Building Real-Time Leaderboards Using Amazon DynamoDB and Amazon Kinesis Data Streams

Introduction

This blog will explore how to build a scalable, real-time leaderboard system using Amazon DynamoDB and Amazon Kinesis Data Streams. Leaderboards are crucial in gaming, e-commerce, and sports applications, where rankings must be updated in near real-time based on user actions or scores. By leveraging AWS services, we can ensure a solution that is not only real-time but also highly available, durable, and capable of handling spikes in traffic.

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Why Use Amazon DynamoDB and Amazon Kinesis Data Streams?

AWS offers several services tailored for real-time applications. Here’s why we chose Amazon DynamoDB and Kinesis:

Amazon DynamoDB: A fully managed NoSQL database designed for high throughput and low latency, making it ideal for maintaining constantly updated data like leaderboard scores. Features like Global Tables allow for multi-region replication, ensuring low latency globally.
Amazon Kinesis Data Streams: Provides a platform to collect, process, and analyze real-time streaming data. It ensures that score updates are processed in order and sent to the right consumers, even under high traffic.

These services create a scalable architecture for handling real-time leaderboard updates.

Key Architecture Components

Data Producers

These are the sources generating score updates. For example

Players in a gaming application.
Users making purchases in an e-commerce app.

Producers send updates (e.g., player scores) to Kinesis Data Streams.

Amazon Kinesis Data Streams

Amazon Kinesis ingests real-time data. Each event (e.g., a player scoring points) is sent to the stream as a record, which is then processed by consumers.

Key configurations:

Shard Management: Ensure the stream has sufficient shards to handle your application’s throughput.
Partition Keys: Use keys like player IDs to ensure ordering within the same partition.

AWS Lambda for Stream Processing

AWS Lambda processes records from Kinesis. Lambda functions:

Parse incoming events.
Perform data transformations if needed (e.g., calculating aggregate scores).
Write updated scores to Amazon DynamoDB.

Amazon DynamoDB

Amazon DynamoDB stores leaderboard data in a table designed for high throughput. A typical schema:

Primary Key: A composite key of GameID (partition key) and PlayerID (sort key).
Attributes: Fields like Score, LastUpdatedTime, etc.

Indexes like Global Secondary Indexes (GSI) can be used to sort by score.

Frontend API or Application

The leaderboard application fetches data from Amazon DynamoDB, displaying sorted scores. Amazon DynamoDB’s Query API efficiently retrieves top-scoring players.

Step-by-Step Implementation

Step 1: Set Up Amazon Kinesis Data Stream

Create Amazon Kinesis Data Stream with a sufficient number of shards.
Configure producers to send score updates as JSON records. Example:

JSON

{
    "GameID": "game123",
    "PlayerID": "player456",
    "ScoreDelta": 50
}

{

"GameID": "game123",

"PlayerID": "player456",

"ScoreDelta": 50

}

Step 2: Write AWS Lambda Function for Processing

Create AWS Lambda function triggered by Amazon Kinesis. The function will:

Parse the incoming record.
Update the player’s score in Amazon DynamoDB. Example code in Python (using boto3):

import boto3
from datetime import datetime

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('LeaderboardTable')

def lambda_handler(event, context):
    for record in event['Records']:
        data = json.loads(record['body'])
        game_id = data['GameID']
        player_id = data['PlayerID']
        score_delta = data['ScoreDelta']
        
        # Update DynamoDB
        table.update_item(
            Key={
                'GameID': game_id,
                'PlayerID': player_id
            },
            UpdateExpression="SET Score = if_not_exists(Score, :start) + :delta, LastUpdatedTime = :time",
            ExpressionAttributeValues={
                ':start': 0,
                ':delta': score_delta,
                ':time': datetime.utcnow().isoformat()
            }
        )

import boto3

from datetime import datetime

dynamodb = boto3.resource('dynamodb')

table = dynamodb.Table('LeaderboardTable')

def lambda_handler(event, context):

for record in event['Records']:

data = json.loads(record['body'])

game_id = data['GameID']

player_id = data['PlayerID']

score_delta = data['ScoreDelta']

# Update DynamoDB

table.update_item(

Key={

'GameID': game_id,

'PlayerID': player_id

UpdateExpression="SET Score = if_not_exists(Score, :start) + :delta, LastUpdatedTime = :time",

ExpressionAttributeValues={

':start': 0,

':delta': score_delta,

':time': datetime.utcnow().isoformat()

}

)

Step 3: Design Amazon DynamoDB Table

Create Amazon DynamoDB table:

Partition key: GameID
Sort key: PlayerID
Add a GSI to sort by Score in descending order.

