path: root/synapse/storage/stats.py

# -*- coding: utf-8 -*-
# Copyright 2018, 2019 New Vector Ltd
# Copyright 2019 The Matrix.org Foundation C.I.C.
#
# 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 logging
from itertools import chain

from twisted.internet import defer
from twisted.internet.defer import DeferredLock

from synapse.api.constants import EventTypes, Membership
from synapse.storage import PostgresEngine
from synapse.storage.engines import Sqlite3Engine
from synapse.storage.state_deltas import StateDeltasStore
from synapse.util.caches.descriptors import cached

logger = logging.getLogger(__name__)

# these fields track absolutes (e.g. total number of rooms on the server)
# You can think of these as Prometheus Gauges.
# You can draw these stats on a line graph.
# Example: number of users in a room
ABSOLUTE_STATS_FIELDS = {
    "room": (
        "current_state_events",
        "joined_members",
        "invited_members",
        "left_members",
        "banned_members",
        "total_events",
        "total_event_bytes",
    ),
    "user": ("public_rooms", "private_rooms"),
}

# these fields are per-timeslice and so should be reset to 0 upon a new slice
# You can draw these stats on a histogram.
# Example: number of events sent locally during a time slice
PER_SLICE_FIELDS = {"room": (), "user": ()}

TYPE_TO_TABLE = {"room": ("room_stats", "room_id"), "user": ("user_stats", "user_id")}

# these are the tables which contain our actual subjects
TYPE_TO_ORIGIN_TABLE = {"room": "rooms", "user": "users"}


class StatsStore(StateDeltasStore):
    def __init__(self, db_conn, hs):
        super(StatsStore, self).__init__(db_conn, hs)

        self.server_name = hs.hostname
        self.clock = self.hs.get_clock()
        self.stats_enabled = hs.config.stats_enabled
        self.stats_bucket_size = hs.config.stats_bucket_size

        self.stats_delta_processing_lock = DeferredLock()

        self.register_background_update_handler(
            "populate_stats_prepare", self._populate_stats_prepare
        )
        self.register_background_update_handler(
            "populate_stats_process_rooms", self._populate_stats_process_rooms
        )
        self.register_background_update_handler(
            "populate_stats_process_users", self._populate_stats_process_users
        )
        # we no longer need to perform clean-up, but we will give ourselves
        # the potential to reintroduce it in the future – so documentation
        # will still encourage the use of this no-op handler.
        self.register_noop_background_update("populate_stats_cleanup")

    def quantise_stats_time(self, ts):
        """
        Quantises a timestamp to be a multiple of the bucket size.

        Args:
            ts (int): the timestamp to quantise, in milliseconds since the Unix
                Epoch

        Returns:
            int: a timestamp which
              - is divisible by the bucket size;
              - is no later than `ts`; and
              - is the largest such timestamp.
        """
        return (ts // self.stats_bucket_size) * self.stats_bucket_size

    @defer.inlineCallbacks
    def _unwedge_incremental_processor(self, forced_promise):
        """
        Make a promise about what this stats regeneration will handle,
        so that we can allow the incremental processor to start doing things
        right away – 'unwedging' it.

        Args:
            forced_promise (dict of positions):
                If supplied, this is the promise that is made.
                Otherwise, a promise is made that reduces the amount of work
                that must be performed by the incremental processor.
        """

        if forced_promise is None:
            promised_stats_delta_pos = (
                yield self.get_max_stream_id_in_current_state_deltas()
            )
            promised_max = self.get_room_max_stream_ordering()
            promised_min = self.get_room_min_stream_ordering()

            promised_positions = {
                "state_delta_stream_id": promised_stats_delta_pos,
                "total_events_min_stream_ordering": promised_min,
                "total_events_max_stream_ordering": promised_max,
            }
        else:
            promised_positions = forced_promise

        # this stores it for our reference later
        yield self.update_stats_positions(
            promised_positions, for_initial_processor=True
        )

        # this unwedges the incremental processor
        yield self.update_stats_positions(
            promised_positions, for_initial_processor=False
        )

        # with the delta processor unwedged, now let it catch up in case
        # anything was missed during the wedge period
        self.clock.call_later(0, self.hs.get_stats_handler().notify_new_event)

