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Navigate to IAM and create a new user.Before starting with any of the deployment steps, go ahead and create an IAM user and note down the credentials: One more point to note here is that we will need an IAM user which will used throughout the process to perform the deployments to AWS. I am using few python packages in the function which is specified in the requirements file: The function returns the API response in JSON format. So in this scenario this is returning the Visitorcount counting the visitor records in the table. /getcount: returns the count of records in the table.Its basically a Flask API which exposes one API endpoint. This is a Lambda function developed in Python.

The API Gateway is pointed to this Lambda function and routes all API requests to this function. I will create the DB with some default options and wont be customizing much. The Lambda function calls the Dynamo DB endpoint to get the data and return as a response. This is a NoSQL database provided by AWS. This is the backend database which the API will call to get the data.

#Npm serverless command not found code

You can handle the API authentication either in your Lambda code itself or offload the auth to the API Gateway using custom authorizers. But you should always have some kind of authentication added to your APIs. The API endpoint is accessible publicly for this demo API. For this sample project I am using a basic API Gateway without any authentication. I wont be getting into details of those as those are separate topics in itself. In a real scenario there are lot of functionalities which get activated within the API Gateway like Authentication, rate limiting rules, API keys etc. I am using a really simple version of the Gateway. This handles the routing the API requests for different API routes to the specific Lambda function.

This is like a gate to the backend Lambda function. This exposes the API endpoint to the internet. Below image shows an overall architecture of the API and its different components. The app is basically a Flask API built using Python. To demonstrate the Lambda deployment methods in this post, I have developed a simple app which I will be deploying.

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But in this post I will go through steps to deploy a full Lambda function app using some common methods which can automate the Lambda deployment. These are some generic steps to create a Lambda function.

Upload the package and create the Lambda function from AWS console.

Write the Lambda code in a choice language of yours.

To get a simple Lambda function running, your typical steps will be: It is very useful to build application backends where the Lambda function will act as an API backend fronted by an API gateway. Lambda supports multiple languages and frameworks which can be used to write a whole application using a Lambda Function.

How much memory the code execution may use up.

As a developer using Lambda, you only have to worry about (at the least): You provide the code as a Lambda function and once its executed, Lambda takes care of provisioning the required infra on the backend.

Using a Lambda function you can run or execute your application code without actually provisioning any App servers. Lambda is the Serverless computing platform service provided on AWS.

Basic understanding of Docker and building Docker images.

I will go through the installations in later sections: Here I will follow PythonДЇew installations.

Basic understanding of at least one coding language.

Pre-RequisitesД«efore I start, if you want to follow along and deploy your own Lambda, here are few pre-requisites you need to take care of in terms of installation and understanding: The sample code for each of the ways are included in the repo.

Using SAM (Serverless Application Model) templates and Lambda LayersДЄs always the whole code base is available on my GitHub Repo Here.

Deploy Lambda functions from Docker images.

On a high level these are the ways which I will go through here and I found easier to deploy a Lambda function: Hopefully this will help someone else who is starting to learn the wonderful Lambda functions. So this post is where I try to explain what I learnt about the various ways to deploy Lambda functions. ThatвЂ™s when I started learning these different ways of working on the Lambda functions locally and deploying them to AWS.

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It was very tough to work on the editor within the AWS console and it was tougher if you had to install packages with the function. So when I started learning Lambda functions and developing some of my own, one thing I always struggled with at the start was, how do I work on them locally. Now if you donвЂ™t know about Lambda functions, donвЂ™t worry I will get to it later. Recently I have been involved in developing a lot of AWS Lambda functions.