ray/dashboard/datacenter.py
Alan Guo 50b20809b8
[Dashboard] Stop caching logs in memory. Use state observability api to fetch on demand. (#26818)
Signed-off-by: Alan Guo <aguo@anyscale.com>

## Why are these changes needed?
Reduces memory footprint of the dashboard.
Also adds some cleanup to the errors data.

Also cleans up actor cache by removing dead actors from the cache.

Dashboard UI no longer allows you to see logs for all workers in a node. You must click into each worker's logs individually.
<img width="1739" alt="Screen Shot 2022-07-20 at 9 13 00 PM" src="https://user-images.githubusercontent.com/711935/180128633-1633c187-39c9-493e-b694-009fbb27f73b.png">


## Related issue number
fixes #23680 
fixes #22027
fixes #24272
2022-07-26 03:10:57 -07:00

344 lines
13 KiB
Python

import asyncio
import logging
import ray.dashboard.consts as dashboard_consts
import ray.dashboard.memory_utils as memory_utils
# TODO(fyrestone): Not import from dashboard module.
from ray.dashboard.modules.actor.actor_utils import actor_classname_from_task_spec
from ray.dashboard.utils import Dict, Signal, async_loop_forever
logger = logging.getLogger(__name__)
class GlobalSignals:
node_info_fetched = Signal(dashboard_consts.SIGNAL_NODE_INFO_FETCHED)
node_summary_fetched = Signal(dashboard_consts.SIGNAL_NODE_SUMMARY_FETCHED)
job_info_fetched = Signal(dashboard_consts.SIGNAL_JOB_INFO_FETCHED)
worker_info_fetched = Signal(dashboard_consts.SIGNAL_WORKER_INFO_FETCHED)
class DataSource:
# {node id hex(str): node stats(dict of GetNodeStatsReply
# in node_manager.proto)}
node_stats = Dict()
# {node id hex(str): node physical stats(dict from reporter_agent.py)}
node_physical_stats = Dict()
# {actor id hex(str): actor table data(dict of ActorTableData
# in gcs.proto)}
actors = Dict()
# {job id hex(str): job table data(dict of JobTableData in gcs.proto)}
jobs = Dict()
# {node id hex(str): dashboard agent [http port(int), grpc port(int)]}
agents = Dict()
# {node id hex(str): gcs node info(dict of GcsNodeInfo in gcs.proto)}
nodes = Dict()
# {node id hex(str): ip address(str)}
node_id_to_ip = Dict()
# {node id hex(str): hostname(str)}
node_id_to_hostname = Dict()
# {node id hex(str): worker list}
node_workers = Dict()
# {node id hex(str): {actor id hex(str): actor table data}}
node_actors = Dict()
# {job id hex(str): worker list}
job_workers = Dict()
# {job id hex(str): {actor id hex(str): actor table data}}
job_actors = Dict()
# {worker id(str): core worker stats}
core_worker_stats = Dict()
# {job id hex(str): {event id(str): event dict}}
events = Dict()
# {node ip (str): log counts by pid
# (dict from pid to count of logs for that pid)}
ip_and_pid_to_log_counts = Dict()
# {node ip (str): error entries by pid
# (dict from pid to list of latest err entries)}
ip_and_pid_to_errors = Dict()
class DataOrganizer:
@staticmethod
@async_loop_forever(dashboard_consts.PURGE_DATA_INTERVAL_SECONDS)
async def purge():
# Purge data that is out of date.
# These data sources are maintained by DashboardHead,
# we do not needs to purge them:
# * agents
# * nodes
# * node_id_to_ip
# * node_id_to_hostname
logger.info("Purge data.")
alive_nodes = {
node_id
for node_id, node_info in DataSource.nodes.items()
if node_info["state"] == "ALIVE"
}
for key in DataSource.node_stats.keys() - alive_nodes:
DataSource.node_stats.pop(key)
for key in DataSource.node_physical_stats.keys() - alive_nodes:
DataSource.node_physical_stats.pop(key)
@classmethod
@async_loop_forever(dashboard_consts.ORGANIZE_DATA_INTERVAL_SECONDS)
async def organize(cls):
job_workers = {}
node_workers = {}
core_worker_stats = {}
# await inside for loop, so we create a copy of keys().
for node_id in list(DataSource.nodes.keys()):
workers = await cls.get_node_workers(node_id)
for worker in workers:
job_id = worker["jobId"]
job_workers.setdefault(job_id, []).append(worker)
for stats in worker.get("coreWorkerStats", []):
