For enterprises that use serverless computing, it is critical to be able to keep an eye on applications. Distributed, event-driven architectures make it tricky to get clear visibility. This is where serverless observability can help you spot issues faster and fine-tune performance. Achieving full observability in a serverless world comes with its own unique set of challenges. In this article, we will get into what serverless observability means and the common hurdles you may encounter along the way.

What is serverless observability?

Understanding serverless observability

Serverless observability extends traditional observability practices to serverless environments. It  ensures real-time visibility into function execution, resource utilization and dependencies across microservices. With the right observability strategy, you can quickly identify bottlenecks, optimize workloads and enhance security. Effective observability also improves debugging and troubleshooting efficiency by providing detailed insights into function behaviors and interactions.

The three pillars of serverless observability

1. Logs capture detailed records of function execution, including request inputs, processing times and errors. Logs provide historical context for understanding failures and performance issues.
2. Metrics provide quantitative data on system performance, including latency, resource utilization and invocation rates. Proper metric tracking enables proactive scaling and optimization.
3. Traces track the journey of a request across distributed services to pinpoint bottlenecks and dependencies. Tracing helps diagnose service dependencies and response time inefficiencies.

Common challenges of observability for serverless systems

Serverless observability poses distinct challenges because serverless applications often run as short-lived functions that spin up and shut down on demand. Once the container hosting the function terminates, traditional troubleshooting approaches (such as logging into a server) are no longer possible. 

To overcome this, you need observability solutions that can capture and retain detailed metrics, logs and traces in near real-time. These solutions enable you to “look back in time,” inspecting the state and behavior of serverless functions at the moment they are executed. This retrospective view is critical for pinpointing issues and maintaining a clear understanding of what happened in your cluster, even after containers have disappeared.

Common challenges to serverless observability include:

Distributed and ephemeral workloads

• Serverless functions execute in a stateless manner and only exist for brief durations. This makes it difficult to collect continuous monitoring data.
• Functions often run in multiple locations, which complicates cross-service visibility and requires more sophisticated tracking tools.
• Traditional Application Performance Management (APM) solutions often struggle to adapt to serverless workloads, which necessitates new observability approaches.

Limited native monitoring tools

• Cloud provider monitoring tools may not offer detailed insights across multiple cloud services.
• Organizations need to integrate additional serverless observability tools to achieve comprehensive insights.
• Vendor-specific observability tools may create fragmentation and require multi-tool integration for complete visibility.

Data overload

• Large volumes of logs and traces can make it difficult to extract relevant insights.
• Without efficient log aggregation and filtering, debugging is often a time-consuming task.
• Centralized logging solutions are required to avoid overwhelming storage and analysis systems.

Cold start latency

• Functions may experience delays when invoked after periods of inactivity.
• Observability enables teams to detect and mitigate cold start latency through optimization strategies.
• Using warm function strategies and reducing dependencies can help mitigate cold start delays.

Best practices for serverless observability

1. Implement distributed tracing

Distributed tracing tracks requests as they journey through different services and functions, providing a complete picture of an application’s workflow. Tools exist to help collect and visualize trace data for better performance analysis. Proper trace correlation across different cloud services and third-party APIs helps maintain end-to-end observability.

2. Leverage centralized logging

Aggregating logs from multiple serverless functions in a centralized platform enhances visibility and troubleshooting. Solutions like SUSE Rancher Prime and SUSE Security enable you to manage and analyze logs efficiently. Implementing log retention policies and automated log parsing will improve usability and minimize storage costs.

3. Optimize metrics collection

Selecting key performance indicators (KPIs) such as function execution time, memory consumption and error rates will help you monitor serverless workloads effectively. Automated alerting is key to achieving timely issue resolution. As you finetune your metrics collection, you avoid unnecessary overhead and ensure that you capture essential performance data.

4. Adopt AI-powered insights

AI-driven analytics enhance observability by detecting anomalies, predicting failures and recommending optimizations. By leveraging full stack observability solutions, you can gain proactive insights into serverless performance. AI-based pattern recognition can identify trends that human analysts might overlook, for enabling preemptive issue resolution.

5. Ensure security and compliance

Observability plays a critical role in detecting security threats and maintaining compliance in serverless environments. Implementing container observability solutions will help you safeguard workloads and prevent vulnerabilities. Security observability tools can track API access patterns, detect unusual activity and enforce least privilege access controls.

The future of serverless observability

Serverless computing simplifies infrastructure management but introduces new observability challenges. By implementing best practices such as distributed tracing and centralized logging, you can enhance visibility and optimize serverless applications. For effective, robust performance monitoring and security compliance, consider investing in cloud-native observability. To stay ahead of evolving serverless workloads, prioritize continuous improvement in observability strategies and tool adoption. For a hands-on experience, start a 30-day free trial of SUSE Cloud Observability on AWS Marketplace. Continuously refine your observability strategies and tools to stay ahead of evolving serverless workloads and deliver reliable, high-performing services.

Serverless observability FAQs

Is Kubernetes serverless?

No, Kubernetes is not inherently serverless; it is a container orchestration platform that manages workloads. However, Kubernetes can support serverless frameworks like Knative to enable serverless deployment models. Kubernetes enables hybrid approaches that combine containerized workloads with serverless execution models.

What is the difference between observability and monitoring?

Monitoring tracks predefined metrics and alerts. Observability provides deeper insights into system behavior using logs, metrics and traces. It also enables proactive issue resolution by providing a comprehensive view of application performance. While monitoring identifies when issues occur, observability takes it to the next level by explaining why they happen and how to prevent them.

What are the advantages of a serverless approach?

Serverless computing offers benefits such as automatic scaling, reduced operational overhead and cost efficiency. You can focus on innovation without managing infrastructure, making serverless an ideal choice for modern cloud-native applications. Additionally, serverless architectures improve fault tolerance, reduce time to market and allow you to build resilient applications with lower operational complexity.