TL;DR: Something introduced the following bad join order:
```
(227s) Tuple counts for dom#TObject::TPythonTuple#ff/2@i2#8f58670w after 3m46s:
25000 ~0% {2} r1 = SCAN PointsToContext::PointsToContext::appliesToScope_dispred#ff#prev_delta OUTPUT In.1, In.0 'context'
24000 ~1% {2} r2 = JOIN r1 WITH @py_scope#f ON FIRST 1 OUTPUT Lhs.1 'context', Lhs.0
1076876712 ~6% {3} r3 = JOIN r2 WITH Flow::TupleNode#class#f CARTESIAN PRODUCT OUTPUT Rhs.0, Lhs.0 'context', Lhs.1
870129666 ~0% {3} r4 = JOIN r3 WITH Flow::ControlFlowNode::isLoad_dispred#f ON FIRST 1 OUTPUT Lhs.1 'context', Lhs.2, Lhs.0 'origin'
870129000 ~0% {3} r5 = r4 AND NOT dom#TObject::TPythonTuple#ff#prev(Lhs.2 'origin', Lhs.0 'context')
870129000 ~1% {3} r6 = SCAN r5 OUTPUT In.2 'origin', In.1, In.0 'context'
9000 ~0% {2} r7 = JOIN r6 WITH Flow::ControlFlowNode::getScope_dispred#ff ON FIRST 2 OUTPUT Lhs.0 'origin', Lhs.2 'context'
return r7
```
(...the above being the tuple counts _at the point when I cancelled the
query_!)
Rewriting the code to force a join between `TupleNode#class` and
`getScope` results in the following join orders:
```
(0s) Tuple counts for TObject::scope_loads_tuplenode#ff/2@b3cf0bo5 after 13ms:
37369 ~3% {1} r1 = JOIN Flow::TupleNode#class#f WITH Flow::ControlFlowNode::isLoad_dispred#f ON FIRST 1 OUTPUT Lhs.0 'origin'
37369 ~3% {2} r2 = JOIN r1 WITH Flow::ControlFlowNode::getScope_dispred#ff ON FIRST 1 OUTPUT Rhs.1 's', Lhs.0 'origin'
return r2
```
and
```
(78s) Tuple counts for dom#TObject::TPythonTuple#ff/2@i53#121c440w after 6ms:
34736 ~3% {2} r1 = SCAN PointsToContext::PointsToContext::appliesToScope_dispred#ff#prev_delta OUTPUT In.1, In.0 'context'
7370 ~5% {2} r2 = JOIN r1 WITH TObject::scope_loads_tuplenode#ff ON FIRST 1 OUTPUT Lhs.1 'context', Rhs.1 'origin'
7370 ~5% {2} r3 = r2 AND NOT dom#TObject::TPythonTuple#ff#prev(Lhs.1 'origin', Lhs.0 'context')
7370 ~1% {2} r4 = SCAN r3 OUTPUT In.1 'origin', In.0 'context'
return r4
```
the latter being the largest iteration of `dom#TPythonTuple` throughout
the log.
No other major performance issues were observed.
- move from custom concept `LogOutput` to standard concept `Logging`
- remove `Log.qll` from experimental frameworks
- fold models into standard models (naively for now)
- stdlib:
- make Logger module public
- broaden definition of instance
- add `extra` keyword as possible source
- flak: add app.logger as logger instance
- django: `add django.utils.log.request_logger` as logger instance
(should we add the rest?)
- remove LogOutput from experimental concepts
I am slightly concerned that the test now generates many more
intermediate results. I suppose that maes the analysis heavy.
Should the new library get a new name instead, so the old code
does not get evaluated?
I did a test locally, something like
import requests
req = requests.Request(
"POST",
"http://127.0.0.1:8000/app/upload-test/",
data={"name": "foo"},
files={"upload" : ("wat/haha|!#$%^&", open("foo.txt", "rb"))},
)
# print(req.prepare().body.decode('ascii'))
requests.session().send(req.prepare())
and the `wat/` part was stripped from the filename
The original configuration did not match sinks with sanitizers.
Here it is resolved using flow state,
it could also be done by using two configurations.
Particularly in value and literal patterns.
This is getting a little bit into the guards aspect of matching.
We could similarly add reverse flow in terms of
sub-patterns storing to a sequence pattern,
a flow step from alternatives to an-or-pattern, etc..
It does not seem too likely that sources are embedded in patterns
to begin with, but for secrets perhaps?
It is illustrated by the literal test. The value test still fails.
I believe we miss flow in general from the static attribute.
