I was recently called upon to secure an Nginx web server with HTTPS, and my goal was to set this up with a certificate obtained from AWS Certificate Manager. It took me a while to figure out how to get everything configured and working. Hopefully someone else who is attempting to do the same thing will read this and I can save you some time!
One of the great promises of serverless has always been that it would free developers to focus on writing code without having to give too much consideration to the underlying infrastructure. But the advantages presented by the instantly, infinitely scalable nature of serverless come with limitations and unique considerations that you need to take into account.
Ever wondered what’s under the hood of your neighbors’ car, the situation in their wallet or the configuration of their serverless stack? Well wonder no more! Today we will bring you the statistics of Dashbird so you could compare your Lambda functions with others. Unfortunately, the car and the wallet thingy you should figure out on your own. Let’s start… (I hope you like charts)
A while back, I wrote about how you can shave latency off every AWS SDK operation by enabling HTTP keep-alive, like this. It had the desired effect and I saw lots of people apply this technique in their projects. But it also resulted in the same 10 lines of code being copied and pasted everywhere! I began thinking about ways to distribute an optimized version of AWS SDK so everyone can benefit.
Serverless first came onto the scene in 2014 when AWS Lambda was launched. It offers a dynamic cloud-computing execution model where the server is run by the cloud provider. As with any relatively recent technology, its novelty results in a steep learning curve, and it comes with its own set of benefits and drawbacks.
It’s five o’clock on a Friday afternoon. There are no new bug reports and everything is looking smooth. Your plan of a relaxing weekend is in sight when you get a call—the website you look after isn’t responding. Yikes. AWS Lambda minimizes the chance of this truly terrifying event from happening by taking care of server maintenance while you focus on coding robust applications.
Back in Part I of Deploying a Serverless Data Processing Workflow with AWS Step Functions, Nuatu mentioned one key benefit of using step functions is their visibility into business critical workflows. Outside stakeholders, support staff, and other engineers can look at a state machine execution in AWS or Stackery, and can easily understand the process.
We’re pleased to announce that the Lumigo serverless intelligence platform now supports the Python microframework, AWS Chalice. Chalice was created by AWS to simplify the process of writing serverless apps in Python. Similar in structure to the Flask web framework, Chalice handles much of the configuration on behalf of the user, including automated IAM role policy generation.
Serverless has the potential to bring massive ops advantages to projects of all sizes, but while it presents great business benefits, we need to spare a thought for how teams develop on serverless. I recently published ‘Serverless Development is Broken’ a list of concerns about how developers can work with long deploy times inherent in a cloud-only code environment.
Many people writing about AWS Lambda view Node as the code-default. I’ve been guilty of this in my own articles, but it’s important to remember that Python is a ‘first-class citizen’ within AWS and is a great option for writing readable Lambda code. Take a look at these two starter examples of writing functionality in Python.
