Auto loader in adb

Question

Hi,

how to create autoloader using below script and how it will trigger it

https://learn.microsoft.com/en-us/azure/databricks/delta/update-schema#enable-schema-evolution-for-writes-to-add-new-columns

Answer 1

@Vineet S

Thanks for using Microsoft Q&A forum and posting your query.

To create an Auto Loader in Azure Databricks (ADB) and trigger it, you can use the following script as a starting point. This script sets up Auto Loader to incrementally and efficiently process new data files as they arrive in cloud storage.

Here’s an example in Python:

from pyspark.sql import SparkSession
# Initialize Spark session
spark = SparkSession.builder.appName("AutoLoaderExample").getOrCreate()
# Define the source and target paths
source_path = "<path-to-source-data>"
checkpoint_path = "<path-to-checkpoint>"
target_path = "<path-to-target>"
# Configure Auto Loader
df = (spark.readStream.format("cloudFiles")
      .option("cloudFiles.format", "parquet")  # Specify the format of your source files
      .option("cloudFiles.schemaLocation", checkpoint_path)  # Schema location for schema evolution
      .load(source_path))
# Write the streaming data to the target path
(df.writeStream
   .option("checkpointLocation", checkpoint_path)
   .start(target_path))

Explanation:

1. Initialize Spark Session: Start by initializing a Spark session.
2. Define Paths: Set the paths for the source data, checkpoint, and target location.
3. Configure Auto Loader: Use spark.readStream.format("cloudFiles") to set up Auto Loader. Specify the format of your source files (e.g., “parquet”, “json”, etc.) and the schema location for schema evolution.
4. Write Stream: Write the streaming data to the target path, using the checkpoint location to track progress and ensure exactly-once processing.

Triggering the Auto Loader:

The Auto Loader will automatically trigger and process new files as they arrive in the specified source path. The checkpoint location ensures that the state is maintained, and the stream can resume from where it left off in case of any interruptions.

For more detailed information on configuring schema inference and evolution, you can refer to the official documentation.

Hope this helps. Do let us know if you any further queries.