async def __init__(
self,
controller_name: str,
http_config: HTTPOptions,
checkpoint_path: str,
detached: bool = False,
_override_controller_namespace: Optional[str] = None,
):
# Used to read/write checkpoints.
self.controller_namespace = ray.get_runtime_context().namespace
self.controller_name = controller_name
self.checkpoint_path = checkpoint_path
kv_store_namespace = f"{self.controller_name}-{self.controller_namespace}"
self.kv_store = make_kv_store(checkpoint_path,
namespace=kv_store_namespace)
self.snapshot_store = RayInternalKVStore(namespace=kv_store_namespace)
# Dictionary of deployment_name -> proxy_name -> queue length.
self.deployment_stats = defaultdict(lambda: defaultdict(dict))
# Used to ensure that only a single state-changing operation happens
# at any given time.
self.write_lock = asyncio.Lock()
self.long_poll_host = LongPollHost()
self.http_state = HTTPState(
controller_name,
detached,
http_config,
_override_controller_namespace=_override_controller_namespace,
)
self.endpoint_state = EndpointState(self.kv_store, self.long_poll_host)
# Fetch all running actors in current cluster as source of current
# replica state for controller failure recovery
all_current_actor_names = ray.util.list_named_actors()
self.deployment_state_manager = DeploymentStateManager(
controller_name,
detached,
self.kv_store,
self.long_poll_host,
all_current_actor_names,
_override_controller_namespace=_override_controller_namespace,
)
# TODO(simon): move autoscaling related stuff into a manager.
self.autoscaling_metrics_store = InMemoryMetricsStore()
asyncio.get_event_loop().create_task(self.run_control_loop())
def test_basics(self):
s = InMemoryMetricsStore()
s.add_metrics_point({"m1": 1}, timestamp=1)
s.add_metrics_point({"m1": 2}, timestamp=2)
assert s.window_average("m1", window_start_timestamp_s=0) == 1.5
def test_multiple_metrics(self):
s = InMemoryMetricsStore()
s.add_metrics_point({"m1": 1, "m2": -1}, timestamp=1)
s.add_metrics_point({"m1": 2, "m2": -2}, timestamp=2)
assert s.window_average("m1", window_start_timestamp_s=0) == 1.5
assert s.window_average("m2", window_start_timestamp_s=0) == -1.5