Implement Spark-compatible `regexp_substr`

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
38/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
rust, sql
Área
databases

Línea de trabajo

Start by reading Spark's RegExpSubStr in regexpExpressions.scala, which is defined purely as NullIf(RegExpExtract(left, right, 0), "") — the new function in crates/spark/src/function/string/ must reproduce that nullif-on-empty semantics, not just a regex search. Check whether regexp_extract exists yet in this crate; if not, the shared core decides the shape of the work. Add crates/sqllogictest/test_files/string/regexp_substr.slt covering the listed NULL/empty-match/invalid-pattern cases, and annotate the UDF with #[user_doc].

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

enhancement

Part of #174 and apache/datafusion-comet#7300.

What the function does

regexp_substr(str, regexp) returns the first substring of str that matches the Java regular expression regexp, or NULL if there is no match. It is RuntimeReplaceable: the analyzer rewrites it to nullif(regexp_extract(str, regexp, 0), ''), so an empty match also returns NULL. Available since 3.4.0.

Both arguments are implicitly cast to STRING. Result type is the type of str (STRING; collation-aware in 4.0+). NULL if either argument is NULL. There are no position / occurrence / flags arguments (unlike some other engines' regexp_substr).

Examples
SELECT regexp_substr('Steven Jones and Stephen Smith are the best players', 'Ste(v|ph)en');
-- Steven
SELECT regexp_substr('Steven Jones and Stephen Smith are the best players', 'Jeck');
-- NULL
SELECT regexp_substr('1a 2b 14m', '\\d+');
-- 1
SELECT regexp_substr('1a 2b 14m', '\\d+(a|b|m)');
-- 1a
SELECT regexp_substr('1a 2b 14m', '\\d{2}(a|b|m)');
-- 14m
SELECT regexp_substr('1a 2b 14m', '');
-- NULL
SELECT regexp_substr('Spark', null);
-- NULL
Edge cases to test
  • NULL str or NULL regexp, literal and column: NULL.
  • Empty pattern returns NULL (the first match is the empty string at position 0, which nullif turns into NULL).
  • A pattern whose first match is empty returns NULL even if a later match is non-empty: regexp_substr('abc', 'b*') matches '' at position 0, so the result is NULL.
  • No match returns NULL (not '' as regexp_extract does).
  • Invalid pattern: INVALID_PARAMETER_VALUE.PATTERN. Because of the rewrite, the functionName in the error is `regexp_extract`.
  • Column patterns that differ per row.
  • spark.sql.parser.escapedStringLiterals (default false) only affects SQL literal parsing before the function. In .slt, DataFusion keeps backslashes literal ('\d+').
  • Java vs Rust divergences to decline or test: lookaround / backreferences / atomic / possessive (Java only); ASCII \d \w \s in Java vs Unicode in Rust (regexp_substr('\u0663 1', '\\d') is 1 in Spark; U+0663 ARABIC-INDIC DIGIT THREE); . excludes \r, \u0085, \u2028, \u2029 in Java (regexp_substr('a\rb', 'a.b') is NULL in Spark); $ matches before a final line terminator in Java; (?i) ASCII-only in Java; inline flag letters U / u / d; Java-only \Q..\E, \h, \R, \Z, octal; Rust-only (?P<n>), \<, [[:alpha:]]. Leftmost-first alternation is the same on both sides (regexp_substr('ab', 'a|ab') is a).
  • Unicode: matches containing multi-byte and supplementary characters are returned whole.
  • UTF8_LCASE collation (4.0+) matches case-insensitively.
Differences across Spark versions
  • 4.0: collation-aware inputs; the nullif literal is created with str's type. Otherwise none found.
Implementation notes
  • Spark: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala, RegExpSubStr (replacement is NullIf(RegExpExtract(left, right, 0), "")).
  • Because of the rewrite, Spark plans never contain RegExpSubStr; an engine translating Spark plans sees nullif(regexp_extract(...), ''). regexp_extract is not in datafusion-spark either; implementing it with the same core (group-index checks as in RegExpExtractBase.checkGroupIndex) would serve both. A direct regexp_substr UDF is still useful for SQL users.
  • DataFusion core has no regexp_substr. regexp_match returns capture groups of the first match as a list and uses Rust syntax.
  • Use the shared Java-regex compatibility helper proposed for rlike (verified subset, decline otherwise, decided at planning time for literal patterns).
  • Belongs in crates/spark/src/function/string/. Add crates/sqllogictest/test_files/string/regexp_substr.slt and document with #[user_doc].

This issue was generated by an LLM (Claude) from the Apache Spark source code and tests. Examples come from Spark's ExpressionDescription examples, test suites and .sql.out golden files, but please verify them against Spark before relying on them.

Lenguaje dominante
Rust
Estrellas
7
Forks
15
Merge medio
16 h 43 min
PR fusionados (30 d)
26

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