Step 4: Create the Frontend or API

Use AWS SDKs or APIs to query DynamoDB for the top players. Example query:

response = table.query(
    KeyConditionExpression=Key('GameID').eq('game123'),
    IndexName='ScoreIndex',
    ScanIndexForward=False,  # Descending order
    Limit=10  # Top 10 players
)

response = table.query(

KeyConditionExpression=Key('GameID').eq('game123'),

IndexName='ScoreIndex',

ScanIndexForward=False, # Descending order

Limit=10 # Top 10 players

)

Step 5: Handle Scaling

Use Amazon DynamoDB Auto Scaling for throughput adjustments.
Monitor Amazon Kinesis shard utilization and scale up/down shards as needed.
Use Amazon CloudWatch Alarms for proactive monitoring.

Best Practices

Amazon DynamoDB

Enable On-Demand Capacity for unpredictable traffic.
Use Time-to-Live (TTL) to remove old or irrelevant data.
Optimize indexes for leaderboard queries.

Amazon Kinesis

Use Enhanced Fan-Out if multiple consumers are needed.
Implement error handling and retries in the AWS Lambda function.

Security

Use AWS IAM roles with the least privilege to access Amazon DynamoDB and Amazon Kinesis.
Encrypt data in transit using HTTPS and at rest with AWS KMS.

Real-Life Use Cases

Gaming Applications: Real-time ranking based on scores, kills, or other game metrics.
E-Commerce Platforms: Leaderboards for top spenders or most active users.
Sports Analytics: Real-time rankings for players during live matches.

Monitoring and Optimization

Use Amazon CloudWatch Metrics for:
- Monitoring Amazon Kinesis shard utilization.
- Tracking Amazon DynamoDB read/write capacity.
Employ AWS X-Ray to trace and debug the end-to-end process.
Optimize AWS Lambda function concurrency for faster processing

Conclusion

Building a real-time leaderboard using Amazon DynamoDB and Amazon Kinesis Data Streams ensures a scalable and efficient solution. By leveraging AWS managed services, you can focus on your application’s functionality without worrying about infrastructure complexities.

This architecture supports massive data ingestion, low-latency updates, and global accessibility, making it suitable for industry applications.

Drop a query if you have any questions regarding Amazon DynamoDB or Amazon Kinesis Data Streams and we will get back to you quickly.

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FAQs

1. How do I ensure the leaderboard updates are processed in order?

ANS: – Using a consistent partition key (e.g., PlayerID) to maintain order when writing to Amazon Kinesis Data Streams. Within a partition, records are processed in the order they are received. Ensure your processing logic (e.g., AWS Lambda function) respects this sequence, especially for scenarios like cumulative score calculations.

2. What are the cost considerations for using Amazon DynamoDB and Amazon Kinesis?

ANS: –

Amazon DynamoDB: Costs depend on read/write capacity and storage. Use Auto Scaling or On-Demand Capacity to manage variable traffic efficiently.
Amazon Kinesis Data Streams: Costs are based on the number of shards, data ingestion, and data retention. Optimize shard count and retention period to reduce costs while meeting application needs.
Monitoring with AWS Cost Explorer can help identify cost-saving opportunities.

WRITTEN BY Sunil H G

Sunil H G is a highly skilled and motivated Research Associate at CloudThat. He is an expert in working with popular data analysis and visualization libraries such as Pandas, Numpy, Matplotlib, and Seaborn. He has a strong background in data science and can effectively communicate complex data insights to both technical and non-technical audiences. Sunil's dedication to continuous learning, problem-solving skills, and passion for data-driven solutions make him a valuable asset to any team.