    @defer.inlineCallbacks
    def _populate_stats_prepare(self, progress, batch_size):
        """
        This is a background update, which prepares the database for
        statistics regeneration.
        """

        if not self.stats_enabled:
            yield self._end_background_update("populate_stats_prepare")
            return 1

        def _wedge_incremental_processor(txn):
            """
            Wedge the incremental processor (by setting its positions to NULL),
            and return its previous positions – atomically.
            """

            with self.stats_delta_processing_lock:
                old = self._get_stats_positions_txn(txn, for_initial_processor=False)
                self._update_stats_positions_txn(txn, None, for_initial_processor=False)

            return old

        def _make_skeletons(txn, stats_type):
            """
            Get all the rooms and users that we want to process, and create
            'skeletons' (incomplete _stats_current rows) for them, if they do
            not already have a row.
            """

            if isinstance(self.database_engine, Sqlite3Engine):
                sql = """
                        INSERT OR IGNORE INTO %(table)s_current
                        (%(id_col)s, completed_delta_stream_id, %(zero_cols)s)
                        SELECT %(id_col)s, NULL, %(zeroes)s FROM %(origin_table)s
                    """
            else:
                sql = """
                        INSERT INTO %(table)s_current
                        (%(id_col)s, completed_delta_stream_id, %(zero_cols)s)
                        SELECT %(id_col)s, NULL, %(zeroes)s FROM %(origin_table)s
                        ON CONFLICT DO NOTHING
                    """

            table, id_col = TYPE_TO_TABLE[stats_type]
            origin_table = TYPE_TO_ORIGIN_TABLE[stats_type]
            zero_cols = list(
                chain(ABSOLUTE_STATS_FIELDS[stats_type], PER_SLICE_FIELDS[stats_type])
            )

            txn.execute(
                sql
                % {
                    "table": table,
                    "id_col": id_col,
                    "origin_table": origin_table,
                    "zero_cols": zero_cols,
                    "zeroes": ", ".join(["0"] * len(zero_cols)),
                }
            )

        def _delete_dirty_skeletons(txn):
            """
            Delete pre-existing rows which are incomplete.
            """
            sql = """
                    DELETE FROM %s_current
                    WHERE completed_delta_stream_id IS NULL
            """

            for _k, (table, id_col) in TYPE_TO_TABLE:
                txn.execute(sql % (table,))

        # first wedge the incremental processor and reset our promise
        old_positions = yield self.runInteraction(
            "populate_stats_wedge", _wedge_incremental_processor
        )

        if None in old_positions.values():
            old_positions = None

        # with the incremental processor wedged, we delete dirty skeleton rows
        # since we don't want to double-count them.
        yield self.runInteraction(
            "populate_stats_delete_dirty_skeletons", _delete_dirty_skeletons
        )

        yield self._unwedge_incremental_processor(old_positions)

        yield self.runInteraction("populate_stats_make_skeletons", _make_skeletons)
        self.get_earliest_token_for_stats.invalidate_all()

        yield self._end_background_update("populate_stats_prepare")
        return 1

    @defer.inlineCallbacks
    def _populate_stats_process_users(self, progress, batch_size):
        """
        This is a background update which regenerates statistics for users.
        """
        if not self.stats_enabled:
            yield self._end_background_update("populate_stats_process_users")
            return 1

        def _get_next_batch(txn):
            # Only fetch 250 users, so we don't fetch too many at once, even
            # if those 250 users have less than batch_size state events.
            sql = """
                    SELECT user_id FROM user_stats_current
                    WHERE completed_delta_stream_id IS NULL
                    LIMIT 250
                """
            txn.execute(sql)
            users_to_work_on = txn.fetchall()

            if not users_to_work_on:
                return None

            # Get how many are left to process, so we can give status on how
            # far we are in processing
            txn.execute(
                "SELECT COUNT(*) FROM room_stats_current"
                " WHERE completed_delta_stream_id IS NULL"
            )
            progress["remaining"] = txn.fetchone()[0]

            return users_to_work_on

        users_to_work_on = yield self.runInteraction(
            "populate_stats_users_get_batch", _get_next_batch
        )

