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Auto Loader provides a Structured Streaming source called cloudFiles to incrementally and efficiently processes new data files as they arrive in cloud storage.
Given an input directory path on the cloud file storage, the cloudFiles source automatically processes new files as they arrive, with the option of also processing existing files in that directory.
Auto Loader can ingest JSON, CSV, PARQUET, AVRO, ORC, TEXT, and BINARYFILE file formats.
As files are discovered, their metadata is persisted in a scalable key-value store (RocksDB) in the checkpoint location of your Auto Loader pipeline. This key-value store ensures that data is processed exactly once.
Databricks Autoloader supports two methods to detect new files in your Cloud storage namely:
Directory Listing: This approach is useful for cases when only a few files are required to be streamed regularly. Here, the new files are recognised from listing the input directory. With just access to your Cloud Storage data, you can swiftly enable your Databricks Autoloader Streams.
From the beginning, Databricks Autoloader automatically detects if the input directory is good for Incremental Listing. Though, you have the option to explicitly choose between the Incremental Listing or Full Directory Listing by setting cloudFiles.useIncrementalListing as true or false.
File Notification: As your directory size increases, you may want to switch over to the file notification mode for better scalability and faster performance. Using the Cloud services like Azure Event Grid and Queue Storage services, AWS SNS and SQS or GCS Notifications, and Google Cloud Pub/Sub services, it subscribes to file events in the input directory.