apache-sparkdatabricksspark-structured-streamingdatabricks-autoloader

Databricks Autoloader Schema Evolution throws StateSchemaNotCompatible exception


I am trying to use Databricks Autoloader for a very simple use case:

Reading JSONs from S3 and loading them into a delta table, with schema inference and evolution.

This is my code:

self.spark \
      .readStream \
      .format("cloudFiles") \
      .option("cloudFiles.format", "json") \
      .option("cloudFiles.inferColumnTypes", "true") \
      .option("cloudFiles.schemaLocation", f"{self.target_s3_bucket}/_schema/{source_table_name}") \
      .load(f"{self.source_s3_bucket}/{source_table_name}") \
      .distinct() \
      .writeStream \
      .trigger(availableNow=True) \
      .format("delta") \
      .option("mergeSchema", "true") \
      .option("checkpointLocation", f"{self.target_s3_bucket}/_checkpoint/{source_table_name}") \
      .option("streamName", source_table_name) \
      .start(f"{self.target_s3_bucket}/{target_table_name}")

When a JSON with an unknown column arrives, the Stream fails, as expected, with a NEW_FIELDS_IN_RECORD_WITH_FILE_PATH exception.

But when I retry the job, I get the following exception:

StateSchemaNotCompatible: Provided schema doesn't match to the schema for existing state! Please note that Spark allow difference of field name: check count of fields and data type of each field.

This is my first time using Autoloader, am I doing something obviously wrong?


Solution

  • You are using .distinct() which creates a state for which changes in schema are not allowed. This is an expected behaviour, you may find more info here: https://docs.databricks.com/en/structured-streaming/query-recovery.html#:~:text=Changes%20in%20stateful%20operations