        # No more users -- complete the transaction.
        if not users_to_work_on:
            yield self._end_background_update("populate_stats_process_users")
            return 1

        logger.info(
            "Processing the next %d users of %d remaining",
            len(users_to_work_on),
            progress["remaining"],
        )

        processed_membership_count = 0

        promised_positions = yield self.get_stats_positions(for_initial_processor=True)

        if None in promised_positions:
            logger.error(
                "There is a None in promised_positions;"
                " dependency task must not have been run."
                " promised_positions: %r",
                promised_positions,
            )
            yield self._end_background_update("populate_stats_process_users")
            return 1

        for (user_id,) in users_to_work_on:
            now = self.hs.get_reactor().seconds()

            def _process_user(txn):
                # Get the current token
                current_token = self._get_max_stream_id_in_current_state_deltas_txn(txn)

                sql = """
                        SELECT
                            (
                                join_rules = 'public'
                                OR history_visibility = 'world_readable'
                            ) AS is_public,
                            COUNT(*) AS count
                        FROM room_memberships
                        JOIN room_state USING (room_id)
                        WHERE
                            user_id = ? AND membership = 'join'
                        GROUP BY is_public
                    """
                txn.execute(sql, (user_id,))
                room_counts_by_publicness = dict(txn.fetchall())

                self._update_stats_delta_txn(
                    txn,
                    now,
                    "user",
                    user_id,
                    {},
                    complete_with_stream_id=current_token,
                    absolute_field_overrides={
                        # these are counted absolutely because it is
                        # more difficult to count them from the promised time,
                        # because counting them now can use the quick lookup
                        # tables.
                        "public_rooms": room_counts_by_publicness.get(True, 0),
                        "private_rooms": room_counts_by_publicness.get(False, 0),
                    },
                )

                # we use this count for rate-limiting
                return sum(room_counts_by_publicness.values())

            processed_membership_count += yield self.runInteraction(
                "update_user_stats", _process_user
            )

            # Update the remaining counter.
            progress["remaining"] -= 1

            if processed_membership_count > batch_size:
                # Don't process any more users, we've hit our batch size.
                return processed_membership_count

        yield self.runInteraction(
            "populate_stats",
            self._background_update_progress_txn,
            "populate_stats_process_users",
            progress,
        )

        return processed_membership_count

    @defer.inlineCallbacks
    def _populate_stats_process_rooms(self, progress, batch_size):
        """
        This is a background update which regenerates statistics for rooms.
        """
        if not self.stats_enabled:
            yield self._end_background_update("populate_stats_process_rooms")
            return 1

        def _get_next_batch(txn):
            # Only fetch 250 rooms, so we don't fetch too many at once, even
            # if those 250 rooms have less than batch_size state events.
            sql = """
                    SELECT room_id FROM room_stats_current
                    WHERE completed_delta_stream_id IS NULL
                    LIMIT 250
                """
            txn.execute(sql)
            rooms_to_work_on = txn.fetchall()

            if not rooms_to_work_on:
                return None

            # Get how many are left to process, so we can give status on how
            # far we are in processing
            txn.execute(
                "SELECT COUNT(*) FROM room_stats_current"
                " WHERE completed_delta_stream_id IS NULL"
            )
            progress["remaining"] = txn.fetchone()[0]

            return rooms_to_work_on

        rooms_to_work_on = yield self.runInteraction(
            "populate_stats_rooms_get_batch", _get_next_batch
        )

        # No more rooms -- complete the transaction.
        if not rooms_to_work_on:
            yield self._end_background_update("populate_stats_process_rooms")
            return 1

        logger.info(
            "Processing the next %d rooms of %d remaining",
            len(rooms_to_work_on),
            progress["remaining"],
        )

