duckdb / duckdb/duckdb-python

ComputeError: caught exception during execution of a Python source

Abierto
#128 2 comentarios 0 reacciones 0 asignados Ver en GitHub
needs triage
Lenguaje dominante
Python
Estrellas
187
Forks
112
Merge medio
13 h 29 min
PR fusionados (30 d)
17

Descripción

### What happens?

First of all nice, feature with the lazy DataFrame. I have a problem with this new feature in duckdb v1.4.1 also tested with 1.4.0 and polars version 1.34.0, but also tested with versions earlier than this.
Mostly the first loading and filtering ... with pl(lazy = True) works but e.g. joins with other tables are not working and results in this Error:

ComputeError: caught exception during execution of a Python source, exception: InvalidInputException: Invalid Input Error: Attempting to execute an unsuccessful or closed pending query result.

Full Trace:

```console
File ~/.venv/lib/python3.9/site-packages/polars/_utils/deprecation.py:97, in deprecate_streaming_parameter..decorate..wrapper(*args, **kwargs)
93 kwargs["engine"] = "in-memory"
95 del kwargs["streaming"]
---> [97](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a225468726f6d626f7365227d.vscode-resource.vscode-cdn.net/home/cdsw/notebooks/~/.venv/lib/python3.9/site-packages/polars/_utils/deprecation.py:97) return function(*args, **kwargs)

File ~/.venv/lib/python3.9/site-packages/polars/lazyframe/opt_flags.py:328, in forward_old_opt_flags..decorate..wrapper(*args, **kwargs)
325 optflags = cb(optflags, kwargs.pop(key)) # type: ignore[no-untyped-call,unused-ignore]
327 kwargs["optimizations"] = optflags
--> [328](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a225468726f6d626f7365227d.vscode-resource.vscode-cdn.net/home/cdsw/notebooks/~/.venv/lib/python3.9/site-packages/polars/lazyframe/opt_flags.py:328) return function(*args, **kwargs)

File ~/.venv/lib/python3.9/site-packages/polars/lazyframe/frame.py:2415, in LazyFrame.collect(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, engine, background, optimizations, **_kwargs)
2413 # Only for testing purposes
2414 callback = _kwargs.get("post_opt_callback", callback)
-> [2415](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a225468726f6d626f7365227d.vscode-resource.vscode-cdn.net/home/cdsw/notebooks/~/.venv/lib/python3.9/site-packages/polars/lazyframe/frame.py:2415) return wrap_df(ldf.collect(engine, callback))
```

### To Reproduce

```
con = duckdb.connect(db.db_path, read_only= True)
df_lab = con.sql("SELECT * FROM data1").pl(lazy = True)
df_main = con.sql("SELECT * FROM data2").pl(lazy = True)
df_lab.join(df_main, on = "account_id").collect()
```

this would be the LazyFrame:

naive plan: (run LazyFrame.explain(optimized=True) to see the optimized plan)
INNER JOIN:
LEFT PLAN ON: [col("account_id")]
PYTHON SCAN []
PROJECT */10 COLUMNS
RIGHT PLAN ON: [col("account_id")]
PYTHON SCAN []
PROJECT */70 COLUMNS
END INNER JOIN

### OS:

linux

### DuckDB Version:

1.41.0

### DuckDB Client:

Python

### Hardware:

_No response_

### Full Name:

Maximilian Zeidler

### Affiliation:

Helios

### What is the latest build you tested with? If possible, we recommend testing with the latest nightly build.

I have tested with a stable release

### Did you include all relevant data sets for reproducing the issue?

No - I cannot share the data sets because they are confidential

### Did you include all code required to reproduce the issue?

- [x] Yes, I have

### Did you include all relevant configuration (e.g., CPU architecture, Python version, Linux distribution) to reproduce the issue?

- [x] Yes, I have

Guía de contribución

Abrir la guía de contribución

Línea de trabajo

Empieza ejecutando la reproducción proporcionada de DuckDB Python con dos Lazy DataFrames y el join; después inspecciona la salida de LazyFrame.explain(optimized=True) y la ruta de integración de Python .pl(lazy=True). Se considera completado cuando el join y collect terminan correctamente sin el error de resultado de consulta pendiente cerrado; los datasets confidenciales pueden limitar la validación.

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

Evaluación

Stack tecnológico
python
Área
databases
Tipo de issue
Error
Dificultad
4/5
Tiempo estimado
3-5 días
Estado de actividad
Estancado
Claridad
Bastante claro
Aptitud para principiantes
38/100

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