Category: Aws

What is AWS Lambda

Amazon Web Services (AWS) Lambda is a serverless compute service that runs your code in response to events and automatically manages the underlying compute resources for you.

With Lambda, you can run code for virtually any type of application or backend service, without the need to provision or maintain servers. This makes it easy to build and deploy applications and services quickly, without worrying about infrastructure.

Lambda functions are defined by a combination of a trigger and a function. A trigger is an event that causes the function to be executed, such as an HTTP request, a change in a database, or the arrival of a message in a message queue. The function is the code that is executed in response to the trigger.

Lambda supports multiple programming languages, including Node.js, Python, Java, C#, and Go. You can write your functions using one of these languages and then run them on Lambda.

Lambda automatically scales your functions to meet the demands of your applications, so you don’t have to worry about capacity planning. You only pay for the compute time that you consume, making it an efficient and cost-effective way to run your applications and services.

Pros and Cons when writting AWS Lambda with Python vs Java vs Nodejs

Python
>>> Pros:

Python is a popular and easy-to-learn programming language with a large and active developer community.
There are many libraries and frameworks available for Python that can be easily integrated into a Lambda function, such as the AWS SDK for Python (Boto3), which makes it easy to interact with other AWS services.

>>> Cons:

Python can be slower than some other languages, such as Java or C++, due to the dynamic nature of the language and the use of a bytecode interpreter. This may not be a significant issue for many Lambda functions, but it could be a concern for high-performance workloads.

Java

>>> Pros:

Java is a mature and well-established programming language with good performance and scalability.
Java has good support for concurrency and parallelism, which can be useful for certain types of Lambda functions that need to perform multiple tasks concurrently.

>>> Cons:

Java can be more verbose and require more code than some other languages, such as Python or Node.js.
There may be a steeper learning curve for developers who are not already familiar with Java.

Node.js

>>> Pros:

Node.js is a popular runtime for building serverless applications and is well-suited for event-driven programming.
Node.js has a large and active developer community, and there are many libraries and frameworks available for building serverless applications with Node.js.

>>> Cons:

Node.js can be less performant than some other languages, such as Java or C++, due to the use of a single-threaded event loop. This may not be a significant issue for many Lambda functions, but it could be a concern for high-performance workloads.
There may be a steeper learning curve for developers who are not already familiar with JavaScript and Node.js.

How to write Lambda AWS with Python

AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda to execute code in response to triggers such as changes in data, shifts in system state, or actions by users.

To write an AWS Lambda function in Python, you will need to:

1. Choose a trigger that will invoke your function. This could be an event such as a change in a data stream or an HTTP request.

2. Create a function in Python that will be executed when the trigger occurs. This function should include the necessary logic to perform the desired tasks.

3. Package your function code and any dependencies into a deployment package.

4. Create a new Lambda function in the AWS Management Console and upload your deployment package.

5. Set the trigger for your function and configure any necessary function settings.

Here is an example of a simple AWS Lambda function written in Python:

import json

def lambda_handler(event, context):
    # TODO implement
    message = 'Hello from Lambda!'
    return {
        'statusCode': 200,
        'body': json.dumps(message)
    }

This function will return a message when it is invoked. The event and context parameters are objects that contain information about the current execution environment. The lambda_handler function is the default entry point for Lambda functions and will be invoked when the function is executed.

Example Code AWS Lambda with Python and RDS

Here is an example of a simple AWS Lambda function written in Python that connects to an Amazon Relational Database Service (RDS) database and executes a SQL query:

import os
import pymysql

def lambda_handler(event, context):
    # Connect to the RDS database
    host = os.environ['DB_HOST']
    port = os.environ['DB_PORT']
    user = os.environ['DB_USER']
    password = os.environ['DB_PASSWORD']
    database = os.environ['DB_DATABASE']
    connection = pymysql.connect(host=host, port=port, user=user, password=password, db=database)
    
    try:
        # Execute a SQL query
        with connection.cursor() as cursor:
            sql = "SELECT * FROM table"
            cursor.execute(sql)
            result = cursor.fetchall()
        
        # Commit the transaction
        connection.commit()
        
        # Return the result
        return result
    finally:
        # Close the connection
        connection.close()

In this example, the function connects to the RDS database using the pymysql library and the connection details stored in environment variables. The function then executes a SQL query and returns the result.

To use this function with AWS Lambda, you would need to create an RDS instance and set up the appropriate IAM roles and policies to allow the Lambda function to access the database. You would also need to set the environment variables with the connection details for the RDS instance.

