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org.apache.spark.sql.pipelines.util

SchemaInferenceUtils

object SchemaInferenceUtils

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SchemaInferenceUtils.scala
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  6. def diffSchemas(currentSchema: StructType, targetSchema: StructType): Seq[TableChange]

    Determines the column changes needed to transform the current schema into the target schema.

    Determines the column changes needed to transform the current schema into the target schema.

    This function compares the current schema with the target schema and produces a sequence of TableChange objects representing: 1. New columns that need to be added 2. Existing columns that need type updates

    currentSchema

    The current schema of the table

    targetSchema

    The target schema that we want the table to have

    returns

    A sequence of TableChange objects representing the necessary changes

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  11. def inferSchemaFromFlows(flows: Seq[ResolvedFlow], userSpecifiedSchema: Option[StructType]): StructType

    Given a set of flows that write to the same destination and possibly a user-specified schema, we infer the schema of the destination dataset.

    Given a set of flows that write to the same destination and possibly a user-specified schema, we infer the schema of the destination dataset. The logic is as follows: 1. If there are no incoming flows, return the user-specified schema (if provided) or an empty schema. 2. If there are incoming flows, we merge the schemas of all flows that write to the same destination. 3. If a user-specified schema is provided, we merge it with the inferred schema. The user-specified schema will take precedence over the inferred schema. Returns an error if encountered during schema inference or merging the inferred schema with the user-specified one.

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