Monitoring and Troubleshooting Serverless Applications

Gone are the days when developers handled app development, server logs, infrastructures, and other resources single-handedly. With the introduction of serverless computing, businesses can build and deploy applications much faster. Serverless architectures offload routine tasks from developers and let them focus on app building. They offer scalable, flexible, and cost-effective solutions that eliminate the need to manage servers.

This blog dives deep into serverless app monitoring and the tools that can help you monitor and troubleshoot effectively.

What Is Serverless? 

Serverless is an application development and execution model that allows developers to build apps and run codes without managing servers or backend infrastructure.  

Unlike in traditional server-based architecture, where developers had to provision, configure, and manage servers to run apps, serverless computing has no operational responsibilities. Developers can write codes and deploy them as individual functions or microservices. 

Here’s how serverless functions work:

• Write a function in the app code for a specific purpose.
• Define an event that triggers the service provider to execute the function (i.e., an HTTP request).
• As users trigger the event, the cloud service provider starts the function.
• The result of the function executed within the application is then displayed to the user.

Benefits of Serverless Architecture

Serverless architecture offers key benefits for developers and businesses. Here are a few of them:

1. Cost Efficiency

Serverless architecture works on a pay-per-use basis. You pay only for cloud-based computing time as needed. Unlike traditional server-based architecture, there’s no need to pay for server management or idle capacity. 

2. Scalability and Flexibility

Serverless architectures have built-in autoscaling to scale up and down based on demand. They ensure apps remain responsive without manual processes. They are dynamic and adjust to traffic surges to maintain performance consistency. 

3. Improved Productivity

Developers no longer need to perform provisioning and managing servers. As serverless architectures offload routine tasks, developers can focus more on app building and streamlining the development process. 

4. Better Observability

With serverless architectures, developers can break down applications into smaller parts to gain more visibility into them. The decomposition process improves app observability and makes it easier to fix issues. 

Why Is Serverless Monitoring and Troubleshooting Important? 

Serverless monitoring allows developers to track and address issues critical to the system’s health and user experience. 

With serverless architectures, developers can no longer access the underlying server resources, logs, and other operational data. While traditional architectures give full visibility into environments, the cloud provider manages infrastructure, scaling, and resource allocation in serverless architectures. This means businesses have lesser visibility and control over environments. As a result, identifying bugs or issues can be difficult.

With serverless monitoring and troubleshooting, your teams can track these managed resources and services that power serverless functions. They can effectively identify issues and manage complex digital environments. 

Besides helping you identify and address issues and improve efficiency, here are a few benefits of serverless monitoring:

1. Predict and Optimize Cost

Monitor serverless spending to understand how many resources are used at a specific time. This enables you to control spending and scale when needed. Further, a total cost of ownership (TCO) serverless model can improve cost prediction by considering development, infrastructure, and maintenance costs.

2. Prevent Cold Starts

Leverage serverless monitoring to identify which functions are used and when. It helps you optimize performance by fixing latency issues caused by "cold" functions.

Since serverless architectures are event-based, they use functions only when an event demands. If functions remain unused for a period, they go "cold" leading to cold starts when the function is called on again after a period. 

3. Monitor Memory Usage

In serverless architectures, memory usage is a configurable element. You can allocate memory space to a function depending on the cloud provider. This affects the processing power required to run a function when an event triggers it. 

Top 5 Serverless Platforms in 2024

1. AWS Lambda

AWS Lambda is a serverless computing tool with in-built autoscaling and infrastructure management functions.

The platform supports several programming languages, including PowerShell, Ruby, C#, and Go. It offers automated scaling capabilities and administration without manually managing servers.

2. Vercel

Vercel is a cloud platform that provides developer tools and infrastructure to build apps faster. The platform is optimized for the front end, including static sites and Jamstack projects built with frameworks like Next.js, Vue.js, and React.

The tool also offers global CDN, cache validation, and serverless functions. 

3. Azure Functions

Azure Functions is a serverless computing service enabling you to run events triggered by events without provisioning or managing infrastructures.

The tool supports Java, Python, C#, F#, and Node.js. It integrates with Azure and other third-party services and provides auto-scaling capabilities.

4. Google Cloud Functions

Google’s Cloud Functions allow you to trigger codes from Google Cloud, Google Assistant, Firebase, or directly from any web or backend app via HTTP.

Based on pay-per-use pricing, the platform auto-scales based on incoming requests integrates with Google Cloud services and supports Go, Java, .NET Core, Ruby, Node.js, Python, and PHP.

5. OpenFaaS

OpenFaaS helps you deploy event-triggered functions and microservices to Kubernetes without boilerplate coding.

The platform supports any programming language via Docker/OCI images and scales functions based on custom metrics and HTTP requests. 

