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What is Observability?

Ayooluwa Isaiah
Updated on April 19, 2024

The term "observability" comes from Rudolf E. Kálmán's famous 1960 paper where he applied it to describe mathematical control systems. Simply put: "Observability is a measure of how well internal states of a system can be inferred from knowledge of its external outputs".

In software systems, observability is the capacity to deduce the internal state of an application from its external outputs (telemetry) alone. Essentially, this means that the behavior of the entire system can be understood and predicted by examining the data it generates no matter how novel the behavior is.

If a software system is fully observable, it means you can understand the complete behavior of the system by analyzing the data it generates. Conversely, a lack of observability means there are certain states or behaviors that cannot be discerned or predicted just by looking at its outputs.

Why observability is important

Software professionals have long relied on a patchwork of tools to grasp the inner workings of their systems. Dashboards, thresholds, and automated alerts have been their eyes and ears.

But as software becomes more intricate and interconnected, this approach is failing. The metrics and monitoring approach which were once dependable, now leave many teams in the dark.

The issue lies in outdated assumptions about how systems function. Your application is no longer a single entity residing within your control but a vast, ever-changing ecosystem of services, often dependent on elements you don't own.

Observability offers a fundamental paradigm shift. Instead of educated guesses based on limited data, it grants true insight into the inner workings of your system. Think of the difference between a dim flashlight and a powerful searchlight!

Developers can pinpoint the root of errors and understand how code changes cascade. SREs gain the ability to proactively prevent outages, not just react to them. DevOps teams collaborate more effectively using a shared language built on data. And businesses benefit from higher customer satisfaction, reduced costs through prevented downtime, and increased agility.

Ultimately, observability represents a transformation from reactive troubleshooting to exploring the unknown, uncovering hidden relationships, and revealing insights that lead to optimization and continuous improvement. The era of relying on alerts and guesswork is over – observability provides the tools for a brighter, more resilient future.

Observability vs monitoring

Screenshot from 2024-04-19 18-20-37.png

Traditional monitoring relies on metrics and dashboards to track known patterns and trigger alerts. This reactive approach is well-suited for identifying pre-defined issues. However, it leaves teams dependent on the past experience and intuition of their most seasoned engineers in order to debug novel problems in complex systems.

Observability offers a fundamentally different approach. Instead of requiring the foresight to predict potential issues, it provides tools that empower engineers to ask open-ended questions of their systems. This allows them to:

  • Discover hidden issues: Observability uncovers problems that dashboards and alerts might obscure, preventing engineers from treating symptoms rather than root causes.

  • Investigate without bias: By providing granular data, observability enables engineers to follow a trail of evidence directly to the issue's source, reducing reliance on gut feelings or preconceived notions.

  • Democratize debugging: Observability tools make system insights accessible to all engineers, not just the most experienced. Curiosity and investigative skills replace a reliance on institutional knowledge.

  • Increase confidence: The ability to definitively pinpoint problems, regardless of system complexity or an engineer's prior familiarity, enhances the team's confidence in their diagnoses and solutions.

To summarize, while monitoring excels at detecting known problems, observability facilitates the methodical investigation essential for diagnosing the unanticipated issues inherent in today's complex software systems.

How do you make a system observable?

Observability represents a fundamental shift from reactive monitoring to truly understanding how your system operates. In this section, I'll break down the elements needed to illuminate your system and transform it into one that reveals its inner workings.

1. Logs

Traditional, unstructured logs are designed for human readability. A system administrator might look at the provided log snippet and understand the basic flow of events: an order process was initiated, a payment gateway connection was made, but ultimately, the order failed due to invalid card details.

2023-04-17 14:35:20 Order processing started for order ID: 12345
2023-04-17 14:35:23 Payment gateway connection established
2023-04-17 14:35:25 Payment authorization failed: Invalid card details XXXX XXXX XXXX 3456
2023-04-17 14:35:26 Customer notification email sent to:
2023-04-17 14:35:26 Order processing failed for order ID: 12345

However, at the scale of modern systems, where numerous processes happen concurrently and leave vast trails of logs, it's almost impossible to piece together these interconnected events and why the order failed, or efficiently search across numerous orders to identify similar patterns of failure.

