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palazzem
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Feb 5, 2017
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palazzem
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Looks good! Not totally sure about making the Span.tracer() method as part of the Span "public API".
| lines.extend((" ", "%s:%s" % kv) for kv in sorted(self.meta.items())) | ||
| return "\n".join("%10s %s" % l for l in lines) | ||
|
|
||
| def tracer(self): |
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do we need to make it "available" as a Span public API? or do we need it only for internal instrumentation? in that case I suggest to keep it "internal".
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I think it's totally fine. So harm at all.
| def trace_pyramid(config): | ||
| config.add_tween('ddtrace.contrib.pyramid:trace_tween_factory') | ||
| # ensure we only patch the renderer once. | ||
| import pyramid.renderers |
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the import should be here because of a particular import order?
gh-worker-dd-mergequeue-cf854d Bot
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Apr 23, 2026
…llocator (#17664) ## Description This PR fixes a segmentation fault in the memory allocation profiler that occurs when a hook call races with `memalloc` start/stop operations. The issue arises from concurrent access to the saved allocator struct, which could be partially written while being read, resulting in`NULL` function pointers being dereferenced. The key indicator in that case is that `#1 0x0000000000000000` frame -- we are trying to execute a null function pointer. ```` Error UnixSignal: Process terminated with SEGV_MAPERR (SIGSEGV) #0 0x00007ff3c303a8d4 #1 0x0000000000000000 memalloc_alloc (/go/src/github.com/DataDog/apm-reliability/dd-trace-py/ddtrace/profiling/collector/_memalloc.cpp:68) #2 0x00007ff39dcb3b20 memalloc_alloc (/go/src/github.com/DataDog/apm-reliability/dd-trace-py/ddtrace/profiling/collector/_memalloc.cpp:68) #3 0x00007ff39dcb3b20 memalloc_malloc(void*, unsigned long) (/go/src/github.com/DataDog/apm-reliability/dd-trace-py/ddtrace/profiling/collector/_memalloc.cpp:80) #4 0x00007ff3c3087e1b PyUnicode_New #5 0x00007ff3c30889f4 #6 0x00007ff3c3170c84 #7 0x00007ff3c316b931 #8 0x00007ff3c31aaac8 #9 0x00007ff3c31033ac #10 0x00007ff3c310e2a6 PyObject_CallMethodObjArgs #11 0x00007ff3c310e46d #12 0x00007ff3c31a96c2 #13 0x00007ff3c3102fd7 PyObject_Vectorcall #14 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #15 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_PyObject_FastCallDictTstate #173 0x00007ff3c30e958d _PyObject_Call_Prepend #174 0x00007ff3c3109150 #175 0x00007ff3c30e917c _PyObject_MakeTpCall #176 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #177 0x00007ff3c323c094 #178 0x00007ff3c30e92f1 _PyObject_FastCallDictTstate #179 0x00007ff3c30e958d _PyObject_Call_Prepend #180 0x00007ff3c3109150 #181 0x00007ff3c30e917c _PyObject_MakeTpCall #182 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #183 0x00007ff3c323c094 #184 0x00007ff3c30e92f1 _PyObject_FastCallDictTstate #185 0x00007ff3c30e958d _PyObject_Call_Prepend #186 0x00007ff3c3109150 #187 0x00007ff3c30e917c _PyObject_MakeTpCall #188 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #189 0x00007ff3c323c094 #190 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #191 0x00007ff3c323c094 #192 0x00007ff3c30e92f1 _PyObject_FastCallDictTstate #193 0x00007ff3c30e958d _PyObject_Call_Prepend #194 0x00007ff3c3109150 #195 0x00007ff3c30e917c _PyObject_MakeTpCall #196 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #197 0x00007ff3c323c094 #198 0x00007ff3c317d0fd #199 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #200 0x00007ff3c323c094 #201 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #202 0x00007ff3c323c094 #203 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #204 0x00007ff3c323c094 #205 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #206 0x00007ff3c323c094 #207 0x00007ff3c317d23c #208 0x00007ff3c31a7ec5 #209 0x00007ff3c301ac77 #210 0x00007ff3c357c573 ```` The fix implements two key changes. 1. **Hook functions (`memalloc_alloc`, `memalloc_realloc`)**: Snapshot the allocator struct locally before use and guard indirect function calls with `NULL` checks. This prevents crashes if a partially-written struct is observed during a start/stop race. 2. **Start/stop operations (`memalloc_start`, `memalloc_stop`)**: Use local variables and single assignments when publishing the allocator struct to `global_memalloc_ctx.pymem_allocator_obj`. This ensures concurrent hook calls observe either the old or new struct, never a partially-written intermediate state. The real root cause is that `PyMem_GetAllocator` is not documented as atomic, and the struct could be read field-by-field while being written to concurrently. By using local copies and single assignments, we ensure atomicity at the C level and prevent observation of inconsistent state. Co-authored-by: thomas.kowalski <thomas.kowalski@datadoghq.com>
