Kelly is working as a Data Engineer of Whizlabs Inc.
She's working on Databricks Spark job execution using data frames but faces the following error message: “Serialized task is too large” Which of the following Spark configuration properties is required to be amended?
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A. B. C. D.Correct Answer: A
Call parallelize with a large list or convert a large R DataFrame to a Spark DataFrame.
When working with Databricks Spark, it is common to encounter the "Serialized task is too large" error message. This error message usually occurs when a task's serialized size exceeds the maximum allowed size of 2GB.
To fix this issue, one needs to adjust the Spark configuration property to increase the maximum allowed size of serialized tasks. The correct configuration property to amend depends on the nature of the task being performed.
Option A is incorrect as it provides a solution to a different problem. Parallelizing a large list or converting a large R DataFrame to a Spark DataFrame may cause performance issues, but it will not fix the "Serialized task is too large" error.
Option C is also incorrect as it sets up a property related to Delta Lake, which is not relevant to the error message.
Option D is not the optimal solution to the problem. Running the job on a job cluster might reduce the frequency of the error, but it does not address the root cause.
The correct answer is option B, which suggests setting the Spark configuration property using "spark.conf.set()". The specific configuration property to set is "spark.driver.maxResultSize", which defines the maximum size in bytes of the serialized results returned to the driver.
Here's an example of how to set this configuration property in a notebook:
pythonspark.conf.set("spark.driver.maxResultSize", "4g") This sets the maximum size of the serialized results returned to the driver to 4GB, which should be sufficient for most use cases. However, one can adjust the value based on the specific requirements of the job being performed.