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chore(tracer): deprecates patch_all #11918

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github-actions bot commented Jan 13, 2025

CODEOWNERS have been resolved as:

releasenotes/notes/deprecate_patch_all-244eddcfacb1da14.yaml            @DataDog/apm-python
benchmarks/ddtrace_run/scenario.py                                      @DataDog/apm-core-python
ddtrace/_monkey.py                                                      @DataDog/apm-core-python
ddtrace/bootstrap/preload.py                                            @DataDog/apm-core-python
ddtrace/contrib/aws_lambda/__init__.py                                  @DataDog/apm-core-python @DataDog/apm-idm-python
ddtrace/contrib/httplib/__init__.py                                     @DataDog/apm-core-python @DataDog/apm-idm-python
ddtrace/contrib/internal/pytest/plugin.py                               @DataDog/apm-core-python @DataDog/apm-idm-python
ddtrace/contrib/kombu/__init__.py                                       @DataDog/apm-core-python @DataDog/apm-idm-python
ddtrace/contrib/sanic/__init__.py                                       @DataDog/apm-core-python @DataDog/apm-idm-python
ddtrace/contrib/snowflake/__init__.py                                   @DataDog/apm-core-python @DataDog/apm-idm-python
ddtrace/contrib/urllib3/__init__.py                                     @DataDog/apm-core-python @DataDog/apm-idm-python
docs/api.rst                                                            @DataDog/python-guild
tests/appsec/iast/fixtures/entrypoint/views.py                          @DataDog/asm-python
tests/commands/test_runner.py                                           @DataDog/apm-core-python
tests/contrib/algoliasearch/test.py                                     @DataDog/apm-core-python @DataDog/apm-idm-python
tests/contrib/django/test_django.py                                     @DataDog/apm-core-python @DataDog/apm-idm-python
tests/contrib/psycopg/test_psycopg_snapshot.py                          @DataDog/apm-core-python @DataDog/apm-idm-python
tests/contrib/psycopg2/test_psycopg.py                                  @DataDog/apm-core-python @DataDog/apm-idm-python
tests/contrib/psycopg2/test_psycopg_snapshot.py                         @DataDog/apm-core-python @DataDog/apm-idm-python
tests/contrib/starlette/test_starlette.py                               @DataDog/apm-core-python @DataDog/apm-idm-python
tests/tracer/test_monkey.py                                             @DataDog/apm-sdk-api-python

@ZStriker19 ZStriker19 force-pushed the zachg/deprecate_patch_all branch from 12513d0 to fb33c53 Compare January 13, 2025 18:43
@ZStriker19 ZStriker19 changed the title chore(tracer): deprecate patch_all chore(tracer): deprecates patch_all Jan 13, 2025
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Datadog Report

Branch report: zachg/deprecate_patch_all
Commit report: 5b7f14c
Test service: dd-trace-py

✅ 0 Failed, 130 Passed, 1468 Skipped, 4m 47.85s Total duration (35m 39s time saved)

@ZStriker19 ZStriker19 changed the title chore(tracer): deprecates patch_all chore(tracer): deprecates patch_all [3.0] Jan 13, 2025
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Benchmarks

Benchmark execution time: 2025-01-30 22:35:19

Comparing candidate commit 4e3ac9f in PR branch zachg/deprecate_patch_all with baseline commit af9098c in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 394 metrics, 2 unstable metrics.

@ZStriker19 ZStriker19 changed the title chore(tracer): deprecates patch_all [3.0] chore(tracer): deprecates patch_all Jan 16, 2025
ddtrace/__init__.py Outdated Show resolved Hide resolved
mabdinur added a commit that referenced this pull request Jan 21, 2025
…all integrations [3.0] (#11916)

Requires: [chore: avoids using deprecated modules in tests and log
injection [3.0
prep]](#11835).

Follow up to: #11918

This change internalizes the following functions:
- `ddtrace.contrib.<integration_name>.patch()`
- `ddtrace.contrib.<integration_name>.unpatch()`
- `ddtrace.contrib.<integration_name>.get_version()`

This change also internalizes all integrations that only expose
`patch()`, `unpatch()`, and `get_version()` functions.

## Checklist
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- The PR description includes an overview of the change
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strategy
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guidelines](https://ddtrace.readthedocs.io/en/stable/releasenotes.html)
- The change includes or references documentation updates if necessary
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ZStriker19 and others added 15 commits January 22, 2025 17:38
Continuation of #12009

## Checklist
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…ses (#12030)

Auto Test Retries had a bug where retries of tests defined inside
unittest classes would always succeed, even if the test failed. This was
because for unittest classes, pytest saves the exception status in the
[`pytest_runtest_makereport`
hook](https://github.com/pytest-dev/pytest/blob/8.3.x/src/_pytest/unittest.py#L368),
which was not called during retries. This PR fixes it so that the hook
is called.