This is where structured logging comes in to provide machine parseability. Unlike multi-line entries of traditional logs, structured logs represent events as machine-readable data. For example, the previous logs can be represented as JSON objects:

{"time":"2023-04-17T14:35:20", "msg":"Order processing started", "order_id":"12345", "request_id":"550e8400-e29b-41d4-a716-446655440000"}
{"time":"2023-04-17T14:35:23", "msg":"Payment gateway connection established", "order_id":"12345", "request_id":"550e8400-e29b-41d4-a716-446655440000"}
{"time":"2023-04-17T14:35:25", "msg":"Payment authorization failed", "error":"Invalid card details", "card_number": "XXXX XXXX XXXX 3456", "order_id":"12345", "request_id":"550e8400-e29b-41d4-a716-446655440000"}
{"time":"2023-04-17T14:35:26", "msg":"Customer notification email sent", "user_email": "", "order_id":"12345", "request_id":"550e8400-e29b-41d4-a716-446655440000"}
{"time":"2023-04-17T14:35:26", "msg":"Order processing failed", "order_id":"12345", "request_id":"550e8400-e29b-41d4-a716-446655440004"}

This structured approach transforms logs from simple textual records into actionable, data-rich events that are easily parsed, analyzed, and correlated, enhancing your observability efforts.

Instead of having several log lines for a single request which are correlated by a request ID, you can output a canonical log line that contains every relevant contextual request. Doing so would make the above log lines look like this:

  "time": "2023-04-17T14:35:20",
  "msg": "Customer order failed",
  "order_id": "12345",
  "error": "Invalid card details",
  "response_status": 400,
  "user_email": "",
  "card_number": "XXXX XXXX XXXX 3456",
  "request_id": "550e8400-e29b-41d4-a716-446655440000"

After emitting logs, the next step is to put them in a log management system where it can be made available for filtering, grouping, searching and more.

2. Metrics

Metrics are scalar values that represent the state of a system at a given time, often enhanced with tags for easier grouping and analysis. They form the backbone of traditional monitoring systems, providing a snapshot of system health through predefined numerical values over set intervals.

By tracking things like CPU usage, error rates, and queue lengths, they can be a useful component of your observability strategy. However, their use comes with significant limitations, primarily due to their aggregated nature. By compressing all underlying data into a single figure, the nuances and anomalies of individual events that contributed to the metric are completely obfuscated.

For example, a healthy average database query time might mask a subset of extremely slow queries impacting a subset of users. Furthermore, metrics lack the connective tissue to easily follow an individual's journey through the system. The volume of metrics needed to achieve this level of detail quickly becomes impractical and overwhelming for traditional monitoring systems.

To truly observe your system, you need to combine metrics with richer data that captures the specifics of each interaction. Think of it like a blockbuster novel: you can't understand the plot just by reading the summary.

Metrics remain a valuable tool, but they offer only a limited window into the complex workings of modern software.

3. Distributed tracing

Distributed tracing provides a way to trace the journey of a request across various services and system boundaries, offering clarity on how different components interact and where potential issues might arise.

In essence, distributed tracing collects detailed data about each segment of a request's path through a system. These segments are known as "spans", and each span captures essential information such as start time, duration, and operational context.

This data is organized hierarchically, with spans nested within one another to reflect the flow of the request through the system. For example, if a user's request first hits a web service, which then queries a database before calling an authentication service, each of these steps would be represented by separate spans within a single trace.

Spans have the following key components:

  • Trace ID: A unique identifier that connects all spans within a single request.
  • Span ID: A unique identifier for each individual span.
  • Parent ID: Defines the parent-child relationships between spans (absent for the root span).
  • Timestamp: When the span began.
  • Duration: How long the work represented by the span took.