        # Number of state events we've processed by going through each room
        processed_event_count = 0

        promised_positions = yield self.get_stats_positions(for_initial_processor=True)

        if None in promised_positions:
            logger.error(
                "There is a None in promised_positions;"
                " dependency task must not have been run."
                " promised_positions: %s",
                promised_positions,
            )
            yield self._end_background_update("populate_stats_process_rooms")
            return 1

        for (room_id,) in rooms_to_work_on:
            current_state_ids = yield self.get_current_state_ids(room_id)

            join_rules_id = current_state_ids.get((EventTypes.JoinRules, ""))
            history_visibility_id = current_state_ids.get(
                (EventTypes.RoomHistoryVisibility, "")
            )
            encryption_id = current_state_ids.get((EventTypes.RoomEncryption, ""))
            name_id = current_state_ids.get((EventTypes.Name, ""))
            topic_id = current_state_ids.get((EventTypes.Topic, ""))
            avatar_id = current_state_ids.get((EventTypes.RoomAvatar, ""))
            canonical_alias_id = current_state_ids.get((EventTypes.CanonicalAlias, ""))

            event_ids = [
                join_rules_id,
                history_visibility_id,
                encryption_id,
                name_id,
                topic_id,
                avatar_id,
                canonical_alias_id,
            ]

            state_events = yield self.get_events(
                [ev for ev in event_ids if ev is not None]
            )

            def _get_or_none(event_id, arg):
                event = state_events.get(event_id)
                if event:
                    return event.content.get(arg)
                return None

            yield self.update_room_state(
                room_id,
                {
                    "join_rules": _get_or_none(join_rules_id, "join_rule"),
                    "history_visibility": _get_or_none(
                        history_visibility_id, "history_visibility"
                    ),
                    "encryption": _get_or_none(encryption_id, "algorithm"),
                    "name": _get_or_none(name_id, "name"),
                    "topic": _get_or_none(topic_id, "topic"),
                    "avatar": _get_or_none(avatar_id, "url"),
                    "canonical_alias": _get_or_none(canonical_alias_id, "alias"),
                },
            )

            now = self.clock.time_msec()

            def _fetch_data(txn):
                # Get the current token of the room
                current_token = self._get_max_stream_id_in_current_state_deltas_txn(txn)

                current_state_events = len(current_state_ids)

                membership_counts = self._get_user_counts_in_room_txn(txn, room_id)

                room_total_event_count, room_total_event_bytes = self._count_events_and_bytes_in_room_txn(
                    txn,
                    room_id,
                    promised_positions["total_events_min_stream_ordering"],
                    promised_positions["total_events_max_stream_ordering"],
                )

                self._update_stats_delta_txn(
                    txn,
                    now,
                    "room",
                    room_id,
                    {
                        "total_events": room_total_event_count,
                        "total_event_bytes": room_total_event_bytes,
                    },
                    complete_with_stream_id=current_token,
                    absolute_field_overrides={
                        # these are counted absolutely because it is
                        # more difficult to count them from the promised time,
                        # because counting them now can use the quick lookup
                        # tables.
                        "current_state_events": current_state_events,
                        "joined_members": membership_counts.get(Membership.JOIN, 0),
                        "invited_members": membership_counts.get(Membership.INVITE, 0),
                        "left_members": membership_counts.get(Membership.LEAVE, 0),
                        "banned_members": membership_counts.get(Membership.BAN, 0),
                    },
                )

                # we use this count for rate-limiting
                return room_total_event_count

            room_event_count = yield self.runInteraction(
                "update_room_stats", _fetch_data
            )

            # Update the remaining counter.
            progress["remaining"] -= 1

            processed_event_count += room_event_count

            if processed_event_count > batch_size:
                # Don't process any more rooms, we've hit our batch size.
                return processed_event_count

        yield self.runInteraction(
            "populate_stats",
            self._background_update_progress_txn,
            "populate_stats_process_rooms",
            progress,
        )

        return processed_event_count

    def get_stats_positions(self, for_initial_processor=False):
        """
        Returns the stats processor positions.

        Args:
            for_initial_processor (bool, optional): If true, returns the position
                promised by the latest stats regeneration, rather than the current
                incremental processor's position.
                Otherwise (if false), return the incremental processor's position.

