# -*- coding: utf-8 -*- # Copyright 2014-2016 OpenMarket Ltd # Copyright 2017-2018 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 import time from sys import intern from time import monotonic as monotonic_time from typing import ( Any, Callable, Dict, Iterable, Iterator, List, Optional, Tuple, TypeVar, cast, overload, ) import attr from prometheus_client import Histogram from typing_extensions import Literal from twisted.enterprise import adbapi from synapse.api.errors import StoreError from synapse.config.database import DatabaseConnectionConfig from synapse.logging.context import ( LoggingContext, current_context, make_deferred_yieldable, ) from synapse.metrics.background_process_metrics import run_as_background_process from synapse.storage.background_updates import BackgroundUpdater from synapse.storage.engines import BaseDatabaseEngine, PostgresEngine, Sqlite3Engine from synapse.storage.types import Connection, Cursor from synapse.types import Collection # python 3 does not have a maximum int value MAX_TXN_ID = 2 ** 63 - 1 logger = logging.getLogger(__name__) sql_logger = logging.getLogger("synapse.storage.SQL") transaction_logger = logging.getLogger("synapse.storage.txn") perf_logger = logging.getLogger("synapse.storage.TIME") sql_scheduling_timer = Histogram("synapse_storage_schedule_time", "sec") sql_query_timer = Histogram("synapse_storage_query_time", "sec", ["verb"]) sql_txn_timer = Histogram("synapse_storage_transaction_time", "sec", ["desc"]) # Unique indexes which have been added in background updates. Maps from table name # to the name of the background update which added the unique index to that table. # # This is used by the upsert logic to figure out which tables are safe to do a proper # UPSERT on: until the relevant background update has completed, we # have to emulate an upsert by locking the table. # UNIQUE_INDEX_BACKGROUND_UPDATES = { "user_ips": "user_ips_device_unique_index", "device_lists_remote_extremeties": "device_lists_remote_extremeties_unique_idx", "device_lists_remote_cache": "device_lists_remote_cache_unique_idx", "event_search": "event_search_event_id_idx", } def make_pool( reactor, db_config: DatabaseConnectionConfig, engine: BaseDatabaseEngine ) -> adbapi.ConnectionPool: """Get the connection pool for the database. """ # By default enable `cp_reconnect`. We need to fiddle with db_args in case # someone has explicitly set `cp_reconnect`. db_args = dict(db_config.config.get("args", {})) db_args.setdefault("cp_reconnect", True) return adbapi.ConnectionPool( db_config.config["name"], cp_reactor=reactor, cp_openfun=lambda conn: engine.on_new_connection( LoggingDatabaseConnection(conn, engine, "on_new_connection") ), **db_args, ) def make_conn( db_config: DatabaseConnectionConfig, engine: BaseDatabaseEngine, default_txn_name: str, ) -> Connection: """Make a new connection to the database and return it. Returns: Connection """ db_params = { k: v for k, v in db_config.config.get("args", {}).items() if not k.startswith("cp_") } native_db_conn = engine.module.connect(**db_params) db_conn = LoggingDatabaseConnection(native_db_conn, engine, default_txn_name) engine.on_new_connection(db_conn) return db_conn @attr.s(slots=True) class LoggingDatabaseConnection: """A wrapper around a database connection that returns `LoggingTransaction` as its cursor class. This is mainly used on startup to ensure that queries get logged correctly """ conn = attr.ib(type=Connection) engine = attr.ib(type=BaseDatabaseEngine) default_txn_name = attr.ib(type=str) def cursor( self, *, txn_name=None, after_callbacks=None, exception_callbacks=None ) -> "LoggingTransaction": if not txn_name: txn_name = self.default_txn_name return LoggingTransaction( self.conn.cursor(), name=txn_name, database_engine=self.engine, after_callbacks=after_callbacks, exception_callbacks=exception_callbacks, ) def close(self) -> None: self.conn.close() def commit(self) -> None: self.conn.commit() def rollback(self, *args, **kwargs) -> None: self.conn.rollback(*args, **kwargs) def __enter__(self) -> "Connection": self.conn.__enter__() return self def __exit__(self, exc_type, exc_value, traceback) -> Optional[bool]: return self.conn.