[coverage] Conformance findings: RESULTFETCH-020

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
49/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
go
Área
database, databases

Línea de trabajo

Start with the failing TestDuplicateColumnNamesSurviveResultFetch in the coverage PR and trace the SEA/kernel path through kernel.newKernelRows and cdata.ImportCArrowSchema, where duplicate fields trigger the panic. Reproduce with the single-row query, then check row-oriented and Arrow fetches plus multi-chunk and JOIN cases. Done means duplicate column names and both positional values survive every fetch surface without an exception.

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

Descripción

Summary

Surfaced by the multi-language coverage fan-out while conformance-testing these SPEC-IDs against databricks/databricks-sql-go. Each finding is committed as an expected-failure (xfail) test in the coverage PR — the test asserts the CORRECT (post-fix) behavior and stays red until THIS driver (databricks/databricks-sql-go) is fixed, then flips green as a tripwire.

Findings

  • RESULTFETCH-020 [sea]: SEA/kernel backend panics with arrow: duplicate field with name "a" (arrow.StructOf via cdata.ImportCArrowSchema in kernel.newKernelRows) for any result with two identically named columns, including a single-row single-chunk SELECT 1 AS a, 2 AS a; the server returns both columns, so the failure is client-side Arrow schema assembly
    • failing test: TestDuplicateColumnNamesSurviveResultFetch (see the coverage PR diff under tests/)
  • RESULTFETCH-020: SEA/kernel backend panics with arrow: duplicate field with name "a" (arrow.StructOf via cdata.ImportCArrowSchema in kernel.newKernelRows) for any result containing two identically named columns, including a single-row single-chunk SELECT 1 AS a, 2 AS a; the server returns both columns correctly, so the failure is client-side Arrow schema assembly

Reproduce & Expected

RESULTFETCH-020 — A query whose result set contains two columns with the SAME name is valid SQL (e.g.

Reproduce:

  • Execute a one-row query with two identically named columns and read the
    result fully, through every fetch surface the driver exposes (row-oriented
    fetch-all / fetch-one, and the native Arrow/columnar accessor if present).
  • Execute a duplicate-named query whose result is large enough to arrive in
    more than one chunk/batch, and read every row. Exercises concatenation of
    multiple equal-schema chunks. Chunk/batch COUNT is server- and driver-
    dependent and is deliberately NOT asserted -- only that all rows and both
    columns survive. ORDER BY id makes the first data row deterministic so the
    two columns can be told apart positionally (0 vs 1000000).
  • Execute a JOIN whose two sides share a column name, so the duplication comes
    from SELECT * rather than from explicit aliasing.

Expected (per the shared spec):

  • completes without an exception
  • result has exactly 1 row(s)
  • result has 2 column(s)
  • col 0 is named a
  • col 1 is named a
  • col 0, row 0 == 1 (type Int32)
  • col 1, row 0 == 2 (type Int32)
  • completes without an exception
  • result has exactly 200000 row(s)
  • result has 2 column(s)
  • col 0 is named a
  • col 1 is named a
  • col 0, row 0 == 0 (type Int64)
  • col 1, row 0 == 1000000 (type Int64)
  • completes without an exception
  • result has exactly 1 row(s)
  • result has 2 column(s)
  • col 0 is named id
  • col 1 is named id
  • col 0, row 0 == 1 (type Int32)
  • col 1, row 0 == 1 (type Int32)

Context

Lenguaje dominante
Go
Estrellas
53
Forks
70
Merge medio
2 d 12 h
PR fusionados (30 d)
16

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.

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