Implement Spark-compatible `floor(expr, scale)` (two-argument form)

Abierto
  • ¿Algo no está bien?
#262 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
18/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
rust, sql
Área
data

Línea de trabajo

Start by reading crates/spark/src/function/math/floor.rs and round.rs to see how the existing one-argument SparkFloor and the HALF_UP decimal rounding on unscaled integers are wired up, then compare against Spark's FloorExpressionBuilder/RoundBase in mathExpressions.scala. The work is to accept an optional literal scale, derive the result DECIMAL in return_field_from_args, and share a RoundBase-style helper with ceil. Done means the new two-argument cases in crates/sqllogictest/test_files/math/floor.slt pass and SparkFloor's #[user_doc] is regenerated via dev/update_function_docs.sh.

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

floor(expr, scale) rounds expr down (toward negative infinity) to scale decimal places; a negative scale rounds to the left of the decimal point. Spark builds it through FloorExpressionBuilder: one argument gives Floor (already implemented as crates/spark/src/function/math/floor.rs), two arguments give RoundFloor, which uses RoundBase with RoundingMode.FLOOR. Available since 3.3.0. The matching ceil(expr, scale) form is already tracked in apache/datafusion-spark#15 and apache/datafusion-spark#22 and should share the implementation.

scale must be a foldable, non-NULL expression whose type is exactly INT (checked before type coercion, so 2L or CAST(2 AS SMALLINT) is rejected). expr is implicitly cast to DECIMAL (inputTypes = Seq(DecimalType, IntegerType)): TINYINT to DECIMAL(3,0), SMALLINT to DECIMAL(5,0), INT to DECIMAL(10,0), BIGINT to DECIMAL(20,0), FLOAT to DECIMAL(14,7), DOUBLE to DECIMAL(30,15). For an input DECIMAL(p, s) the result is DECIMAL(min(p - s + 1 + min(s, scale), 38), min(s, scale)) when scale >= 0, and DECIMAL(min(max(p - s + 1, -scale + 1), 38), 0) when scale < 0. The result is always nullable.

Examples
SELECT floor(3.1411, 3);
-- 3.141
SELECT floor(3.1411, -3);
-- 0
SELECT floor(-0.35, 1);
-- -0.4        decimal(2,1)
SELECT floor(-35, -1);
-- -40         decimal(11,0)
SELECT floor(5, 0);
-- 5           decimal(11,0)
SELECT floor(-9.9, 0);
-- -10         decimal(2,0)
SELECT floor(CAST(-99 AS DECIMAL(2, 0)), -1);
-- -100        decimal(3,0)
SELECT floor(3.14115, -3);
-- 0           decimal(4,0)
SELECT floor(2.5, null);
-- error NON_FOLDABLE_ARGUMENT
Edge cases to test
  • NULL expr returns NULL. NULL or non-literal scale fails analysis: NON_FOLDABLE_ARGUMENT (SQLSTATE 42K08) The function `floor` requires the parameter `scale` to be a foldable expression of the type "INT", but the actual argument is a non-foldable. (also for floor(2.5, 'a')). Three arguments: WRONG_NUM_ARGS.WITHOUT_SUGGESTION.
  • Result types for every input type, including the p - s + 1 growth and the cap at 38 (floor(CAST(-99 AS DECIMAL(2,0)), -1) is DECIMAL(3,0)).
  • scale larger than the input scale leaves the value unchanged (RoundFloor(bdPi, 8..10) returns bdPi in MathExpressionsSuite).
  • Negative numbers round away from zero: floor(-0.1, 0) is -1, floor(-2.5, 0) is -3, floor(135.135, -2) is 100.
  • Negative scale beyond the integral digits: floor(3.1411, -3) is 0, floor(-3.1411, -3) is -1000.
  • Overflow on the negative-scale path when the precision is already 38: floor(CAST(-99999999999999999999999999999999999999 AS DECIMAL(38,0)), -1) needs 39 digits and throws NUMERIC_VALUE_OUT_OF_RANGE.WITHOUT_SUGGESTION from Decimal.set (derived from code, regardless of ANSI mode).
  • FLOAT / DOUBLE inputs go through the implicit cast first:
    • Values that do not fit (|x| >= 10^7 for FLOAT, |x| >= 10^15 for DOUBLE) are NULL with ANSI off and fail with NUMERIC_VALUE_OUT_OF_RANGE.WITH_SUGGESTION with spark.sql.ansi.enabled=true (the 4.0+ default).
    • NaN and +/-Infinity become NULL in both modes (the cast catches NumberFormatException).
    • The cast uses BigDecimal.valueOf(double) (shortest decimal string), then HALF_UP to the cast scale, so FLOAT 0.1f becomes 0.1000000.
  • BIGINT inputs near Long.MaxValue fit DECIMAL(20,0).
  • Decimal32/64/256 Arrow inputs if accepted (Spark only has 128-bit-range decimals up to precision 38).
Differences across Spark versions
  • 3.4 and 3.5: a NULL or non-foldable scale fails with _LEGACY_ERROR_TEMP_1100 (The 'scale' parameter of function 'floor' needs to be a int literal.); 4.0+ uses NON_FOLDABLE_ARGUMENT.
  • 3.4: the positive-scale path called decimal.toPrecision(decimal.precision, s, mode) (the value's precision); 3.5+ uses the result type precision p.
  • 4.0: ANSI on by default, which changes the FLOAT/DOUBLE overflow case from NULL to an error.
Implementation notes
  • Spark: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala, FloorExpressionBuilder, CeilFloorExpressionBuilderBase, RoundFloor and RoundBase (dataType, nullSafeEval).
  • DataFusion core floor takes one argument and keeps the input type. DataFusion's trunc / round with a precision argument use different rounding modes and keep floating-point types.
  • In datafusion-spark, extend crates/spark/src/function/math/floor.rs to accept the optional scale (decided in return_field_from_args from the literal), and share a RoundBase-style decimal rounding helper with ceil and round (round.rs already has HALF_UP decimal rounding on unscaled integers; add FLOOR/CEILING modes). Implement the implicit casts in the signature (or require the caller to cast), with DOUBLE/FLOAT conversion matching Spark's cast.
  • Tests: extend crates/sqllogictest/test_files/math/floor.slt with the two-argument cases, and update the #[user_doc] on SparkFloor (then run dev/update_function_docs.sh).

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

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de apache/datafusion-spark

Todos los issues de apache/datafusion-spark

Issues similares

Más issues de Rust

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.