DynamoDB Streams Interview Questions and Answers for 7 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, immutable stream of all the changes that occur in a DynamoDB table. These changes include additions, modifications, and deletions of items. They provide a mechanism for building applications that react to changes in DynamoDB data in real-time or near real-time.

  2. Explain the different stream view types available in DynamoDB Streams.

    • Answer: DynamoDB offers two stream view types: `NEW_IMAGE` and `NEW_AND_OLD_IMAGES`. `NEW_IMAGE` only provides the new item's state after the modification. `NEW_AND_OLD_IMAGES` provides both the old and new item states, allowing you to track changes effectively. Choosing the right view depends on the application's requirements. If you only need the current state, `NEW_IMAGE` suffices; if you need to track modifications, `NEW_AND_OLD_IMAGES` is necessary.

  3. How do DynamoDB Streams handle large tables? What are the performance implications?

    • Answer: DynamoDB Streams handle large tables by processing changes in batches. While the stream captures every change, reading from the stream is done in shards. Performance implications depend on the write throughput of the table and the frequency of changes. High write throughput tables will generate a larger stream, potentially increasing read latency and costs if not properly managed. Efficient stream consumption strategies, such as parallel processing and batching, are crucial for handling large tables.

  4. Describe the concept of shards in DynamoDB Streams.

    • Answer: Shards are essentially partitions of the stream. DynamoDB automatically manages shard creation and splitting based on the write activity in the table. Each shard represents a continuous sequence of changes within the stream. Consumers read from individual shards, allowing for parallel processing and improved scalability. The number of shards directly relates to the write throughput of the table.

  5. How do you consume DynamoDB Streams? Explain different consumption methods.

    • Answer: DynamoDB Streams can be consumed using various methods: AWS Lambda is a common choice for event-driven processing. Kinesis Data Streams can be used as an intermediary, allowing for more sophisticated stream processing and fault tolerance. Self-managed applications can directly interact with the stream using the DynamoDB API, but this requires more complex error handling and management.

  6. What are the best practices for designing applications using DynamoDB Streams?

    • Answer: Best practices include: designing idempotent consumers to handle retries effectively; using parallel processing to improve throughput; implementing proper error handling and retry mechanisms; monitoring stream consumption to detect and resolve bottlenecks; and optimizing stream processing logic to minimize latency.

  7. Explain the concept of iterator types in DynamoDB Streams.

    • Answer: Iterators are used to traverse the stream records. There are different iterator types, such as `TRIM_HORIZON` (starting from the oldest record), `LATEST` (starting from the most recent record), and `AT_SEQUENCE_NUMBER` (starting from a specific sequence number). Choosing the correct iterator depends on whether you want to process all historical records or only new changes.

  8. How do you handle stream sharding when scaling your application?

    • Answer: When scaling, you need to ensure your consumers can handle the increasing number of shards. This usually involves adding more consumers, perhaps using a distributed system, to process the shards in parallel. Dynamically assigning shards to consumers may be necessary using techniques like shard assignment services or load balancers.

  9. How can you ensure exactly-once processing of DynamoDB stream records?

    • Answer: True exactly-once processing is challenging. You can strive for *at-least-once* processing and then implement idempotency in your consumer logic. This ensures that even if a record is processed multiple times, the outcome is the same, preventing unintended side effects. Techniques like using unique identifiers for processing and updating a status table can help achieve this.

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