Accelerating Data Processing: Leveraging Apache Hudi with DynamoDB for Faster Commit Time Retrieval with Source Code

Data processing at scale is a constant challenge in the world of big data. As organizations strive to analyze and extract insights from ever-growing data sets, the need for efficient data processing solutions becomes paramount. Apache Hudi, an open-source data management framework, provides the flexibility and performance required for large-scale data operations. In this blog, we'll explore a powerful solution that leverages Apache Hudi and DynamoDB to accelerate data processing by significantly improving commit time retrieval.

Videos Based guide

The Challenge: Faster, Better, Easier Data Processing

One of the key challenges in big data processing is efficiently managing and retrieving commit times. Commit times are crucial for tasks like point-in-time queries, incremental queries, and batch processing pipelines. Traditional approaches to commit time management often involve complex setups and infrastructure maintenance. This can result in slow processing times, increased operational overhead, and scalability challenges. As the size of the data and the number of commits increase, fetching commits and scanning files can become expensive.

The Solution: Leveraging Apache Hudi with DynamoDB

Our solution revolves around leveraging Apache Hudi's capabilities and DynamoDB, a highly scalable and serverless NoSQL database service provided by AWS. Here's how it works

1. Hudi HTTP Callback URI: To initiate the process, we use Hudi's HTTP callback feature. Whenever a data event occurs, Hudi sends this event to a Lambda function via a callback URL.
2. Lambda Function for Data Storage: The Lambda function is responsible for receiving the Hudi event and storing the data in DynamoDB. This Lambda function takes the event data and organizes it efficiently within DynamoDB.
3. Efficient Data Storage: The Lambda function extracts the essential information from the event, such as tableName, commitTime, and basePath. It also calculates the ingestion timestamp and various date components like year, month, day, hour, and minute.
4. Storing Data in DynamoDB: Using these extracted values, the Lambda function creates a DynamoDB item as a dictionary and inserts it into the DynamoDB table. This approach ensures efficient and organized data storage for future retrieval.
5. Serverless Scalability: The serverless nature of AWS Lambda ensures that the system can scale up or down automatically based on the number of incoming events. DynamoDB, being a managed NoSQL database, also scales efficiently based on the event load.

The result is a streamlined and serverless solution that efficiently stores commit time data for any given table. This data can be retrieved for point-in-time queries, incremental queries, or as part of batch processing pipelines.

Steps for Labs

Step 1: Create DynamoDB Table hudi_commits

The DynamoDB table structure is designed as follows:

• Primary Key: TableName
• Sort Key: CommitTime
• Additional Fields: Year, Month, Day, Hour, Minute, and IngestionTime (or Event Time)

You can setup additional GSI and LSI if needed based on your access pattern

Step 2: AWS Lambda Functions

Lets Enable Functional URL on lambda Function

Click on save and COPY functional URL

Configure IAM policy for Lambda Function and lets give permission so it can insert data into DynamoDB

After this we need to add 3 config on hudi side

Hudi Configuration

To enable Hudi to utilize this solution, you need to configure the following Hudi settings:

• hoodie.write.commit.callback.http.url: Set this to the appropriate HTTP callback URL.
• hoodie.write.commit.callback.on: Enable this callback feature.
• hoodie.write.commit.callback.http.timeout.seconds: Set a reasonable timeout value for the HTTP callback.

By configuring Hudi with these settings, you ensure that it communicates effectively with the Lambda function for commit time storage.

Write Data into Hudi and you will see Lambdas function scaling and processing and inserting events into DyanamoDB

Dealing with Increasing Data Size

As data size increases, fetching commits and scanning files can become expensive operations. This is where the combination of Apache Hudi and DynamoDB shines. DynamoDB's scalability and performance capabilities make it well-suited to handle growing data volumes. DynamoDB can efficiently handle the increasing load, ensuring that fetching commits remains fast and cost-effective.

Furthermore, Apache Hudi's data management capabilities, including indexing and compaction, help in optimizing data access. This means that as data grows, you won't experience a linear increase in retrieval costs. Instead, with efficient data management, you can maintain reasonable retrieval times and cost-effectiveness even with large datasets.

Simplifying Downstream Data Processing

With commit times stored efficiently in DynamoDB, downstream applications, batch jobs, and incremental ETL processes can access commits at lightning speed. Whether you need to perform incremental updates or back-filling jobs, DynamoDB provides a central repository for all your commit time data. This approach allows you to focus on the active commits in the Hudi timeline and archive older commits, enhancing query performance.

Complete Code

https://github.com/soumilshah1995/-Accelerating-Data-Processing-Leveraging-Apache-Hudi-with-DynamoDB-for-Faster-Commit-Time-Retrieval/tree/main

Conclusion

Efficient data processing and commit time retrieval are critical components of big data operations. The combination of Apache Hudi and DynamoDB offers an elegant solution to address these challenges. By leveraging serverless infrastructure and automation, this approach simplifies the process, accelerates data processing, and reduces operational overhead. It enables organizations to perform point-in-time queries, incremental queries, and batch processing pipelines with ease. As data volumes continue to grow, solutions like this become invaluable for managing and extracting insights from large datasets.