In 2023, native cloud applications and platforms will develop rapidly. A large number of enterprises are striving to realize the maximum potential of their applications to ensure the ultimate user experience and drive business growth.
The rise of hybrid cloud environments and the adoption of containerization technologies such as Kubernetes have revolutionized the way modern applications are developed, deployed and scaled.
In this digital arena, K8s is the go-to platform for most cloud-native applications and workloads, and this popular open-source tool facilitates container orchestration and discovery, load balancing, and other functions. According to a report, 96% of companies are currently using or are evaluating K8S implementation in their cloud systems.
However, with that comes a new set of challenges. As application complexity increases, so does the need for effective observability solutions that enable enterprises to gain insight into their containerized workloads, and to manage and optimize containerized applications in hybrid cloud environments an important prerequisite.
This article will dive deep into K8S observability to explore six effective strategies that allow enterprises to unlock the full potential of their containerized applications in hybrid cloud environments.
1. Use cluster logging and log aggregation
To gain insight into distributed systems, cluster logging is a fundamental strategy. In a K8S environment, applications span multiple containers and nodes, so collecting and analyzing logs from various sources becomes crucial.
Cluster logging consists of consolidating logs from different components into one easily accessible location. The importance of cluster logging is its ability to provide a holistic view of system behavior and performance.
With K8S cluster logs, you can correlate events and identify patterns in the K8S cluster, enabling efficient troubleshooting and root cause analysis.
To implement cluster logging in K8S, you can use powerful log aggregation tools or cloud-native solutions, such as Amazon Observation Log or Google Cloud Log. These tools provide scalable and efficient ways to collect, store and analyze logs from K8S clusters.
2. Leveraging Distributed Tracing for End-to-End Visibility
In a complex K8S environment where microservices are distributed across multiple containers and nodes, it is challenging to understand the flow of requests and interactions between different components. This is where distributed tracing comes into play - providing end-to-end visibility into the path of a request's execution as it traverses various services.
Distributed tracing allows you to trace the flow of a request from ingress to contact across all microservices, capturing valuable information about each step. By instrumenting your application with a tracing library or proxy, trace data can be generated to show the duration, latency, and potential bottlenecks for each service.
The benefits of leveraging distributed tracing in Kubernetes are significant.
First, it helps you understand dependencies between services, allowing for better troubleshooting and performance optimization. When requests are delayed or erroneous, you can quickly identify the problematic service or component and take corrective action.
Second, distributed tracing allows you to measure and monitor the performance of individual services and their interactions.
By analyzing trace data, you can identify performance bottlenecks, detect inefficient resource usage, and optimize the overall responsiveness of your system. This information is important for container planning and ensuring scalability in Kubernetes environments.
For enterprises, there are some popular distributed tracing solutions to choose from. These tools provide the tools and infrastructure necessary to efficiently collect and visualize trace data. By integrating these solutions into your K8S deployment, you can gain a comprehensive understanding of your microservice behavior and drive continuous improvement.
3. Integrate K8S with APM solution
In order to achieve comprehensive observability in K8S, it is necessary to integrate your environment with an application performance monitoring (APM) solution. APM solutions provide advanced monitoring capabilities that go beyond traditional metrics and logs, providing insight into the performance and behavior of individual application components.
One of the main benefits of APM integration is the ability to detect and diagnose performance bottlenecks in K8S applications.
With an APM solution, you can track requests as they pass through various services and identify areas of high latency or resource conflicts. Armed with this information, targeted actions can be taken to optimize the critical path and improve the overall performance of the application.
Many APM solutions offer specialized K8S integrations that simplify monitoring and management of containerized applications. These integrations provide a library of pre-configured dashboards, alerts, and tools that simplify the capture and analysis of APM data in K8S environments.
4. Use metrics-based monitoring
Metrics-based monitoring forms the basis of observability in K8s. It includes collecting and analyzing key metrics that provide insight into the health, performance, and resource utilization of your K8S clusters and applications.
When it comes to metrics-based monitoring in Kubernetes, there are a few basic components to consider:
Node-level metrics: Monitoring the resource utilization of individual nodes in a Kubernetes cluster is critical for capacity planning and infrastructure optimization. Metrics such as CPU usage, memory usage, disk I/O, and network bandwidth help you identify potential resource bottlenecks and ensure optimal allocation.
Pod-level metrics: Pods are the basic deployment unit of Kubernetes. By monitoring metrics related to pods, you can evaluate their resource consumption, health, and overall performance. Key pod-level metrics include CPU and memory usage, network throughput, and request success rates.
