826 lines
29 KiB
Python
826 lines
29 KiB
Python
# Copyright 2014-2016 OpenMarket Ltd
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# Copyright 2018 New Vector Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import heapq
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import logging
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from collections import defaultdict, namedtuple
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from typing import (
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TYPE_CHECKING,
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Any,
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Awaitable,
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Callable,
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Collection,
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DefaultDict,
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Dict,
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FrozenSet,
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Iterable,
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List,
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Optional,
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Sequence,
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Set,
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Tuple,
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Union,
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overload,
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)
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import attr
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from frozendict import frozendict
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from prometheus_client import Counter, Histogram
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from typing_extensions import Literal
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from synapse.api.constants import EventTypes
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from synapse.api.room_versions import KNOWN_ROOM_VERSIONS, StateResolutionVersions
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from synapse.events import EventBase
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from synapse.events.snapshot import EventContext
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from synapse.logging.context import ContextResourceUsage
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from synapse.logging.utils import log_function
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from synapse.state import v1, v2
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from synapse.storage.databases.main.events_worker import EventRedactBehaviour
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from synapse.storage.roommember import ProfileInfo
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from synapse.types import StateMap
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from synapse.util.async_helpers import Linearizer
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from synapse.util.caches.expiringcache import ExpiringCache
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from synapse.util.metrics import Measure, measure_func
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if TYPE_CHECKING:
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from synapse.server import HomeServer
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from synapse.storage.databases.main import DataStore
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logger = logging.getLogger(__name__)
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metrics_logger = logging.getLogger("synapse.state.metrics")
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# Metrics for number of state groups involved in a resolution.
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state_groups_histogram = Histogram(
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"synapse_state_number_state_groups_in_resolution",
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"Number of state groups used when performing a state resolution",
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buckets=(1, 2, 3, 5, 7, 10, 15, 20, 50, 100, 200, 500, "+Inf"),
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)
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KeyStateTuple = namedtuple("KeyStateTuple", ("context", "type", "state_key"))
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EVICTION_TIMEOUT_SECONDS = 60 * 60
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_NEXT_STATE_ID = 1
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POWER_KEY = (EventTypes.PowerLevels, "")
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def _gen_state_id() -> str:
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global _NEXT_STATE_ID
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s = "X%d" % (_NEXT_STATE_ID,)
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_NEXT_STATE_ID += 1
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return s
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class _StateCacheEntry:
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__slots__ = ["state", "state_group", "state_id", "prev_group", "delta_ids"]
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def __init__(
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self,
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state: StateMap[str],
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state_group: Optional[int],
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prev_group: Optional[int] = None,
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delta_ids: Optional[StateMap[str]] = None,
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):
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# A map from (type, state_key) to event_id.
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self.state = frozendict(state)
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# the ID of a state group if one and only one is involved.
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# otherwise, None otherwise?
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self.state_group = state_group
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self.prev_group = prev_group
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self.delta_ids = frozendict(delta_ids) if delta_ids is not None else None
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# The `state_id` is a unique ID we generate that can be used as ID for
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# this collection of state. Usually this would be the same as the
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# state group, but on worker instances we can't generate a new state
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# group each time we resolve state, so we generate a separate one that
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# isn't persisted and is used solely for caches.
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# `state_id` is either a state_group (and so an int) or a string. This
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# ensures we don't accidentally persist a state_id as a stateg_group
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if state_group:
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self.state_id: Union[str, int] = state_group
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else:
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self.state_id = _gen_state_id()
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def __len__(self):
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return len(self.state)
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class StateHandler:
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"""Fetches bits of state from the stores, and does state resolution
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where necessary
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"""
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def __init__(self, hs: "HomeServer"):
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self.clock = hs.get_clock()
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self.store = hs.get_datastore()
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self.state_store = hs.get_storage().state
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self.hs = hs
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self._state_resolution_handler = hs.get_state_resolution_handler()
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@overload
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async def get_current_state(
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self,
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room_id: str,
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event_type: Literal[None] = None,
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state_key: str = "",
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latest_event_ids: Optional[List[str]] = None,
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) -> StateMap[EventBase]:
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...
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@overload
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async def get_current_state(
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self,
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room_id: str,
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event_type: str,
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state_key: str = "",
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latest_event_ids: Optional[List[str]] = None,
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) -> Optional[EventBase]:
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...