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`profile 16/20` has been flaky with the following test
```
FAILED tests/profiling_v2/test_main.py::test_call_script_gevent[False] - AssertionError: (b'', b'')
assert -11 == 0
```
Since the test is for stack v1+libdd exporter and we don't plan to fix
stack v1, we're removing those tests.


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Co-authored-by: Dmytro Yurchenko <dmytro.yurchenko@datadoghq.com>
## Checklist
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…2034)

In [MLOS-42](https://datadoghq.atlassian.net/browse/MLOS-42) a customer
was experiencing the following error:
```
ddtrace.internal.utils.ArgumentError: contents (at position 0)
``` 
where the `content` argument was not being extracted properly from the
list of keyword arguments inputted into the `chat.send_message` method.

This is because in the Vertex AI integration, we look for the `contents`
keyword argument. However, the content field is titled `content` in the
[send_message](https://github.com/google-gemini/generative-ai-python/blob/main/google/generativeai/generative_models.py#L514)
method and `contents` in the
[generate_content](https://github.com/google-gemini/generative-ai-python/blob/main/google/generativeai/generative_models.py#L239)
method, so it is necessary to differentiate between these two cases.

This PR is a small fix that corrects this error by differentiating
between chat and completion requests in order to extract either
`content` or `contents` respectively.
 
## Testing

### Automatic Testing
I edited some of the currently existing tests to use the keyword
argument extraction rather than the positional argument extraction to
get the content in order to confirm that this fix resolves the error.

### Manual Testing
Running the following code reproduced the error; furthermore, I
confirmed that with this fix, the error is no longer present and the
request completes successfully.
```
llm = GenerativeModel("gemini-1.5-flash")
chat = llm.start_chat()
resp = chat.send_message(content="hello")
```

I also verified that the following code which uses the generate_content
method is not impacted (continues to work as before) as a result of this
fix.

```
llm = GenerativeModel("gemini-1.5-flash")
resp = llm.generate_content(contents="hello")
```


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[MLOS-42]:
https://datadoghq.atlassian.net/browse/MLOS-42?atlOrigin=eyJpIjoiNWRkNTljNzYxNjVmNDY3MDlhMDU5Y2ZhYzA5YTRkZjUiLCJwIjoiZ2l0aHViLWNvbS1KU1cifQ

---------

Co-authored-by: Yun Kim <35776586+Yun-Kim@users.noreply.github.com>
## Checklist
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[applicable](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting))

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## Checklist
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## Description

Add django modules by default to the allowlist. This will still deny the
django submodules in the denylist.

## Checklist
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Signed-off-by: Juanjo Alvarez <juanjo.alvarezmartinez@datadoghq.com>
Co-authored-by: Brett Langdon <brett.langdon@datadoghq.com>
## Description

Implement the stacktrace leak vulnerability detection.

STATUS: Waiting for DataDog/system-tests#3874 to
be merged.


Signed-off-by: Juanjo Alvarez <juanjo.alvarezmartinez@datadoghq.com>
## Checklist
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---------

Signed-off-by: Juanjo Alvarez <juanjo.alvarezmartinez@datadoghq.com>
Co-authored-by: Alberto Vara <alberto.vara@datadoghq.com>
We add the setting option to specifiy the maximum number of frames to
capture while collecting exception replay data. By default this is
infinite.

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ZStriker19 and others added 22 commits January 30, 2025 16:13
)

This fixes the benchmarking test to reflect the config change made in
this PR: #11925

It also updates the config behavior slightly since the removing of
"none" propagation style at the end of the loop was repetitive since we
were already excluding that value.

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This PR depends on #12063.

Rename src/core to src/native. This is laying ground as a [following
PR](#11839) will need to
import native code in `ddtrace.__init__.py`. Without this change this
would create a dependency loop between `ddtrace` & `ddtrace.core`
through `event_hub.py`.

This PR only renames files, so a non-regression test is the only thing
needed.

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guidelines](https://ddtrace.readthedocs.io/en/stable/releasenotes.html)
- The change includes or references documentation updates if necessary
- Backport labels are set (if
[applicable](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting))

## Reviewer Checklist
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- Title is accurate
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- Avoids breaking
[API](https://ddtrace.readthedocs.io/en/stable/versioning.html#interfaces)
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## Description

Prefixes constants that are internal to the `ddtrace` library with an
underscore. These constants are not publicly documented and should not
be referenced by users. Using the deprecated constants will log a
deprecation warning in 2.X and an error in 3.X

## Changes

- Update constants in `ddtrace/contants.py`:
https://github.com/DataDog/dd-trace-py/pull/12010/files#diff-9fdb2ad4ee57c87a82b368d46e86cbc4726001da7e23cff7404f694813644cc6
- Ensure deprecated constants are not used in ddtrace internals 

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Resolves #11994.

