Observability Best Practices: How to Future-proof Your Software
When collaborating with developers, you might have come across the phrase “but it works on my machine,” a well-known programmer joke that holds a valuable lesson.
Software engineers often focus on code within controlled environments, neglecting its behavior in the real world. Observability addresses this issue by enabling the development of software capable of handling real-life situations and unpredictability that real users encounter.
The paradigm shift in software development
Observability encompasses the easiness of evaluating an application’s internal state by analyzing its external outputs, including logs, metrics, and traces. For a more detailed understanding of observability, consider exploring the article “What is observability in custom software development?” to gain a comprehensive overview.
The increasing need for data observability
Observability serves as a response to the growing complexity and distribution of systems. With numerous containers, microservices, and cloud components generating vast amounts of activity data, the observability becomes a means for IT teams to comprehend the entire ecosystem.
By harnessing the dynamic nature of this data, observability allows teams to gain insights into various environments and technologies. This shift marks a departure from traditional monitoring, which concentrates on predefined patterns and properties.
Working with the unpredictable. Monitoring versus observability
As the complexity of systems continues to grow, it becomes evident that many tools were originally designed for a more predictable world.
The distinction between monitoring and observability goes beyond just the tools themselves; it also encompasses how engineers interact with these tools to identify and resolve issues in an unpredictable environment.
Observability allows engineers to adapt to the dynamic nature of modern systems, enabling them to detect and troubleshoot problems effectively in the face of complexity and uncertainty.
Monitoring
Monitoring operates by collecting predefined sets of metrics from individual systems. If an engineer wants to detect a problem in a log, they must have prior knowledge of what to search for.
Additionally, dashboards utilized for monitoring performance metrics and usage are preconfigured, leading to two challenges:
a) engineers need to anticipate specific issues to set up alerts, and
b) investigating issues in detail becomes harder due to limitations in pre-aggregated metrics, which do not support high cardinality data exploration.
Observability
Observability, in contrast, offers a more dynamic perspective by examining the health and status of various applications and resources throughout the entire infrastructure. Its objective is to provide teams with insights that align closely with the behavior of complex, distributed systems, where predicting issues in advance is challenging or when incidents might involve multiple root causes.
Although observability builds upon monitoring, its metrics go beyond the limitations of typical monitoring tools. Consequently, prioritizing the investment in an observability architecture and strategy becomes essential for any software development company.
Observability use cases in custom application development
Custom software development encompasses a diverse array of applications designed to address specific business requirements, offering notable advantages over off-the-shelf SaaS integrations.
In this context, observability plays a pivotal role, guaranteeing that custom software aligns with business and end-user needs while delivering optimal performance. These common use cases demonstrate the value of observability in custom software development:
Application Performance Monitoring and Optimization
Observability provides a comprehensive view of a custom application’s health and performance by aggregating data from all systems, including microservices and cloud-native environments.
This enables engineers to gain valuable insights into latency issues, resource allocation problems, and other performance-related issues, empowering them to optimize the application’s performance.
Application Security and Compliance Monitoring
Another role observability can serve is as a powerful tool for detecting potential security threats and vulnerabilities in custom software. By monitoring logs and metrics related to security events, developers can proactively identify suspicious activities and bolster the software’s security.
Additionally, observability ensures that the software remains compliant with relevant regulations and industry standards.
Continuous Integration and Deployment (CI/CD)
Integrating observability practices into the CI/CD pipeline enables end-to-end visibility, allowing teams to monitor various stages of development, including the build process, test executions, commits, and pre-deployment and post-deployment checks.
Real-time monitoring during CI/CD mitigates the risk of deploying faulty code to production.
Observability in a Microservice Architecture
In a microservice environment, where the number of services and interactions can be complex, observability becomes vital.
It provides granular insights through the utilization of service meshes and distributed tracing, helping engineers track and comprehend the overall system behavior.
Observability in Cloud-Native Environments
Cloud-native environments, characterized by containerization, orchestration, and dynamic scaling, require observability for effective management and understanding of application behavior.
Observability leverages platform-specific features and third-party tools to monitor and optimize applications in dynamic cloud ecosystems.
How to develop highly observable software. Implementing an observability strategy
To fully leverage observability, software development teams must go beyond just using the right tools. They need to adopt a deliberate approach and focus on observability throughout the entire software development process.
Observability-driven development (ODD) expands the concept of observability to the very core of software creation, including the initial stages.
Before delving into building an observability system, it is crucial to ensure that the system itself is observable. This entails designing a transparent Software Development Life Cycle where applications openly and intelligibly expose all their events.
Instrumenting your code for observability
Incorporating observability into the software development process begins by instrumenting the code to capture relevant data and events. This involves integrating observability tools and libraries into the application code to gather essential metrics, logs, and traces.
