SanityCheckPlan fails after ProjectionPushdown for GROUP BY over a mark join on a struct field (regression from #26747)

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Desde 11/11/2026.

  • #27319 de @cetra3 — aberto

Avaliação

Dificuldade
4/5
Tempo estimado
3-5 dias
Facilidade para iniciantes
20/100
Tipo de issue
Bug
Clareza
Claramente especificada
Status de atividade
Estagnada
Stack de tecnologia
rust, sql
Domínio
backend, databases

Direção de pesquisa

Start by tracing the equivalence handling described in the issue through EnsureRequirements and the ProjectionPushdown physical optimizer rule, then inspect the related regression tests around #26747. Reproduce the SQL query from the issue and check how the join’s output partitioning is represented after projection pushdown. Done means the query plans and returns a and b without the final sanity check rejecting its distribution requirements.

Escrita pelo modelo de indexação a partir do texto da issue.

Descrição

Describe the bug

This query fails to plan on main:

SET datafusion.execution.target_partitions = 2;
SET datafusion.optimizer.hash_join_single_partition_threshold_rows = 0;
COPY (SELECT named_struct('k', 'a') AS s) TO 'data/t/1.parquet';
COPY (SELECT named_struct('k', 'b') AS s) TO 'data/t/2.parquet';
CREATE EXTERNAL TABLE t STORED AS PARQUET LOCATION 'data/t/';

SELECT s['k'] FROM t WHERE s['k'] = 'a' OR s['k'] IN (SELECT s['k'] FROM t) GROUP BY 1;

SanityCheckPlan: order and distribution requirements stopped holding after physical optimizer rule 'ProjectionPushdown'
caused by
Error during planning: Plan: [
AggregateExec: mode=SinglePartitioned, gby=[__datafusion_extracted_2@0 as __datafusion_extracted_2], aggr=[]
  FilterExec: __datafusion_extracted_3@1 = a OR mark@0, projection=[__datafusion_extracted_2@2]
    ProjectionExec: expr=[mark@0 as mark, get_field(s@1, k) as __datafusion_extracted_3, get_field(s@1, k) as __datafusion_extracted_2]
      HashJoinExec: mode=Partitioned, join_type=LeftMark, on=[(__datafusion_extracted_4@0, t.s[k]@0)], projection=[mark@2, s@1]
        RepartitionExec: partitioning=Hash([__datafusion_extracted_4@0], 2), input_partitions=2
          DataSourceExec: file_groups={...}, projection=[get_field(s@0, k) as __datafusion_extracted_4, s], file_type=parquet
        RepartitionExec: partitioning=Hash([t.s[k]@0], 2), input_partitions=2
          DataSourceExec: file_groups={...}, projection=[get_field(s@0, k) as t.s[k]], file_type=parquet
] does not satisfy distribution requirements: KeyPartitioned[[__datafusion_extracted_2@0]]). Child-0 output partitioning: Hash([__datafusion_extracted_4@0], 2)

The settings make the join Partitioned on this small data set.

Expected behavior

The query returns a and b, as it does at 0d3aac0bde.

Additional context

A bisect points to #26747 (99e723dd47). Its parent ce0e8293f9 plans the query.

#26747 records get_field(s, k) ≡ __datafusion_extracted_4 when projecting equivalences. That lets EnsureRequirements use the join's hash partitioning for the group key and skip a repartition, which is correct. Then ProjectionPushdown moves get_field(s, k) into the DataSourceExec projection and drops __datafusion_extracted_4 through the join's embedded projection. Neither step records the equivalence again. So the join's output partitioning becomes an unknown column, and the final check rejects the plan.

No rows move in that rewrite, so the query fails to plan and never returns wrong results.

Claude Code (an AI coding assistant) investigated this and wrote this issue on behalf of cetra3. A PR with a fix follows.

Linguagem predominante
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