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async def get_current_state(
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self,
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room_id: str,
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event_type: Optional[str] = None,
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state_key: str = "",
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latest_event_ids: Optional[List[str]] = None,
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) -> Union[Optional[EventBase], StateMap[EventBase]]:
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"""Retrieves the current state for the room. This is done by
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calling `get_latest_events_in_room` to get the leading edges of the
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event graph and then resolving any of the state conflicts.
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This is equivalent to getting the state of an event that were to send
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next before receiving any new events.
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Returns:
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If `event_type` is specified, then the method returns only the one
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event (or None) with that `event_type` and `state_key`.
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Otherwise, a map from (type, state_key) to event.
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"""
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if not latest_event_ids:
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latest_event_ids = await self.store.get_latest_event_ids_in_room(room_id)
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assert latest_event_ids is not None
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logger.debug("calling resolve_state_groups from get_current_state")
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ret = await self.resolve_state_groups_for_events(room_id, latest_event_ids)
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state = ret.state
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if event_type:
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event_id = state.get((event_type, state_key))
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event = None
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if event_id:
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event = await self.store.get_event(event_id, allow_none=True)
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return event
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state_map = await self.store.get_events(
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list(state.values()), get_prev_content=False
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)
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return {
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key: state_map[e_id] for key, e_id in state.items() if e_id in state_map
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}
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async def get_current_state_ids(
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self, room_id: str, latest_event_ids: Optional[Iterable[str]] = None
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) -> StateMap[str]:
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"""Get the current state, or the state at a set of events, for a room
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Args:
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room_id:
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latest_event_ids: if given, the forward extremities to resolve. If
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None, we look them up from the database (via a cache).
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Returns:
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the state dict, mapping from (event_type, state_key) -> event_id
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"""
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if not latest_event_ids:
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latest_event_ids = await self.store.get_latest_event_ids_in_room(room_id)
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assert latest_event_ids is not None
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logger.debug("calling resolve_state_groups from get_current_state_ids")
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ret = await self.resolve_state_groups_for_events(room_id, latest_event_ids)
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return ret.state
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async def get_current_users_in_room(
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self, room_id: str, latest_event_ids: List[str]
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) -> Dict[str, ProfileInfo]:
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"""
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Get the users who are currently in a room.
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Note: This is much slower than using the equivalent method
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`DataStore.get_users_in_room` or `DataStore.get_users_in_room_with_profiles`,
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so this should only be used when wanting the users at a particular point
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in the room.
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Args:
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room_id: The ID of the room.
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latest_event_ids: Precomputed list of latest event IDs. Will be computed if None.
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Returns:
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Dictionary of user IDs to their profileinfo.
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"""
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assert latest_event_ids is not None
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logger.debug("calling resolve_state_groups from get_current_users_in_room")
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entry = await self.resolve_state_groups_for_events(room_id, latest_event_ids)
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return await self.store.get_joined_users_from_state(room_id, entry)
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async def get_current_hosts_in_room(self, room_id: str) -> Set[str]:
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event_ids = await self.store.get_latest_event_ids_in_room(room_id)
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return await self.get_hosts_in_room_at_events(room_id, event_ids)
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async def get_hosts_in_room_at_events(
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self, room_id: str, event_ids: List[str]
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) -> Set[str]:
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"""Get the hosts that were in a room at the given event ids
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Args:
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room_id:
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event_ids:
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Returns:
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The hosts in the room at the given events
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"""
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entry = await self.resolve_state_groups_for_events(room_id, event_ids)
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return await self.store.get_joined_hosts(room_id, entry)
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async def compute_event_context(
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self, event: EventBase, old_state: Optional[Iterable[EventBase]] = None
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) -> EventContext:
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"""Build an EventContext structure for the event.
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This works out what the current state should be for the event, and
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generates a new state group if necessary.
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Args:
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event:
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old_state: The state at the event if it can't be
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calculated from existing events. This is normally only specified
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when receiving an event from federation where we don't have the
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prev events for, e.g. when backfilling.
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Returns:
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The event context.
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"""
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if event.internal_metadata.is_outlier():
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# If this is an outlier, then we know it shouldn't have any current
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# state. Certainly store.get_current_state won't return any, and
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# persisting the event won't store the state group.
