Spark, Databricks: allow an interval string without a unit

[moshap-firebolt]

Spark's multi-units interval syntax lets the string carry its own units, so no unit follows the literal:

SELECT INTERVAL '1 YEAR 2 DAYS 3 HOURS';
SELECT INTERVAL '2 seconds' * 2;

Both SparkSqlDialect and DatabricksDialect set require_interval_qualifier, so these fail with INTERVAL requires a unit after the literal value. Databricks Runtime parses with Spark's grammar and accepts them too; the Databricks docs show only the qualified form.

This sets require_interval_qualifier to false for both dialects. A unit after a literal is still parsed, so INTERVAL '1' DAY, INTERVAL 12 HOURS and INTERVAL '1-2' YEAR TO MONTH are unchanged. The form this gives up is an expression as the interval value (INTERVAL 1 + 1 DAY), which Spark's grammar does not allow: it takes a literal there.

Tests cover the string forms, the qualified forms, signs and arithmetic for both dialects.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 95.34884% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 81.16%. Comparing base (14cbf75) to head (8ea987e).

Files with missing lines	Patch %	Lines
src/dialect/spark.rs	83.33%	0 Missing and 1 partial ⚠️
src/parser/mod.rs	96.42%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2614      +/-   ##
==========================================
+ Coverage   81.14%   81.16%   +0.01%     
==========================================
  Files          42       42              
  Lines       33736    33778      +42     
  Branches    33736    33778      +42     
==========================================
+ Hits        27376    27416      +40     
  Misses       2797     2797              
- Partials     3563     3565       +2     

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[LucaCappelletti94]

You could gate unqualified intervals on string literals through a dialect capability, retaining qualifiers for numeric literals and excluding identifiers from literal values.

Setting require_interval_qualifier() to false makes both dialects accept SELECT INTERVAL 1, which Spark 4.0.1 rejects, and misparse SELECT interval FROM t into SELECT INTERVAL FROM AS t, losing the table reference.

I added some red tests to help out.

[LucaCappelletti94]

Suggested change

	}
	}
	#[test]
	fn test_interval_numeric_requires_unit() {
	assert_eq!(
	spark()
	.parse_sql_statements("SELECT INTERVAL 1")
	.unwrap_err()
	.to_string(),
	"sql parser error: INTERVAL requires a unit after the literal value"
	);
	}
	#[test]
	fn test_interval_rejects_value_arithmetic() {
	assert!(spark()
	.parse_sql_statements("SELECT INTERVAL 1 + 1 DAY")
	.is_err());
	}
	#[test]
	fn test_interval_preserves_column_query() {
	let query = spark()
	.run_parser_method("SELECT interval FROM t", |parser| parser.parse_query())
	.unwrap();
	let SetExpr::Select(select) = *query.body else {
	panic!("Expected SELECT");
	};
	assert_eq!(
	select.from,
	vec![TableWithJoins {
	relation: table("t"),
	joins: vec![],
	}]
	);
	assert_eq!(
	select.projection,
	vec![SelectItem::UnnamedExpr(Expr::Identifier(Ident::new(
	"interval"
	)))]
	);
	}

[LucaCappelletti94]

Suggested change

	}
	}
	#[test]
	fn test_interval_numeric_requires_unit() {
	assert_eq!(
	databricks()
	.parse_sql_statements("SELECT INTERVAL 1")
	.unwrap_err()
	.to_string(),
	"sql parser error: INTERVAL requires a unit after the literal value"
	);
	}
	#[test]
	fn test_interval_rejects_value_arithmetic() {
	assert!(databricks()
	.parse_sql_statements("SELECT INTERVAL 1 + 1 DAY")
	.is_err());
	}
	#[test]
	fn test_interval_preserves_column_query() {
	let query = databricks()
	.run_parser_method("SELECT interval FROM t", |parser| parser.parse_query())
	.unwrap();
	let SetExpr::Select(select) = *query.body else {
	panic!("Expected SELECT");
	};
	assert_eq!(
	select.from,
	vec![TableWithJoins {
	relation: table("t"),
	joins: vec![],
	}]
	);
	assert_eq!(
	select.projection,
	vec![SelectItem::UnnamedExpr(Expr::Identifier(Ident::new(
	"interval"
	)))]
	);
	}

[moshap-firebolt]

You could gate unqualified intervals on string literals through a dialect capability, retaining qualifiers for numeric literals and excluding identifiers from literal values.

Setting require_interval_qualifier() to false makes both dialects accept SELECT INTERVAL 1, which Spark 4.0.1 rejects, and misparse SELECT interval FROM t into SELECT INTERVAL FROM AS t, losing the table reference.

Right, so kept require_interval_qualifier on true and added supports_interval_string_without_qualifier enabled for Spark/Databricks; also unreserved INTERVAL for both dialects similar to PG

I added some red tests to help out.

Thanks - that was super helpful 🙏