worker_id = stats["workerId"]
core_worker_stats[worker_id] = stats
node_workers[node_id] = workers
DataSource.job_workers.reset(job_workers)
DataSource.node_workers.reset(node_workers)
DataSource.core_worker_stats.reset(core_worker_stats)
@classmethod
async def get_node_workers(cls, node_id):
workers = []
node_ip = DataSource.node_id_to_ip[node_id]
node_log_counts = DataSource.ip_and_pid_to_log_counts.get(node_ip, {})
node_errs = DataSource.ip_and_pid_to_errors.get(node_ip, {})
node_physical_stats = DataSource.node_physical_stats.get(node_id, {})
node_stats = DataSource.node_stats.get(node_id, {})
# Merge coreWorkerStats (node stats) to workers (node physical stats)
pid_to_worker_stats = {}
pid_to_language = {}
pid_to_job_id = {}
pids_on_node = set()
for core_worker_stats in node_stats.get("coreWorkersStats", []):
pid = core_worker_stats["pid"]
pids_on_node.add(pid)
pid_to_worker_stats.setdefault(pid, []).append(core_worker_stats)
pid_to_language[pid] = core_worker_stats["language"]
pid_to_job_id[pid] = core_worker_stats["jobId"]
# Clean up logs from a dead pid.
dead_pids = set(node_log_counts.keys()) - pids_on_node
for dead_pid in dead_pids:
if dead_pid in node_log_counts:
node_log_counts.mutable().pop(dead_pid)
for worker in node_physical_stats.get("workers", []):
worker = dict(worker)
pid = worker["pid"]
worker["logCount"] = node_log_counts.get(str(pid), 0)
worker["errorCount"] = len(node_errs.get(str(pid), []))
worker["coreWorkerStats"] = pid_to_worker_stats.get(pid, [])
worker["language"] = pid_to_language.get(
pid, dashboard_consts.DEFAULT_LANGUAGE
)
worker["jobId"] = pid_to_job_id.get(pid, dashboard_consts.DEFAULT_JOB_ID)
await GlobalSignals.worker_info_fetched.send(node_id, worker)
workers.append(worker)
return workers
@classmethod
async def get_node_info(cls, node_id):
node_physical_stats = dict(DataSource.node_physical_stats.get(node_id, {}))
node_stats = dict(DataSource.node_stats.get(node_id, {}))
node = DataSource.nodes.get(node_id, {})
node_ip = DataSource.node_id_to_ip.get(node_id)
# Merge node log count information into the payload
log_counts = DataSource.ip_and_pid_to_log_counts.get(node_ip, {})
node_log_count = 0
for entries in log_counts.values():
node_log_count += entries
error_info = DataSource.ip_and_pid_to_errors.get(node_ip, {})
node_err_count = 0
for entries in error_info.values():
node_err_count += len(entries)
node_stats.pop("coreWorkersStats", None)
view_data = node_stats.get("viewData", [])
ray_stats = cls._extract_view_data(
view_data, {"object_store_used_memory", "object_store_available_memory"}
)
node_info = node_physical_stats
# Merge node stats to node physical stats under raylet
node_info["raylet"] = node_stats
node_info["raylet"].update(ray_stats)
# Merge GcsNodeInfo to node physical stats
node_info["raylet"].update(node)
# Merge actors to node physical stats
node_info["actors"] = DataSource.node_actors.get(node_id, {})
# Update workers to node physical stats
node_info["workers"] = DataSource.node_workers.get(node_id, [])
node_info["logCount"] = node_log_count
node_info["errorCount"] = node_err_count
await GlobalSignals.node_info_fetched.send(node_info)
return node_info
@classmethod
async def get_node_summary(cls, node_id):
node_physical_stats = dict(DataSource.node_physical_stats.get(node_id, {}))
node_stats = dict(DataSource.node_stats.get(node_id, {}))
node = DataSource.nodes.get(node_id, {})
node_physical_stats.pop("workers", None)
node_stats.pop("workersStats", None)
view_data = node_stats.get("viewData", [])
ray_stats = cls._extract_view_data(
view_data, {"object_store_used_memory", "object_store_available_memory"}
)
node_stats.pop("viewData", None)
node_summary = node_physical_stats
# Merge node stats to node physical stats
node_summary["raylet"] = node_stats
node_summary["raylet"].update(ray_stats)
# Merge GcsNodeInfo to node physical stats
node_summary["raylet"].update(node)