        Returns (dict):
            Dict containing :-
                state_delta_stream_id: stream_id of last-processed state delta
                total_events_min_stream_ordering: stream_ordering of latest-processed
                    backfilled event, in the context of total_events counting.
                total_events_max_stream_ordering: stream_ordering of latest-processed
                    non-backfilled event, in the context of total_events counting.
        """
        return self._simple_select_one(
            table="stats_incremental_position",
            keyvalues={"is_background_contract": for_initial_processor},
            retcols=(
                "state_delta_stream_id",
                "total_events_min_stream_ordering",
                "total_events_max_stream_ordering",
            ),
            desc="stats_incremental_position",
        )

    def _get_stats_positions_txn(self, txn, for_initial_processor=False):
        """
        See L{get_stats_positions}.

        Args:
             txn (cursor): Database cursor
        """
        return self._simple_select_one_txn(
            txn=txn,
            table="stats_incremental_position",
            keyvalues={"is_background_contract": for_initial_processor},
            retcols=(
                "state_delta_stream_id",
                "total_events_min_stream_ordering",
                "total_events_max_stream_ordering",
            ),
        )

    def update_stats_positions(self, positions, for_initial_processor=False):
        """
        Updates the stats processor positions.

        Args:
            positions: See L{get_stats_positions}
            for_initial_processor: See L{get_stats_positions}
        """
        if positions is None:
            positions = {
                "state_delta_stream_id": None,
                "total_events_min_stream_ordering": None,
                "total_events_max_stream_ordering": None,
            }
        return self._simple_update_one(
            table="stats_incremental_position",
            keyvalues={"is_background_contract": for_initial_processor},
            updatevalues=positions,
            desc="update_stats_incremental_position",
        )

    def _update_stats_positions_txn(self, txn, positions, for_initial_processor=False):
        """
        See L{update_stats_positions}
        """
        if positions is None:
            positions = {
                "state_delta_stream_id": None,
                "total_events_min_stream_ordering": None,
                "total_events_max_stream_ordering": None,
            }
        return self._simple_update_one_txn(
            txn,
            table="stats_incremental_position",
            keyvalues={"is_background_contract": for_initial_processor},
            updatevalues=positions,
        )

    def update_room_state(self, room_id, fields):
        """
        Args:
            room_id (str)
            fields (dict[str:Any])
        """

        # For whatever reason some of the fields may contain null bytes, which
        # postgres isn't a fan of, so we replace those fields with null.
        for col in (
            "join_rules",
            "history_visibility",
            "encryption",
            "name",
            "topic",
            "avatar",
            "canonical_alias",
        ):
            field = fields.get(col)
            if field and "\0" in field:
                fields[col] = None

        return self._simple_upsert(
            table="room_stats_state",
            keyvalues={"room_id": room_id},
            values=fields,
            desc="update_room_state",
        )

    @cached()
    def get_earliest_token_for_stats(self, stats_type, id):
        """
        Fetch the "earliest token". This is used by the room stats delta
        processor to ignore deltas that have been processed between the
        start of the background task and any particular room's stats
        being calculated.

        Returns:
            Deferred[int]
        """
        table, id_col = TYPE_TO_TABLE[stats_type]

        return self._simple_select_one_onecol(
            "%s_current" % (table,),
            {id_col: id},
            retcol="completed_delta_stream_id",
            allow_none=True,
        )

    def update_stats_delta(
        self, ts, stats_type, stats_id, fields, complete_with_stream_id=None
    ):
        """
        Updates the statistics for a subject, with a delta (difference/relative
        change).

        Args:
            ts (int): timestamp of the change
            stats_type (str): "room" or "user" – the kind of subject
            stats_id (str): the subject's ID (room ID or user ID)
            fields (dict[str, int]): Deltas of stats values.
            complete_with_stream_id (int, optional):
                If supplied, converts an incomplete row into a complete row,
                with the supplied stream_id marked as the stream_id where the
                row was completed.
        """

        return self.runInteraction(
            "update_stats_delta",
            self._update_stats_delta_txn,
            ts,
            stats_type,
            stats_id,
            fields,
            complete_with_stream_id=complete_with_stream_id,
        )

