Cloud native observability is an approach to managing applications built on modern, dynamic environments like microservices, containers, and Kubernetes. It goes beyond monitoring, focusing on gathering insights from metrics, logs, and traces to better understand system behavior. This helps developers and infrastructure teams navigate cloud native systems.
In cloud native systems, components are highly distributed and dynamic, making traditional monitoring methods insufficient. Observability provides a richer, more granular understanding of system states and interactions, allowing for proactive problem-solving and optimization of workflows, ensuring systems are performing optimally and cost-efficiently.
This is part of a series of articles about microservices monitoring.
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Traditional observability techniques hinge on predefined metrics and logs generated by static infrastructure. These methods often can’t cope with the agility and scalability demands of cloud native applications, which undergo frequent changes due to continuous integration and deployment practices.
Cloud native observability embraces change and complexity as part of its core functionality. It leverages advanced data analytics, machine learning technologies, and automated tools to provide real-time insights into highly dynamic environments. This enables timely detection of anomalies, helping to maintain system health and availability under rapid deployment cycles.
Cloud native observability offers several key benefits that are crucial for managing and optimizing modern applications and infrastructure. Here are some of the main advantages:
By integrating cloud native observability into their operations, organizations can not only manage their applications and systems more effectively but also drive improvements that contribute to long-term success and stability.
Related content: Read our guide to cloud native monitoring
Cloud native observability solutions typically offer some or all of the following capabilities.
Infrastructure monitoring in cloud native environments covers virtualized and containerized resources. It involves the continuous observation of infrastructure components such as servers, storage, network devices, and Kubernetes clusters and containers. Effective infrastructure monitoring ensures that the underlying resources are performing as expected.
The use of dynamic scaling and management tools within cloud native infrastructure makes real-time monitoring essential for managing system performance and costs. Insights gained from infrastructure monitoring allow for proactive capacity planning and optimization.
Log management includes storing, centralizing, and analyzing log data from various parts of a system. This functionality helps in understanding the sequence of events leading up to failures or unexpected behavior within cloud native applications. It provides historical context for diagnosing issues after they have occurred.
Log management tools accumulate logs and use advanced analytics to sift through them and highlight relevant anomalies or patterns. This filtering of potentially vast amounts of log data enhances the speed and accuracy of issue detection in complex, distributed architectures.
Distributed tracing is a method for tracking requests as they travel through the components of a distributed system, including microservices, databases, and caching systems. It provides visibility into the performance and behavior of individual services and the system as a whole.
By tracing the journey of a single request from start to finish, teams can identify where delays occur, trace dependencies, and understand how data flows through their applications. This capability is particularly valuable in complex systems where services interact through asynchronous and often non-linear workflows, pinpointing the point of failure or bottleneck.
Application Performance Monitoring (APM) focuses on tracking and managing software application performance. APM tools provide visualizations of application operations, making it easier to pinpoint bottlenecks or failures in real time. They scrutinize every aspect of application behavior to ensure optimal performance and quick troubleshooting of issues.
The insights gathered from APM enable in-depth analysis and understanding of how applications consume resources, respond to user requests, and interact with other services. This information is useful for scaling applications and meeting SLA requirements within cloud native environments.
Alerting and incident response mechanisms are designed to quickly identify and respond to operational issues. Alerting systems detect anomalous behavior or metric thresholds being breached, and promptly notify the relevant teams. This rapid notification helps mitigate potential disruptions or degradation of service quality.
Incident response often leverages automation to handle initial diagnostics and remedial actions, reducing the mean time to resolution and freeing up human resources for more complex problem-solving tasks.
Here are some of the measures that organizations can take to improve observability in their cloud native environments.
When developing cloud native applications, incorporate observability into the application design from the outset. This ensures that every component is built with monitoring, logging, and tracing capabilities that can expose internal states and make them observable.
Embedding observability into the design phase allows developers to include specific hooks and metrics that are vital for understanding application behavior under various conditions. This makes diagnostics and performance tuning easier from the early stages of development.
Instrumentation must be context-rich to provide meaningful insights. Implement tools and techniques that capture not just basic metrics but also detailed contextual information about the state of the application and its environment. Such rich data includes user sessions, API calls, and service interactions.
By enriching instrumentation with context, teams can gain a deeper understanding of issues and performance metrics within their applications. This leads to more effective troubleshooting and optimization.
High cardinality data involves metrics with many unique values, such as user IDs or transaction IDs, which are essential for pinpointing specific issues. High dimensionality refers to data with many attributes or tags, providing a detailed view of the system’s status and behavior under different scenarios.
Focusing on these aspects allows teams to perform precise, granular analysis and improve the detection of anomalies and trends in system performance.
End-to-end observability aims to provide an understanding of the complete user journey and the systemic health of cloud native applications. This involves monitoring and analyzing every touchpoint of the application stack—from front-end user interfaces through backend services and out to third-party integrations.
Use tools designed specifically for dynamic, distributed environments. These tools should support real-time data collection, processing, and analysis across various layers of the cloud infrastructure. They must be scalable, flexible, and capable of handling the complexity and volume of data generated by modern applications.
Key tool categories include advanced APM platforms, log analyzers, and distributed tracing systems. Employing such tools enhances visibility into cloud native architectures and supports cloud native observability strategies.
Lumigo is a cloud native observability and troubleshooting platform, purpose-built to navigate the complexities of microservices. Through automated distributed tracing, Lumigo is able to stitch together the distributed components of an application in one complete view, and track every service of every request. Taking an agentless approach to monitoring, Lumigo sees through the black boxes of third parties, APIs, and managed services.
With Lumigo users can:
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