Once the function is set up and deployed to Lambda, it can be triggered by various events, such as an HTTP request or a change in a database, and the result of the SQL query will be returned.

Example code Aws Lambda with Python and S3

Here is an example of a simple AWS Lambda function written in Python that reads a file from an Amazon Simple Storage Service (S3) bucket and returns the contents:

import boto3

def lambda_handler(event, context):
    # Get the S3 client
    s3 = boto3.client('s3')
    
    # Set the bucket and file name
    bucket = 'my-bucket'
    key = 'path/to/file.txt'
    
    # Read the file from S3
    response = s3.get_object(Bucket=bucket, Key=key)
    contents = response['Body'].read()
    
    # Return the file contents
    return contents

In this example, the function uses the boto3 library to connect to the S3 service and read the specified file. The file contents are then returned by the function.

To use this function with AWS Lambda, you would need to set up the appropriate IAM roles and policies to allow the Lambda function to access the S3 bucket. You would also need to set the bucket and key variables to the correct values for the bucket and file you want to read.

Once the function is set up and deployed to Lambda, it can be triggered by various events, such as an HTTP request or a change in a database, and the contents of the file will be returned.

Example code AWS Lambda with Python and SNS

Here is an example of a simple AWS Lambda function written in Python that sends a message to an Amazon Simple Notification Service (SNS) topic:

import boto3

def lambda_handler(event, context):
    # Get the SNS client
    sns = boto3.client('sns')
    
    # Set the topic and message
    topic_arn = 'arn:aws:sns:us-east-1:123456789012:my-topic'
    message = 'Hello, world!'
    
    # Send the message to the topic
    response = sns.publish(TopicArn=topic_arn, Message=message)
    
    # Return the response
    return response

In this example, the function uses the boto3 library to connect to the SNS service and publish a message to the specified topic. The response from the publish method is then returned by the function.

To use this function with AWS Lambda, you would need to set up the appropriate IAM roles and policies to allow the Lambda function to access the SNS service and publish to the specified topic. You would also need to set the topic_arn variable to the correct value for the topic you want to send the message to.

Once the function is set up and deployed to Lambda, it can be triggered by various events, such as an HTTP request or a change in a database, and the message will be sent to the SNS topic.

Example code AWS Lambda with Python and SQS

Here is an example of a simple AWS Lambda function written in Python that reads a message from an Amazon Simple Queue Service (SQS) queue and processes it:

import boto3

def lambda_handler(event, context):
    # Get the SQS client
    sqs = boto3.client('sqs')
    
    # Set the queue URL
    queue_url = 'https://sqs.us-east-1.amazonaws.com/123456789012/my-queue'
    
    # Receive a message from the queue
    response = sqs.receive_message(QueueUrl=queue_url)
    
    # Get the message body
    try:
        message = response['Messages'][0]
        body = message['Body']
        
        # Process the message
        print('Received message:', body)
        
        # Delete the message from the queue
        sqs.delete_message(QueueUrl=queue_url, ReceiptHandle=message['ReceiptHandle'])
        
        # Return the message body
        return body
    except KeyError:
        # No message was received
        print('No message received')
        return None

In this example, the function uses the boto3 library to connect to the SQS service and receive a message from the specified queue. The function then processes the message and deletes it from the queue. If no message is received, the function returns None.

To use this function with AWS Lambda, you would need to set up the appropriate IAM roles and policies to allow the Lambda function to access the SQS queue. You would also need to set the queue_url variable to the correct value for the queue you want to read from.

Once the function is set up and deployed to Lambda, it can be triggered by various events, such as the arrival of a new message in the queue, and the message will be processed and deleted.

Example code AWS Lambda with Python and DynamoDB

Here is an example of a simple AWS Lambda function written in Python that reads an item from an Amazon DynamoDB table:

import boto3

def lambda_handler(event, context):
    # Get the DynamoDB client
    dynamodb = boto3.client('dynamodb')
    
    # Set the table name and key
    table_name = 'my-table'
    key = {'id': {'N': '123'}}
    
    # Get the item from the table
    response = dynamodb.get_item(TableName=table_name, Key=key)
    
    # Return the item
    return response['Item']

In this example, the function uses the boto3 library to connect to the DynamoDB service and retrieve an item from the specified table using its primary key. The item is then returned by the function.

To use this function with AWS Lambda, you would need to set up the appropriate IAM roles and policies to allow the Lambda function to access the DynamoDB table. You would also need to set the table_name and key variables to the correct values for the table and item you want to retrieve.

Once the function is set up and deployed to Lambda, it can be triggered by various events, such as an HTTP request or a change in a database, and the item will be retrieved and returned.