import asyncio import logging from dataclasses import fields from itertools import islice from typing import List, Tuple from ray.core.generated.common_pb2 import TaskStatus import ray.dashboard.utils as dashboard_utils import ray.dashboard.memory_utils as memory_utils from ray.experimental.state.common import ( filter_fields, StateSchema, SupportedFilterType, ActorState, PlacementGroupState, NodeState, WorkerState, TaskState, ObjectState, RuntimeEnvState, ListApiOptions, ListApiResponse, ) from ray.experimental.state.state_manager import ( StateDataSourceClient, DataSourceUnavailable, ) from ray.runtime_env import RuntimeEnv from ray._private.utils import binary_to_hex logger = logging.getLogger(__name__) GCS_QUERY_FAILURE_WARNING = ( "Failed to query data from GCS. It is due to " "(1) GCS is unexpectedly failed. " "(2) GCS is overloaded. " "(3) There's an unexpected network issue. " "Please check the gcs_server.out log to find the root cause." ) NODE_QUERY_FAILURE_WARNING = ( "Failed to query data from {type}. " "Queryed {total} {type} " "and {network_failures} {type} failed to reply. It is due to " "(1) {type} is unexpectedly failed. " "(2) {type} is overloaded. " "(3) There's an unexpected network issue. Please check the " "{log_command} to find the root cause." ) def _convert_filters_type( filter: List[Tuple[str, SupportedFilterType]], schema: StateSchema ) -> List[Tuple[str, SupportedFilterType]]: """Convert the given filter's type to SupportedFilterType. This method is necessary because click can only accept a single type for its tuple (which is string in this case). Args: filter: A list of filter which is a tuple of (key, val). schema: The state schema. It is used to infer the type of the column for filter. Returns: A new list of filters with correctly types that match the schema. """ new_filter = [] schema = {field.name: field.type for field in fields(schema)} for col, val in filter: if col in schema: column_type = schema[col] if isinstance(val, column_type): # Do nothing. pass elif column_type is int: try: val = int(val) except ValueError: raise ValueError( f"Invalid filter `--filter {col} {val}` for a int type " "column. Please provide an integer filter " f"`--filter {col} [int]`" ) elif column_type is float: try: val = float(val) except ValueError: raise ValueError( f"Invalid filter `--filter {col} {val}` for a float " "type column. Please provide an integer filter " f"`--filter {col} [float]`" ) elif column_type is bool: # Without this, "False" will become True. if val == "False" or val == "false" or val == "0": val = False elif val == "True" or val == "true" or val == "1": val = True else: raise ValueError( f"Invalid filter `--filter {col} {val}` for a boolean " "type column. Please provide " f"`--filter {col} [True|true|1]` for True or " f"`--filter {col} [False|false|0]` for False." ) new_filter.append((col, val)) return new_filter # TODO(sang): Move the class to state/state_manager.py. # TODO(sang): Remove *State and replaces with Pydantic or protobuf. # (depending on API interface standardization). class StateAPIManager: """A class to query states from data source, caches, and post-processes the entries. """ def __init__(self, state_data_source_client: StateDataSourceClient): self._client = state_data_source_client @property def data_source_client(self): return self._client def _filter( self, data: List[dict], filters: List[Tuple[str, SupportedFilterType]], state_dataclass: StateSchema, ) -> List[dict]: """Return the filtered data given filters. Args: data: A list of state data. filters: A list of KV tuple to filter data (key, val). The data is filtered if data[key] != val. state_dataclass: The state schema. Returns: A list of filtered state data in dictionary. Each state data's unncessary columns are filtered by the given state_dataclass schema. """ filters = _convert_filters_type(filters, state_dataclass) result = [] for datum in data: match = True for filter_column, filter_value in filters: filterable_columns = state_dataclass.filterable_columns() if filter_column not in filterable_columns: raise ValueError( f"The given filter column {filter_column} is not supported. " f"Supported filter columns: {filterable_columns}" ) if datum[filter_column] != filter_value: match = False break if match: result.append(filter_fields(datum, state_dataclass)) return result async def list_actors(self, *, option: ListApiOptions) -> ListApiResponse: """List all actor information from the cluster. Returns: {actor_id -> actor_data_in_dict} actor_data_in_dict's schema is in ActorState """ try: reply = await self._client.get_all_actor_info(timeout=option.timeout) except DataSourceUnavailable: raise DataSourceUnavailable(GCS_QUERY_FAILURE_WARNING) result = [] for message in reply.actor_table_data: data = self._message_to_dict(message=message, fields_to_decode=["actor_id"]) result.append(data) result = self._filter(result, option.filters, ActorState) # Sort to make the output deterministic. result.sort(key=lambda entry: entry["actor_id"]) return ListApiResponse( result={d["actor_id"]: d for d in islice(result, option.limit)} ) async def list_placement_groups(self, *, option: ListApiOptions) -> ListApiResponse: """List all placement group information from the cluster. Returns: {pg_id -> pg_data_in_dict} pg_data_in_dict's schema is in PlacementGroupState """ try: reply = await self._client.get_all_placement_group_info( timeout=option.timeout ) except DataSourceUnavailable: raise DataSourceUnavailable(GCS_QUERY_FAILURE_WARNING) result = [] for message in reply.placement_group_table_data: data = self._message_to_dict( message=message, fields_to_decode=["placement_group_id"], ) result.append(data) result = self._filter(result, option.filters, PlacementGroupState) # Sort to make the output deterministic. result.sort(key=lambda entry: entry["placement_group_id"]) return ListApiResponse( result={d["placement_group_id"]: d for d in islice(result, option.limit)} ) async def list_nodes(self, *, option: ListApiOptions) -> ListApiResponse: """List all node information from the cluster. Returns: {node_id -> node_data_in_dict} node_data_in_dict's schema is in NodeState """ try: reply = await self._client.get_all_node_info(timeout=option.timeout) except DataSourceUnavailable: raise DataSourceUnavailable(GCS_QUERY_FAILURE_WARNING) result = [] for message in reply.node_info_list: data = self._message_to_dict(message=message, fields_to_decode=["node_id"]) result.append(data) result = self._filter(result, option.filters, NodeState) # Sort to make the output deterministic. result.sort(key=lambda entry: entry["node_id"]) return ListApiResponse( result={d["node_id"]: d for d in islice(result, option.limit)} ) async def list_workers(self, *, option: ListApiOptions) -> ListApiResponse: """List all worker information from the cluster. Returns: {worker_id -> worker_data_in_dict} worker_data_in_dict's schema is in WorkerState """ try: reply = await self._client.get_all_worker_info(timeout=option.timeout) except DataSourceUnavailable: raise DataSourceUnavailable(GCS_QUERY_FAILURE_WARNING) result = [] for message in reply.worker_table_data: data = self._message_to_dict( message=message, fields_to_decode=["worker_id"] ) data["worker_id"] = data["worker_address"]["worker_id"] result.append(data) result = self._filter(result, option.filters, WorkerState) # Sort to make the output deterministic. result.sort(key=lambda entry: entry["worker_id"]) return ListApiResponse( result={d["worker_id"]: d for d in islice(result, option.limit)} ) def list_jobs(self, *, option: ListApiOptions) -> ListApiResponse: # TODO(sang): Support limit & timeout & async calls. try: result = self._client.get_job_info() except DataSourceUnavailable: raise DataSourceUnavailable(GCS_QUERY_FAILURE_WARNING) return ListApiResponse(result=result) async def list_tasks(self, *, option: ListApiOptions) -> ListApiResponse: """List all task information from the cluster. Returns: {task_id -> task_data_in_dict} task_data_in_dict's schema is in TaskState """ raylet_ids = self._client.get_all_registered_raylet_ids() replies = await asyncio.gather( *[ self._client.get_task_info(node_id, timeout=option.timeout) for node_id in raylet_ids ], return_exceptions=True, ) unresponsive_nodes = 0 running_task_id = set() successful_replies = [] for reply in replies: if isinstance(reply, DataSourceUnavailable): unresponsive_nodes += 1 continue elif isinstance(reply, Exception): raise reply successful_replies.append(reply) for task_id in reply.running_task_ids: running_task_id.add(binary_to_hex(task_id)) partial_failure_warning = None if len(raylet_ids) > 0 and unresponsive_nodes > 0: warning_msg = NODE_QUERY_FAILURE_WARNING.format( type="raylet", total=len(raylet_ids), network_failures=unresponsive_nodes, log_command="raylet.out", ) if unresponsive_nodes == len(raylet_ids): raise DataSourceUnavailable(warning_msg) partial_failure_warning = ( f"The returned data may contain incomplete result. {warning_msg}" ) result = [] for reply in successful_replies: assert not isinstance(reply, Exception) tasks = reply.owned_task_info_entries for task in tasks: data = self._message_to_dict( message=task, fields_to_decode=["task_id"], ) if data["task_id"] in running_task_id: data["scheduling_state"] = TaskStatus.DESCRIPTOR.values_by_number[ TaskStatus.RUNNING ].name result.append(data) result = self._filter(result, option.filters, TaskState) # Sort to make the output