We were previously not testing our OpenAI integration with Python
versions >= 3.12 (at the time, OpenAI did not support those newer Python
versions). We recently saw that our bytecode wrapping for OpenAI methods
broke in Python 3.13, so this PR attempts to address that:
- Switch out our bytecode wrapping with `wrapt.wrap_function_wrapper()`
to make our patching compatible with newer Python versions.
- Add Python 3.12 and 3.13 to tested versions for the OpenAI
integration.
- Implements unpatching and adds patching tests

## Wrapping Changes
We previously were using direct bytecode wrapping in the OpenAI
integration, which was not supported for Python 3.13, and instead
switched to using `wrapt.wrap_function_wrapper()` to wrap OpenAI
methods. This meant that we needed to change wrapping formats,
including:
- use function/attribute names (string) rather than references as inputs
to the wrapping functions
- replace nested functions with `@with_traced_module()` to pass the
traced OpenAI module reference between traced methods
- implement unpatching
- add patching tests (note this is messy because we support both v0 and
v1 openai versions which have separate method names)


**Note**: the issue in #11994 was only reproducible via the
`AsyncOpenAI.Moderations.create(), AsyncOpenAI.Embeddings.create()`
endpoints (chat and completions were not affected). However to avoid any
risk introduced by the now unsupported bytecode wrapping, we are
changing the entire OpenAI integration to use
`wrapt.wrap_function_wrapper()` instead.

## Testing Changes

### Python 3.7 removal (Drop support for OpenAI v0)

Since ddtrace 3.0 will be removing support for Python 3.7, we are also
dropping support accordingly in this PR. This also coincides with
removing support for OpenAI v0 since v0 was last released more than a 16
months ago, and requires a large maintenance burden and pollutes the
codebase. Note that this PR will just drop testing support, but a future
PR will remove tracing support for v0 in the form of a refactor/cleanup
of the OpenAI integration.

### Azure OpenAI tests

Azure OpenAI requests are changed in latest versions of OpenAI, which
fail our cassette tests (recorded in previous versions of OpenAI).
However on manual testing, our openai integration is unchanged in
support for newer versions. With consideration for time/effort to
produce new cassette test files which is time consuming, we are going to
skip cassette tests for Azure endpoints with latest openai versions
until we improve our testing framework entirely (move off cassette
files). Here are the manual traces submitted with Azure OpenAI for
reference:
<img width="578" alt="Screenshot 2025-01-23 at 2 20 00 PM"
src="https://github.com/user-attachments/assets/19f34edc-4c20-4cfd-804f-3e7bce26f4df"
/>
<img width="545" alt="Screenshot 2025-01-23 at 2 20 14 PM"
src="https://github.com/user-attachments/assets/a58bc888-6bee-4d67-9fda-0661aed14423"
/>


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…ted span creation (#11983)

Adds telemetry to track the number of LLMObs spans created either via
auto instrumentation or manual instrumentation.

## Checklist
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Prefixes the following `ddtrace.config` attributes with an `_`. These
attributes are internal to the tracer and should not be referenced or
modified by users:
- http
- http_server
- trace_headers
- header_is_traced
- convert_rc_trace_sampling_rules
- enable_remote_configuration
- get_from

Users should enable remote configuration and set `http` configurations
via environment variables.

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[applicable](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting))

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The profiling native tests have a 5 minute timeout. The test themselves
typically finish in 1-2 minutes. But we see a pretty wide variance in
how long the "Install llvm 19" step takes. Sometimes it takes 20 seconds
and sometimes it takes over 3 minutes, making us time out. Increase the
timeout just a bit to make the tests less flaky while we figure out a
better way (caching? pre-building an image with llvm 19?)
Fix the following CI failures:
https://gitlab.ddbuild.io/DataDog/apm-reliability/dd-trace-py/-/jobs/774636553

