What Is Observability? Key Components and Best PracticesBy Emil Protalinski | Last modified on January 11, 2024
Software systems are increasingly complex. Applications can no longer simply be understood by examining their source code or relying on traditional monitoring methods. The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach: observability.
Observability is not just a buzzword; it's a fundamental shift in how we perceive and manage the health, performance, and behavior of software systems. In this article, we will demystify observability—a concept that has become indispensable in modern software development and operations. We will delve into observability's key components, how observability differs from monitoring, observability's benefits and challenges, and even go over how to implement observability with engineering teams.
Observability (sometimes referred to as o11y) is the concept of gaining an understanding into the behavior and performance of applications and systems. Observability starts by collecting system telemetry data, such as logs, metrics, and traces. More important is how that telemetry is analyzed to diagnose issues, understand the interconnectivity of dependencies, and ensure reliability.
The key components of observability: telemetry types and the core analysis loop
Observability emphasizes collecting and correlating diverse data sources to gain a holistic understanding of a system’s behavior. This is done through the core analysis loop, which involves a continuous cycle of data collection, analysis, and action, allowing teams to monitor, troubleshoot, and optimize their systems effectively.
To gain a more complete picture, observability tools collect data from various components of the software system: logs, metrics, and traces (typically considered the “three pillars of observability” but don’t get us started on that rant).
- Logs provide a textual narrative, helping you understand the "what" and "why" of events and issues.
- Metrics offer quantitative data on system performance and resource utilization, helping you gain insights into the "how much" and "when" aspects.
- Traces let you visualize the entire journey of a request or transaction, revealing the "flow" and "where" latency occurs.
Leveraged in unison, logs, metrics, and traces empower teams to troubleshoot, optimize, and maintain complex systems to ensure reliability, performance, and a better user experience.
Logs (or log files) are chronological records of events, actions, and messages generated by an application program or software system during its operation. Log messages capture information about what software is doing, including execution, performance, errors, warnings, user actions, and other relevant system events. Logs are valuable for debugging issues, diagnosing errors, and auditing system activity. They provide a textual narrative of system events, making it easier to understand the sequence of actions leading up to a problem.
There are different types of log files, including error logs, access logs, application logs, security logs, and transaction logs. These vary in the type of information they can include, such as by listing who has accessed an application or providing a time-stamped view of what happened in an application.
Metrics are quantitative measurements or numerical values that represent specific aspects of a system's performance, resource utilization, or behavior. Metrics are typically collected at regular intervals and can be split into two groups: infrastructure metrics and application metrics.
Examples of metrics include CPU usage as a percentage, memory usage in megabytes, response times in milliseconds, requests per second, and the number of connections to a load balancer. Metrics offer a quantitative understanding when tracking system performance, identifying trends, setting baselines, and detecting system anomalies.
Distributed traces, also known as just traces, capture a chronological record of the events and processing steps that occur with each end-to-end transaction or request as they move through various components, services, and nodes of a distributed system. Each trace records the timing and context of individual operations, enabling a visualization of the entire flow. By providing a detailed view of how requests propagate through microservices, traces are critical for understanding the end-to-end performance of distributed systems, identifying bottlenecks, and diagnosing latency issues.
The core analysis loop
Once collected, the data often needs to be normalized and centralized into a single data store to help correlate information from different sources and create a unified view of system behavior. From here, visualization tools can be used to provide real-time insights into system performance, issues, and user interactions. Visualizations like charts and graphs help teams quickly identify anomalies and trends.
The core analysis loop helps isolate where a fault is happening. As part of triage and isolation, observability data can be used to identify the underlying cause of the problem through deep dives into logs, metrics, and traces. A freeform investigation allows for flexible, ad-hoc exploration of data, empowering engineering teams to dig deeper into observability data, explore correlations, and identify patterns or anomalies that may not have been anticipated. It allows for the discovery of insights that pre-configured dashboards might overlook.
Once the cause is determined, engineering teams take action to resolve the issue, often involving code changes, configuration updates, or resource scaling. The goal is to minimize downtime and restore normal operation.
