MaxGekk opened a new pull request #27804: [SPARK-31020][SPARK-31023][SPARK-31025][SPARK-31044][SQL] Support foldable args by `from_csv/json` and `schema_of_csv/json` URL: https://github.com/apache/spark/pull/27804 ### What changes were proposed in this pull request? In the PR, I propose: 1. To replace matching by `Literal` in `ExprUtils.evalSchemaExpr()` to checking foldable property of the `schema` expression. 2. To replace matching by `Literal` in `ExprUtils.evalTypeExpr()` to checking foldable property of the `schema` expression. 3. To change checking of the input parameter in the `SchemaOfCsv` expression, and allow foldable `child` expression. 4. To change checking of the input parameter in the `SchemaOfJson` expression, and allow foldable `child` expression. Closes #27771 Closes #27774 Closes #27777 Closes #27797 ### Why are the changes needed? This should improve Spark SQL UX for `from_csv`/`from_json`. Currently, Spark expects only literals: ```sql spark-sql> select from_csv('1,Moscow', replace('dpt_org_id INT, dpt_org_city STRING', 'dpt_org_', '')); Error in query: Schema should be specified in DDL format as a string literal or output of the schema_of_csv function instead of replace('dpt_org_id INT, dpt_org_city STRING', 'dpt_org_', '');; line 1 pos 7 spark-sql> select from_json('{"id":1, "city":"Moscow"}', replace('dpt_org_id INT, dpt_org_city STRING', 'dpt_org_', '')); Error in query: Schema should be specified in DDL format as a string literal or output of the schema_of_json function instead of replace('dpt_org_id INT, dpt_org_city STRING', 'dpt_org_', '');; line 1 pos 7 ``` and only string literals are acceptable as CSV examples by `schema_of_csv`/`schema_of_json`: ```sql spark-sql> select schema_of_csv(concat_ws(',', 0.1, 1)); Error in query: cannot resolve 'schema_of_csv(concat_ws(',', CAST(0.1BD AS STRING), CAST(1 AS STRING)))' due to data type mismatch: The input csv should be a string literal and not null; however, got concat_ws(',', CAST(0.1BD AS STRING), CAST(1 AS STRING)).; line 1 pos 7; 'Project [unresolvedalias(schema_of_csv(concat_ws(,, cast(0.1 as string), cast(1 as string))), None)] +- OneRowRelation spark-sql> select schema_of_json(regexp_replace('{"item_id": 1, "item_price": 0.1}', 'item_', '')); Error in query: cannot resolve 'schema_of_json(regexp_replace('{"item_id": 1, "item_price": 0.1}', 'item_', ''))' due to data type mismatch: The input json should be a string literal and not null; however, got regexp_replace('{"item_id": 1, "item_price": 0.1}', 'item_', '').; line 1 pos 7; 'Project [unresolvedalias(schema_of_json(regexp_replace({"item_id": 1, "item_price": 0.1}, item_, )), None)] +- OneRowRelation ``` ### Does this PR introduce any user-facing change? Yes, after the changes users can pass any foldable string expression as the `schema` parameter to `from_csv()/from_json()`. For the example above: ```sql spark-sql> select from_csv('1,Moscow', replace('dpt_org_id INT, dpt_org_city STRING', 'dpt_org_', '')); {"id":1,"city":"Moscow"} spark-sql> select from_json('{"id":1, "city":"Moscow"}', replace('dpt_org_id INT, dpt_org_city STRING', 'dpt_org_', '')); {"id":1,"city":"Moscow"} ``` After change the `schema_of_csv`/`schema_of_json` functions accept foldable expressions, for example: ```sql spark-sql> select schema_of_csv(concat_ws(',', 0.1, 1)); struct<_c0:double,_c1:int> spark-sql> select schema_of_json(regexp_replace('{"item_id": 1, "item_price": 0.1}', 'item_', '')); struct<id:bigint,price:double> ``` ### How was this patch tested? Added new test to `CsvFunctionsSuite` and to `JsonFunctionsSuite`.
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