Observability in Software Engineering – Metrics, Logs, Traces

What is Observability?

In control theory, observability, is the concept of deducing a system’s state from its external outputs. When applied to IT, observability refers to how you evaluate the current state of an application through the data it produces.

Essentially, observability goes beyond collecting data; it involves the ability to delve into the system, tracing inputs as they navigate through every aspect of the system, gathering diverse signals and data, and yielding precise outputs of the entire system. Simply put, observability enables teams to gain complete visibility into their application’s and infrastructure’s health by combining data from metrics, logs, and traces.

To enable observability, four key components must be considered:

• Instrumentation: This entails measuring tools to retrieve telemetry (metrics, logs, traces) data from services, container applications, and host systems.
• Data correlation: This involves processing these different datasets.
• Incident response mechanisms: This refers to how efficiently a failure is delivered to the appropriate personnel.
• AIOps: Artificial Intelligence for IT Operations (AIOps) is an automated system that aggregates, correlates, and prioritizes incident data.

These components work together to improve a system’s overall observability.

Why is observability critical?

Modern IT infrastructure and applications have grown in complexity due to the introduction of containers, microservice architectures, and multi- and hybrid-cloud approaches. The continuous communication between these distributed systems can sometimes be unpredictable or result in failures and performance issues. This means that a single failure in one component could result in a ripple effect across other system components and significantly affect the application’s performance.

Observability is essential because it dives into the problem, why it exists, and even traces back to how it all began, offering real-time data into the state of the software system. Enterprises can leverage this to make informed, data-driven decisions that lead to positive business outcomes. Engineering, Operations, and DevOps teams can also gain visibility into the system through observability, allowing them to spend less time troubleshooting and refactoring.

Observability vs. monitoring

Observability and monitoring are complementary concepts for managing and understanding complex systems: a system must be observable for effective monitoring.

Monitoring keeps track of overall system health and tells you when something is wrong. It involves aggregating and displaying system performance data in predefined metrics like CPU usage, memory consumption, response times, error rates, etc.

The concept of observability, however, uses data collection to tell you what is wrong and why it happened. It involves developing a comprehensive understanding of the system’s internal operations.

Benefits of observability

Observability offers a range of capabilities that can benefit software developers and engineers, operations teams, and business stakeholders.

• Businesses & enterprises: According to the State of Observability report of 2023, observability is essential for the operation and success of a company. 64% of organizations are spending a day or longer on typical investigations after an incident. Businesses can utilize observability tools to analyze the end-to-end customer journey, understand resource utilization, and identify opportunities for optimization. As a result, teams can reduce useless or redundant information while prioritizing critical occurrences, saving costs, and enhancing resource allocation.
• Enhance end-user experience: The customer experience and overall satisfaction with the product/service is a primary goal for businesses. With Observability tools, businesses can monitor and analyze the performance of their applications and services in real-time. This helps track, identify, and address issues that could impact the end-user experience and content delivery, like slow response times or service outages.
• DevOps and SRE: Observability supports a culture of continuous improvement by providing real-time insights into system performance. When implemented, it can offer end-to-end data visibility and monitoring across multi-layered IT architecture. Thus, DevOps and SRE teams leverage observability to make informed decisions about infrastructure changes and instantly resolve unanticipated problems, saving time.
• Security and Compliance: With full visibility into your application or infrastructure, observability enables the team to detect abnormal patterns or behaviors within a system. Observability data (metrics, logs, and traces) can be instrumental in understanding the scope and impact of threats like data breaches or outright software attacks.

Challenges of observability

While observability provides essential insights into complex systems, it also has drawbacks. Some challenges with observability can include:

• Data overload: The volume, velocity, and variety of raw data and alerts generated from components in distributed systems are not constant. As a result, extracting data as these systems scale and become more complex is more challenging.
• Complexity of modern software systems: The complexity of an infrastructure substantially impacts the effectiveness of observability in a system. The intricate nature of modern IT environments, especially in dynamic multi-cloud setups, introduces challenges that can lead to gaps in monitoring and hinder real-time insights. For instance, a rise in servers and IoT devices inside some large-scale systems may delay sending logs to logging.
• Siloed Infra, Dev, Ops, Apps, and Biz teams: When data or infrastructure silos exist within an organization, implementing a unified observability solution becomes challenging, which can result in high costs. DevOps, business, and operations teams must learn to collaborate efficiently to achieve observability and reduce risk.

