require specialized log management capabilities. Solutions such as the ELK (Elasticsearch, Logstash, Kibana) are cloud native applications for logging, storage, search, analysis and visualization features suitable even for complex observability implementations.

Application Performance Monitoring
APM solutions like the open source SigNoz, Elastic APM, and Pinpoint instrument application code to track performance data from the user's perspective. APM provides unified visibility into key metrics, traces. This granular app telemetry delivers actionable insights for troubleshooting issues.
Moreover, APM tools continuously monitors application behavior over time, allowing teams to identify regressions, inefficiencies, and optimization areas based on real user traffic patterns. Historical analytics enable data-driven tuning of application code, resources, and architecture.
When used in conjunction with other observability systems monitoring infrastructure and services , APM provides critical application-centric insights which traditional monitoring tools might struggle with.

Observability Use Cases

Observability delivers value across domains by providing meaningful insights that answers diverse questions:

In fintech, observability helps firms manage risk:

  • Traces give fraud teams meaningful insights to tune detection models minimizing false positives and losses.
  • Granular metrics identify latency issues degrading trade execution speeds so developers can optimize performance.
  • Logging user flows improves conversion funnel analysis resulting in higher engagement.

A great example of this is Payment processor Paystack, they have an excellent blog on how they handle observability

For e-commerce companies, an observability platform drives continuous innovation, resilience and informed decision making:

  • Traces empower quick experimentation and canary launches to accelerate feature development.
  • Metrics correlated with business KPIs help quantify software impact and prioritize tech investments.
  • Logs can reveal checkout fall-offs guide placement and personalization's to improve customer experience.

Food delivery platforms like Swiggy use observability to monitor the end user experience for over 30 million uses

In gaming, observability unlocks key capabilities around retention, quality of service, and new features:

  • Metrics on session length optimize game design to increase user engagement.
  • Traces provide a single source to diagnose lag or crashes due to game server overload under new release demand.
  • Logs of in-game behaviors inform balancing of economy systems and better tutorial flows.

Implementation and Best Practices

Realizing the benefits of observability in complex systems requires an intentional gradual implementation approach. Every organizations observability strategy will be different but in general:

Start with Metrics
Begin by instrumenting key infrastructure and application metrics using a time-series database like Prometheus. Focus on language-native instrumentation before introducing agents. Gradually expand metrics coverage across critical components.

Add Logging Best Practices
Next, implement structured logging with a standard schema. Send application logs to a centralized analysis tool like the ELK stack. Normalize and tune logs to reduce noise, alert on anomalies across application components.

Traces for Critical Services
Then introduce distributed tracing selectively for high priority services using OpenTelemetry. Visualize relationships between interconnected components with Jaeger. Resist over-instrumentation and trace sampling is key.
It's also important to note that tracing requires using specific SDK's depending on the language you choose and may  require changes to application logic

Identifying Bottlenecks with Profiling
Identify a profiling tool that integrates well with your target systems and languages. Popular options include pprof, pyroscope. Determine what support is available for capturing and visualizing profile data. Start profiling high-priority hot paths or resource-intensive components first. Utilize profiling data visualizations to identify CPU and memory bottlenecks.

There’s no shortage of observability tools, however picking the one that suits your needs is where the challenge lies, every team is structured differently and your needs might vary. Selecting the “right” observability tool means trying things out and iterating.

Observability in DevOps and AIOps

Observability empowers engineers with detailed system visibility to optimize DevOps workflows while accelerating incident response. Its application spans:

Identifying Issues
Granular metrics, logs and traces shine a flashlight into dark corners, spotlighting inefficiencies, anomalies or failures. This enables operations teams to get to the root cause behind degraded service performance which indirect monitoring alerts could easily miss. It also creates a record of event's that can be used  as source of truth for future events.

Informing Improvements
Observability provides a steady stream of signals which can be targeted for tuning across infrastructure, application code, APIs, databases, and more. It enables data-driven decisions on refactors that can prevent future issues.

Quantifying Releases
By tracking key indicators before and after deployments, observability quantifies the impact of changes across traces, logs and metrics - informing better release engineering. Gradual feature rollouts depend deeply on observability.

AIOps Integration
Feeding observability data into machine learning systems powers automated insights around issue triage, anomaly detection, and predictive capabilities. AIOps needs observability signals to learn operational patterns and performance metrics in order to provide real-time insights about application performance and infrastructure monitoring

Without comprehensive observability, AIOps would remain blind to much of the inner workings behind complex system behaviors and failures. Observability thus unlocks crucial new capabilities for AI-assisted operations. For modern engineering teams, observability delivers resilience and efficiency gains at both human and automated levels.

Improving User Experiences

Observability gives teams crucial visibility to continually optimize user journeys. Metrics reveal performance degradation like high latency that directly worsens responsiveness. Traces pinpoint system failures explaining poor experiences.
Together these signals paint an accurate portrait of interrelated factors driving user outcomes. Observability may connect buffering issues to CDN or load balancer under provisioning. By linking symptoms to root causes, it enables proactively fixing pains before customers feel impact.  DevOps Teams can optimize stability and performance

Bringing Observability Everywhere

The goal of observability is to provide development teams and operations a big picture of system health and behavior patterns.

As services span across environments, teams need unified observability connecting insights from multiple sources. To operate effectively, they require a single source of truth, making sense of massive volumes of metrics, logs, and traces.

Without properly aggregating and analyzing observability data, the signals risk becoming just noise. Teams get overwhelmed sorting through the firehose of alerts, charts, and visualizations. Isolating where issues originate and correlating root causes becomes near impossible.
With unified observability, teams gain focus. Disjointed signals condense into an accurate portrait of system health.

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Jubril Oyetunji Twitter

Jubril is a software engineer, primarily focused on building infrastructure using cloud native technologies. When he’s not coding or ranting, he’s sharing his learnings through technical writing.