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# FIXME: why do we populate current_state_ids? I thought the point was
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# that we weren't supposed to have any state for outliers?
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if old_state:
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prev_state_ids = {(s.type, s.state_key): s.event_id for s in old_state}
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if event.is_state():
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current_state_ids = dict(prev_state_ids)
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key = (event.type, event.state_key)
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current_state_ids[key] = event.event_id
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else:
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current_state_ids = prev_state_ids
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else:
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current_state_ids = {}
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prev_state_ids = {}
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# We don't store state for outliers, so we don't generate a state
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# group for it.
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context = EventContext.with_state(
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state_group=None,
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state_group_before_event=None,
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current_state_ids=current_state_ids,
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prev_state_ids=prev_state_ids,
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)
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return context
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#
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# first of all, figure out the state before the event
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#
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if old_state:
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# if we're given the state before the event, then we use that
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state_ids_before_event: StateMap[str] = {
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(s.type, s.state_key): s.event_id for s in old_state
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}
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state_group_before_event = None
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state_group_before_event_prev_group = None
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deltas_to_state_group_before_event = None
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entry = None
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else:
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# otherwise, we'll need to resolve the state across the prev_events.
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logger.debug("calling resolve_state_groups from compute_event_context")
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entry = await self.resolve_state_groups_for_events(
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event.room_id, event.prev_event_ids()
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)
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state_ids_before_event = entry.state
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state_group_before_event = entry.state_group
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state_group_before_event_prev_group = entry.prev_group
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deltas_to_state_group_before_event = entry.delta_ids
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#
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# make sure that we have a state group at that point. If it's not a state event,
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# that will be the state group for the new event. If it *is* a state event,
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# it might get rejected (in which case we'll need to persist it with the
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# previous state group)
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#
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if not state_group_before_event:
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state_group_before_event = await self.state_store.store_state_group(
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event.event_id,
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event.room_id,
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prev_group=state_group_before_event_prev_group,
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delta_ids=deltas_to_state_group_before_event,
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current_state_ids=state_ids_before_event,
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)
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# Assign the new state group to the cached state entry.
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#
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# Note that this can race in that we could generate multiple state
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# groups for the same state entry, but that is just inefficient
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# rather than dangerous.
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if entry and entry.state_group is None:
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entry.state_group = state_group_before_event
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#
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# now if it's not a state event, we're done
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#
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if not event.is_state():
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return EventContext.with_state(
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state_group_before_event=state_group_before_event,
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state_group=state_group_before_event,
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current_state_ids=state_ids_before_event,
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prev_state_ids=state_ids_before_event,
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prev_group=state_group_before_event_prev_group,
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delta_ids=deltas_to_state_group_before_event,
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)
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#
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# otherwise, we'll need to create a new state group for after the event
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#
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key = (event.type, event.state_key)
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if key in state_ids_before_event:
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replaces = state_ids_before_event[key]
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if replaces != event.event_id:
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event.unsigned["replaces_state"] = replaces
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state_ids_after_event = dict(state_ids_before_event)
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state_ids_after_event[key] = event.event_id
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delta_ids = {key: event.event_id}
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state_group_after_event = await self.state_store.store_state_group(
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event.event_id,
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event.room_id,
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prev_group=state_group_before_event,
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delta_ids=delta_ids,
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current_state_ids=state_ids_after_event,
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)
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return EventContext.with_state(
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state_group=state_group_after_event,
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state_group_before_event=state_group_before_event,
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current_state_ids=state_ids_after_event,
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prev_state_ids=state_ids_before_event,
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prev_group=state_group_before_event,
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delta_ids=delta_ids,
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)
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@measure_func()
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async def resolve_state_groups_for_events(
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self, room_id: str, event_ids: Iterable[str]
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) -> _StateCacheEntry:
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"""Given a list of event_ids this method fetches the state at each
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event, resolves conflicts between them and returns them.
