Two Rust benches cannot link, so they can never run; the CI gate only type-checks them
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Evaluación
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Aptitud para principiantes
- 35/100
- Tipo de issue
- Error
- Claridad
- Bastante claro
- Estado de actividad
- Activo
- Área
- build-system, ci-cd, testing-qa
Línea de trabajo
Empieza reproduciendo los fallos con cargo bench --manifest-path fraiseql_rs/Cargo.toml --all-features --bench memory -- --test y el comando equivalente de la pipeline. Lee fraiseql_rs/Cargo.toml, benches/memory.rs, benches/pipeline.rs y el paso de CI añadido por #500. Se considera terminado cuando el alcance elegido esté resuelto y ambos benchmarks se ejecuten correctamente o se eliminen, con CI comprobando los targets resultantes.
Escrito por el modelo de indexación a partir del texto del issue.
Descripción
Found while verifying what remained actionable in #491. #500 fixed the bench compile errors and added a CI step that keeps them compiling, which holds — but "compiling" here means cargo check, and cargo check does not link. Two of the seven benches cannot produce an executable at all.
What happens
Each bench built individually, --all-features, on b85762dee:
connection_pool builds
core_benchmark builds
memory LINK FAILS
mutation_benchmark builds
pipeline LINK FAILS
query_execution builds
streaming builds
cargo bench --manifest-path fraiseql_rs/Cargo.toml --all-features --bench memory -- --test:
mold: error: undefined symbol: PyUnicode_FromStringAndSize
mold: error: undefined symbol: PyEval_RestoreThread
mold: error: undefined symbol: _Py_Dealloc
>>> referenced 52 more times
clang: error: linker command failed with exit code 1
error: could not compile `fraiseql_rs` (bench "memory")
The five that build do run — cargo bench --bench core_benchmark -- --test reports Testing components/byte_reader_parsing … Success.
Why
Both failing benches, and only those two, call into the PyO3-dependent half of the crate:
// fraiseql_rs/benches/memory.rs:2, fraiseql_rs/benches/pipeline.rs:3
use fraiseql_rs::pipeline::builder::build_graphql_response;
pyo3 is declared with extension-module unconditionally (fraiseql_rs/Cargo.toml:62), which tells it not to link libpython — the interpreter resolves those symbols at import time. Any target that produces an executable and actually references them therefore cannot link. It is the same constraint the comment above the CI build step already records for cargo test. There is no feature combination that avoids it: extension-module is on the dependency line, not behind a feature.
The other five link because nothing they call reaches a Python symbol.
Why CI does not catch it
The gate added by #500 is:
- name: Check all targets (benches and test binaries)
run: cargo check --manifest-path fraiseql_rs/Cargo.toml --all-targets --all-features
cargo check stops after type-checking, so it validates that these two benches still match the API they call — the #471 drift problem, which it does solve — while never discovering that they cannot be executed. Adding --all-targets to the neighbouring cargo build step does not work either; that is exactly what #491 proposed, and it fails on memory, pipeline and the lib test target.
Scope
Both benches are declared with harness = false in Cargo.toml, so they are meant to be run. As things stand they are dead weight of the same kind as the test directories removed in #506: present, type-checked, and unrunnable.
Worth deciding rather than assuming: remove them, or move build_graphql_response behind a boundary the benches can call without dragging in PyO3. The second is only worth it if the pipeline builder is genuinely something we want benchmarked, which is a performance question, not a build one.
Credit to @Joemon24, whose #491 was pulling on this same thread — the benches not really being built — and whose PR is what prompted the check.
- Lenguaje dominante
- Python
- Estrellas
- 22
- Forks
- 3
- Merge medio
- 30 min
- PR fusionados (30 d)
- 35
Preparar el entorno
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
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