What should I do if I encounter a 'FROM_JSON_CORRUPT_RECORD_COLUMN_IN_SCHEMA' error in Databricks SQL?
If you encounter a 'FROM_JSON_CORRUPT_RECORD_COLUMN_IN_SCHEMA' error in Databricks SQL, it means that the `from_json` function found the `columnNameOfCorruptRecord` present in a JSON object and can no longer proceed. To resolve this issue, you should configure a different value for the `columnNameOfCorruptRecord` option. Here's an example of how to do this:

Suppose you have a DataFrame `df` with a column named `corrupt_col` that contains corrupt JSON records. To parse the JSON records using the `from_json` function, you can set the `columnNameOfCorruptRecord` option to a different column name, such as `corrupt_record`, as follows:
```python
from pyspark.sql.functions import from_json, col

# Set the columnNameOfCorruptRecord option to a different column name
spark.conf.set("spark.sql.columnNameOfCorruptRecord", "corrupt_record")

# Parse the JSON records using the from_json function
df = df.withColumn("parsed_json", from_json(col("corrupt_col"), schema))

# Select the parsed JSON columns
df = df.select("id", "parsed_json.*")
```
In this example, the `columnNameOfCorruptRecord` option is set to `corrupt_record` before parsing the JSON records using the `from_json` function. This allows the function to proceed even if it encounters corrupt records in the `corrupt_col` column. After parsing the JSON records, you can select the parsed JSON columns using the `select` method.

By setting the `columnNameOfCorruptRecord` option to a different column name, you can avoid the 'FROM_JSON_CORRUPT_RECORD_COLUMN_IN_SCHEMA' error and successfully parse the JSON records in your DataFrame.