Reproduction:
```
from ddtrace.appsec._metrics import _set_waf_init_metric
```

- `ddtrace/appsec/_metrics` defines
[_set_waf_init_metric](https://github.com/DataDog/dd-trace-py/blob/130b69b367e22311fa5fea7e3cc0910396e968c4/ddtrace/appsec/_metrics.py#L48-L49)
- `ddtrace/appsec/_metrics` imports
[ddtrace.appsec._proccessors](https://github.com/DataDog/dd-trace-py/blob/130b69b367e22311fa5fea7e3cc0910396e968c4/ddtrace/appsec/_metrics.py#L4-L5)
- `ddtrace.appsec._proccessors` imports
[_set_waf_init_metric](https://github.com/DataDog/dd-trace-py/blob/130b69b367e22311fa5fea7e3cc0910396e968c4/ddtrace/appsec/_processor.py#L443-L444)
- Boom: Circular import

The fix here is to avoid importing `ddwaf` from
`ddtrace.appsec._processor` in `ddtrace.appsec._metrics`. Instead we can
import ddwaf directly from `ddtrace.appsec._ddwaf`.

This issue does not occur if the `ddtrace.appsec._processor` module is
imported before `ddtrace.appsec._metrics`. This is why we do not see
this error in most of our appsec tests. However the order of imports is
not guaranteed by the slots check. This explains the flakiness in this
job.


## Checklist
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policy](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting)
Fixes token chunk extraction to account for the `choices` field in a
chunk being an empty list

#### Before
```
Error generating LLMObs span event for span <Span(id=16151817411339450163,trace_id=137677390470467884790869841527646927357,parent_id=None,name=openai.request)>, likely due to malformed span
Traceback (most recent call last):
  File "/XXXXX/ddtrace/contrib/internal/openai/utils.py", line 118, in __aiter__
    await self._extract_token_chunk(chunk)
  File "/XXXXX/ddtrace/contrib/internal/openai/utils.py", line 157, in _extract_token_chunk
    choice = getattr(chunk, "choices", [None])[0]
IndexError: list index out of range
```

#### After
Traced succesfully
<img width="904" alt="image"
src="https://github.com/user-attachments/assets/43c68edd-03f7-4105-a3d3-213eeb5fb0ab"
/>



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---------

Co-authored-by: lievan <evan.li@datadoqhq.com>
Co-authored-by: Yun Kim <35776586+Yun-Kim@users.noreply.github.com>
)

Creates a GHA job that checks if the `main` branch can be cleanly merged
to a 3.x-staging feature branch. This is to avoid scenarios where `main`
has changes that conflict with `3.x-staging`, so we can resolve those
before merging anything.

This is mainly for the 3.x migration effort. We can clean this job up
afterwards.

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## Checklist
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---------

Co-authored-by: Nicole Cybul <nicole.cybul@datadoghq.com>
#12075)

We added locking to the memory profiler to address crashes. These locks
are mostly "try" locks, meaning we bail out if we can't acquire them
right away. This was done defensively to mitigate the possibility of
deadlock until we fully understood why the locks are needed and could
guarantee their correctness. But as a result of using try locks, the
`iter_events` function in particular can fail if the memory profiler lock
is contended when it tries to collect profiling events. The function
then returns NULL, leading to SystemError exceptions because we don't
set an error.

Even if we set an error, returning NULL isn't the right thing to do.
It'll basically mean we wait until the next profile iteration, still
accumulating events in the same buffer, and try again to upload the
events. So we're going to get multiple iteration's worth of events. The
right thing to do is take the lock unconditionally in `iter_events`. We
can allocate the new tracker outside the memory allocation profiler lock
so that we don't need to worry about reentrancy/deadlock issues if
we start profiling that allocation. Then, the only thing we do under the
lock is swap out the global tracker, so it's safe to take the lock
unconditionally.

Fixes #11831

TODO - regression test?
…le (#12035)

## Motivation

Refactors all web server integrations still using `tracer.trace` to
instead use `core.context_with_data`. This is in preparation for
supporting AWS API Gateway to ensure all web servers share the same code
path.

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## Checklist
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Depending of the timing, libddwaf loading process could create triggers
that would create loops in our instrumentation.
From what I investigated:
- if loaded too early, it could have bad interactions with gevent.
- if loaded too late, it could be self instrumented by the tracer,
creating a loop, as ctypes is using Popen and subprocess.

while keeping the late loading introduced by 2 previous PRs
- #11987
- #12013
this PR introduced a mechanism to bypass tracer instrumentation during
ctypes loading, to avoid a possible loop that would prevent the WAF to
be loaded.


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…t config (#12089)

This PR makes a change to our shared distributed tracing header
injection method to dispatch signals/events instead of relying on the
global config settings, which is only modifiable via env vars. This
fixes distributed tracing for users that might rely solely on the
`LLMObs.enable()` setup config.

