Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

datalake_fdw: read path — planned fragments, CustomScan, projection and pruning

Abierto
#2,019 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
c, postgresql, sql
Área
data-engineering, databases, distributed-systems

Línea de trabajo

Start with the engine plan and the CustomScan planner hook, using dependencies A, B2, and B4; validate the skeleton, projection, and pruning against the stub and local files first. Done means matching Spark rows across schema evolution and fragment splits, reporting correct EXPLAIN ANALYZE pruning, refusing unsupported or dictionary-encoded columns by name, and releasing readers on cancellation.

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

Descripción

datalake

Part of #2008. Letters (A, B0–B7, C, D, E) are the PRs listed there; this is D.

Scope
  • Fragments from the engine's plan (files, row-group ranges) assigned to segments; one large file may be split by row group.
  • A CustomScan (planner hook, design decision D10) that opens a reader per fragment and decodes batches into slots.
  • Projection by field id from the table's Iceberg schema (ProjectionSet), so files written before a column was added or renamed read correctly; only needed columns are read.
  • Row-group pruning from the query's quals against Parquet statistics; EXPLAIN ANALYZE reports row groups skipped.
  • With it: #1989 name mapping for files without field ids.
Out of scope

Delete files (E), time travel (#1683 §2.3), ANALYZE beyond the current zero-sample no-op.

Depends on

A, B2, B4. Skeleton, projection and pruning are testable against the stub and local files first.

Acceptance
  • Same rows as Spark on the same table, including one Spark evolved (column added, renamed, dropped, int promoted to long).
  • A file split into three fragments reads the same as whole; every segment reads only its fragments.
  • EXPLAIN ANALYZE shows pruning for a selective predicate and none for WHERE true.
  • Unsupported or dictionary-encoded columns are refused naming the column; cancel mid-scan releases the reader.
Lenguaje dominante
C
Estrellas
1.4k
Forks
248
Merge medio
4 d 10 h
PR fusionados (30 d)
40

Guía de contribución

Abrir la guía de contribución

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/cloudberry

Todos los issues de apache/cloudberry

Issues similares

Más issues de C

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.