AI Companion for Developers and SREs

The euphoria around ChatGPT is undeniable and deservedly so. If you are not living under a rock, you may have heard about ChantGPT and OpenAI.

Over the last few months, CloudAEye has built an AI-powered observability solution for AWS Lambda. Naturally, we spent some time looking at this serverless domain. I was curious about what ChatGPT knows about this AWS service. My interest peaked after CloudAEye was named top AI startups along with OpenAI by AngelList.

Here is what I found. I have created an account with OpenAI and used their Playground UI interface.

Q. Write a blog on what key metrics to monitor for AWS Lambda for CloudAEye

Answer from ChatGPT:

In the ever-evolving world of cloud computing, AWS Lambda is quickly becoming one of the most popular services for businesses to leverage. This serverless compute service allows businesses to deploy and execute code without having to manage servers, enabling them to quickly and cost-effectively scale. While the service is incredibly useful, it is important to monitor certain key metrics in order to ensure the performance and reliability of your applications.

At CloudAEye, we recommend monitoring the following key metrics to ensure the best performance and reliability of your AWS Lambda applications.

1. Request Count: This metric tracks the number of requests made to your functions, allowing you to measure the usage of your applications. Monitoring request counts can help you identify peak usage times and optimize your architecture to accommodate the increased demand.

2. Error Count: This metric tracks the number of errors encountered when requests are made to your functions, allowing you to quickly identify and address any issues that arise.

3. Execution Duration: This metric tracks how long it takes for a function to execute, allowing you to identify performance bottlenecks and adjust your architecture to improve performance.

4. Throttling Count: This metric tracks the number of requests that are throttled, allowing you to understand when your functions are being overwhelmed. This can help you identify potential scaling issues and adjust your architecture to accommodate the increased demand.

5. Memory Usage: This metric tracks how much memory your functions are using, allowing you to identify any potential memory leakage or other issues that could affect performance.

By monitoring these key metrics, you can ensure that your AWS Lambda applications are performing optimally and reliably. If you need assistance monitoring your AWS Lambda applications, contact CloudAEye today. Our experienced team can help you identify and address any performance issues and ensure that your applications are running smoothly.

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Review

This blog was fairly good and succinct. ChatGPT was able to identify the key metrics one should monitor for lambda. What could be better? AWS documentation groups the metrics in few categories such as Invocation metrics, Performance metrics, Concurrency metrics, Asynchronous invocation metrics. It would have been nice if the blog included this perspective. Refer to AWS Lambda function metrics for more context.

Q. Write a blog on how to effectively monitor AWS Lambda with code examples

Answer from ChatGPT:

AWS Lambda is a powerful and cost-effective serverless computing platform. It enables developers to quickly and easily deploy code and scale applications without the need for any additional infrastructure. However, it can be difficult to properly monitor and debug AWS Lambda functions in production. Fortunately, there are several tools and services available that can help you better understand and optimize your Lambda functions.

In this blog, we’ll discuss how to effectively monitor AWS Lambda with code examples. We’ll discuss the following topics:

1. CloudWatch Logs
2. CloudWatch Metrics
3. AWS X-Ray
4. AWS Lambda Performance Insights
5. AWS CloudTrail

Let’s get started!

1. CloudWatch Logs
CloudWatch Logs is a service provided by AWS that allows you to monitor and analyze log files generated by your AWS Lambda functions. It provides you with information about the execution of your functions, such as the number of invocations, the duration of each invocation, and any errors that occurred.

To get started with CloudWatch Logs, you need to configure your Lambda functions to send log messages to CloudWatch. This can be done through the AWS Lambda Console or via the CLI or SDK. Once your Lambda functions are configured to send logs to CloudWatch, you can then use the CloudWatch Logs console to view, search, and analyze your log data.

2. CloudWatch Metrics
In addition to CloudWatch Logs, you can also use CloudWatch Metrics to further monitor and troubleshoot your AWS Lambda functions. CloudWatch Metrics provides you with real-time metrics about the performance and usage of your functions. You can also set up alarms and notifications to be alerted when certain thresholds are met.

To get started with CloudWatch Metrics, you need to configure your Lambda functions to send metrics to CloudWatch. This can be done through the AWS Lambda Console or via the CLI or SDK. Once your Lambda functions are configured to send metrics to CloudWatch, you can then use the CloudWatch Metrics console to view, search, and analyze your metrics data.

[Stopped…]

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Review:

I would rate this blog as average. The blog was not done. So, it is not fair to rate it. Based on the initial text, this lacked the real insights, references and code examples.

Q. Write a blog on how to effectively monitor AWS Lambda with code examples (take 2)

Answer from ChatGPT:

AWS Lambda is a powerful and easy-to-use serverless platform that allows developers to create applications without the need for provisioning or managing servers. It enables developers to rapidly build and deploy applications in the cloud. However, with the power and convenience of Lambda comes the need to effectively monitor its performance and stability.

Fortunately, there are several ways to effectively monitor AWS Lambda functions. This blog post will discuss some of the best practices for monitoring Lambda functions, including code examples.

First, you should use CloudWatch Logs to monitor the performance of your Lambda functions. CloudWatch Logs provides detailed information about the execution of a Lambda function, including CPU and memory usage, execution duration, and errors. You can use the CloudWatch Logs API to programmatically access the logs and analyze them for performance and stability issues.

Second, you should use Amazon CloudWatch Metrics to monitor the performance of your Lambda functions. CloudWatch Metrics provides a variety of metrics that can be used to measure the performance of Lambda functions, including the number of invocations, the number of errors, and the average execution time. You can use the CloudWatch Metrics API to access these metrics and create custom dashboards for monitoring and troubleshooting your Lambda functions.

Third, you should use Amazon CloudWatch Events to monitor the state of your Lambda functions. CloudWatch Events provide an easy way to set up triggers that can be used to detect changes in your Lambda functions, such as when a function is created or deleted, or when its configuration is modified.

Finally, you should use Amazon X-Ray to monitor the performance of your Lambda functions. X-Ray provides detailed information about the performance of your Lambda functions, including the average latency, the number of concurrent invocations, and the number of errors. You can use the X-Ray API to access this information and create custom dashboards to monitor and troubleshoot your Lambda functions.

These are just some of the best practices for monitoring AWS Lambda functions. To learn more about monitoring Lambda functions, you can take a look at our comprehensive guide to monitoring AWS Lambda.

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Review:

This blog was underwhelming for me. While it talked about four ways to monitor lambda using AWS services, it was introductory with mostly boiler plate commentary. No code examples were given. Perhaps I made a mistake in using the interface.

Looking ahead...

(Generative) AI will change how we do things in coming months and years. There will be a significant leap in productivity. Enterprises will re-imagine workflows with AI-native Apps. Here at CloudAEye, we are experimenting with few concepts. Please stay tuned.

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