Dx 32 nested types #17
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edurdevic
reviewed
Jun 3, 2023
| @@ -43,6 +43,7 @@ def above_threshold(self): | |||
| "database": "table_schema", | |||
| "table": "table_name", | |||
| "column": "column_name", | |||
| "type": "data_type", | |||
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I renamed the columns from the source SQL query, so you don't need this rename any more
| ).withColumn("effective_timestamp", func.current_timestamp()) | ||
| # merge using scd-typ2 | ||
| logger.friendly(f"Update classification table {self.classification_table_name}") | ||
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| self.classification_table.alias("target").merge( | ||
| staged_updates_df.alias("source"), | ||
| "target.table_catalog <=> source.table_catalog AND target.table_schema = source.table_schema AND target.table_name = source.table_name AND target.column_name = source.column_name AND target.tag_name = source.tag_name AND target.current = true", | ||
| "target.table_catalog <=> source.table_catalog AND target.table_schema = source.table_schema AND target.table_name = source.table_name AND target.column_name = source.column_name AND target.data_type = source.data_type AND target.tag_name = source.tag_name AND target.current = true", |
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Why do we need to match on data_type?
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| @dataclass | ||
| class TaggedColumn: | ||
| name: str | ||
| data_type: str |
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We also need the full_data_type, which contains the full definition of the composed columns
| @@ -66,6 +66,15 @@ def compile_msql(self, table_info: TableInfo) -> list[SQLRow]: | |||
| temp_sql = msql | |||
| for tagged_col in tagged_cols: | |||
| temp_sql = temp_sql.replace(f"[{tagged_col.tag}]", tagged_col.name) | |||
| # TODO: Can we avoid "replacing strings" for the different types in the future? This is due to the generation of MSQL. Maybe we should rather generate SQL directly from the search method... | |||
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Yes, we should do that instead.
| col_name_splitted = col_name.split(".") | ||
| return ".".join(["`" + col + "`" for col in col_name_splitted]) | ||
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| def recursive_flatten_complex_type(self, col_name, schema, column_list): |
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Oh, wow! This was more complex than I expected.
| {"col_name": self.backtick_col_name(col_name + "." + field.name), "type": "string"} | ||
| ) | ||
| elif type(field.dataType) in self.COMPLEX_TYPES: | ||
| column_list = self.recursive_flatten_complex_type(col_name + "." + field.name, field, column_list) |
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I think you should be appending to column_list instead of replacing column_list. Otherwise you overwrite previously appended string types
| @@ -23,3 +23,6 @@ hive_metastore,default,tb_all_types,str_part_col,STRING,1 | |||
| ,default,tb_1,ip,STRING, | |||
| ,default,tb_1,mac,STRING, | |||
| ,default,tb_1,description,STRING, | |||
| ,default,tb_2,active,BOOLEAN, | |||
| ,default,tb_2,categories,"map<string,string>", | |||
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Here we need to add a "full_data_type" column to reflect the UC structure
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A first version supporting complex types (struct with any level and arrays up to the first level only). Structs are scanned recursively but as soon as an array (or map) is found we don't continue for that column.
Still requires some code refactoring but it would be great if you could review the approach.
Steps remaining: