understanding how to use mergeSchema=True

Mantri Sai Ashish 0 Reputation points
2024-11-07T09:17:43.31+00:00

In COPY INTO under FORMAT_OPTIONS generally i use mergeSchema=True but in few queries i am seeing that it is also metioned under copy_options why ? and I have also seen mergeSchema=true given under options while read or readStream, i have seen that in readStream sometimes ,mergeSchema=True is used along with inferSchema which doesnt make sense as each readStream read as individual batches right

Azure Databricks
Azure Databricks
An Apache Spark-based analytics platform optimized for Azure.
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  1. Ganesh Gurram 1,825 Reputation points Microsoft Vendor
    2024-11-07T18:45:01.68+00:00

    @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 of mergeSchema=True in different contexts:

    COPY INTO Command:

    • FORMAT_OPTIONS: When you use mergeSchema=True under FORMAT_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 with mergeSchema=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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