How to Make DynamoDB Streams Trigger at PK Level: A Step-by-Step Guide

Prerequisites

Before we dive into the tutorial, make sure you have the following:

• A DynamoDB table with a stream enabled
• An AWS Lambda function (we’ll use Python as an example)
• The AWS CLI installed on your machine
• A basic understanding of DynamoDB and AWS Lambda

Step 1: Create a DynamoDB Table with a Stream

Create a DynamoDB table with a stream enabled. You can do this using the AWS CLI or the AWS Management Console. For this example, we’ll use the AWS CLI:

aws dynamodb create-table --table-name my-table \
  --attribute-definitions AttributeName=PK,AttributeType=S \
  --key-schema AttributeName=PK,KeyType=HASH \
  --provisioned-throughput ReadCapacityUnits=10,WriteCapacityUnits=5 \
  --stream-specification StreamViewType=NEW_IMAGE

This creates a table named “my-table” with a primary key (PK) of type string and a stream enabled with a view type of NEW_IMAGE.

Step 2: Create an AWS Lambda Function

Create an AWS Lambda function that will process the stream events. For this example, we’ll use a Python function:

import json
import boto3

dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context):
    # Process the stream event here
    print(event)
    return {
        'statusCode': 200
    }

This function simply prints the stream event to the console. We’ll modify this function later to process the event at the PK level.

Step 3: Configure the Lambda Function to Process the Stream

Configure the Lambda function to process the stream events. We’ll add a trigger to the function that subscribes to the DynamoDB stream:

aws lambda create-event-source-mapping --function-name my-lambda-function \
  --event-source-arn arn:aws:dynamodb:REGION:ACCOUNT_ID:table/my-table/stream/LATEST

Replace REGION with the region where your DynamoDB table is located, and ACCOUNT_ID with your AWS account ID.

Step 4: Modify the Lambda Function to Process at the PK Level

Modify the Lambda function to process the stream event at the PK level. We’ll use the NewImage attribute of the stream event to get the PK value:

import json
import boto3

dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context):
    # Get the PK value from the NewImage attribute
    pk_value = event['Records'][0]['dynamodb']['NewImage']['PK']['S']

    # Process the event at the PK level
    print(f"Processing event for PK {pk_value}")

    # Perform any additional processing or actions here

    return {
        'statusCode': 200
    }

In this modified function, we extract the PK value from the NewImage attribute and print a message indicating that we’re processing the event at the PK level. You can modify this function to perform any additional processing or actions required for your use case.

Step 5: Test the Configuration

Test the configuration by inserting a new item into the DynamoDB table:

aws dynamodb put-item --table-name my-table \
  --item '{
    "PK": {"S": "partition-key-1"},
    "SK": {"S": "sort-key-1"},
    "Data": {"S": "Hello, World!"}
  }'

This should trigger the Lambda function, which will process the event at the PK level. Check the CloudWatch logs to verify that the function is correctly processing the event:

Processing event for PK partition-key-1

Bonus: Handling Multiple PK Values

In some cases, you may need to handle multiple PK values in a single stream event. To do this, you can modify the Lambda function to iterate over the Records array and process each PK value individually:

import json
import boto3

dynamodb = boto3.resource('dynamodb')

def lambda_handler(event, context):
    # Iterate over the Records array
    for record in event['Records']:
        pk_value = record['dynamodb']['NewImage']['PK']['S']

        # Process the event at the PK level
        print(f"Processing event for PK {pk_value}")

        # Perform any additional processing or actions here

    return {
        'statusCode': 200
    }

This modified function iterates over the Records array and processes each PK value individually, allowing you to handle multiple PK values in a single stream event.

Conclusion

In this article, we showed you how to make DynamoDB streams trigger at the PK level, giving you more control and flexibility over your data processing pipeline. By following these steps, you can process data at the PK level, reducing latency and improving performance. Remember to test your configuration thoroughly to ensure that it’s working as expected.

By following these steps, you’ll be able to make DynamoDB streams trigger at the PK level, unlocking new possibilities for real-time data processing and analytics.

Happy coding!

Frequently Asked Question

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