CodeQL Python query runs extremely slow on medium-sized project using TaintTracking::Global
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Description
I'm experiencing very slow performance when running a CodeQL query on a Python project using `TaintTracking::Global`. The analysis never finishes, even after more than **2 hours**, on a project that I believe is not very large. Below are some details:
* **CVE project**: CVE-2024-23637
* **Python files**: 263
* **Total lines**: \~88,981
* **Sources**: < 200
* **Sinks**: < 200
* **Tracking config**: `TaintTracking::Global`
My query looks like this:
```ql
module RemoteToFileConfiguration implements DataFlow::ConfigSig {
predicate isSource(DataFlow::Node source) {
MySources::isSource(source)
}
predicate isSink(DataFlow::Node sink) {
MySinks::isMySink(sink)
}
}
module Flow = TaintTracking::Global;
import Flow::PathGraph
from Flow::PathNode source, Flow::PathNode sink
where Flow::flowPath(source, sink)
select sink.getNode(), source, sink, "Flow path from source to sink"
```
I defined sinks or sources like this (simplified):
```ql
module MySinks {
class Sink extends DataFlow::Node {
Sink() {
exists(FunctionValue func, Call call |
func.getQualifiedName() = "run_code" or
func.getQualifiedName() = "check_syntax_error" or
...
call.getFunc().pointsTo(func) and
this = DataFlow::exprNode(call.getAnArg())
)
}
}
predicate isMySink(DataFlow::Node sink) {
exists(Sink s | s = sink)
}
}
```
### My questions:
1. Why is the performance so slow in this case?
2. Are there any best practices for optimizing `TaintTracking::Global` on Python?
3. I tried using `func.getQualifiedName()` with a full path like `"Module xml.etree.ElementInclude.Function default_loader"`, but it didn’t work in VSCode (the function wasn't found). Is there a correct way to define sinks using fully qualified names for Python?
Thank you very much for any guidance or suggestions!
Contributor guide
Research direction
No repository file or test is named. Start by reproducing the shown TaintTracking::Global query against the CVE-2024-23637 project, then inspect the source and sink definitions, including the getQualifiedName usage in VSCode. Done means documenting the performance cause or optimization guidance and the correct Python sink naming approach.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, vscode
- Domain
- devtools, security
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100