Choosing Serverless Monitoring Tools

There are several serverless monitoring tools that you can use to get the one that fits your bill. Here are a few popular ones:

• AWS CloudWatch is the default monitoring tool for AWS Lambda and other AWS serverless services. It helps track the performance of serverless functions with metrics, logs, etc.
• Datadog is a cloud-based monitoring and analytics tool that supports serverless monitoring across several cloud providers, like AWS, Google Cloud, and Azure. It offers dashboards, alerting, and analytics features based on your chosen Datadog pricing plan.
• Middleware.io is a full-stack cloud observability tool that lets you monitor logs, metrics, events, and traces in real time on unified dashboards. It provides end-to-end visibility into serverless apps.

Apart from choosing the best serverless monitoring tool, setting up monitoring metrics to track serverless apps is essential. Here are a few of them:

• Invocation duration: Track the invocation duration of each function to identify performance issues and optimize function execution.
• Memory usage: Monitor this metric to ensure the functions are configured with the apt memory allocation and identify inefficient usage and memory leaks.
• Error rates: Monitor error rates of function invocations to resolve issues quickly. 
• Cold starts: Track the frequency and duration of cold starts to improve the performance of serverless functions, especially during new deployments and traffic spikes. 
• Concurrency: Monitor this metric to identify the number of function executions happening simultaneously to ensure they can manage the workload and identify scaling and resource issues.

Best Practices for Monitoring Serverless Applications 

Here are some best practices for monitoring serverless applications:

1. Configuring Alerts

Setting threshold alerts on critical metrics like invocation errors, concurrency, memory usage, etc. helps you proactively monitor your apps and resources. You can set alerts for early detection of potential issues and enable prompt responses.

You can also integrate your emails, Slack channels, and SMS for urgent notifications and real-time updates.

2. Visualizing Data 

Create custom dashboards to get a unified view of all performance data and metrics. It helps you track key metrics and KPIs, identify patterns, and detect anomalies in one place. Also, use line charts and scatter plots to identify trends in performance metrics and highlight unusual data points. 

3. Advanced Techniques 

Leverage AI-powered advanced analytics to analyze historical data and predict future trends accurately. AI algorithms can also help spot potential issues early before they significantly impact user experience. You can also use AI to schedule maintenance tasks based on estimated resource usage and performance metrics. 

With the integration of AI and serverless monitoring, there are a few potential future trends that can take serverless monitoring to the next level:

• Increase in automation: You can leverage more automated monitoring and alerting systems that adapt quickly to changing app behavior.
• Enhanced integration: Improved integration of monitoring tools with CI/CD pipelines for better tracking and deployment of applications. 
• Advanced data visualization: Get actionable performance data with AI-generated insights to improve app performance. 

Troubleshooting Serverless Applications 

1. Identifying Issues 

The first step to troubleshooting is identifying issues in serverless applications. Some common issues that may arise in serverless apps are:

• Latency spikes: Sudden increase in function invocation duration, impacting overall app performance
• Cold starts: Function execution delays due to long periods of inactivity
• Memory leaks: Excess memory usage by serverless functions, resulting in declining function performance and failures
• Errors: Timeouts, exceptions, invocation errors, etc. occurring during function execution, disrupting app performance

To identify the above issues in serverless applications and ensure effective troubleshooting, perform a thorough root cause analysis. Leverage traces, logs, metrics, and data monitoring to spot the potential problem areas and the underlying causes of the issues. This will help you:

• Understand the issue, its nature, and its scope.
• Identify the root cause by evaluating the context and events surrounding issues.
• Implement sustainable solutions that address the issue at its source rather than temporary fixes. 

2. Resolving Issues

One of the most critical steps in troubleshooting is log analysis. Logs provide valuable insights into function execution, including:

• Error messages that help identify specific errors and get cues to resolve issues
• Execution details like event sequence, input parameters, and other contextual information
• Performance metrics data like memory usage, invocation duration, etc., to help detect performance issues

In short, analyzing logs gives you a deeper understanding of serverless app behavior and ensures effective troubleshooting. 

FAQs

1. What Are the Benefits of Monitoring Serverless Applications?

The key benefits of monitoring serverless applications include faster troubleshooting, cost optimization, improved developer productivity, better observability across environments and metrics, and faster app development and deployment.

2. What Are the Key Metrics for Serverless Applications?

The key metrics of serverless applications include memory usage, concurrency, cold starts, invocation duration, function error rates, response times, and latency. 

3. How Do Serverless Applications Differ in Troubleshooting From Traditional Apps?

In traditional apps, developers can access server logs, tools, and infrastructures to debug codes. However, serverless applications need an observability tool to collect function-related metrics. Also, in serverless, cloud providers control access to tools, so developers need help troubleshooting.

4. What Are the Best Practices for Monitoring and Troubleshooting Serverless Apps?

Best practices for tracking and troubleshooting serverless apps include using custom dashboards, setting alerts for critical metrics, leveraging AI and ML analytics, performing load testing, and implementing centralized logs. 

Conclusion

Serverless architectures make it easier for developers to focus on building quality apps. However, they also pose monitoring challenges due to a lack of direct access to resources.

The good news is that you can overcome the monitoring challenges with serverless monitoring solutions and ensure effective monitoring and troubleshooting by adopting a few best practices. These best practices can help you reduce complexities in serverless environments and proactively address issues before they significantly impact user experience.