optimize expensive operations

Before executing computationally expensive operations, implement conditional checks to determine if the operation is actually necessary. This optimization technique can significantly improve performance by avoiding costly computations when simpler alternatives exist or when the operation can be skipped entirely.

Key principles:

1. Identify expensive operations: Look for operations that involve data collection, type conversion, or complex computations
2. Add necessity checks: Implement conditional logic to determine if the expensive operation is required
3. Provide efficient alternatives: When the expensive operation isn’t needed, use simpler approaches

Example from Arrow conversion optimization:

# Check if expensive conversion is needed
needs_conversion = any(
    LocalDataToArrowConversion._need_converter(field.dataType, field.nullable)
    for field in return_type.fields
)

if not needs_conversion:
    try:
        # Use direct, efficient approach when conversion isn't needed
        return [pa.RecordBatch.from_pylist(data, schema=pa.schema(list(arrow_return_type)))]
    except:
        # Fall back to expensive conversion only if necessary
        pass

This pattern is particularly important in data processing pipelines where operations may be called frequently on large datasets. The upfront cost of the necessity check is typically much lower than the expensive operation itself.