Question
Answer and Explanation
To retrieve the file name during Databricks Structured Streaming, you can leverage the built-in input_file_name() function in Spark SQL.
Here’s a comprehensive guide:
1. Understanding input_file_name() Function:
The input_file_name() function returns the name of the file being processed by the current record in Spark. It is particularly useful in Structured Streaming when you need to know which file contributed to a specific row of data.
2. Implementation Steps:
- Read Data Stream: Start by reading your data stream from a file source, such as a directory on cloud storage (e.g., Azure Data Lake Storage, AWS S3).
- Apply input_file_name(): Use the input_file_name() function within a Spark SQL query or DataFrame transformation to add a new column containing the file name.
3. Example Code in PySpark:
Here's an example of how to implement this in PySpark:
from pyspark.sql.functions import input_file_name
# Define the schema (optional, but recommended)
schema = "col1 STRING, col2 INT"
# Read the stream of data
df = spark.readStream.format("csv") \\
.option("header", "true") \\
.schema(schema) \\
.load("/path/to/your/data/")
# Add the input file name column
df_with_filename = df.withColumn("filename", input_file_name())
# Display the stream (for testing)
query = df_with_filename.writeStream \\
.outputMode("append") \\
.format("console") \\
.start()
query.awaitTermination()
4. Explanation:
- The spark.readStream reads the data stream from the specified path in CSV format.
- df.withColumn("filename", input_file_name()) adds a new column named filename to the DataFrame, which contains the name of the file from which each record was read.
- The writeStream configuration writes the stream to the console for testing purposes. In a production environment, you might write it to a Delta table, database, or another sink.
5. Handling Different File Formats and Schemas:
- Ensure the schema is correctly defined to match the data in your files. If the schema is not defined, Spark will attempt to infer it, which can sometimes lead to errors or incorrect data types.
- Adjust the format option ("csv", "json", "parquet", etc.) to match the type of files you are reading.
6. Considerations:
- Performance: Using input_file_name() can have a slight performance overhead, so use it judiciously. If you only need the file name for debugging or auditing purposes, consider limiting its usage to specific scenarios.
- File Path Changes: Be aware that the full file path is returned, including the directory. You may need to use additional string manipulation functions to extract just the file name if desired.
7. Example: Extracting Just the File Name:
If you need only the file name without the path, you can use Spark SQL functions like split and element_at:
from pyspark.sql.functions import input_file_name, split, element_at
df = spark.readStream.format("csv").option("header", "true").load("/path/to/your/data/")
df_with_filename = df.withColumn("full_path", input_file_name())
df_with_filename = df_with_filename.withColumn("filename", element_at(split("full_path", "/"), -1))
query = df_with_filename.writeStream.outputMode("append").format("console").start()
query.awaitTermination()
In summary, utilizing the input_file_name() function is the most direct and efficient way to retrieve the file name during Databricks Structured Streaming. Remember to consider the performance implications and adjust your implementation based on your specific requirements and file formats.