    def _update_stats_delta_txn(
        self,
        txn,
        ts,
        stats_type,
        stats_id,
        fields,
        complete_with_stream_id=None,
        absolute_field_overrides=None,
    ):
        """
        See L{update_stats_delta}
        Additional Args:
            absolute_field_overrides (dict[str, int]): Current stats values
                (i.e. not deltas) of absolute fields.
                Does not work with per-slice fields.
        """
        table, id_col = TYPE_TO_TABLE[stats_type]

        quantised_ts = self.quantise_stats_time(int(ts))
        end_ts = quantised_ts + self.stats_bucket_size

        abs_field_names = ABSOLUTE_STATS_FIELDS[stats_type]
        slice_field_names = PER_SLICE_FIELDS[stats_type]
        for field in chain(fields.keys(), absolute_field_overrides.keys()):
            if field not in abs_field_names and field not in slice_field_names:
                # guard against potential SQL injection dodginess
                raise ValueError(
                    "%s is not a recognised field"
                    " for stats type %s" % (field, stats_type)
                )

        # only absolute stats fields are tracked in the `_current` stats tables,
        # so those are the only ones that we process deltas for when
        # we upsert against the `_current` table.

        # This calculates the deltas (`field = field + ?` values)
        # for absolute fields,
        # * defaulting to 0 if not specified
        #     (required for the INSERT part of upserting to work)
        # * omitting overrides specified in `absolute_field_overrides`
        deltas_of_absolute_fields = {
            key: fields.get(key, 0)
            for key in abs_field_names
            if key not in absolute_field_overrides
        }

        if absolute_field_overrides is None:
            absolute_field_overrides = {}

        if complete_with_stream_id is not None:
            absolute_field_overrides = absolute_field_overrides.copy()
            absolute_field_overrides[
                "completed_delta_stream_id"
            ] = complete_with_stream_id

        # first upsert the `_current` table
        self._upsert_with_additive_relatives_txn(
            txn=txn,
            table=table + "_current",
            keyvalues={id_col: stats_id},
            absolutes=absolute_field_overrides,
            additive_relatives=deltas_of_absolute_fields,
        )

        if self.has_completed_background_updates():
            # TODO want to check specifically for stats regenerator, not all
            #   background updates…
            # then upsert the `_historical` table.
            # we don't support absolute_fields for per-slice fields as it makes
            # no sense.
            per_slice_additive_relatives = {
                key: fields.get(key, 0) for key in slice_field_names
            }
            self._upsert_copy_from_table_with_additive_relatives_txn(
                txn=txn,
                into_table=table + "_historical",
                keyvalues={id_col: stats_id},
                extra_dst_keyvalues={
                    "end_ts": end_ts,
                    "bucket_size": self.stats_bucket_size,
                },
                additive_relatives=per_slice_additive_relatives,
                src_table=table + "_current",
                copy_columns=abs_field_names,
                additional_where=" AND completed_delta_stream_id IS NOT NULL",
            )

    def _upsert_with_additive_relatives_txn(
        self, txn, table, keyvalues, absolutes, additive_relatives
    ):
        """Used to update values in the stats tables.

        Args:
            txn: Transaction
            table (str): Table name
            keyvalues (dict[str, any]): Row-identifying key values
            absolutes (dict[str, any]): Absolute (set) fields
            additive_relatives (dict[str, int]): Fields that will be added onto
                if existing row present.
        """
        if self.database_engine.can_native_upsert:
            absolute_updates = [
                "%(field)s = EXCLUDED.%(field)s" % {"field": field}
                for field in absolutes.keys()
            ]

            relative_updates = [
                "%(field)s = EXCLUDED.%(field)s + %(table)s.%(field)s"
                % {"table": table, "field": field}
                for field in additive_relatives.keys()
            ]

            insert_cols = []
            qargs = [table]

            for (key, val) in chain(
                keyvalues.items(), absolutes.items(), additive_relatives.items()
            ):
                insert_cols.append(key)
                qargs.append(val)

            sql = """
                INSERT INTO %(table)s (%(insert_cols_cs)s)
                VALUES (%(insert_vals_qs)s)
                ON CONFLICT DO UPDATE SET %(updates)s
            """ % {
                "table": table,
                "insert_cols_cs": ", ".join(insert_cols),
                "insert_vals_qs": ", ".join(
                    ["?"] * (len(keyvalues) + len(absolutes) + len(additive_relatives))
                ),
                "updates": ", ".join(chain(absolute_updates, relative_updates)),
            }

