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ce428a1abe
Figuring out how to correctly limit updates from this stream without dropping entries is far more complicated than just counting the number of rows being returned. We need to consider each query separately and, if any one query hits the limit, truncate the results from the others. I think this also fixes some potentially long-standing bugs where events or state changes could get missed if we hit the limit on either query.
227 lines
7.9 KiB
Python
227 lines
7.9 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2017 Vector Creations Ltd
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# Copyright 2019 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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from collections import Iterable
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from typing import List, Tuple, Type
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import attr
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from ._base import Stream, StreamUpdateResult, Token
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"""Handling of the 'events' replication stream
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This stream contains rows of various types. Each row therefore contains a 'type'
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identifier before the real data. For example::
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RDATA events batch ["state", ["!room:id", "m.type", "", "$event:id"]]
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RDATA events 12345 ["ev", ["$event:id", "!room:id", "m.type", null, null]]
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An "ev" row is sent for each new event. The fields in the data part are:
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* The new event id
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* The room id for the event
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* The type of the new event
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* The state key of the event, for state events
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* The event id of an event which is redacted by this event.
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A "state" row is sent whenever the "current state" in a room changes. The fields in the
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data part are:
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* The room id for the state change
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* The event type of the state which has changed
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* The state_key of the state which has changed
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* The event id of the new state
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"""
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@attr.s(slots=True, frozen=True)
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class EventsStreamRow(object):
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"""A parsed row from the events replication stream"""
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type = attr.ib() # str: the TypeId of one of the *EventsStreamRows
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data = attr.ib() # BaseEventsStreamRow
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class BaseEventsStreamRow(object):
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"""Base class for rows to be sent in the events stream.
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Specifies how to identify, serialize and deserialize the different types.
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"""
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# Unique string that ids the type. Must be overriden in sub classes.
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TypeId = None # type: str
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@classmethod
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def from_data(cls, data):
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"""Parse the data from the replication stream into a row.
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By default we just call the constructor with the data list as arguments
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Args:
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data: The value of the data object from the replication stream
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"""
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return cls(*data)
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@attr.s(slots=True, frozen=True)
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class EventsStreamEventRow(BaseEventsStreamRow):
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TypeId = "ev"
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event_id = attr.ib() # str
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room_id = attr.ib() # str
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type = attr.ib() # str
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state_key = attr.ib() # str, optional
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redacts = attr.ib() # str, optional
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relates_to = attr.ib() # str, optional
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@attr.s(slots=True, frozen=True)
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class EventsStreamCurrentStateRow(BaseEventsStreamRow):
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TypeId = "state"
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room_id = attr.ib() # str
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type = attr.ib() # str
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state_key = attr.ib() # str
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event_id = attr.ib() # str, optional
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_EventRows = (
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EventsStreamEventRow,
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EventsStreamCurrentStateRow,
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) # type: Tuple[Type[BaseEventsStreamRow], ...]
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TypeToRow = {Row.TypeId: Row for Row in _EventRows}
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class EventsStream(Stream):
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"""We received a new event, or an event went from being an outlier to not
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"""
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NAME = "events"
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def __init__(self, hs):
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self._store = hs.get_datastore()
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super().__init__(
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self._store.get_current_events_token, self._update_function,
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)
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async def _update_function(
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self, from_token: Token, current_token: Token, target_row_count: int
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) -> StreamUpdateResult:
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# the events stream merges together three separate sources:
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# * new events
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# * current_state changes
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# * events which were previously outliers, but have now been de-outliered.
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#
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# The merge operation is complicated by the fact that we only have a single
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# "stream token" which is supposed to indicate how far we have got through
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# all three streams. It's therefore no good to return rows 1-1000 from the
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# "new events" table if the state_deltas are limited to rows 1-100 by the
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# target_row_count.
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#
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# In other words: we must pick a new upper limit, and must return *all* rows
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# up to that point for each of the three sources.
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#
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# Start by trying to split the target_row_count up. We expect to have a
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# negligible number of ex-outliers, and a rough approximation based on recent
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# traffic on sw1v.org shows that there are approximately the same number of
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# event rows between a given pair of stream ids as there are state
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# updates, so let's split our target_row_count among those two types. The target
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# is only an approximation - it doesn't matter if we end up going a bit over it.
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target_row_count //= 2
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# now we fetch up to that many rows from the events table
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event_rows = await self._store.get_all_new_forward_event_rows(
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from_token, current_token, target_row_count
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) # type: List[Tuple]
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# we rely on get_all_new_forward_event_rows strictly honouring the limit, so
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# that we know it is safe to just take upper_limit = event_rows[-1][0].
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assert (
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len(event_rows) <= target_row_count
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), "get_all_new_forward_event_rows did not honour row limit"
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# if we hit the limit on event_updates, there's no point in going beyond the
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# last stream_id in the batch for the other sources.
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if len(event_rows) == target_row_count:
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limited = True
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upper_limit = event_rows[-1][0] # type: int
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else:
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limited = False
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upper_limit = current_token
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# next up is the state delta table
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state_rows = await self._store.get_all_updated_current_state_deltas(
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from_token, upper_limit, target_row_count
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) # type: List[Tuple]
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# again, if we've hit the limit there, we'll need to limit the other sources
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assert len(state_rows) < target_row_count
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if len(state_rows) == target_row_count:
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assert state_rows[-1][0] <= upper_limit
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upper_limit = state_rows[-1][0]
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limited = True
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# FIXME: is it a given that there is only one row per stream_id in the
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# state_deltas table (so that we can be sure that we have got all of the
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# rows for upper_limit)?
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# finally, fetch the ex-outliers rows. We assume there are few enough of these
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# not to bother with the limit.
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ex_outliers_rows = await self._store.get_ex_outlier_stream_rows(
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from_token, upper_limit
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) # type: List[Tuple]
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# we now need to turn the raw database rows returned into tuples suitable
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# for the replication protocol (basically, we add an identifier to
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# distinguish the row type). At the same time, we can limit the event_rows
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# to the max stream_id from state_rows.
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event_updates = (
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(stream_id, (EventsStreamEventRow.TypeId, rest))
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for (stream_id, *rest) in event_rows
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if stream_id <= upper_limit
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) # type: Iterable[Tuple[int, Tuple]]
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state_updates = (
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(stream_id, (EventsStreamCurrentStateRow.TypeId, rest))
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for (stream_id, *rest) in state_rows
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) # type: Iterable[Tuple[int, Tuple]]
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ex_outliers_updates = (
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(stream_id, (EventsStreamEventRow.TypeId, rest))
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for (stream_id, *rest) in ex_outliers_rows
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) # type: Iterable[Tuple[int, Tuple]]
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# we need to return a sorted list, so merge them together.
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updates = list(heapq.merge(event_updates, state_updates, ex_outliers_updates))
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return updates, upper_limit, limited
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@classmethod
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def parse_row(cls, row):
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(typ, data) = row
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data = TypeToRow[typ].from_data(data)
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return EventsStreamRow(typ, data)
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