Databricks Data Engineer Professional Question 91
Single answerYou are implementing a Spark Structured Streaming job to process a stream of clickstream data. The data contains duplicate records, and you want to deduplicate the data based on a combination of 'user_id' and 'event_time' columns. The stream is configured with a watermark on 'event_time' of 10 minutes. Which approach should you use to ensure deduplication happens efficiently while maintaining correctness?
- A
Use the dropDuplicates method with 'user_id' and 'event_time' columns, and ensure a watermark is applied to the 'event_time' column.
- B
Add a unique identifier column to each record and use the dropDuplicates method without specifying any columns.
- C
Use the distinct method to deduplicate the stream.
- D
Use the dropDuplicates method with 'user_id' and 'event_time' columns but do not configure any watermark.
Show answer and explanation
Correct answer: A
Explanation
In Spark Structured Streaming, deduplication can be achieved using the dropDuplicates method with specific columns. When working with time-based data in a streaming context, configuring a watermark is crucial to manage state efficiently and prevent unbounded memory usage. Using the 'user_id' and 'event_time' columns ensures deduplication happens based on the business logic requirement.
- A. Correct.
This is the correct approach. The dropDuplicates method, when used with specific columns like 'user_id' and 'event_time', ensures that duplicates are removed based on those columns. Additionally, the configured watermark ensures that Spark can manage state efficiently by dropping old state records beyond the watermark threshold.
- B. Incorrect.
Adding a unique identifier does not address the deduplication requirement based on 'user_id' and 'event_time'. This approach would not correctly remove duplicates based on the specified criteria.
- C. Incorrect.
The distinct method removes duplicates across the entire dataset, which is inefficient and not suitable for streaming use cases where deduplication is needed based on specific columns.
- D. Incorrect.
Using dropDuplicates without a watermark can lead to unbounded state growth, as Spark will retain all records in memory indefinitely to ensure correctness. Watermarking is essential for managing state efficiently in streaming applications.