When planning to observe an application, it is essential to determine the necessary data for the application’s health, reliability, and user experience, and prioritize the most likely failure modes of the system.
Depending on the chosen tech stack, utilizing the built-in support of the framework can automate code instrumentation and reduce potential errors. Ensuring that the instrumentation remains lightweight and does not introduce substantial overhead is fundamental for maintaining performance and efficiency.
Collecting observability data
Observability relies on the collection of data through three key pillars: logs, metrics, and distributed traces.
1. Logs: These encompass historical records of various events, including system and server logs, network system logs, and application logs.
2. Metrics: Metrics involve the measurement of specific activities over time intervals, such as monitoring CPU and memory usage, infrastructure metrics, or user and web tracking scripts.
3. Distributed traces: Distributed traces are records of service calls corresponding to a request, primarily used to track the performance of microservices.
To achieve comprehensive observability, data collection should span the entire technology stack, including the data layer, container layer, and upper cloud application layers.
Data quality is a critical aspect of an effective observability strategy, demanding accuracy, consistency, timeliness, and completeness to support informed decision-making.
To maintain the quality of collected data, data sources from various monitoring systems should be standardized to prevent redundancy, reduce clutter, and minimize noise. Regular audits and pruning of monitored data also help maintain a streamlined and efficient observability system, such as deleting unused dashboards, alerts, or logging entries.
Analyzing observability data
Observability promotes a holistic analysis of all systems and layers, which can make interpreting health and performance data both more manageable and challenging. The challenge arises not only from the sheer volume of generated data but also from the risk of overestimating the team’s ability to consistently observe and respond to these insights effectively.
To address this challenge, it is essential to present information in a concise and shareable format. Tools like Kibana or Grafana can aid engineers in creating visual representations of the data, making it easier to share across teams and team members. This practice fosters a culture of knowledge sharing between development, operations, and support teams, enhancing collaboration and collective problem-solving.
When analyzing observability data, it is vital to consider user experience insights. Understanding how users interact with the application in real-world scenarios can enrich the observability strategy. It provides valuable clues regarding application performance that may not be apparent solely by examining raw telemetry data from back-end applications.
By factoring in user experience, organizations can gain a more comprehensive understanding of their software’s behavior and identify potential areas for improvement.
Building an observability infrastructure
Incorporating observability into the software development lifecycle asks for a robust implementation strategy and a suitable infrastructure. The landscape of observability systems offers a wide array of options to choose from.
Choosing a system fit for your needs
Choosing an observability system comes with several options, from acquiring a complete package from a vendor and sending data to a third party to utilizing multiple tools to create a comprehensive infrastructure.
Observability tools focus on monitoring specific aspects of applications, infrastructure, or networks. Examples include Prometheus for metrics monitoring, Jaeger for distributed tracing, and Sentry for error tracking.
On the other hand, observability platforms offer all-in-one solutions that integrate multiple observability tools into a unified environment, such as Google Cloud’s operations suite.
The decision to opt for a particular approach depends on your monitoring needs and the complexity of your ecosystem.
Observability best practices in software product development
Effective observability practices go beyond just having a strategy and infrastructure in place. To foster an observability culture and ensure resilient software, specific aspects should be considered.
Here are some actionable and sustainable approaches to achieve this within your team:
1. Integrate observability practices into the entire software development lifecycle (SDLC) from the outset, ensuring transparency in code and data collection throughout the development, testing, and deployment stages.
2. Align observability goals with broader business objectives, using collected data to optimize application performance, resource allocation, and time-to-market.
3. Focus on monitoring relevant data and regularly review metrics to maintain their significance. Customize dashboards, set appropriate thresholds for alerts, and utilize tracing, logs, user-centric data, and team feedback to adjust metrics iteratively.
4. Maximize automation and standardization to minimize errors and manual interventions. Utilize machine learning algorithms and AI for tasks like anomaly detection and root cause analysis, and establish standardized data formats and contextual information to improve observability efficiency.
5. Prioritize the end user’s perspective by incorporating user insights into observability practices. Analyze user interactions through monitoring and session recordings to gain better visibility on performance issues and potential business impact.
Observability: The key to software transparency and developer accountability
Observability is a crucial factor in achieving high performance and delivering quality custom software development services. Without it, developers rely on guesswork and pattern matching, facing challenges in identifying cause-and-effect relationships promptly.
By adopting strong observability practices, software development teams gain a clear view of their systems, fostering a culture of transparency and responsibility.
Observability empowers developers by providing them with essential data and insights. Equipped with this knowledge, developers take ownership of their code and become accountable for its performance and dependability.
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