Hi @The_Demigorgan, Certainly! When using Autoloader in Databricks for ingesting data from Parquet files, you can enforce your custom schema and avoid schema inference.
Let’s address this issue:
Schema Enforcement:
- Autoloader allows you to explicitly define the schema for your data.
- By doing so, you ensure that the schema is consistent during both read and write operations.
Common Causes of Schema Mismatch:
- The schema mismatch error you’re encountering during writestream could be due to several reasons:
- Conflicting Schema: The schema inferred during readstream might not match the custom schema you’ve defined.
- Data Type Mismatch: Fields with different data types can cause schema mismatches.
- Missing Fields: If the custom schema defines additional fields that are not present in the data, it can lead to errors.
Troubleshooting Steps:
- Ensure that you explicitly set the schema during both read and write operations.
- Check if there are any conflicting or overriding settings in your code or configuration that may cause the schema to be interpreted differently.
- Verify that the custom schema you’ve defined aligns with the actual data in your Parquet files.
Additional Considerations:
- If you encounter issues related to specific fields or data types, review your custom schema and the actual data.
- Double-check that the schema definition matches the Parquet files’ structure.
Remember to adapt the above example to your specific use case, ensuring that your custom schema aligns with the data you’re ingesting. If you need further assistance or have more questions, feel free to ask! 🚀
1: Databricks Community: Autoloader issue 2: Databricks Community: How to enforce schema with Autoloader? 3: Databricks Knowledge Base: Explicit path to data or a defined schema required for Auto Loader 4: Ust Does: Using and Abusing Auto Loader’s Inferred Schema
