Databricks-Spark-Assoc Pandas API on Spark Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Set the configuration 'compute.ops_on_diff_frames' to True using spark.conf.set.

  Why this is correct

  Setting the configuration 'compute.ops_on_diff_frames' to True allows operations between different DataFrames by enabling the computation of operations on different frames. This is the intended way to resolve the error when the operation is necessary, though it may have performance implications because it can trigger a shuffle or collect data. It is a valid solution when the operation is required.

• ✓

  Convert both DataFrames to PySpark DataFrames and perform the operation using Spark SQL functions.

  Why this is correct

  Converting to PySpark DataFrames and using Spark SQL functions bypasses the Pandas API on Spark restriction entirely. This is a valid workaround because the error is specific to Pandas API on Spark's handling of different frames. By using native Spark operations, you can perform the required computations without encountering the 'compute.ops_on_diff_frames' error, though it may require rewriting the logic.

• ✗

  Ensure both DataFrames are derived from the same base DataFrame or have the same index.

  Why it's wrong here

  While having the same index can sometimes avoid the error, it is not a guaranteed solution. The error is specifically about operations on different frames, and having the same index does not necessarily mean they are the same frame. The configuration setting is the definitive way to allow such operations. This action may not resolve the error if the DataFrames are still considered different.

• ✗

  Use the 'psdf1.merge(psdf2)' method instead of direct comparison.

  Why it's wrong here

  Using merge is not a general solution for all operations that trigger the error. The error occurs when operations involve columns from different DataFrames, such as arithmetic between columns of two DataFrames. Merge is only applicable for joining DataFrames, not for element-wise operations. Therefore, this action does not resolve the error for all cases.

• ✗

  Call 'psdf1.to_pandas()' and 'psdf2.to_pandas()' to perform the operation in pandas.

  Why it's wrong here

  Converting to pandas and performing the operation locally may work for small datasets but is not scalable and defeats the purpose of using Pandas API on Spark. It can cause out-of-memory errors on the driver for large datasets. While it technically resolves the error by avoiding the distributed operation, it is not a recommended solution for large data and is not a general fix.