For the core analysis loop to be useful, the data being analyzed must be rich in context and must contain high-cardinality data. When an issue arises, observability tools can use high-cardinality data to pinpoint the specific components, transactions, or users affected, making it easier to identify the issue.
Issues aside, teams leveraging observability practices can analyze how users interact with applications and services to optimize user experiences and meet business goals. The practice of observability is an ongoing process: teams continuously collect data, analyze it, act, and learn from the results. This data-driven and feedback-driven approach fosters a culture of continuous improvement and cross-team collaboration.
How observability is different from monitoring
Monitoring is the collection of predefined metrics. Monitoring tracks and measures specific aspects of a software system's performance and availability. Its primary goal is to provide alerts and notifications when predefined thresholds or conditions are met, signaling potential issues. Monitoring is suitable for quickly identifying critical issues, such as server downtime, high CPU utilization, or low disk space. It is more reactive in nature and excels at providing early warnings for well-defined problems.
Observability’s primary purpose is to facilitate proactive issue detection and resolution. It emphasizes real-time or near-real-time data collection and analysis, enabling teams to monitor the system's current state and detect issues as they occur. Observability is useful for diagnosing complex issues in distributed systems, optimizing system performance, understanding user behavior, and maintaining system reliability in dynamic and cloud-native environments.
Monitoring and observability serve different purposes and can be applied at different stages of the software development and operations lifecycle. Monitoring focuses on predefined metrics and alerts, while observability provides a comprehensive view of system behavior. Imagine attending a dinner with friends: monitoring keeps track of how many dishes to order, and observability ensures the dinner is a success no matter what happens.
The benefits of observability
Observability offers a slew of benefits, including:
- Proactive issue detection: Allows for real-time monitoring of system components. This proactive approach helps detect and address issues as they occur, reducing downtime and minimizing user impact.
- Efficient troubleshooting: Provides valuable context through data when diagnosing issues. Teams can quickly identify the root causes of problems, streamline the debugging process, and reduce mean time to resolution (MTTR).
- Optimization opportunities: Helps identify performance bottlenecks, inefficiencies, and areas for optimization. As such, teams can fine-tune software systems for improved efficiency and cost savings.
- Improved user experience: Lets teams monitor user interactions and user behavior within an application. Teams can then use this information to optimize the user experience, improve usability, and address pain points.
- Better decision-making: Provides real-world performance data, so teams can make informed choices about system improvements, resource allocation, and scaling strategies.
- Scalability: Details resource utilization and identifies performance bottlenecks. Teams can plan for and implement scalable solutions.
- Resilience and reliability: Helps teams understand failure patterns so they can implement strategies such as automated failover, graceful degradation, and fault tolerance to enhance system reliability.
- Collaboration: Fosters collaboration and knowledge sharing, helping stakeholders understand system behavior and to make informed decisions.
- Compliance and auditing: Provides a trail of activities and events, letting you support compliance requirements and auditing processes based on industry standards and regulations.
Observability is not just a tool or a set of practices. It's a mindset that lets teams gain deep insights into the performance and behavior of software systems. Coupled with real-time monitoring and proactive issue detection, these insights empower companies to build, maintain, and optimize software systems that are reliable, performant, and responsive to user needs.
The challenges in observability
While observability can be a powerful practice, it also comes with challenges that companies and teams must address:
- Data volume, noise, and costs: Vast amounts of data that is not equally valuable can be overwhelming to manage, evaluate, and analyze. Sampling can be useful in lessening the time and financial burdens of telemetry.
- Data variety: Combining and correlating data from logs, metrics, and traces can be complex, especially when different components use different data types, formats, structures, or standards. Frameworks like OpenTelemetry can alleviate this pain point.
- Real-time processing: Achieving low-latency data processing of observability data at scale can be technically difficult and resource-intensive.
- Data privacy and security: Protecting observability data, which may contain sensitive information such as user data or access logs, requires investment and planning.
