Avoid per-batch field lookups in SchemaMapping #6563
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tustvold
commented
Jun 6, 2023
| let rows_num = batch.num_rows(); | ||
| let mapped_batch = mapping.map_batch(batch).unwrap(); | ||
| let projected = batch.project(&projection).unwrap(); |
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This highlights the major change, the schema adaptor assumes that the projection it output has been applied to the file_schema batches.
tustvold
commented
Jun 6, 2023
| } | ||
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| /// Creates a `SchemaMapping` that can be used to cast or map the columns from the file schema to the table schema. | ||
| /// | ||
| /// If the provided `file_schema` contains columns of a different type to the expected | ||
| /// `table_schema`, the method will attempt to cast the array data from the file schema | ||
| /// to the table schema where possible. | ||
| /// | ||
| /// Returns a [`SchemaMapping`] that can be applied to the output batch | ||
| /// along with an ordered list of columns to project from the file |
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The ordered is important as parquet::ProjectionMask is not order preserving
alamb
approved these changes
Jun 6, 2023
| .zip(&self.field_mappings) | ||
| .map(|(field, file_idx)| match file_idx { | ||
| Some(batch_idx) => cast(&batch_cols[*batch_idx], field.data_type()), | ||
| None => Ok(new_null_array(field.data_type(), batch_rows)), |
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Which issue does this PR close?
Closes #.
Rationale for this change
Follow up to #6458. This reworks the mapping logic to avoid needing to do column lookups per batch
What changes are included in this PR?
Are these changes tested?
Are there any user-facing changes?