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Spark, Databricks: parse multi-unit interval literals - #2616
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…2616 (#19) * Revert "Spark, Databricks: read multi-units interval literals (#17)" This reverts commit bae1cb2. * Revert "Spark, Databricks: accept an interval string without a unit (#16)" This reverts commit ee51174. * Spark, Databricks: allow an interval string without a unit Spark's multi-units interval syntax lets the string carry its own units, as in INTERVAL '1 YEAR 2 DAYS 3 HOURS', so no unit follows the literal. Databricks Runtime parses with the same grammar. A unit after a literal is still read; the expression form this gives up (INTERVAL 1 + 1 DAY) is not Spark syntax. * Spark, Databricks: keep the qualifier for numbers; a string may omit it Address review: turning require_interval_qualifier off accepted INTERVAL 1 and read SELECT interval FROM t as SELECT INTERVAL FROM AS t. Keep it on and add supports_interval_string_without_qualifier: the value must be a literal, and only an unsigned string may omit the qualifier. INTERVAL is not reserved for these dialects, so SELECT interval FROM t reads a column. * Spark, Databricks: double-quoted interval strings; a signed number after interval is arithmetic Address review: a double-quoted string is a Spark string literal, so it may be an interval value and may omit the qualifier (INTERVAL "2 months", INTERVAL "1" DAY). Only a bare number or a string after INTERVAL keeps the interval error; a signed number falls back to the identifier, so interval - 1 and INTERVAL -1 are arithmetic on the column, as in Spark. A string window frame bound stays a string literal in these dialects (RANGE BETWEEN '1 day' PRECEDING), as in Spark, rather than being read as an interval without a qualifier. * Spark, Databricks: assert TO x, not TO; drop two duplicate tests Address review: Spark accepts INTERVAL '1' DAY TO as an alias, so assert the error on TO x instead. parse_interval_number_without_unit_error and parse_interval_expression_value repeat test_interval_numeric_requires_unit and parse_interval_require_qualifier. The guard comment now covers strings as well as numbers. * Spark, Databricks: parse multi-unit interval literals Spark's multi-units interval syntax lists several value / unit pairs, INTERVAL 10 YEAR 20 MONTH, documented as the same as the string form INTERVAL '10 YEAR 20 MONTH'. A new supports_interval_multi_units hook, enabled for SparkSqlDialect and DatabricksDialect, reads the extra pairs into that string form, so the AST is unchanged. * Spark, Databricks: double-quoted values in multi-unit intervals A double-quoted string is a Spark string literal, so it may be any value of a multi-units interval, e.g. INTERVAL "1" DAY "2" HOUR. * Spark, Databricks: assert the error text in test_interval_rejects_value_arithmetic clippy's assertions_on_result_states rejects assert!(... .is_err()). Compare the message instead, as test_interval_numeric_requires_unit does.
Spark's multi-units interval syntax lets the string carry its own units, as in INTERVAL '1 YEAR 2 DAYS 3 HOURS', so no unit follows the literal. Databricks Runtime parses with the same grammar. A unit after a literal is still read; the expression form this gives up (INTERVAL 1 + 1 DAY) is not Spark syntax.
Address review: turning require_interval_qualifier off accepted INTERVAL 1 and read SELECT interval FROM t as SELECT INTERVAL FROM AS t. Keep it on and add supports_interval_string_without_qualifier: the value must be a literal, and only an unsigned string may omit the qualifier. INTERVAL is not reserved for these dialects, so SELECT interval FROM t reads a column.
…ter interval is arithmetic Address review: a double-quoted string is a Spark string literal, so it may be an interval value and may omit the qualifier (INTERVAL "2 months", INTERVAL "1" DAY). Only a bare number or a string after INTERVAL keeps the interval error; a signed number falls back to the identifier, so interval - 1 and INTERVAL -1 are arithmetic on the column, as in Spark. A string window frame bound stays a string literal in these dialects (RANGE BETWEEN '1 day' PRECEDING), as in Spark, rather than being read as an interval without a qualifier.
Address review: Spark accepts INTERVAL '1' DAY TO as an alias, so assert the error on TO x instead. parse_interval_number_without_unit_error and parse_interval_expression_value repeat test_interval_numeric_requires_unit and parse_interval_require_qualifier. The guard comment now covers strings as well as numbers.
…ue_arithmetic clippy's assertions_on_result_states rejects assert!(... .is_err()). Compare the message instead, as test_interval_numeric_requires_unit does.
Spark's multi-units interval syntax lists several value / unit pairs, INTERVAL 10 YEAR 20 MONTH, documented as the same as the string form INTERVAL '10 YEAR 20 MONTH'. A new supports_interval_multi_units hook, enabled for SparkSqlDialect and DatabricksDialect, reads the extra pairs into that string form, so the AST is unchanged.
A double-quoted string is a Spark string literal, so it may be any value of a multi-units interval, e.g. INTERVAL "1" DAY "2" HOUR.
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Stacked on #2614, whose commits this branch includes; only the last two commits are new. It needs #2614 because the string form below has no unit, which Spark and Databricks reject until #2614 lands. I will rebase onto
mainonce #2614 merges.Spark's multi-units interval syntax lists several value / unit pairs,
INTERVAL 10 YEAR 20 MONTH, documented as equivalent toINTERVAL '10 YEAR 20 MONTH'.This adds
Dialect::supports_interval_multi_units, enabled forSparkSqlDialectandDatabricksDialect. When set,parse_intervalreads the extra pairs into that string form, so the AST is unchanged. Values may be numbers or single- or double-quoted strings, optionally signed. A single unit, or a unit followed by something else (INTERVAL 3 DAY + 1), parses as before. Tests insqlparser_common.rscover dialects with and without the hook.