The three pillars of observability

In observability, three pillars collectively provide insights into the behavior and performance of a system.

Metrics

Metrics are quantitative data points with attributes that indicate the health of one aspect of the system—like the infrastructure, applications, load balancers, and other sources. They provide real-time visibility into a system’s performance using parameters such as timestamp, name, KPIs, and value. Businesses and Engineering teams often utilize popular metrics like CPU, network traffic, memory, latency, and disk utilization, which are natural indicators of a system’s health. Metrics, however, often focus on one system region at a time, making tracking issues throughout a distributed system difficult.

Logs: A chronicle of events

Logs are granular text records of events and errors of an application’s request processing. Each log entry provides chronology records with valuable insight into the series of events, diagnosing issues, and auditing system activities. Observability into logs will help developers identify when a problem occurred and which events correlate. These log entries are grouped based on similar log field content in either plain text, structured, or binary format.

Whenever a query is run, the system examines the results for patterns and automatically groups log entries based on similar log field content. Logs are often temporal; however, most organizations extend their log retention period for security and compliance measures.

Traces: Mapping the journey

The third pillar, traces, plays a pivotal role in unraveling the end-to-end behavior of a request in distributed systems. Traces capture the lifecycle of a request as it passes through multiple components, providing a visual map of dependencies and interactions. Although traces cannot tell you why a system is misbehaving, they can help you understand the flow of requests and narrow down which of the hundreds of components is faulty. During distributed tracing, resource names can be used to identify the correct resource ID to query and track the flow of requests across services.

Alone, each pillar provides valuable insight but does not guarantee observability due to its unique limitations. To create a successful observability strategy, teams should explore solutions that integrate all three pillars for total visibility of your IT health in terms of performance and availability.

Observability in software engineering

In software engineering, observability is the ability to understand, measure, and analyze the internal state of a system by inspecting its external outputs. This capability plays a crucial role in the maintenance, debugging, and enhancement of the performance of intricate software systems.
Implementing an observability strategy is a proactive approach to enhance system reliability, improve issues, and enable continuous delivery and improvement—which means your engineering team can sleep well at night.

Extending observability to platform engineering

As platforms evolve, they inherently become more complex, containing many interconnected services, microservices, containers, and other components. Therefore, teams often require real-time feedback on performance, faults, and user engagement from numerous configurations and versions of the same product. Platform engineers play a critical role in handling the complicated nature of these distributed systems by provisioning and configuring observability tools.

However, observability enables platform engineers to delve deeper into how services interact, track the flow of data, and comprehend the performance of the entire platform. So, metrics, logs, and event data can be continuously collected, analyzed, and correlated to facilitate fast troubleshooting. The ultimate goal is to provide a seamless experience for DevOps teams while accomplishing optimal efficiency across the platform.

Mia-Platform and Observability

There are several open-source and commercial vendors that provide SaaS products for observing, managing, and analyzing telemetry data. However, Mia-Platform caters to software engineering teams by providing an unparalleled development experience. At its core is Mia-Platform Console, an Internal Developer Platform (IDP) crafted for Platform Engineers and DevOps professionals. This solution improves end-to-end visibility, traceability, and observability across the DevOps cycle. With Mia-Platform, teams can seamlessly navigate and optimize their development journey, ensuring a holistic and streamlined approach to software engineering.

Observability in software engineering is not just another technical requirement; it’s an integral part of managing software in distributed systems. So, if you are looking to establish robust platform engineering practices, check out this comprehensive Role-Based Access Control (RBAC) White Paper. You can also uncover the full potential of observability by exploring other Mia-Platform’s open-source projects.

Wrapping up

Observability has become a requirement for most modern-day applications. Although not required for every application, observability bridges the gap between the known and the unknown within a distributed system. It empowers IT operations, DevOps, and software engineers to track and navigate any challenges that affect a software’s performance and stability.