Container-level metrics: Containers within a pod encapsulate individual application components. Monitoring container-level metrics can help you understand the resource consumption and behavior of a specific application service or process. Metrics such as CPU usage, memory usage, and file system usage provide insight into container performance.
- Application-specific metrics: Depending on the needs of your application, you may need to monitor custom metrics for specific business logic or domains. These metrics can include interaction rates, error rates, cache hit rates, or other relevant performance metrics.
5. Customize Kubernetes events to enhance observability
Custom events communicate between K8S components and between K8S and external systems. They can signal important events such as deployments, scaling operations, configuration changes, and even application-specific events within containers.
By utilizing custom events, you gain several observability benefits:
- Proactive Monitoring: Custom events allow you to define and monitor specific conditions that require attention. For example, events can be created to indicate when resources run low, when pods fail, or when certain thresholds are crossed. By capturing these events, you can proactively detect and resolve problems before they escalate.
- Contextual information : Custom events include additional contextual information to aid in troubleshooting and root cause analysis. You can attach relevant details such as error messages, timestamps, affected resources, or any other metadata to gain insight into the significance of the event. This extra context helps to understand and solve problems more effectively.
- Integration with external systems: K8S custom events can be consumed by external systems, such as monitoring platforms or incident management tools. Integrating these systems allows you to trigger automatic responses or notifications based on specific events. This simplifies the incident response process and ensures critical issues are resolved in a timely manner.
With custom K8S events, you can use K8S event hooks (event hooks), custom controllers, and even use K8S APIs to develop your event-driven applications. By defining event triggers, capturing relevant information, and reacting to events, you can establish a robust observability framework that complements traditional monitoring methods.
6. Integrate comprehensive monitoring to realize active discovery of observability
Synthetic monitoring simulates user behavior or specific transactions that represent day-to-day interactions with the application. These synthetic monitors can be set to run periodically at different locations, simulating user behavior and measuring key performance indicators.
Integrating synthetic monitoring in your K8S environment has several major benefits:
- Proactive Problem Detection: Synthetic monitoring allows you to spot problems before real users are affected. By periodically simulating user interactions, you can identify degraded, erroneous, or unresponsive components. This early detection enables you to proactively resolve issues and maintain high application availability.
- Performance Benchmarking: Synthetic monitoring provides a baseline for performance benchmarking and SLA compliance. By running consistent tests from different locations, you can measure response time, latency, and availability under normal conditions. These benchmarks serve as a reference for detecting anomalies and ensuring optimal performance.
- Location monitoring: Synthetic monitoring can be configured to run from different locations, providing insight into application performance in different areas. This helps identify latency issues or location discrepancies that might affect the user experience. Using this information to optimize your application performance, you can ensure a globally consistent user experience.
You can use professional tools to integrate comprehensive monitoring into your K8S environment. These tools provide the ability to create and deploy synthetic monitors, monitor performance metrics, and generate reports.
One way to get K8S observability for microservices-based applications is to use third-party tools like Datadog, Splunk, Middleware, and Dynatrace. These tools capture metrics and events, providing multiple out-of-the-box reports, graphs and alerts to save time.
7. Development tools that support microservices and k8s
There are many development tools on the market. The JNPF rapid development platform is a full-stack development platform based on SpringBoot+Vue3. To build business applications, the platform can be deployed locally and also supports K8S deployment.
Through the visual drag-and-drop method, it takes only 2 hours to complete the development of an application in the traditional mode with JNPF. To build applications with JNPF, you only need to focus on the business itself. Data storage, operating environment, server, network security, etc., the platform handles everything for you.
It is also equipped with visual function engines such as chart engine, interface engine, portal engine, organization user engine, etc., to basically realize the visual construction of page UI. There are hundreds of functional controls and templates built in, so that it can meet the personalized needs of users to the greatest extent under the simple operation of dragging and dropping. Experience address: https://www.jnpfsoft.com/?csdn
Due to the limited space, here is only an introduction, so you can experience and understand it yourself.
8. Summary
By utilizing the 6 strategies mentioned above, you can enhance your understanding of the behavior and performance of your K8S deployment. Implementing these strategies will provide comprehensive insight into distributed systems, enabling efficient troubleshooting, performance optimization, proactive problem detection, and improved user experience.
Whether you are operating a small-scale K8S environment or managing a complex hybrid cloud deployment, applying these strategies will contribute to the success and reliability of your applications.