DynamoDB Streams: A Better Alternative to Table Scans

While working on a backend service which was using DynamoDB, I encountered a problem where I needed to retrieve a set of data of a specific type. However, this data type did not have an index, and the dataset was frequently updating.

Since the data was constantly changing, my initial thought was to attach a trigger. However, after reviewing the AWS documentation on how to add triggers to DynamoDB, I decided to explore alternative approaches.

Initial Solutions Considered

By the time I had properly considered my options, three viable solutions stood out:

1. Complete Table Scan and Filtering

Performing a full scan of the table and filtering the results was the first idea that came to mind. However, I quickly dismissed it due to its inefficiency. Scanning a large dataset without an index is:

• Computationally expensive
• Significantly slows down performance
• Not scalable for large datasets

2. Adding an Index for the Data Type

Adding an index seemed like a reasonable approach, but I had two concerns:

• The data in question was not frequently queried, making an additional index potentially unnecessary overhead
• The table already had multiple indexes, and I wanted to avoid adding another unless absolutely necessary

3. Running a Cron Job

Another idea was to set up a cron job to periodically scan the table, cache the results, or store them in another table.

As a wise engineer once said: If you can't think of anything else, run a cron job.

However, I wasn't completely convinced by any of these solutions, so I decided to revisit DynamoDB triggers.

Exploring DynamoDB Streams

I had heard about DynamoDB Streams before but had never implemented them. In essence, DynamoDB Streams work similarly to database triggers in relational databases. The key difference is that they:

• Generate a stream of events whenever data changes
• Allow you to process these changes in real time
• Can be integrated with AWS services such as Amazon Kinesis or AWS Lambda (which I used in my case)

Implementation Steps

Surprisingly, setting up DynamoDB Streams was much simpler than AWS documentation made it seem. Here's what I had to do:

1. Enable a DynamoDB Stream

   • This was a one-click action in the AWS console

2. Create a Lambda Function

   • Set up a new AWS Lambda function
   • Attached it to the DynamoDB stream

3. Process the Stream Data

   • The Lambda function listened to changes
   • Processed the data
   • Saved it to a new table

Here's a high-level architecture diagram illustrating the setup:

Conclusion

DynamoDB Streams turned out to be an efficient solution for handling frequently changing data without resorting to inefficient table scans or additional indexes. By leveraging AWS Lambda, I was able to:

• Process data changes in real time
• Store the results in a more structured manner
• Avoid unnecessary table scans and indexes

DynamoDB Streams offer much more functionality than I explored in this use case. If you're interested in learning more, check out AWS's official blog post: DynamoDB Streams Use Cases and Design Patterns.