            txn.execute(sql, qargs)
        else:
            self.database_engine.lock_table(txn, table)
            retcols = chain(absolutes.keys(), additive_relatives.keys())
            current_row = self._simple_select_one_txn(
                txn, table, keyvalues, retcols, allow_none=True
            )
            if current_row is None:
                merged_dict = {**keyvalues, **absolutes, **additive_relatives}
                self._simple_insert_txn(txn, table, merged_dict)
            else:
                for (key, val) in additive_relatives.items():
                    current_row[key] += val
                current_row.update(absolutes)
                self._simple_update_one_txn(txn, table, keyvalues, current_row)

    def _upsert_copy_from_table_with_additive_relatives_txn(
        self,
        txn,
        into_table,
        keyvalues,
        extra_dst_keyvalues,
        additive_relatives,
        src_table,
        copy_columns,
        additional_where="",
    ):
        """
        Args:
             txn: Transaction
             into_table (str): The destination table to UPSERT the row into
             keyvalues (dict[str, any]): Row-identifying key values
             extra_dst_keyvalues (dict[str, any]): Additional keyvalues
                for `into_table`.
             additive_relatives (dict[str, any]): Fields that will be added onto
                if existing row present. (Must be disjoint from copy_columns.)
             src_table (str): The source table to copy from
             copy_columns (iterable[str]): The list of columns to copy
             additional_where (str): Additional SQL for where (prefix with AND
                if using).
        """
        if self.database_engine.can_native_upsert:
            ins_columns = chain(
                keyvalues, copy_columns, additive_relatives, extra_dst_keyvalues
            )
            sel_exprs = chain(
                keyvalues,
                copy_columns,
                ("?" for _ in chain(additive_relatives, extra_dst_keyvalues)),
            )
            keyvalues_where = ("%s = ?" % f for f in keyvalues)

            sets_cc = ("%s = EXCLUDED.%s" % (f, f) for f in copy_columns)
            sets_ar = (
                "%s = EXCLUDED.%s + %s.%s" % (f, f, into_table, f) for f in copy_columns
            )

            sql = """
                INSERT INTO %(into_table)s (%(ins_columns)s)
                SELECT %(sel_exprs)s
                FROM %(src_table)s
                WHERE %(keyvalues_where)s %(additional_where)s
                ON CONFLICT (%(keyvalues)s)
                DO UPDATE SET %(sets)s
            """ % {
                "into_table": into_table,
                "ins_columns": ", ".join(ins_columns),
                "sel_exprs": ", ".join(sel_exprs),
                "keyvalues_where": " AND ".join(keyvalues_where),
                "src_table": src_table,
                "keyvalues": ", ".join(
                    chain(keyvalues.keys(), extra_dst_keyvalues.keys())
                ),
                "sets": ", ".join(chain(sets_cc, sets_ar)),
                "additional_where": additional_where,
            }

            qargs = chain(additive_relatives.values(), keyvalues.values())
            txn.execute(sql, qargs)
        else:
            self.database_engine.lock_table(txn, into_table)
            src_row = self._simple_select_one_txn(
                txn, src_table, keyvalues, copy_columns
            )
            dest_current_row = self._simple_select_one_txn(
                txn,
                into_table,
                keyvalues,
                chain(additive_relatives.keys(), copy_columns),
                allow_none=True,
            )

            if dest_current_row is None:
                merged_dict = {**keyvalues, **src_row, **additive_relatives}
                self._simple_insert_txn(txn, into_table, merged_dict)
            else:
                for (key, val) in additive_relatives.items():
                    src_row[key] = dest_current_row[key] + val
                self._simple_update_txn(txn, into_table, keyvalues, src_row)

    def incremental_update_room_total_events_and_bytes(self, in_positions):
        """
        Counts the number of events and total event bytes per-room and then adds
        these to the respective total_events and total_event_bytes room counts.

        Args:
            in_positions (dict): Positions,
                as retrieved from L{get_stats_positions}.

