Why is a long-running stage in my Spark job only showing one task, and how can I resolve this issue?
A long-running stage with one task in a Spark job could be due to several reasons such as:

1. Expensive User Defined Functions (UDFs) on small data
2. Window function without a PARTITION BY statement
3. Reading from an unsplittable file type like gzip
4. Setting the multiLine option when reading a JSON or CSV file
5. Schema inference of a large file
6. Use of repartition(1) or coalesce(1)

To resolve this issue, you can:

1. Optimize your UDFs or replace them with built-in functions if possible.
2. Ensure that you have a proper PARTITION BY statement in your window functions.
3. Avoid using unsplittable file types like gzip. Instead, use splittable file types like snappy or lz4.
4. Avoid setting the multiLine option when reading JSON or CSV files.
5. Perform schema inference on a small sample of your data and then apply it to the entire dataset.
6. Avoid using repartition(1) or coalesce(1) unless necessary.

By implementing these changes, you should be able to resolve the issue of a long-running stage with only one task in your Spark job.