Implement Spark-compatible `map_filter` (higher-order map function)

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
rust, sql
Área
backend

Línea de trabajo

Start at crates/spark/src/function/lambda/mod.rs, an empty ScalarUDF placeholder that needs a HigherOrderUDF plus registration via register_higher_order_function; crates/spark/src/function/map/utils.rs has the MapArray helpers. Model the body on DataFusion's array_filter (datafusion/functions-nested/src/array_filter.rs) and HigherOrderUDFImpl/spread_captures in datafusion/expr/src/higher_order_function.rs. Done means the new crates/sqllogictest/test_files/lambda/map_filter.slt passes the NULL-map, NULL-predicate, order and arity cases, with #[user_doc] added.

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

Descripción

enhancement

Part of #176 and apache/datafusion-comet#7300.

What the function does

map_filter(expr, func) keeps the entries of the map expr for which the lambda (k, v) -> BOOLEAN returns
true. It drops entries where the lambda returns false or NULL. Entries keep their original order. The result
type is the input map type, unchanged. A NULL map gives NULL, and the result is nullable if the input is nullable.
The key variable is non-nullable, and the value variable has the map's valueContainsNull nullability. The lambda
must take exactly 2 parameters and return BOOLEAN. A non-boolean body, or a non-lambda literal such as
map_filter(m, 1), fails with DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE. expr must be a MAP. Available since
Spark 3.0.0. No aliases.

Examples
SELECT map_filter(map(1, 0, 2, 2, 3, -1), (k, v) -> k > v);
-- {1:0,3:-1}

From the ExpressionDescription. HigherOrderFunctionsSuite adds the following cases:

map lambda result
{1:0, 2:10, 3:-1} k > v {1:0, 3:-1}
{1:null, 2:10, 3:null} k > v {} (NULL predicate drops the entry)
{1:0, 2:10, 3:-1} v IS NULL {}
{1:null, 2:10, 3:null} v IS NULL {1:null, 3:null}
{"abcdf":5, "abc":10, "":0} length(k) = v {"abcdf":5, "":0}
{"abcdf":5, "abc":10, "":null} length(k) = v {"abcdf":5}
{1:[0,1,2], 2:null, -3:[-1,0,-2,3]} size(v) + k > 3 {1:[0,1,2]}
NULL map any NULL
Edge cases to test
  • A NULL map gives NULL. An empty map gives {}. A map whose entries are all filtered out gives {}, not NULL.
  • A NULL predicate result is treated as false. Spark checks f.eval(...).asInstanceOf[Boolean], and a NULL unboxes
    to false.
  • NULL values in the map.
  • In the size(v) + k > 3 case, size(NULL) is -1 when ANSI is off (spark.sql.legacy.sizeOfNull, default true)
    and NULL when ANSI is on. The 2:null entry is dropped either way, because 1 > 3 is false and a NULL predicate
    also drops the entry. Test both settings.
  • Entry order is preserved, and duplicate keys cannot appear (the input map is already deduplicated).
  • Captured outer columns: map_filter(m, (k, v) -> v > threshold_col).
  • Nondeterministic predicates, evaluated once per entry.
  • Errors inside the predicate under ANSI mode (v / 0 > 1), raised for every evaluated entry.
  • Lambda arity: k -> ... and (k, v, x) -> ... both fail with INVALID_LAMBDA_FUNCTION_CALL.NUM_ARGS_MISMATCH.
    DataFusion's planner accepts lambdas with fewer parameters than offered, so the Spark arity check has to be
    enforced explicitly.
  • Key types: integers, strings, binary, structs, and floating point (including NaN and -0.0 keys).
  • Sliced MapArray input, where offsets do not start at 0.
Differences across Spark versions
  • None found in MapFilter between 3.4 and 4.2. The literal-argument error tests were added to the golden file in
    4.1, which only adds tests. ANSI mode is on by default from 4.0.
Implementation notes
  • Spark: MapFilter in sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/higherOrderFunctions.scala.
    It builds the result with ArrayBasedMapData directly, with no dedup and no key normalization.
  • Infrastructure. DataFusion's higher-order function framework (HigherOrderUDFImpl in
    datafusion/expr/src/higher_order_function.rs, and LambdaArgument::evaluate with spread_captures for captured
    columns) is on branch-56, which this crate uses. lambda_parameters should offer [key (non-null), value].
  • DataFusion core has no higher-order map functions. array_filter (datafusion/functions-nested/src/array_filter.rs)
    is the closest model: evaluate the predicate over the flattened keys and values of the MapArray entries, then
    filter the entries struct and rebuild the offsets. Its handling of NULL predicates (treated as not matching) and
    of values under NULL rows matches what is needed here.
  • This crate's crates/spark/src/function/lambda/mod.rs is an empty ScalarUDF placeholder. It needs a
    HigherOrderUDF list and registration through register_higher_order_function /
    SessionStateBuilder::higher_order_functions. crates/spark/src/function/map/utils.rs has MapArray
    construction helpers.
  • Add crates/sqllogictest/test_files/lambda/map_filter.slt and document the function 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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