DynamoDB Streams Interview Questions and Answers for 2 years experience

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DynamoDB Streams Interview Questions

  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 additions, modifications, and deletions of items. Streams provide a mechanism to build applications that react to changes in your data in real-time or near real-time, enabling event-driven architectures.

  2. What are the different stream view types in DynamoDB Streams?

    • Answer: There are two main stream view types: `NEW_IMAGE` and `NEW_AND_OLD_IMAGES`. `NEW_IMAGE` only captures the new item after a modification or insertion. `NEW_AND_OLD_IMAGES` captures both the old and new item states, allowing you to see the changes made.

  3. Explain the concept of shards in DynamoDB Streams.

    • Answer: Streams are divided into shards. Each shard represents a segment of the stream's data. Sharding allows DynamoDB to handle a high volume of write operations efficiently. You read data from a stream by reading from specific shards.

  4. How do you access data from DynamoDB Streams?

    • Answer: You can access data from DynamoDB Streams using AWS Lambda, Kinesis Data Streams, or other AWS services that can consume streams. Lambda functions are a common choice for near real-time processing.

  5. What are the limitations of DynamoDB Streams?

    • Answer: Limitations include a retention period limit (24 hours to 7 days depending on the configuration), throughput limitations per shard, and the fact that streams only capture changes; they don't contain the entire table data.

  6. How do you handle stream processing failures?

    • Answer: Implement robust error handling in your stream consumer (e.g., Lambda function). Use retries with exponential backoff to handle transient failures. Utilize dead-letter queues (DLQs) to capture unrecoverable errors for later investigation.

  7. Describe the process of enabling DynamoDB Streams on a table.

    • Answer: You can enable streams on a DynamoDB table through the AWS Management Console, AWS CLI, or AWS SDKs. You specify the stream view type (NEW_IMAGE or NEW_AND_OLD_IMAGES) during enabling. The process involves updating the table's attributes.

  8. How can you manage the cost of DynamoDB Streams?

    • Answer: Costs are based on read and write capacity units consumed by the stream. Optimize your stream processing to minimize the number of reads. Choose an appropriate retention period based on your needs (shorter retention periods are cheaper).

  9. Explain the difference between DynamoDB Streams and DynamoDB Global Tables.

    • Answer: DynamoDB Streams capture changes within a single table, providing a mechanism for reacting to those changes. DynamoDB Global Tables replicate a table across multiple regions for low latency access, but don't inherently provide a change stream for those replications.

  10. How can you use DynamoDB Streams with AWS Kinesis Data Streams?

    • Answer: You can configure DynamoDB Streams to write the stream data directly to a Kinesis Data Stream. This allows you to leverage the features of Kinesis, such as fan-out to multiple consumers and increased scalability for high-volume applications.

  11. What is the role of sequence numbers in DynamoDB Streams?

    • Answer: Sequence numbers are unique identifiers for each record in a DynamoDB stream, guaranteeing the order of events within a shard. They help with ensuring that your stream processor processes events in the correct order and avoids data duplication or loss.

  12. How do you handle large data records in DynamoDB Streams?

    • Answer: DynamoDB has limitations on the size of items stored in a table, and therefore in streams. If your data is too large, consider using DynamoDB's features such as attributes for efficient storage and retrieval. Or, split up your large records into smaller chunks before writing to the table.

  13. Explain the concept of DynamoDB Streams' auto-scaling.

    • Answer: DynamoDB Streams doesn't directly auto-scale the stream itself. However, the underlying DynamoDB table can be configured for auto-scaling. By adjusting the provisioned capacity of your table, you indirectly influence the stream's capacity and potential performance bottlenecks.

  14. How do you optimize the performance of DynamoDB Stream processing?

    • Answer: Optimize your Lambda functions to handle records efficiently, use parallel processing to distribute the workload, employ efficient data structures, and minimize unnecessary operations within your stream processing logic.

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