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NO.3 Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs. BigQueryIO.Read .named("ReadLogData") .from("clouddataflow-readonly:samples.log_data") You want to improve the performance of this data read. What should you do?_
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Explanation:
Explanation
The correct answer is D because:
- When reading from BigQuery in Dataflow, using
TableRowobjects directly in the PCollection provides better performance compared to other methods - Each element in the PCollection representing a single row as a
TableRowobject allows for efficient parallel processing - This approach avoids unnecessary serialization/deserialization overhead
- The current code
BigQueryIO.Read.named("ReadLogData").from("clouddataflow-readonly:samples.log_data")is already reading the entire table, but using TableRow objects optimizes how the data is processed in the pipeline
Why other options are less optimal:
- A: Specifying TableReference doesn't significantly improve read performance
- B: Using .fromQuery to read specific fields could help with performance by reducing data volume, but it's not the most direct performance improvement for the read operation itself
- C: Using TableSchema and TableFieldSchema classes is more about schema definition rather than read performance optimization
The key insight is that in Dataflow pipelines, representing BigQuery data as TableRow objects in PCollections provides the most efficient processing model for large-scale data operations._
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