emmettbutler
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Apr 24, 2026
…llocator (#17664) ## Description This PR fixes a segmentation fault in the memory allocation profiler that occurs when a hook call races with `memalloc` start/stop operations. The issue arises from concurrent access to the saved allocator struct, which could be partially written while being read, resulting in`NULL` function pointers being dereferenced. The key indicator in that case is that `#1 0x0000000000000000` frame -- we are trying to execute a null function pointer. ```` Error UnixSignal: Process terminated with SEGV_MAPERR (SIGSEGV) #0 0x00007ff3c303a8d4 #1 0x0000000000000000 memalloc_alloc (/go/src/github.com/DataDog/apm-reliability/dd-trace-py/ddtrace/profiling/collector/_memalloc.cpp:68) #2 0x00007ff39dcb3b20 memalloc_alloc (/go/src/github.com/DataDog/apm-reliability/dd-trace-py/ddtrace/profiling/collector/_memalloc.cpp:68) #3 0x00007ff39dcb3b20 memalloc_malloc(void*, unsigned long) (/go/src/github.com/DataDog/apm-reliability/dd-trace-py/ddtrace/profiling/collector/_memalloc.cpp:80) #4 0x00007ff3c3087e1b PyUnicode_New #5 0x00007ff3c30889f4 #6 0x00007ff3c3170c84 #7 0x00007ff3c316b931 #8 0x00007ff3c31aaac8 #9 0x00007ff3c31033ac #10 0x00007ff3c310e2a6 PyObject_CallMethodObjArgs #11 0x00007ff3c310e46d #12 0x00007ff3c31a96c2 #13 0x00007ff3c3102fd7 PyObject_Vectorcall #14 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #15 0x00007ff3c323c094 #16 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #17 0x00007ff3c323c094 #18 0x00007ff3c30e997d PyObject_CallOneArg #19 0x00007ff3c306a480 _PyObject_GenericGetAttrWithDict #20 0x00007ff3c30c620d PyObject_GetAttr #21 0x00007ff3c32309e7 _PyEval_EvalFrameDefault #22 0x00007ff3c323c094 #23 0x00007ff3c312880e #24 0x00007ff3c30e917c _PyObject_MakeTpCall #25 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #26 0x00007ff3c323c094 #27 0x00007ff3c312880e #28 0x00007ff3c30e917c _PyObject_MakeTpCall #29 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #30 0x00007ff3c323c094 #31 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #32 0x00007ff3c323c094 #33 0x00007ff3c317d0fd #34 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #35 0x00007ff3c323c094 #36 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #37 0x00007ff3c323c094 #38 0x00007ff3c317d1b5 #39 0x00007ff3c3102fd7 PyObject_Vectorcall #40 0x00007ff3c3232f4a _PyEval_EvalFrameDefault #41 0x00007ff3c3240da5 #42 0x00007ff3c324112d #43 0x00007ff3c3233be1 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_PyObject_FastCallDictTstate #173 0x00007ff3c30e958d _PyObject_Call_Prepend #174 0x00007ff3c3109150 #175 0x00007ff3c30e917c _PyObject_MakeTpCall #176 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #177 0x00007ff3c323c094 #178 0x00007ff3c30e92f1 _PyObject_FastCallDictTstate #179 0x00007ff3c30e958d _PyObject_Call_Prepend #180 0x00007ff3c3109150 #181 0x00007ff3c30e917c _PyObject_MakeTpCall #182 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #183 0x00007ff3c323c094 #184 0x00007ff3c30e92f1 _PyObject_FastCallDictTstate #185 0x00007ff3c30e958d _PyObject_Call_Prepend #186 0x00007ff3c3109150 #187 0x00007ff3c30e917c _PyObject_MakeTpCall #188 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #189 0x00007ff3c323c094 #190 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #191 0x00007ff3c323c094 #192 0x00007ff3c30e92f1 _PyObject_FastCallDictTstate #193 0x00007ff3c30e958d _PyObject_Call_Prepend #194 0x00007ff3c3109150 #195 0x00007ff3c30e917c _PyObject_MakeTpCall #196 0x00007ff3c32335a2 _PyEval_EvalFrameDefault #197 0x00007ff3c323c094 #198 0x00007ff3c317d0fd #199 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #200 0x00007ff3c323c094 #201 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #202 0x00007ff3c323c094 #203 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #204 0x00007ff3c323c094 #205 0x00007ff3c3233dd3 _PyEval_EvalFrameDefault #206 0x00007ff3c323c094 #207 0x00007ff3c317d23c #208 0x00007ff3c31a7ec5 #209 0x00007ff3c301ac77 #210 0x00007ff3c357c573 ```` The fix implements two key changes. 1. **Hook functions (`memalloc_alloc`, `memalloc_realloc`)**: Snapshot the allocator struct locally before use and guard indirect function calls with `NULL` checks. This prevents crashes if a partially-written struct is observed during a start/stop race. 2. **Start/stop operations (`memalloc_start`, `memalloc_stop`)**: Use local variables and single assignments when publishing the allocator struct to `global_memalloc_ctx.pymem_allocator_obj`. This ensures concurrent hook calls observe either the old or new struct, never a partially-written intermediate state. The real root cause is that `PyMem_GetAllocator` is not documented as atomic, and the struct could be read field-by-field while being written to concurrently. By using local copies and single assignments, we ensure atomicity at the C level and prevent observation of inconsistent state. Co-authored-by: thomas.kowalski <thomas.kowalski@datadoghq.com>
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adds integration for the pyramid http tracing.