- Distributed systems complexity: Ensuring consistent observability practices across multiple services can be complex and difficult to manage.
- Instrumentation overhead: Adding observability instrumentation to applications can introduce overhead, impacting performance.
- Skills and training: Effectively using observability tools and interpreting data may require training to obtain skills and harness the full potential of observability. This is true of some tools—however, we at Honeycomb understand this challenge and frequently add features to make observability accessible to everyone. Our Query Assistant, for example, allows engineers to query their systems in plain English.
- Cultural shift: Adopting observability may require overcoming resistance to changing towards data-driven decision-making and collaboration across teams.
- Data retention policies: Determining how long to retain observability data for analysis and compliance purposes may require a legal investment.
Not all these challenges apply to every company, but those that do can be addressed through a combination of technical solutions, best practices, and organizational changes.
How to implement observability with engineering teams
Implementing observability effectively with IT teams involves a combination of technical practices, cultural shifts, and organizational strategies. Here are some best practices to ensure a successful observability implementation:
- Set clear objectives: Establish what you aim to achieve with observability, such as improving system reliability, reducing MTTR, or enhancing the user experience.
- Foster a teamwork culture: Promote collaboration between development, operations, and other relevant teams.
- Implement instrumentation: Instrument your applications and infrastructure to collect observability data consistently across your system using libraries, agents, and custom code to capture relevant logs, metrics, and traces.
- Define Key Performance Indicators (KPIs): Establish KPIs that align with your observability goals and set threshold values for alerting.
- Centralize data and correlate information: Centralize observability data from various sources into a single platform, allowing for correlation and analysis of logs, metrics, and traces for deeper insights.
- Create comprehensive dashboards: Set up customizable dashboards that provide real-time visibility into system performance and health, displaying relevant metrics and alerts for different teams and stakeholders.
- Implement automated alerting: Set up automated alerts, based on predefined thresholds and anomaly detection, that are actionable and provide context about the issue's severity and impact.
- Practice incident response and postmortems: Establish incident response processes that use observability data to quickly diagnose and resolve issues, as well as conduct postmortems to analyze the root causes of incidents and implement preventive measures.
- Monitor user behavior: Incorporate observability into user behavior monitoring to understand how users interact with your applications and to improve the user experience.
- Educate and train teams: Provide training and education to engineering teams on observability best practices, tools, and data interpretation. Ensure that team members understand the value and importance of observability in their daily work.
Effective observability is not just a technical endeavor; it requires a cultural shift and ongoing commitment to monitoring, troubleshooting, and optimizing systems. Strive to quantify the impact of observability on your company’s goals and objectives, such as reduced downtime, faster issue resolution, and improved system performance.
You should also encourage knowledge sharing and documentation. Creating a culture of sharing observations, insights, and best practices across teams will further foster learning and advancement.
Finally, the work is never finished. You should continuously assess and improve your observability practices, regularly reviewing dashboards, alerts, and KPIs to ensure they remain relevant and effective. Be open to adopting new tools and practices as technology evolves.
The Honeycomb difference
Honeycomb’s approach is fundamentally different from other tools that claim observability, and is built to help teams answer novel questions about their ever-evolving cloud applications.
Other tools silo your data across disjointed pillars (logs, metrics, and traces), are too slow, and constrain teams to only answering predetermined questions. Honeycomb unifies all data sources in a single type, returning queries in seconds—not minutes—and revealing critical issues that logs and metrics alone can’t see. Using the power of distributed tracing and a query engine designed for highly-contextual telemetry data, Honeycomb reveals both why a problem is happening and who specifically is impacted.
Every interface is interactive, enabling any engineer—no matter how tenured—to ask questions on the fly, drill down by any dimension and solve issues before customers notice. Here’s a more in-depth look at what makes Honeycomb different, and why it’s such a profound change from traditional monitoring tools:
- See what’s happening and who’s impacted: Alert investigations in other tools generally start with an engineer viewing an impenetrable chart, followed by hopping between disjointed trace views and log analysis tools, leaving them guessing at the correlations between all three. Instead of this fragmented ‘three pillar’ approach to observability, Honeycomb unifies all data sources (logs, metrics and traces) in a single type. Using the power of distributed tracing and a query engine designed for highly-contextual telemetry data, Honeycomb reveals both why a problem is happening and who specifically is impacted.