By instrumenting your code to capture and propagate this data, you can visualize the complete journey of a request. This helps identify performance bottlenecks, pinpoint errors, and uncover hidden dependencies within your system.

4. Continuous profiling

Continuous profiling is a modern approach to understanding the resource consumption of software applications, specifically focusing on memory and CPU usage, down to the individual line of code.

This technique differs from traditional profiling, which is generally more resource-intensive and conducted intermittently. Continuous profiling employs statistical profiling with time-based sampling, a method pioneered by Google in 2010.

This approach involves capturing data at a set frequency, such as 100 times per second, and aggregating this information over time to provide a detailed yet efficient analysis with minimal overhead.

In the context of observability, continuous profiling gives you the ability to understand resource usage down to individual lines of code. This allows you to compare profiles to pinpoint code changes that degrade performance, augment your existing metrics by instantly revealing the root cause of performance spikes, and enhance traces of slow transactions by providing detailed profiles of the problematic functions.

Imagine you notice a sudden surge in request latency. Continuous profiling would immediately show you which functions are consuming the most CPU cycles. Or, when examining a trace of delayed database interactions, profiling could reveal inefficient code within the database query function.

Challenges of observability

Observability promises the ability to understand the inner workings of complex software systems. However, achieving this state of true insight is far from simple.

The very characteristics of modern applications – distributed, ephemeral, and built from countless interacting components – create a range of hurdles teams must overcome to reap the benefits of true observability.

Here's a few of the most common roadblocks on the path to achieving observability:

1. Data volume and high costs


Modern systems generate massive amounts of telemetry data, which can be overwhelming. Storing, processing, and extracting meaningful insights from this massive dataset becomes costly and difficult.

Solution: Implement data management strategies such as sampling and tiered retention policies to reduce the data volume without sacrificing insights. Using observability solutions with transparent cost management.

2. Complexity and change

Modern systems are built with microservices, cloud infrastructure, and constantly evolving technologies, creating intricate interdependencies that are difficult to untangle. Maintaining the mappings needed for observability in rapidly changing environments is a constant battle, impacting how well your observability setup keeps pace with system changes.

Solution: Utilize automation wherever possible to maintain instrumentation and mappings in dynamic environments. Invest in tooling that supports auto-discovery and flexible visualization capabilities.

3. Signal overload and false positives

With so much data available, it's easy to overload on-call engineers with alerts, making it challenging to identify the truly important signals from the noise. This leads to alert fatigue, where critical issues are missed and teams drown in low-priority notifications, undermining trust in the system.

Solution: Employ AI/ML techniques to help surface relevant patterns and anomalies. Implement correlation capabilities to reduce noise and provide greater context for alerts.

4. Steep learning curve

Implementing and operating an effective observability solution requires diverse skills across data engineering, software development, and system reliability. Many organizations lack the in-house expertise, making it either expensive to acquire talent or leading to suboptimal implementations that don't deliver full value.

Solution: Invest in training and upskilling your existing teams. Consider strategic hires, partnerships, or managed observability services to supplement your capabilities.

5. Tooling and integration

The observability tooling landscape is fragmented and many solutions are still evolving. Integrating various tools to work seamlessly can be time-consuming, creating friction for teams and limiting their ability to achieve comprehensive observability.

Solution: Prioritize tools with open standards and robust integrations. When possible, consider platforms that consolidate multiple observability functions to reduce overall tool sprawl.

Final thoughts

The reactive monitoring practices of the past are insufficient for the complex, distributed systems of today.

If you're struggling with blind spots, endless troubleshooting sessions, and a reliance on tribal knowledge, it's time to explore the transformative power of observability.

Embrace this new approach and empower your team to truly understand the inner workings of your software.

Thanks for reading!

Author's avatar
Article by
Ayooluwa Isaiah
Ayo is the Head of Content at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including, Digital Ocean, and CSS-Tricks. When he’s not writing or coding, he loves to travel, bike, and play tennis.
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