Databricks Machine Learning Associate Question 335
Single answerYou are using the Pandas API on Spark to process a large dataset in Databricks. While analyzing the performance, you notice that the Pandas API on Spark is not as fast as native Spark operations. What is one of the reasons for this behavior related to the InternalFrame?
- A
The InternalFrame requires additional memory to maintain a mapping between Spark DataFrame and Pandas DataFrame.
- B
The InternalFrame forces the computation to run on a single executor, limiting parallelism.
- C
The InternalFrame introduces additional overhead to ensure the index and schema consistency between Spark and Pandas DataFrames.
- D
The InternalFrame causes data to be shuffled unnecessarily during transformations.
Show answer and explanation
Correct answer: C
Explanation
The InternalFrame is a critical component of the Pandas API on Spark that acts as an intermediary between the Spark DataFrame and Pandas DataFrame. It ensures that the index and schema are consistent between the two frameworks. While this allows for seamless integration, it introduces additional overhead, making the Pandas API on Spark slower compared to native Spark operations in certain cases.
- A. Incorrect.
While the InternalFrame does require memory, this is not the primary reason for the performance difference. Its impact on memory is generally minimal.
- B. Incorrect.
The InternalFrame does not restrict the computation to a single executor. Pandas API on Spark operations can still leverage Spark's distributed architecture.
- C. Correct.
The InternalFrame introduces overhead because it ensures consistency between the schema and index of Spark DataFrames and Pandas DataFrames. This requires extra computation, which can impact performance.
- D. Incorrect.
The InternalFrame itself does not directly cause unnecessary shuffling of data. Shuffling is typically a result of specific transformations or operations.