chore: import upstream snapshot with attribution

doc/source/ray-observability/doc_code/metrics_example.py

@@ -0,0 +1,61 @@
+import time
+
+import ray
+from ray.util.metrics import Counter, Gauge, Histogram
+
+ray.init(_metrics_export_port=8080)
+
+
+@ray.remote
+class MyActor:
+    def __init__(self, name):
+        self._curr_count = 0
+
+        self.counter = Counter(
+            "num_requests",
+            description="Number of requests processed by the actor.",
+            tag_keys=("actor_name",),
+        )
+        self.counter.set_default_tags({"actor_name": name})
+
+        self.gauge = Gauge(
+            "curr_count",
+            description="Current count held by the actor. Goes up and down.",
+            tag_keys=("actor_name",),
+        )
+        self.gauge.set_default_tags({"actor_name": name})
+
+        self.histogram = Histogram(
+            "request_latency",
+            description="Latencies of requests in ms.",
+            boundaries=[0.1, 1],
+            tag_keys=("actor_name",),
+        )
+        self.histogram.set_default_tags({"actor_name": name})
+
+    def process_request(self, num):
+        start = time.time()
+        self._curr_count += num
+
+        # Increment the total request count.
+        self.counter.inc()
+        # Update the gauge to the new value.
+        self.gauge.set(self._curr_count)
+        # Record the latency for this request in ms.
+        self.histogram.observe(1000 * (time.time() - start))
+
+        return self._curr_count
+
+
+print("Starting actor.")
+my_actor = MyActor.remote("my_actor")
+print("Calling actor.")
+my_actor.process_request.remote(-10)
+print("Calling actor.")
+my_actor.process_request.remote(5)
+print("Metrics should be exported.")
+print("See http://localhost:8080 (this may take a few seconds to load).")
+
+# Sleep so we can look at the metrics before exiting.
+time.sleep(30)
+print("Exiting!")