Implement Spark-compatible `positive`
I maintainer di solito rispondono entro 1 giorno
Nessuno ha ancora preso questa issue.
Valutazione
- Difficoltà
- 3/5
- Tempo stimato
- 1-2 giorni
- Idoneità per principianti
- 65/100
Direzione di ricerca
The template is crates/spark/src/function/math/negative.rs: copy its signature, NumericAndInterval types and implicit-cast handling, and register the function with the other math functions. Read Spark's UnaryPositive and datafusion/sql/src/expr/unary_op.rs for the timestamp/Duration divergence, then add ScalarUDFImpl::simplify replacing the call with its child. Enable the commented query in crates/sqllogictest/test_files/math/positive.slt and extend it with the NULL, integer-boundary, float, decimal, string-cast, interval and rejected-type cases; done is that file passing.
Scritto dal modello di indicizzazione a partire dal testo della issue.
Descrizione
Part of #174 and apache/datafusion-comet#7300.
What the function does
positive(expr) (also the unary +expr operator) returns expr unchanged. It accepts numeric and interval types (TypeCollection.NumericAndInterval, with ImplicitCastInputTypes, so a STRING argument is cast to DOUBLE). The result type equals the (cast) input type; NULL in gives NULL out. Available since 1.5.0.
In Spark 4.0+ UnaryPositive is RuntimeReplaceable with replacement = child, so it disappears from optimized plans. In 3.4 and 3.5 it is a regular expression that survives into physical plans.
Examples
SELECT positive(1);
-- 1
SELECT positive('-1.11'), positive(-1.11);
-- -1.11 -1.11 (double, decimal(3,2))
SELECT +interval '-1 year 1 month';
-- -0-11 interval year to month
SELECT +interval '-1 day 1 hour -1 minute 1 second';
-- -0 23:00:59.000000000
SELECT +date '1999-01-01';
-- error DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE
Edge cases to test
- NULL input of each type.
- Every integer type keeps its type and value, including
-2147483648and-9223372036854775808(no overflow, unlikenegative). - FLOAT / DOUBLE: NaN, -0.0 (sign preserved), +/-Infinity returned unchanged.
- DECIMAL keeps precision and scale (
positive(-1.11)isDECIMAL(3,2)). - STRING argument is cast to DOUBLE (
positive('-1.11')is DOUBLE-1.11). With ANSI on an invalid string fails in the cast (CAST_INVALID_INPUT); with ANSI off it is NULL. - Year-month and day-time intervals are returned unchanged (
Interval(YearMonth)andDuration(Microsecond)in this crate). - DATE, TIMESTAMP, BOOLEAN, BINARY are rejected with
DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE(requiredType("NUMERIC" or "INTERVAL DAY TO SECOND" or "INTERVAL YEAR TO MONTH" or "INTERVAL")). - Scalar and array inputs.
Differences across Spark versions
- 3.4 / 3.5:
UnaryPositiveis evaluated as an expression (identitynullSafeEval). - 4.0+:
RuntimeReplaceable, replaced by its child during optimization. Behaviour is the same.
Implementation notes
- Spark:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala,UnaryPositive. - DataFusion core has no
positivefunction. Its SQL planner accepts+exprfor numeric, interval and timestamp operands and returns the operand unchanged (datafusion/sql/src/expr/unary_op.rs); Spark rejects timestamps, and Spark day-time intervals arrive here asDuration, which that check may not cover. - Mirror
crates/spark/src/function/math/negative.rs(same signature and types) and return the input as-is. ImplementingScalarUDFImpl::simplifyto replace the call with its argument is also appropriate. - Tests: the placeholder
crates/sqllogictest/test_files/math/positive.sltexists with a commented-out query; enable and extend it. 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.
- Lingua principale
- Rust
- Stelle
- 7
- Fork
- 15
- Merge medio
- 16h 43m
- PR unite (30g)
- 26
Preparare l'ambiente
- Nessun Dockerfile né file Docker Compose
- Nessun modello di pull request
- Leggi la guida per i contributori
Come iniziare
- Leggi tutta la issue e poi la guida ai contributi del progetto.
- Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
- Fai un fork del repository e lavora su un branch.
- Apri una pull request che faccia riferimento al numero della issue.
Altre issue di apache/datafusion-spark
-
enhancement
Difficoltà 2/5 1-3 ore Idoneità per principianti 70/100
apache/datafusion-spark#287 ·
I maintainer di solito rispondono entro 1 giorno
-
enhancement good first issue
Difficoltà 2/5 1-3 ore Idoneità per principianti 80/100
apache/datafusion-spark#266 ·
I maintainer di solito rispondono entro 1 giorno
-
[Bug] make_dt_interval returns NULL for more than about 1.49 million days, Spark returns the intervalForse già presa @Subodh-17 l’ha presa oggi. Aperta
Difficoltà 2/5 1-3 ore Idoneità per principianti 78/100
apache/datafusion-spark#109 ·
I maintainer di solito rispondono entro 1 giorno
-
enhancement
Difficoltà 5/5 Più di una settimana Idoneità per principianti 18/100
apache/datafusion-spark#299 ·
I maintainer di solito rispondono entro 1 giorno
-
enhancement
Difficoltà 4/5 3-5 giorni Idoneità per principianti 35/100
apache/datafusion-spark#298 ·
I maintainer di solito rispondono entro 1 giorno
Tutte le issue di apache/datafusion-spark
Issue simili
-
Difficoltà 2/5 1-3 ore Idoneità per principianti 70/100
storytold/photocraft#15232 · 1 commento ·
I maintainer di solito rispondono entro 1 giorno
-
Difficoltà 2/5 1-3 ore Idoneità per principianti 68/100
suhina-daw/suhina-daw#1477 ·
I maintainer di solito rispondono entro 1 giorno
-
parity priority: low ui
Difficoltà 2/5 1-3 ore Idoneità per principianti 62/100
storytold/pdfcraft#7483 · 1 commento ·
I maintainer di solito rispondono entro 2 giorni
-
Difficoltà 2/5 1-3 ore Idoneità per principianti 72/100
IngvarConsulting/unica#3097 ·
I maintainer di solito rispondono entro 1 giorno
-
enhancement in-progress
Difficoltà 1/5 Meno di un'ora Idoneità per principianti 64/100