        Returns (Deferred[tuple[dict, bool]]):
            First element (dict):
                The new positions. Note that this is for reference only –
                the new positions WILL be committed by this function.
            Second element (bool):
                true iff there was a change to the positions, false otherwise
        """

        def incremental_update_total_events_and_bytes_txn(txn):
            positions = in_positions.copy()

            max_pos = self.get_room_max_stream_ordering()
            min_pos = self.get_room_min_stream_ordering()
            self.update_total_event_and_bytes_count_between_txn(
                txn,
                low_pos=positions["total_events_max_stream_ordering"],
                high_pos=max_pos,
            )

            self.update_total_event_and_bytes_count_between_txn(
                txn,
                low_pos=min_pos,
                high_pos=positions["total_events_min_stream_ordering"],
            )

            if (
                positions["total_events_max_stream_ordering"] != max_pos
                or positions["total_events_min_stream_ordering"] != min_pos
            ):
                positions["total_events_max_stream_ordering"] = max_pos
                positions["total_events_min_stream_ordering"] = min_pos

                self._update_stats_positions_txn(txn, positions)

                return positions, True
            else:
                return positions, False

        return self.runInteraction(
            "stats_incremental_total_events_and_bytes",
            incremental_update_total_events_and_bytes_txn,
        )

    def update_total_event_and_bytes_count_between_txn(self, txn, low_pos, high_pos):
        """
        Updates the total_events and total_event_bytes counts for rooms,
            in a range of stream_orderings.

        Inclusivity of low_pos and high_pos is dependent upon their signs.
        This makes it intuitive to use this function for both backfilled
        and non-backfilled events.

        Examples:
        (low, high) → (kind)
        (3, 7) → 3 < … <= 7 (normal-filled; low already processed before)
        (-4, -2) → -4 <= … < -2 (backfilled; high already processed before)
        (-7, 7) → -7 <= … <= 7 (both)

        Args:
            txn: Database transaction.
            low_pos: Low stream ordering
            high_pos: High stream ordering
        """

        if low_pos >= high_pos:
            # nothing to do here.
            return

        now = self.clock.time_msec()

        # we choose comparators based on the signs
        low_comparator = "<=" if low_pos < 0 else "<"
        high_comparator = "<" if high_pos < 0 else "<="

        if isinstance(self.database_engine, PostgresEngine):
            new_bytes_expression = "OCTET_LENGTH(json)"
        else:
            new_bytes_expression = "LENGTH(CAST(json AS BLOB))"

        sql = """
            SELECT room_id, COUNT(*) AS new_events, SUM(%s) AS new_bytes
            FROM events INNER JOIN event_json USING (event_id)
            WHERE ? %s stream_ordering AND stream_ordering %s ?
            GROUP BY room_id
        """ % (
            low_comparator,
            high_comparator,
            new_bytes_expression,
        )

        txn.execute(sql, (low_pos, high_pos))

        for room_id, new_events, new_bytes in txn.fetchall():
            self._update_stats_delta_txn(
                txn,
                now,
                "room",
                room_id,
                {"total_events": new_events, "total_event_bytes": new_bytes},
            )

    def _count_events_and_bytes_in_room_txn(self, txn, room_id, low_token, high_token):
        """
        Count the number of events and event bytes in a room between two tokens,
        inclusive.
        Args:
            txn (cursor): The database
            room_id (str): The ID of the room to count events for
            low_token (int): the minimum stream ordering to count
            high_token (int): the maximum stream ordering to count

        Returns (tuple[int, int]):
            First element (int):
                the number of events
            Second element (int):
                the number of bytes in events' event JSON
        """

        if isinstance(self.database_engine, PostgresEngine):
            bytes_expression = "OCTET_LENGTH(json)"
        else:
            bytes_expression = "LENGTH(CAST(json AS BLOB))"

        sql = """
            SELECT COUNT(*) AS num_events, SUM(%s) AS num_bytes
            FROM events
            JOIN event_json USING (event_id)
            WHERE room_id = ?
                AND ? <= stream_ordering
                AND stream_ordering <= ?
        """ % (
            bytes_expression,
        )
        txn.execute(sql, (room_id, low_token, high_token))
        return txn.fetchone()