DynamoDB Streams Interview Questions and Answers for 10 years experience

1. What are DynamoDB Streams?

   • Answer: DynamoDB Streams are a feature that captures a continuous stream of changes to your DynamoDB tables. These changes include inserts, updates, and deletes. Streams provide a mechanism to build applications that react to changes in your DynamoDB data in real-time or near real-time, enabling integration with other services like AWS Lambda, Kinesis, and more.

2. Explain the difference between DynamoDB Streams' New Image and Old Image.

   • Answer: The New Image represents the item *after* a modification (insert, update, or delete). The Old Image represents the item *before* a modification (only available for updates and deletes). If an item is deleted, the Old Image contains its state before deletion; the New Image is null. New Image is always available for inserts; the Old Image is null.

3. What are the different stream view types in DynamoDB Streams? Explain their use cases.

   • Answer: There are two stream view types:
     • NEW_IMAGE: Only contains the New Image. This is efficient when you only need the updated state of the item. Ideal for applications that only need the latest data.
     • NEW_AND_OLD_IMAGES: Contains both New and Old Images. Necessary when you need both the before and after states of an item, often for auditing, change data capture (CDC), or building reconciliation processes.
     • KEYS_ONLY: Only includes the primary key of the modified item. Most efficient for low-latency applications only needing the key for further processing, reducing data transferred.

4. How do you enable DynamoDB Streams on a table?

   • Answer: You can enable DynamoDB Streams using the AWS Management Console, AWS CLI, or AWS SDKs. The process involves specifying the desired stream view type (NEW_IMAGE, NEW_AND_OLD_IMAGES, or KEYS_ONLY) and optionally configuring the Kinesis Data Stream that will receive the stream records.

5. What are the limitations of DynamoDB Streams?

   • Answer: Limitations include stream retention period (up to 24 hours, 7 days, or indefinitely for on-demand), throughput limits (dependent on the table's write capacity), potential for data loss during exceptionally high write throughput, and the need for careful consideration of stream view type selection based on processing requirements.

6. How can you consume DynamoDB Streams using AWS Lambda?

   • Answer: You configure your Lambda function to trigger on DynamoDB Stream events. Lambda receives the stream records (containing the image data) and executes your code to process those changes. The function is invoked asynchronously for each record in the stream.

7. How can you consume DynamoDB Streams using Amazon Kinesis Data Streams?

   • Answer: When enabling DynamoDB Streams, you can specify a Kinesis Data Stream as the target. This sends the stream records directly to your Kinesis stream, allowing for more complex processing using Kinesis applications and other Kinesis-related services like Kinesis Data Analytics.

8. What is the Shard count in DynamoDB Streams and how does it affect performance?

   • Answer: DynamoDB Streams are sharded to manage throughput and scaling. The shard count is determined based on the table's write capacity. A higher shard count enables higher throughput but might increase the complexity of processing. Consumers need to handle multiple shards concurrently.

9. How do you handle errors when processing DynamoDB Stream records?

   • Answer: Implement robust error handling using try-catch blocks or similar mechanisms in your Lambda function or Kinesis consumer. Employ retry strategies (exponential backoff) to handle temporary failures. Consider using dead-letter queues (DLQs) to capture and log records that consistently fail to process.

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