The use of toPandas in PySpark can lead to performance and memory management issues.
PySpark is designed to handle large-scale data processing in a distributed manner, leveraging the power of cluster computing to efficiently manage
and process big data. Calling toPandas collects all data from a Spark DataFrame into a Pandas DataFrame on a
single machine. This can lead to memory issues and performance bottlenecks, especially with large datasets, which is contrary to the distributed
nature of Spark.
For this reason, it is generally advisable to avoid using toPandas unless you are certain that the dataset is small enough to be
handled comfortably by a single machine. Instead, consider using Spark’s built-in functions and capabilities to perform data processing tasks in a
distributed manner.
If the conversion to Pandas is necessary, ensure that the dataset size is manageable and that the conversion is justified by specific requirements,
such as integration with libraries that require Pandas DataFrames.
Exceptions
This rule will not raise issues in the following context:
- When visualization is performed with other libraries such as
matplotlib, seaborn, etc.
- When the
DataFrame is of a limited size (e.g. following a call to limit or an aggregation through
groupBy)
- In tests
