Ganesha S created SPARK-58366:
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Summary: [SQL] Support JSON_TABLE table-valued function
Key: SPARK-58366
URL: https://issues.apache.org/jira/browse/SPARK-58366
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 4.2.0
Reporter: Ganesha S
Add the ANSI SQL:2016 JSON_TABLE table-valued function, which shreds a JSON
document into a relational table. A row path selects a sequence of JSON items,
and a COLUMNS clause projects a typed value out of each item into a column.
*Syntax (flat, non-nested subset):*
```
JSON_TABLE(json_expr, row_path
COLUMNS (
col1 FOR ORDINALITY,
col2 <type> [PATH '<json_path>'],
col3 <type> EXISTS [PATH '<json_path>']
)
[ \{ NULL | ERROR } ON ERROR ]
) [AS] alias
```
*Capabilities:*
- Row path with a trailing [*] expands a JSON array into one row per element;
a non-wildcard path yields a single row for the matched value.
- FOR ORDINALITY: a 1-based BIGINT row counter.
- Value columns: extracted and cast to the declared type; the path may be
explicit (PATH '...') or implicit ('$.<columnName>').
- EXISTS columns: presence test, cast to the declared type. A present-but-null
JSON value counts as existing; only an absent path is false.
- \{ NULL | ERROR } ON ERROR: NULL ON ERROR (the default) produces no rows on
null/malformed input; ERROR ON ERROR raises.
- Usable in a comma join and with LATERAL.
*Motivation:*
JSON_TABLE is the SQL-standard way to turn JSON into rows and columns and is
supported by Oracle, DB2, MySQL 8, PostgreSQL 17, Snowflake, and Trino. Spark
currently requires chaining from_json + explode/inline + get_json_object to
achieve the same result. JSON_TABLE folds that into one declarative, standard
construct and eases migration from those systems.
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