Programmatic `LLMObs.enable()/disable()` calls do not set the global
`config._llmobs_enabled` boolean setting, which is only controlled by
the `DD_LLMOBS_ENABLED` env var. This was problematic for users that
relied on manual `LLMObs.enable()` setup (i.e. no env vars) because our
distributed tracing injection code only checks the global config to
inject llmobs parent IDs into request headers. If users manually enabled
LLMObs without any env vars, then this would not be reflected in the
global config value and thus LLMObs parent IDs would never be injected
into the request headers.

We can't check directly if LLMObs is enabled in the http injection
module because:
1. This would require us to import significant product-specific
LLMObs-code into the shared http injector helper module which would
impact non-LLMObs users' app performance
2. Circular imports in LLMObs which imports http injector logic to use
in its own helpers

Instead of doing our check based on the global `config._llmobs_enabled`
setting, we now send a tracing event to our shared product listeners,
and register a corresponding `LLMObs._inject_llmobs_context()` hook to
be called for all inject() calls if LLMObs is enabled (we check the
LLMObs instance, not the global config setting value).

~One risk and why I don't like changing global config settings is
because this then implies that it is no longer global or tied to an env
var (I want to push for env var configuration where possible over manual
overriding/enabling). If a global enabled config can be toggled
indiscriminately then this could open a can of worms for
enabling/disabling logic in our LLMObs service, which isn't really
designed to be toggled on/off multiple times in the app's lifespan.
However if some users cannot rely on env vars, then I don't see any
other solution that does not couple tracer internal code with LLMObs
code which is a no-option.~ (UPDATE: we avoided this issue by using
signal dispatching)

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ddtrace v3.0 is set to remove `ddtrace.providers` module from the public
API. However we should still allow users to use their own
ContextProviders. This PR ensures the BaseContextProvider remains in the
public API and can be used in `tracer.configure(...)`.

## Checklist
- [x] PR author has checked that all the criteria below are met
- The PR description includes an overview of the change
- The PR description articulates the motivation for the change
- The change includes tests OR the PR description describes a testing
strategy
- The PR description notes risks associated with the change, if any
- Newly-added code is easy to change
- The change follows the [library release note
guidelines](https://ddtrace.readthedocs.io/en/stable/releasenotes.html)
- The change includes or references documentation updates if necessary
- Backport labels are set (if
[applicable](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting))

## Reviewer Checklist
- [x] Reviewer has checked that all the criteria below are met 
- Title is accurate
- All changes are related to the pull request's stated goal
- Avoids breaking
[API](https://ddtrace.readthedocs.io/en/stable/versioning.html#interfaces)
changes
- Testing strategy adequately addresses listed risks
- Newly-added code is easy to change
- Release note makes sense to a user of the library
- If necessary, author has acknowledged and discussed the performance
implications of this PR as reported in the benchmarks PR comment
- Backport labels are set in a manner that is consistent with the
[release branch maintenance
policy](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting)
The shared pipeline is introducing support for windows OCI packages.
This PR ensure dd-trace-py doesn't generate nonsensical packages.
Windows support can be added later.

This was tested by using the WIP branch of one-pipeline

## Checklist
- [x] PR author has checked that all the criteria below are met
- The PR description includes an overview of the change
- The PR description articulates the motivation for the change
- The change includes tests OR the PR description describes a testing
strategy
- The PR description notes risks associated with the change, if any
- Newly-added code is easy to change
- The change follows the [library release note
guidelines](https://ddtrace.readthedocs.io/en/stable/releasenotes.html)
- The change includes or references documentation updates if necessary
- Backport labels are set (if
[applicable](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting))

## Reviewer Checklist
- [x] Reviewer has checked that all the criteria below are met 
- Title is accurate
- All changes are related to the pull request's stated goal
- Avoids breaking
[API](https://ddtrace.readthedocs.io/en/stable/versioning.html#interfaces)
changes
- Testing strategy adequately addresses listed risks
- Newly-added code is easy to change
- Release note makes sense to a user of the library
- If necessary, author has acknowledged and discussed the performance
implications of this PR as reported in the benchmarks PR comment
- Backport labels are set in a manner that is consistent with the
[release branch maintenance
policy](https://ddtrace.readthedocs.io/en/latest/contributing.html#backporting)

---------

Co-authored-by: Brett Langdon <brett.langdon@datadoghq.com>
@datadog-dd-trace-py-rkomorn
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datadog-dd-trace-py-rkomorn bot commented Jan 30, 2025

Datadog Report

Branch report: zachg/deprecate_patch_all
Commit report: 4e3ac9f
Test service: dd-trace-py

✅ 0 Failed, 130 Passed, 1468 Skipped, 4m 27.29s Total duration (35m 44.45s time saved)

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