The issue with your Autoloader setup and foreachBatch approach is likely related to how you're handling the file path metadata. Here's something to try:
File Path Handling in Autoloader
When using Databricks Autoloader, the file path isn't automatically included as a column in your DataFrame. You need to explicitly capture it using one of these approaches:
1. Use the `includeFileName` option:
```python
df = (
spark.readStream.format("cloudFiles")
.option("cloudFiles.format", "csv") # or your specific format
.option("cloudFiles.includeExistingFiles", "true")
.option("cloudFiles.includeFileName", "true") # This adds the file path as metadata
.load(input_path)
)
```
2. Or explicitly add it using `input_file_name()`:
```python
df = (
spark.readStream.format("cloudFiles")
.option("cloudFiles.format", "csv")
.option("cloudFiles.includeExistingFiles", "true")
.load(input_path)
.withColumn("_source_file_path", F.input_file_name())
)
```
Fixing the NoneType Issue
The `distinct().collect()` returning NoneType likely means either:
- The column doesn't exist
- The column contains null values
- The DataFrame is empty
Here's a robust approach for your `foreachBatch` function:
```python
def process_batch(microBatchOutputDF, batchId):
# First check if the batch has data
if microBatchOutputDF.isEmpty():
print(f"Batch {batchId} is empty")
return
# Debug: Print schema to verify column exists
microBatchOutputDF.printSchema()
# Get distinct file paths, handling potential nulls
files_df = (
microBatchOutputDF
.select("_source_file_path") # Or use "_metadata.file_path" if that's your column
.dropna()
.distinct()
)
# Convert to Python list safely
files = [row["_source_file_path"] for row in files_df.collect()]
if not files:
print(f"No valid file paths found in batch {batchId}")
return
# Process files in sorted order
for filename in sorted(files):
print(f"Processing file: {filename}")
file_df = microBatchOutputDF.filter(F.col("_source_file_path") == filename)
process_single_file(file_df, filename, batchId)
```
Additional Considerations
- Schema Inference: For CSV/JSON files, consider using `.option("cloudFiles.schemaLocation", schema_location_path)` to maintain consistent schema across batches.
- Checkpointing: Ensure you have proper checkpointing with `.option("checkpointLocation", checkpoint_path)` in your stream definition.
- Error Handling: Add try/except blocks in your processing logic to handle file-specific failures gracefully.
- Traceability: Consider writing the `_source_file_path` to your target table for data lineage tracking.
- Performance: If processing many files per batch, consider using a thread pool to parallelize the file-level processing.