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Args:
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room_id
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event_ids
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Returns:
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The resolved state
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"""
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logger.debug("resolve_state_groups event_ids %s", event_ids)
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# map from state group id to the state in that state group (where
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# 'state' is a map from state key to event id)
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# dict[int, dict[(str, str), str]]
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state_groups_ids = await self.state_store.get_state_groups_ids(
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room_id, event_ids
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)
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if len(state_groups_ids) == 0:
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return _StateCacheEntry(state={}, state_group=None)
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elif len(state_groups_ids) == 1:
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name, state_list = list(state_groups_ids.items()).pop()
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prev_group, delta_ids = await self.state_store.get_state_group_delta(name)
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return _StateCacheEntry(
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state=state_list,
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state_group=name,
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prev_group=prev_group,
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delta_ids=delta_ids,
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)
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room_version = await self.store.get_room_version_id(room_id)
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result = await self._state_resolution_handler.resolve_state_groups(
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room_id,
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room_version,
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state_groups_ids,
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None,
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state_res_store=StateResolutionStore(self.store),
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)
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return result
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async def resolve_events(
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self,
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room_version: str,
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state_sets: Collection[Iterable[EventBase]],
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event: EventBase,
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) -> StateMap[EventBase]:
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logger.info(
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"Resolving state for %s with %d groups", event.room_id, len(state_sets)
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)
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state_set_ids = [
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{(ev.type, ev.state_key): ev.event_id for ev in st} for st in state_sets
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]
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state_map = {ev.event_id: ev for st in state_sets for ev in st}
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new_state = await self._state_resolution_handler.resolve_events_with_store(
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event.room_id,
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room_version,
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state_set_ids,
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event_map=state_map,
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state_res_store=StateResolutionStore(self.store),
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)
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return {key: state_map[ev_id] for key, ev_id in new_state.items()}
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@attr.s(slots=True)
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class _StateResMetrics:
|
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"""Keeps track of some usage metrics about state res."""
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# System and User CPU time, in seconds
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cpu_time = attr.ib(type=float, default=0.0)
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# time spent on database transactions (excluding scheduling time). This roughly
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# corresponds to the amount of work done on the db server, excluding event fetches.
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db_time = attr.ib(type=float, default=0.0)
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# number of events fetched from the db.
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db_events = attr.ib(type=int, default=0)
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|
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_biggest_room_by_cpu_counter = Counter(
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"synapse_state_res_cpu_for_biggest_room_seconds",
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"CPU time spent performing state resolution for the single most expensive "
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"room for state resolution",
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)
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_biggest_room_by_db_counter = Counter(
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"synapse_state_res_db_for_biggest_room_seconds",
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"Database time spent performing state resolution for the single most "
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"expensive room for state resolution",
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)
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|
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class StateResolutionHandler:
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"""Responsible for doing state conflict resolution.
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Note that the storage layer depends on this handler, so all functions must
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be storage-independent.
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"""
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def __init__(self, hs: "HomeServer"):
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self.clock = hs.get_clock()
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self.resolve_linearizer = Linearizer(name="state_resolve_lock")
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# dict of set of event_ids -> _StateCacheEntry.
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self._state_cache: ExpiringCache[
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FrozenSet[int], _StateCacheEntry
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] = ExpiringCache(
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cache_name="state_cache",
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clock=self.clock,
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max_len=100000,
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expiry_ms=EVICTION_TIMEOUT_SECONDS * 1000,
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iterable=True,
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reset_expiry_on_get=True,
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)
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#
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# stuff for tracking time spent on state-res by room
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#
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# tracks the amount of work done on state res per room
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self._state_res_metrics: DefaultDict[str, _StateResMetrics] = defaultdict(
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_StateResMetrics
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)
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self.clock.looping_call(self._report_metrics, 120 * 1000)
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@log_function
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async def resolve_state_groups(
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self,
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room_id: str,
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room_version: str,
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state_groups_ids: Dict[int, StateMap[str]],
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event_map: Optional[Dict[str, EventBase]],
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state_res_store: "StateResolutionStore",
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) -> _StateCacheEntry:
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"""Resolves conflicts between a set of state groups
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Always generates a new state group (unless we hit the cache), so should
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not be called for a single state group
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Args:
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room_id: room we are resolving for (used for logging and sanity checks)
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room_version: version of the room
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state_groups_ids:
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A map from state group id to the state in that state group
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(where 'state' is a map from state key to event id)
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event_map:
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a dict from event_id to event, for any events that we happen to
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have in flight (eg, those currently being persisted). This will be
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used as a starting point for finding the state we need; any missing
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events will be requested via state_res_store.
