Why & when to use Auto Loader

Auto Loader in Databricks is primarily designed for processing new files as they arrive inCloud Storage, making it an excellent choice for handling data sources that generate log files and store them in cloud storage (e.g., S3, ADLS, Google Cloud Storage). However, it is not designed to directly handle streaming live data from sources like log file generators that continuously produce data without storing it in files.

When you specify the format as CloudFiles in your readStream operation, it indicates that you're using Databricks Auto Loader. Auto Loader automatically processes new data files as they arrive in your cloud storage and handles schema evolution and checkpointing to ensure reliable and scalable ingestion of streaming data.

When to Use Auto Loader: Cloud Storage: When log files are being generated and stored in cloud storage, Auto Loader can efficiently process these files as they arrive. Batch Processing: Auto Loader can be used for near real-time batch processing of log files, ensuring that new files are ingested and processed as they appear in the cloud storage.
Alternatives for Streaming Live Data: For processing streaming live data directly from sources like log file generators, you can use Spark Structured Streaming with different input sources:
Kafka:

Apache Kafka is a popular choice for ingesting and processing streaming data. You can set up Kafka to receive log data from log file generators and then use Spark Structured Streaming to process the data in real-time.
streaming_df = spark.readStream.format("kafka") \ .option("kafka.bootstrap.servers", "host:port") \ .option("subscribe", "topic") \ .load()
# Process the streaming data
transformed_df = streaming_df.selectExpr("CAST(value AS STRING) as message")
# Write the streaming DataFrame to a Delta table
query = transformed_df.writeStream.format("delta") \ .outputMode("append") \ .option("checkpointLocation", "/path/to/checkpoint/dir") \ .start("/path/to/delta/table")
query.awaitTermination()

Socket Source:

If you have a simple use case where data is sent over a network socket, you can use the socket source in Spark Structured Streaming.
streaming_df = spark.readStream.format("socket") \ .option("host", "localhost") \ .option("port", 9999) \ .load()
# Process the streaming data
transformed_df = streaming_df.selectExpr("CAST(value AS STRING) as message")
# Write the streaming DataFrame to a Delta table query = transformed_df.writeStream.format("delta") \ .outputMode("append") \ .option("checkpointLocation", "/path/to/checkpoint/dir") \ .start("/path/to/delta/table")
query.awaitTermination()

File Source with Auto Loader:

If your log file generator stores the logs in cloud storage, you can use Auto Loader to process them.
streaming_df = spark.readStream.format("cloudFiles") \ .option("cloudFiles.format", "json") \ .option("cloudFiles.schemaLocation", "/path/to/checkpoint/schema") \ .load("s3://your-bucket/path/to/logs")
# Process the streaming data
transformed_df = streaming_df.select("timestamp", "log_level", "message")
# Write the streaming DataFrame to a Delta table
query = transformed_df.writeStream.format("delta") \ .outputMode("append") \ .option("checkpointLocation", "/path/to/checkpoint/dir") \ .start("/path/to/delta/table")
query.awaitTermination()
By choosing the appropriate method based on your data source and requirements, you can effectively handle and process streaming live data or log files using Spark Structured Streaming and Databricks.