Before we learn and understand one of the great features provided by AWS, also known as AWS lambdas, let’s try to understand what Serverless Computing is.
Serverless Computing is a cloud computing execution model in which the cloud provider dynamically manages the allocation of machine resources. Pricing is based on the actual amount of resources consumed by an application.
We have a couple of good Serverless Computing environments provided by Google and Amazon,
- Google Cloud Functions
- AWS Lambda
In this article, we will focus and learn what AWS Lambda is, and how easily we can create serverless computing environment/ framework/ architecture at our fingertips.
AWS environment/package offers a ton of services like EC2, S3, API Gateway, etc. AWS Lambda is one of them.
AWS Lambda falls under the Compute category.
Let’s speed up the gear and try to figure out the following:
Q - What does AWS Lambda do?
As we said, it is a serverless computing service provided by Amazon Web Services that runs your code/function in response to your events and manages the underlying computing resource for you. You don’t have to worry about the memory and server configuration and scaling. We can even use AWS Lambda to extend other services or any custom logic.
Q - Is it language-agnostic?
AWS Lambda supports a wide range of languages. It can be written in Node.js, Python, Java, C#, etc.
Q - How AWS Lambda function works?
When any AWS lambda function gets invoked, it manages and provides all necessary resources to start the functioning. We just specify some basic configurations like memory need for function, maximum execution time. If not provided, the default values will be considered.
Firstly, it sets up a container and some necessary bootstrapping to invoke the function which adds some more latency the first time function is invoked, and later on, for every subsequent call, the same container is reused.
It maintains the container for some time and if the function is not called in that time period, resources are de-allocated.
Note
When you write your Lambda function code, do not assume that AWS Lambda always reuses the container because AWS Lambda may choose not to reuse the container. Depending on various other factors, AWS Lambda may simply create a new container instead of reusing an existing container.
On the other side, there are some points/limitations needed to be considered.
| Resource |
Limits |
| Memory allocation range |
Minimum = 128 MB / Maximum = 1536 MB (with 64 MB increments) |
| Ephemeral disk capacity ("/tmp" space) |
512 MB |
| Number of file descriptors |
1,024 |
| Number of processes and threads (combined total) |
1,024 |
| Maximum execution duration per request |
300 seconds |
| Invoke request body payload size (RequestResponse) |
6 MB |
| Invoke request body payload size (Event) |
128 K |
| Invoke response body payload size (RequestResponse) |
6 MB |
Let’s start with creating AWS Lambda.
- Login to your AWS account.
- Click on Lambda under Compute services.
- Click on “Create a Lambda function”, which brings the following screen.
- We are considering “js 6.10” as “Select runtime” in this article and then choose Blank Function.
- Click "Next" as in the below screenshot, since we aren’t covering triggers in this article.
- This screen is the main screen where we configure all the necessary data.
- Name is just a function name
- Description as needed
- Runtime allows us to set the node runtime environment, in our example, it's set to 6.10
- Lambda function code area allows you to write your code. In this article, we are using an inline approach if you have a small source code. There are two other ways Upload as a Zip file and Upload from S3 as you will see this later. But for now, let’s keep it inline.
- Lambda function handler and role allows us to specify the entry point of the function. In the screenshot, it is named as main whereas main is the function name (can be any name). “index.main” will call exports.main.
Role defines the permission of your function whereas Existing Role pulls any existing role if any.
Tags are used to group and filter your functions. A tag consists of key-value pair.
Advanced Settings allow you to configure Memory & Timeout settings along with VPC settings if any.
- Once all this is done, click Next. The Review screen shows up for confirmation; click "Create function".
- In the next screen as below, click "Test" to invoke the function.
You can see the console output as “Hello from Lambda” whereas the Summary screen displays the detailed output as Lambda function duration, Max memory used, etc.
So, in this article, we have learned about Serverless Computing along with AWS Lambda. We also learned how easy it is to create a Lambda function without worrying about resources. In the
next article, we will dive deep into functions and their structure.
I hope you liked it.