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If None, all events will be fetched via state_res_store.
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state_res_store
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Returns:
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The resolved state
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"""
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group_names = frozenset(state_groups_ids.keys())
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with (await self.resolve_linearizer.queue(group_names)):
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cache = self._state_cache.get(group_names, None)
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if cache:
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return cache
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logger.info(
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"Resolving state for %s with groups %s",
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room_id,
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list(group_names),
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)
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state_groups_histogram.observe(len(state_groups_ids))
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new_state = await self.resolve_events_with_store(
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room_id,
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room_version,
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list(state_groups_ids.values()),
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event_map=event_map,
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state_res_store=state_res_store,
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)
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# if the new state matches any of the input state groups, we can
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# use that state group again. Otherwise we will generate a state_id
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# which will be used as a cache key for future resolutions, but
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# not get persisted.
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with Measure(self.clock, "state.create_group_ids"):
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cache = _make_state_cache_entry(new_state, state_groups_ids)
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self._state_cache[group_names] = cache
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return cache
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async def resolve_events_with_store(
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self,
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room_id: str,
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room_version: str,
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state_sets: Sequence[StateMap[str]],
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event_map: Optional[Dict[str, EventBase]],
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state_res_store: "StateResolutionStore",
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) -> StateMap[str]:
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"""
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Args:
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room_id: the room we are working in
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room_version: Version of the room
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state_sets: List of dicts of (type, state_key) -> event_id,
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which are the different state groups to resolve.
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event_map:
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a dict from event_id to event, for any events that we happen to
|
|
have in flight (eg, those currently being persisted). This will be
|
|
used as a starting point for finding the state we need; any missing
|
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events will be requested via state_map_factory.
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If None, all events will be fetched via state_res_store.
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state_res_store: a place to fetch events from
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Returns:
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a map from (type, state_key) to event_id.
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"""
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try:
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with Measure(self.clock, "state._resolve_events") as m:
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room_version_obj = KNOWN_ROOM_VERSIONS[room_version]
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if room_version_obj.state_res == StateResolutionVersions.V1:
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return await v1.resolve_events_with_store(
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room_id,
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room_version_obj,
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state_sets,
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event_map,
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state_res_store.get_events,
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)
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else:
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return await v2.resolve_events_with_store(
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self.clock,
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room_id,
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room_version_obj,
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state_sets,
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event_map,
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state_res_store,
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)
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finally:
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self._record_state_res_metrics(room_id, m.get_resource_usage())
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def _record_state_res_metrics(
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self, room_id: str, rusage: ContextResourceUsage
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) -> None:
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room_metrics = self._state_res_metrics[room_id]
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room_metrics.cpu_time += rusage.ru_utime + rusage.ru_stime
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room_metrics.db_time += rusage.db_txn_duration_sec
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room_metrics.db_events += rusage.evt_db_fetch_count
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def _report_metrics(self) -> None:
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if not self._state_res_metrics:
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# no state res has happened since the last iteration: don't bother logging.
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return
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self._report_biggest(
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lambda i: i.cpu_time,
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"CPU time",
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_biggest_room_by_cpu_counter,
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)
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self._report_biggest(
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lambda i: i.db_time,
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"DB time",
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_biggest_room_by_db_counter,
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)
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self._state_res_metrics.clear()
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def _report_biggest(
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self,
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extract_key: Callable[[_StateResMetrics], Any],
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metric_name: str,
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prometheus_counter_metric: Counter,
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) -> None:
|
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"""Report metrics on the biggest rooms for state res
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Args:
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extract_key: a callable which, given a _StateResMetrics, extracts a single
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metric to sort by.
