@Mantri Sai Ashish - Thanks for the question and using MS Q&A forum.
In Azure Databricks,
mergeSchema
is an option that can be used in various contexts to handle schema evolution when reading or writing data. Let’s break down the usage ofmergeSchema=True
in different contexts:
COPY INTO Command:
- FORMAT_OPTIONS: When you use
mergeSchema=True
underFORMAT_OPTIONS
, it tells the system to infer the schema across multiple source files and merge them. This is useful when your input files might have slight schema variations. - COPY_OPTIONS: Using
mergeSchema=True
here allows the target Delta table’s schema to evolve based on the input schema. This is particularly helpful when the target table needs to adapt to new columns or data types from the source files.
read or readStream Operations:
- mergeSchema=True: This option is used to merge schemas from different files or batches. When used with
readStream
, it ensures that the schema evolves as new data arrives, which can be useful in streaming scenarios where the schema might change over time. - inferSchema=True: This option infers the schema from the data. When used with
readStream
, it can be combined withmergeSchema=True
to handle evolving schemas. While it might seem redundant, combining these options ensures that both schema inference and merging are handled correctly, especially in dynamic data environments.
Using both mergeSchema
and inferSchema
ensures that your data processing pipeline can handle schema changes gracefully, whether you’re loading data in batch or streaming mode. This flexibility is crucial for maintaining robust data workflows.
I hope this clarifies the usage of mergeSchema=True
!
Hope this helps. Do let us know if you have any further queries.
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