- Consolidate your logs and metrics workflows in one tool: Other vendors treat traces as a discrete complement to logs and metrics. Honeycomb’s approach is fundamentally different: wide events make it possible to rely on Honeycomb’s traces as your only debugging tool, consolidating logs and metrics use cases into one workflow. Honeycomb’s traces stitch together events to illuminate what happened within the flow of system interactions. And unlike metrics, which provide indirect signals about user experience, tracing in Honeycomb models how your users are actually interacting with your system, surfacing up relevant events by comparing across all columns. Also unlike metrics-based tools, Honeycomb's traces never break when you need to analyze highly contextual data within your system.
- Dramatically speed up debugging: Speed up debugging by automatically detecting hidden patterns with BubbleUp. Highlight anomalies on any heatmap or query result, and BubbleUp will reveal the hidden attributes that are statistically unique to your selection, making it easy to determine what context matters across millions of fields and values. Because BubbleUp is an easy-to-grasp visualization tool, any team member can quickly identify outliers for further investigation.
- Get the full context on incident severity: Other solutions provide metric-based SLOs, meaning they simply check a count (good minute or bad minute?) with no context on severity (how bad was it?). Honeycomb’s alerts are directly tied to the reality that people are experiencing, so you can better understand severity and meet users’ high performance expectations. Honeycomb’s SLOs are event based, enabling higher-fidelity alerts that give teams insight into the underlying “why.” When errors begin, Honeycomb SLOs can ping your engineers in an escalating series of alerts. Unlike other vendors, Honeycomb SLOs reveal the underlying event data, so anyone can quickly see how to improve performance against a particular objective.
- Avoid lock-in with best-in-class OpenTelemetry (OTel) support: Honeycomb supports and contributes to OpenTelemetry, a vendor-agnostic observability framework that enables teams to instrument, collect and export rich telemetry data. Prior to OTel, teams were stuck using vendors’ proprietary SDKs; with OTel, you can instrument once and send to multiple tools if needed, avoiding lock-in. Using OTel’s automatic instrumentation for popular languages, teams can receive tracing instrumentation data with only a few hours’ work. Or, instrument manually to get even richer data and more flexible configuration. Engineers can also attach their existing logs to traces.
- Make costs predictable, without sacrificing system visibility: With Honeycomb, you simply pay by event volume—not by seats, servers, or fields—solving the tradeoff between system visibility and cost. Unlike legacy metrics and monitoring tools, Honeycomb enables engineers to capture unlimited custom attributes for debugging, with no impact on your spend. Honeycomb charges by number of events, not how much data each event contains or the way you analyze that data. There’s no penalty to instrument rich high-dimensionality telemetry or analyze high-cardinality fields.
- You can consolidate your metrics and logs analysis tools into a single line item (and single workflow), because Honeycomb's traces contain wide events with all of your important debugging context. Add as many team members as you like—your costs won’t change.
- Reduce spend further without missing out on critical debugging data with Refinery, Honeycomb’s intelligent sampling proxy tool. Unlike legacy ‘blunt force’ sampling methods that can miss important context, Refinery can examine whole traces and intelligently keep what's important, and sample the rest based on their importance to you.
The future of observability
Observability empowers software engineers to gain a deep understanding of complex, interconnected systems. This lets teams proactively detect, diagnose, and resolve issues, leading to more reliable and performant software systems.
As the volume of data inside companies increases exponentially, teams will look for even more proactive actionable insights they can glean. Both AI and observability will be instrumental for making sense of this exponential growth of data. Coupling observability with AI and ML algorithms will help surface anomalies and automate IT workflows while using generative AI will democratize observability tools for all.
To learn more about observability and achieving production excellence, watch this video and check out Honeycomb’s O’Reilly Book on Observability Engineering.
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