chore: import upstream snapshot with attribution

doc/source/serve/doc_code/load_shedding.py

@@ -0,0 +1,55 @@
+# flake8: noqa
+# fmt: off
+
+# __example_deployment_start__
+import time
+from ray import serve
+from starlette.requests import Request
+
+@serve.deployment(
+    # Each replica will be sent 2 requests at a time.
+    max_ongoing_requests=2,
+    # Each caller queues up to 2 requests at a time.
+    # (beyond those that are sent to replicas).
+    max_queued_requests=2,
+)
+class SlowDeployment:
+    def __call__(self, request: Request) -> str:
+        # Emulate a long-running request, such as ML inference.
+        time.sleep(2)
+        return "Hello!"
+# __example_deployment_end__
+
+# __client_test_start__
+import ray
+import aiohttp
+
+@ray.remote
+class Requester:
+    async def do_request(self) -> int:
+        async with aiohttp.ClientSession("http://localhost:8000/") as session:
+            return (await session.get("/")).status
+
+r = Requester.remote()
+serve.run(SlowDeployment.bind())
+
+# Send 4 requests first.
+# 2 of these will be sent to the replica. These requests take a few seconds to execute.
+first_refs = [r.do_request.remote() for _ in range(2)]
+_, pending = ray.wait(first_refs, timeout=1)
+assert len(pending) == 2
+# 2 will be queued in the proxy.
+queued_refs = [r.do_request.remote() for _ in range(2)]
+_, pending = ray.wait(queued_refs, timeout=0.1)
+assert len(pending) == 2
+
+# Send an additional 5 requests. These will be rejected immediately because
+# the replica and the proxy queue are already full.
+for status_code in ray.get([r.do_request.remote() for _ in range(5)]):
+    assert status_code == 503
+
+# The initial requests will finish successfully.
+for ref in first_refs:
+    print(f"Request finished with status code {ray.get(ref)}.")
+
+# __client_test_end__