deterministic. result.sort(key=lambda entry: entry["task_id"]) return ListApiResponse( result={d["task_id"]: d for d in islice(result, option.limit)}, partial_failure_warning=partial_failure_warning, ) async def list_objects(self, *, option: ListApiOptions) -> ListApiResponse: """List all object information from the cluster. Returns: {object_id -> object_data_in_dict} object_data_in_dict's schema is in ObjectState """ raylet_ids = self._client.get_all_registered_raylet_ids() replies = await asyncio.gather( *[ self._client.get_object_info(node_id, timeout=option.timeout) for node_id in raylet_ids ], return_exceptions=True, ) unresponsive_nodes = 0 worker_stats = [] for reply, node_id in zip(replies, raylet_ids): if isinstance(reply, DataSourceUnavailable): unresponsive_nodes += 1 continue elif isinstance(reply, Exception): raise reply for core_worker_stat in reply.core_workers_stats: # NOTE: Set preserving_proto_field_name=False here because # `construct_memory_table` requires a dictionary that has # modified protobuf name # (e.g., workerId instead of worker_id) as a key. worker_stats.append( self._message_to_dict( message=core_worker_stat, fields_to_decode=["object_id"], preserving_proto_field_name=False, ) ) partial_failure_warning = None if len(raylet_ids) > 0 and unresponsive_nodes > 0: warning_msg = NODE_QUERY_FAILURE_WARNING.format( type="raylet", total=len(raylet_ids), network_failures=unresponsive_nodes, log_command="raylet.out", ) if unresponsive_nodes == len(raylet_ids): raise DataSourceUnavailable(warning_msg) partial_failure_warning = ( f"The returned data may contain incomplete result. {warning_msg}" ) result = [] memory_table = memory_utils.construct_memory_table(worker_stats) for entry in memory_table.table: data = entry.as_dict() # `construct_memory_table` returns object_ref field which is indeed # object_id. We do transformation here. # TODO(sang): Refactor `construct_memory_table`. data["object_id"] = data["object_ref"] del data["object_ref"] result.append(data) result = self._filter(result, option.filters, ObjectState) # Sort to make the output deterministic. result.sort(key=lambda entry: entry["object_id"]) return ListApiResponse( result={d["object_id"]: d for d in islice(result, option.limit)}, partial_failure_warning=partial_failure_warning, ) async def list_runtime_envs(self, *, option: ListApiOptions) -> ListApiResponse: """List all runtime env information from the cluster. Returns: A list of runtime env information in the cluster. The schema of returned "dict" is equivalent to the `RuntimeEnvState` protobuf message. We don't have id -> data mapping like other API because runtime env doesn't have unique ids. """ agent_ids = self._client.get_all_registered_agent_ids() replies = await asyncio.gather( *[ self._client.get_runtime_envs_info(node_id, timeout=option.timeout) for node_id in agent_ids ], return_exceptions=True, ) result = [] unresponsive_nodes = 0 for node_id, reply in zip(self._client.get_all_registered_agent_ids(), replies): if isinstance(reply, DataSourceUnavailable): unresponsive_nodes += 1 continue elif isinstance(reply, Exception): raise reply states = reply.runtime_env_states for state in states: data = self._message_to_dict(message=state, fields_to_decode=[]) # Need to deseiralize this field. data["runtime_env"] = RuntimeEnv.deserialize( data["runtime_env"] ).to_dict() data["node_id"] = node_id result.append(data) partial_failure_warning = None if len(agent_ids) > 0 and unresponsive_nodes > 0: warning_msg = NODE_QUERY_FAILURE_WARNING.format( type="agent", total=len(agent_ids), network_failures=unresponsive_nodes, log_command="dashboard_agent.log", ) if unresponsive_nodes == len(agent_ids): raise DataSourceUnavailable(warning_msg) partial_failure_warning = ( f"The returned data may contain incomplete result. {warning_msg}" ) result = self._filter(result, option.filters, RuntimeEnvState) # Sort to make the output deterministic. def sort_func(entry): # If creation time is not there yet (runtime env is failed # to be created or not created yet, they are the highest priority. # Otherwise, "bigger" creation time is coming first. if "creation_time_ms" not in entry: return float("inf") elif entry["creation_time_ms"] is None: return float("inf") else: return float(entry["creation_time_ms"]) result.sort(key=sort_func, reverse=True) return ListApiResponse( result=list(islice(result, option.limit)), partial_failure_warning=partial_failure_warning, ) def _message_to_dict( self, *, message, fields_to_decode: List[str], preserving_proto_field_name: bool = True, ) -> dict: return dashboard_utils.message_to_dict( message, fields_to_decode, including_default_value_fields=True, preserving_proto_field_name=preserving_proto_field_name, )