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metric_name: the name of the metric we have extracted, for the log line
|
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prometheus_counter_metric: a prometheus metric recording the sum of the
|
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the extracted metric
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"""
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n_to_log = 10
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if not metrics_logger.isEnabledFor(logging.DEBUG):
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# only need the most expensive if we don't have debug logging, which
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# allows nlargest() to degrade to max()
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n_to_log = 1
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items = self._state_res_metrics.items()
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# log the N biggest rooms
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biggest: List[Tuple[str, _StateResMetrics]] = heapq.nlargest(
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n_to_log, items, key=lambda i: extract_key(i[1])
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)
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metrics_logger.debug(
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"%i biggest rooms for state-res by %s: %s",
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len(biggest),
|
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metric_name,
|
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["%s (%gs)" % (r, extract_key(m)) for (r, m) in biggest],
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)
|
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|
|
# report info on the single biggest to prometheus
|
|
_, biggest_metrics = biggest[0]
|
|
prometheus_counter_metric.inc(extract_key(biggest_metrics))
|
|
|
|
|
|
def _make_state_cache_entry(
|
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new_state: StateMap[str], state_groups_ids: Dict[int, StateMap[str]]
|
|
) -> _StateCacheEntry:
|
|
"""Given a resolved state, and a set of input state groups, pick one to base
|
|
a new state group on (if any), and return an appropriately-constructed
|
|
_StateCacheEntry.
|
|
|
|
Args:
|
|
new_state: resolved state map (mapping from (type, state_key) to event_id)
|
|
|
|
state_groups_ids:
|
|
map from state group id to the state in that state group (where
|
|
'state' is a map from state key to event id)
|
|
|
|
Returns:
|
|
The cache entry.
|
|
"""
|
|
# if the new state matches any of the input state groups, we can
|
|
# use that state group again. Otherwise we will generate a state_id
|
|
# which will be used as a cache key for future resolutions, but
|
|
# not get persisted.
|
|
|
|
# first look for exact matches
|
|
new_state_event_ids = set(new_state.values())
|
|
for sg, state in state_groups_ids.items():
|
|
if len(new_state_event_ids) != len(state):
|
|
continue
|
|
|
|
old_state_event_ids = set(state.values())
|
|
if new_state_event_ids == old_state_event_ids:
|
|
# got an exact match.
|
|
return _StateCacheEntry(state=new_state, state_group=sg)
|
|
|
|
# TODO: We want to create a state group for this set of events, to
|
|
# increase cache hits, but we need to make sure that it doesn't
|
|
# end up as a prev_group without being added to the database
|
|
|
|
# failing that, look for the closest match.
|
|
prev_group = None
|
|
delta_ids: Optional[StateMap[str]] = None
|
|
|
|
for old_group, old_state in state_groups_ids.items():
|
|
n_delta_ids = {k: v for k, v in new_state.items() if old_state.get(k) != v}
|
|
if not delta_ids or len(n_delta_ids) < len(delta_ids):
|
|
prev_group = old_group
|
|
delta_ids = n_delta_ids
|
|
|
|
return _StateCacheEntry(
|
|
state=new_state, state_group=None, prev_group=prev_group, delta_ids=delta_ids
|
|
)
|
|
|
|
|
|
@attr.s(slots=True, auto_attribs=True)
|
|
class StateResolutionStore:
|
|
"""Interface that allows state resolution algorithms to access the database
|
|
in well defined way.
|
|
"""
|
|
|
|
store: "DataStore"
|
|
|
|
def get_events(
|
|
self, event_ids: Iterable[str], allow_rejected: bool = False
|
|
) -> Awaitable[Dict[str, EventBase]]:
|
|
"""Get events from the database
|
|
|
|
Args:
|
|
event_ids: The event_ids of the events to fetch
|
|
allow_rejected: If True return rejected events.
|
|
|
|
Returns:
|
|
An awaitable which resolves to a dict from event_id to event.
|
|
"""
|
|
|
|
return self.store.get_events(
|
|
event_ids,
|
|
redact_behaviour=EventRedactBehaviour.AS_IS,
|
|
get_prev_content=False,
|
|
allow_rejected=allow_rejected,
|
|
)
|
|
|
|
def get_auth_chain_difference(
|
|
self, room_id: str, state_sets: List[Set[str]]
|
|
) -> Awaitable[Set[str]]:
|
|
"""Given sets of state events figure out the auth chain difference (as
|
|
per state res v2 algorithm).
|
|
|
|
This equivalent to fetching the full auth chain for each set of state
|
|
and returning the events that don't appear in each and every auth
|
|
chain.
|
|
|
|
Returns:
|
|
An awaitable that resolves to a set of event IDs.
|
|
"""
|
|
|
|
return self.store.get_auth_chain_difference(room_id, state_sets)
|