fix(rc): skip shared memory allocation in AWS Lambda

AWS Lambda does not provide /dev/shm, so the multiprocessing.Array
allocation in PublisherSubscriberConnector always fails with
FileNotFoundError. Since the single-subscriber refactor (74c3ab4)
the connector is eagerly created at import time via the module-level
remoteconfig_poller singleton, causing a noisy warning on every cold
start even when remote configuration is disabled.

Check in_aws_lambda() before attempting the allocation and fall back
directly to _DummySharedArray without logging a warning, since this
is expected behavior in Lambda — not an error condition.

Resolves: DataDog/datadog-lambda-python#785

Author: zarirhamza

ddtrace/internal/remoteconfig/_connectors.py

@@ -43,20 +43,26 @@ class PublisherSubscriberConnector:
     """
 
     def __init__(self):
-        try:
-            self.data = get_mp_context().Array("c", SHARED_MEMORY_SIZE, lock=False)
-        # FileNotFoundError: /dev/shm may not exist or be inaccessible.
-        # ImportError: multiprocessing.Array imports multiprocessing.sharedctypes,
-        # which imports ctypes and requires the _ctypes C extension module. Some
-        # environments (e.g. Alpine Linux, minimal Docker images, or custom
-        # Python builds without libffi) do not provide _ctypes and raise
-        # ModuleNotFoundError.
-        # See: https://app.datadoghq.com/error-tracking/issue/25b34008-bb9f-11f0-abbd-da7ad0900002
-        except (FileNotFoundError, ImportError):
-            log.warning(
-                "Unable to create shared memory. Features relying on remote configuration will not work as expected."
-            )
+        from ddtrace.internal.serverless import in_aws_lambda
+
+        if in_aws_lambda():
             self.data = _DummySharedArray()
+        else:
+            try:
+                self.data = get_mp_context().Array("c", SHARED_MEMORY_SIZE, lock=False)
+            # FileNotFoundError: /dev/shm may not exist or be inaccessible.
+            # ImportError: multiprocessing.Array imports multiprocessing.sharedctypes,
+            # which imports ctypes and requires the _ctypes C extension module. Some
+            # environments (e.g. Alpine Linux, minimal Docker images, or custom
+            # Python builds without libffi) do not provide _ctypes and raise
+            # ModuleNotFoundError.
+            # See: https://app.datadoghq.com/error-tracking/issue/25b34008-bb9f-11f0-abbd-da7ad0900002
+            except (FileNotFoundError, ImportError):
+                log.warning(
+                    "Unable to create shared memory. "
+                    "Features relying on remote configuration will not work as expected."
+                )
+                self.data = _DummySharedArray()
         # Checksum attr validates if the Publisher send new data
         self.checksum = -1
         # shared_data_counter attr validates if the Subscriber send new data

releasenotes/notes/fix-lambda-shared-memory-warning-4873867125814623.yaml

@@ -0,0 +1,6 @@
+---
+fixes:
+  - |
+    remote config: Fixes a spurious ``Unable to create shared memory`` warning
+    on every AWS Lambda cold start by skipping the ``multiprocessing.Array``
+    allocation when running in Lambda, where ``/dev/shm`` is unavailable.

tests/internal/remoteconfig/test_remoteconfig_connector.py

@@ -68,6 +68,17 @@ def test_connector_fallback_on_array_creation_failure():
         assert isinstance(connector.data, _DummySharedArray)
 
 
+def test_connector_uses_dummy_shared_array_in_aws_lambda(monkeypatch):
+    """In AWS Lambda, shared memory (/dev/shm) is unavailable. The connector
+    should skip the allocation attempt entirely and use _DummySharedArray
+    without logging a warning."""
+    from ddtrace.internal.remoteconfig._connectors import _DummySharedArray
+
+    monkeypatch.setenv("AWS_LAMBDA_FUNCTION_NAME", "my-function")
+    connector = PublisherSubscriberConnector()
+    assert isinstance(connector.data, _DummySharedArray)
+
+
 global_connector = PublisherSubscriberConnector()