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About 90% of all Lambda functions monitored by Dashbird on AWS Lambda are running Nodejs and Python runtimes.
Is this purely a reflection of the general popularity of these programming languages?
Python has grown mainly due to its simplicity and readability, which confers a relatively flat learning curve. As well as its versatility to address a variety of problems. In the last 2 decades, it has been adopted by academia and several universities, which laid the foundation for its current dominance in the data science field.
Nodejs was a game-changer for the web. The rise of complex and rich web applications made JavaScript a fundamental tool for any web developer. Being able to use the same programming language on the backend provided a significant productivity advantage for cutting out context switching.
Apart from these particularities, both are dynamic languages that play really well with the current software trends, as we’ve covered in this article.
If we look at the programming language popularity index (PYPL), Python and Nodejs snap about 50% of the software development market nowadays. There must be something else going on to make it so much more popular on Serverless.
One explanation could be that Serverless functions are commonly used for gluing tasks, in which languages such as Python and JavaScript excel.
Another aspect is that AWS Lambda is popularly used as a backend service behind SaaS and mobile applications. This market is also relatively skewed towards scripting languages, particularly the two in question. Serverless is not as popular in other markets, such as IoT and desktop applications, in which languages such as Java and C tend to dominate.
But we believe it also has some connection to the inner architecture of Serverless as well. One of the limitations in Lambda functions, for example, is cold starts. The heavier the runtime startup footprint, the worse it becomes. Python and Nodejs have advantages on this side. Although Golang usually outperforms both, especially on concurrent asynchronous jobs, the AWS Lambda platform doesn’t seem to be well optimized for it yet.
Just like Python and Nodejs are super popular on AWS Lambda, Dashbird is also the most widely used serverless monitoring tool out there. It provides developers with visibility and peace-of-mind alerting tools that are tailored for serverless resources, including Lambda functions, DynamoDB tables, API Gateway, SQS queues, etc. Join thousands of developers already using Dashbird now, it’s free and requires no credit card.
Today we are announcing a new, updated pricing model and the end of free tier for Dashbird.
In this article, we’re covering 4 tips for AWS Lambda optimization for production. Covering error handling, memory provisioning, monitoring, performance, and more.
In this article we’ll go through the ins and outs of AWS Lambda pricing model, how it works, what additional charges you might be looking at and what’s in the fine print.
Dashbird was born out of our own need for an enhanced serverless debugging and monitoring tool, and we take pride in being developers.
Dashbird gives us a simple and easy to use tool to have peace of mind and know that all of our Serverless functions are running correctly. We are instantly aware now if there’s a problem. We love the fact that we have enough information in the Slack notification itself to take appropriate action immediately and know exactly where the issue occurred.
Thanks to Dashbird the time to discover the occurrence of an issue reduced from 2-4 hours to a matter of seconds or minutes. It also means that hundreds of dollars are saved every month.
Great onboarding: it takes just a couple of minutes to connect an AWS account to an organization in Dashbird. The UI is clean and gives a good overview of what is happening with the Lambdas and API Gateways in the account.
I mean, it is just extremely time-saving. It’s so efficient! I don’t think it’s an exaggeration or dramatic to say that Dashbird has been a lifesaver for us.
Dashbird provides an easier interface to monitor and debug problems with our Lambdas. Relevant logs are simple to find and view. Dashbird’s support has been good, and they take product suggestions with grace.
Great UI. Easy to navigate through CloudWatch logs. Simple setup.
Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.