await GlobalSignals.node_summary_fetched.send(node_summary)
return node_summary
@classmethod
async def get_all_node_summary(cls):
return [
await DataOrganizer.get_node_summary(node_id)
for node_id in DataSource.nodes.keys()
]
@classmethod
async def get_all_node_details(cls):
return [
await DataOrganizer.get_node_info(node_id)
for node_id in DataSource.nodes.keys()
]
@classmethod
async def get_all_actors(cls):
result = {}
for index, (actor_id, actor) in enumerate(DataSource.actors.items()):
result[actor_id] = await cls._get_actor(actor)
# There can be thousands of actors including dead ones. Processing
# them all can take many seconds, which blocks all other requests
# to the dashboard. The ideal solution might be to implement
# pagination. For now, use a workaround to yield to the event loop
# periodically, so other request handlers have a chance to run and
# avoid long latencies.
if index % 1000 == 0 and index > 0:
# Canonical way to yield to the event loop:
# https://github.com/python/asyncio/issues/284
await asyncio.sleep(0)
return result
@staticmethod
async def _get_actor(actor):
actor = dict(actor)
worker_id = actor["address"]["workerId"]
core_worker_stats = DataSource.core_worker_stats.get(worker_id, {})
actor_constructor = core_worker_stats.get(
"actorTitle", "Unknown actor constructor"
)
actor["actorConstructor"] = actor_constructor
actor.update(core_worker_stats)
# TODO(fyrestone): remove this, give a link from actor
# info to worker info in front-end.
node_id = actor["address"]["rayletId"]
pid = core_worker_stats.get("pid")
node_physical_stats = DataSource.node_physical_stats.get(node_id, {})
actor_process_stats = None
actor_process_gpu_stats = []
if pid:
for process_stats in node_physical_stats.get("workers", []):
if process_stats["pid"] == pid:
actor_process_stats = process_stats
break
for gpu_stats in node_physical_stats.get("gpus", []):
# gpu_stats.get("processes") can be None, an empty list or a
# list of dictionaries.
for process in gpu_stats.get("processes") or []:
if process["pid"] == pid:
actor_process_gpu_stats.append(gpu_stats)
break
actor["gpus"] = actor_process_gpu_stats
actor["processStats"] = actor_process_stats
return actor
@classmethod
async def get_actor_creation_tasks(cls):
infeasible_tasks = sum(
(
list(node_stats.get("infeasibleTasks", []))
for node_stats in DataSource.node_stats.values()
),
[],
)
new_infeasible_tasks = []
for task in infeasible_tasks:
task = dict(task)
task["actorClass"] = actor_classname_from_task_spec(task)
task["state"] = "INFEASIBLE"
new_infeasible_tasks.append(task)
resource_pending_tasks = sum(
(
list(data.get("readyTasks", []))
for data in DataSource.node_stats.values()
),
[],
)
new_resource_pending_tasks = []
for task in resource_pending_tasks:
task = dict(task)
task["actorClass"] = actor_classname_from_task_spec(task)
task["state"] = "PENDING_RESOURCES"
new_resource_pending_tasks.append(task)
results = {
task["actorCreationTaskSpec"]["actorId"]: task
for task in new_resource_pending_tasks + new_infeasible_tasks
}
return results
@classmethod
async def get_memory_table(
cls,
sort_by=memory_utils.SortingType.OBJECT_SIZE,
group_by=memory_utils.GroupByType.STACK_TRACE,
):
all_worker_stats = []
for node_stats in DataSource.node_stats.values():
all_worker_stats.extend(node_stats.get("coreWorkersStats", []))
memory_information = memory_utils.construct_memory_table(
all_worker_stats, group_by=group_by, sort_by=sort_by
)
return memory_information
@staticmethod
def _extract_view_data(views, data_keys):
view_data = {}
for view in views:
view_name = view["viewName"]
if view_name in data_keys:
if not view.get("measures"):
view_data[view_name] = 0
continue
measure = view["measures"][0]
if "doubleValue" in measure:
measure_value = measure["doubleValue"]
elif "intValue" in measure:
measure_value = measure["intValue"]
else:
measure_value = 0
view_data[view_name] = measure_value
return view_data