fn try_new_impl()

in arrow-array/src/record_batch.rs [300:369]
58 lines of code
10 McCabe index (conditional complexity)

    fn try_new_impl(
        schema: SchemaRef,
        columns: Vec<ArrayRef>,
        options: &RecordBatchOptions,
    ) -> Result<Self, ArrowError> {
        // check that number of fields in schema match column length
        if schema.fields().len() != columns.len() {
            return Err(ArrowError::InvalidArgumentError(format!(
                "number of columns({}) must match number of fields({}) in schema",
                columns.len(),
                schema.fields().len(),
            )));
        }

        let row_count = options
            .row_count
            .or_else(|| columns.first().map(|col| col.len()))
            .ok_or_else(|| {
                ArrowError::InvalidArgumentError(
                    "must either specify a row count or at least one column".to_string(),
                )
            })?;

        for (c, f) in columns.iter().zip(&schema.fields) {
            if !f.is_nullable() && c.null_count() > 0 {
                return Err(ArrowError::InvalidArgumentError(format!(
                    "Column '{}' is declared as non-nullable but contains null values",
                    f.name()
                )));
            }
        }

        // check that all columns have the same row count
        if columns.iter().any(|c| c.len() != row_count) {
            let err = match options.row_count {
                Some(_) => "all columns in a record batch must have the specified row count",
                None => "all columns in a record batch must have the same length",
            };
            return Err(ArrowError::InvalidArgumentError(err.to_string()));
        }

        // function for comparing column type and field type
        // return true if 2 types are not matched
        let type_not_match = if options.match_field_names {
            |(_, (col_type, field_type)): &(usize, (&DataType, &DataType))| col_type != field_type
        } else {
            |(_, (col_type, field_type)): &(usize, (&DataType, &DataType))| {
                !col_type.equals_datatype(field_type)
            }
        };

        // check that all columns match the schema
        let not_match = columns
            .iter()
            .zip(schema.fields().iter())
            .map(|(col, field)| (col.data_type(), field.data_type()))
            .enumerate()
            .find(type_not_match);

        if let Some((i, (col_type, field_type))) = not_match {
            return Err(ArrowError::InvalidArgumentError(format!(
                "column types must match schema types, expected {field_type:?} but found {col_type:?} at column index {i}")));
        }

        Ok(RecordBatch {
            schema,
            columns,
            row_count,
        })
    }