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# Copyright 2017 Vector Creations Ltd
# Copyright 2019 New Vector Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import heapq
from typing import TYPE_CHECKING, Iterable, Optional, Tuple, Type, TypeVar, cast
import attr
from synapse.replication.tcp.streams._base import (
Stream,
StreamRow,
StreamUpdateResult,
Token,
)
if TYPE_CHECKING:
from synapse.server import HomeServer
"""Handling of the 'events' replication stream
This stream contains rows of various types. Each row therefore contains a 'type'
identifier before the real data. For example::
RDATA events batch ["state", ["!room:id", "m.type", "", "$event:id"]]
RDATA events 12345 ["ev", ["$event:id", "!room:id", "m.type", null, null]]
An "ev" row is sent for each new event. The fields in the data part are:
* The new event id
* The room id for the event
* The type of the new event
* The state key of the event, for state events
* The event id of an event which is redacted by this event.
A "state" row is sent whenever the "current state" in a room changes. The fields in the
data part are:
* The room id for the state change
* The event type of the state which has changed
* The state_key of the state which has changed
* The event id of the new state
"""
@attr.s(slots=True, frozen=True, auto_attribs=True)
class EventsStreamRow:
"""A parsed row from the events replication stream"""
type: str # the TypeId of one of the *EventsStreamRows
data: "BaseEventsStreamRow"
T = TypeVar("T", bound="BaseEventsStreamRow")
class BaseEventsStreamRow:
"""Base class for rows to be sent in the events stream.
Specifies how to identify, serialize and deserialize the different types.
"""
# Unique string that ids the type. Must be overridden in sub classes.
TypeId: str
@classmethod
def from_data(cls: Type[T], data: Iterable[Optional[str]]) -> T:
"""Parse the data from the replication stream into a row.
By default we just call the constructor with the data list as arguments
Args:
data: The value of the data object from the replication stream
"""
return cls(*data)
@attr.s(slots=True, frozen=True, auto_attribs=True)
class EventsStreamEventRow(BaseEventsStreamRow):
TypeId = "ev"
event_id: str
room_id: str
type: str
state_key: Optional[str]
redacts: Optional[str]
relates_to: Optional[str]
membership: Optional[str]
rejected: bool
outlier: bool
@attr.s(slots=True, frozen=True, auto_attribs=True)
class EventsStreamCurrentStateRow(BaseEventsStreamRow):
TypeId = "state"
room_id: str
type: str
state_key: str
event_id: Optional[str]
_EventRows: Tuple[Type[BaseEventsStreamRow], ...] = (
EventsStreamEventRow,
EventsStreamCurrentStateRow,
)
TypeToRow = {Row.TypeId: Row for Row in _EventRows}
class EventsStream(Stream):
"""We received a new event, or an event went from being an outlier to not"""
NAME = "events"
def __init__(self, hs: "HomeServer"):
self._store = hs.get_datastores().main
super().__init__(
hs.get_instance_name(),
self._store._stream_id_gen.get_current_token_for_writer,
self._update_function,
)
async def _update_function(
self,
instance_name: str,
from_token: Token,
current_token: Token,
target_row_count: int,
) -> StreamUpdateResult:
# the events stream merges together three separate sources:
# * new events
# * current_state changes
# * events which were previously outliers, but have now been de-outliered.
#
# The merge operation is complicated by the fact that we only have a single
# "stream token" which is supposed to indicate how far we have got through
# all three streams. It's therefore no good to return rows 1-1000 from the
# "new events" table if the state_deltas are limited to rows 1-100 by the
# target_row_count.
#
# In other words: we must pick a new upper limit, and must return *all* rows
# up to that point for each of the three sources.
#
# Start by trying to split the target_row_count up. We expect to have a
# negligible number of ex-outliers, and a rough approximation based on recent
# traffic on sw1v.org shows that there are approximately the same number of
# event rows between a given pair of stream ids as there are state
# updates, so let's split our target_row_count among those two types. The target
# is only an approximation - it doesn't matter if we end up going a bit over it.
target_row_count //= 2
# now we fetch up to that many rows from the events table
event_rows = await self._store.get_all_new_forward_event_rows(
instance_name, from_token, current_token, target_row_count
)
# we rely on get_all_new_forward_event_rows strictly honouring the limit, so
# that we know it is safe to just take upper_limit = event_rows[-1][0].
assert (
len(event_rows) <= target_row_count
), "get_all_new_forward_event_rows did not honour row limit"
# if we hit the limit on event_updates, there's no point in going beyond the
# last stream_id in the batch for the other sources.
if len(event_rows) == target_row_count:
limited = True
upper_limit: int = event_rows[-1][0]
else:
limited = False
upper_limit = current_token
# next up is the state delta table.
(
state_rows,
upper_limit,
state_rows_limited,
) = await self._store.get_all_updated_current_state_deltas(
instance_name, from_token, upper_limit, target_row_count
)
limited = limited or state_rows_limited
# finally, fetch the ex-outliers rows. We assume there are few enough of these
# not to bother with the limit.
ex_outliers_rows = await self._store.get_ex_outlier_stream_rows(
instance_name, from_token, upper_limit
)
# we now need to turn the raw database rows returned into tuples suitable
# for the replication protocol (basically, we add an identifier to
# distinguish the row type). At the same time, we can limit the event_rows
# to the max stream_id from state_rows.
event_updates: Iterable[Tuple[int, Tuple]] = (
(stream_id, (EventsStreamEventRow.TypeId, rest))
for (stream_id, *rest) in event_rows
if stream_id <= upper_limit
)
state_updates: Iterable[Tuple[int, Tuple]] = (
(stream_id, (EventsStreamCurrentStateRow.TypeId, rest))
for (stream_id, *rest) in state_rows
)
ex_outliers_updates: Iterable[Tuple[int, Tuple]] = (
(stream_id, (EventsStreamEventRow.TypeId, rest))
for (stream_id, *rest) in ex_outliers_rows
)
# we need to return a sorted list, so merge them together.
updates = list(heapq.merge(event_updates, state_updates, ex_outliers_updates))
return updates, upper_limit, limited
@classmethod
def parse_row(cls, row: StreamRow) -> "EventsStreamRow":
(typ, data) = cast(Tuple[str, Iterable[Optional[str]]], row)
event_stream_row_data = TypeToRow[typ].from_data(data)
return EventsStreamRow(typ, event_stream_row_data)
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