__exit__(exc_type, exc_value, traceback) # Proxy through any unknown lookups to the DB conn class. def __getattr__(self, name): return getattr(self.conn, name) # The type of entry which goes on our after_callbacks and exception_callbacks lists. # # Python 3.5.2 doesn't support Callable with an ellipsis, so we wrap it in quotes so # that mypy sees the type but the runtime python doesn't. _CallbackListEntry = Tuple["Callable[..., None]", Iterable[Any], Dict[str, Any]] R = TypeVar("R") class LoggingTransaction: """An object that almost-transparently proxies for the 'txn' object passed to the constructor. Adds logging and metrics to the .execute() method. Args: txn: The database transaction object to wrap. name: The name of this transactions for logging. database_engine after_callbacks: A list that callbacks will be appended to that have been added by `call_after` which should be run on successful completion of the transaction. None indicates that no callbacks should be allowed to be scheduled to run. exception_callbacks: A list that callbacks will be appended to that have been added by `call_on_exception` which should be run if transaction ends with an error. None indicates that no callbacks should be allowed to be scheduled to run. """ __slots__ = [ "txn", "name", "database_engine", "after_callbacks", "exception_callbacks", ] def __init__( self, txn: Cursor, name: str, database_engine: BaseDatabaseEngine, after_callbacks: Optional[List[_CallbackListEntry]] = None, exception_callbacks: Optional[List[_CallbackListEntry]] = None, ): self.txn = txn self.name = name self.database_engine = database_engine self.after_callbacks = after_callbacks self.exception_callbacks = exception_callbacks def call_after(self, callback: "Callable[..., None]", *args: Any, **kwargs: Any): """Call the given callback on the main twisted thread after the transaction has finished. Used to invalidate the caches on the correct thread. """ # if self.after_callbacks is None, that means that whatever constructed the # LoggingTransaction isn't expecting there to be any callbacks; assert that # is not the case. assert self.after_callbacks is not None self.after_callbacks.append((callback, args, kwargs)) def call_on_exception( self, callback: "Callable[..., None]", *args: Any, **kwargs: Any ): # if self.exception_callbacks is None, that means that whatever constructed the # LoggingTransaction isn't expecting there to be any callbacks; assert that # is not the case. assert self.exception_callbacks is not None self.exception_callbacks.append((callback, args, kwargs)) def fetchall(self) -> List[Tuple]: return self.txn.fetchall() def fetchone(self) -> Tuple: return self.txn.fetchone() def __iter__(self) -> Iterator[Tuple]: return self.txn.__iter__() @property def rowcount(self) -> int: return self.txn.rowcount @property def description(self) -> Any: return self.txn.description def execute_batch(self, sql: str, args: Iterable[Iterable[Any]]) -> None: if isinstance(self.database_engine, PostgresEngine): from psycopg2.extras import execute_batch # type: ignore self._do_execute(lambda *x: execute_batch(self.txn, *x), sql, args) else: for val in args: self.execute(sql, val) def execute_values(self, sql: str, *args: Any) -> List[Tuple]: """Corresponds to psycopg2.extras.execute_values. Only available when using postgres. Always sets fetch=True when caling `execute_values`, so will return the results. """ assert isinstance(self.database_engine, PostgresEngine) from psycopg2.extras import execute_values # type: ignore return self._do_execute( lambda *x: execute_values(self.txn, *x, fetch=True), sql, *args ) def execute(self, sql: str, *args: Any) -> None: self._do_execute(self.txn.execute, sql, *args) def executemany(self, sql: str, *args: Any) -> None: self._do_execute(self.txn.executemany, sql, *args) def _make_sql_one_line(self, sql: str) -> str: "Strip newlines out of SQL so that the loggers in the DB are on one line" return " ".join(line.strip() for line in sql.splitlines() if line.strip()) def _do_execute(self, func: Callable[..., R], sql: str, *args: Any) -> R: sql = self._make_sql_one_line(sql) # TODO(paul): Maybe use 'info' and 'debug' for values? sql_logger.debug("[SQL] {%s} %s", self.name, sql) sql = self.database_engine.convert_param_style(sql) if args: try: sql_logger.debug("[SQL values] {%s} %r", self.name, args[0]) except Exception: # Don't let logging failures stop SQL from working pass start = time.time() try: return func(sql, *args) except Exception as e: sql_logger.debug("[SQL FAIL] {%s} %s", self.name, e) raise finally: secs = time.time() - start sql_logger.debug("[SQL time] {%s} %f sec", self.name, secs) sql_query_timer.labels(sql.split()[0]).observe(secs) def close(self) -> None: self.txn.close() def __enter__(self) -> "LoggingTransaction": return self def __exit__(self, exc_type, exc_value, traceback): self.close() class PerformanceCounters: def __init__(self): self.current_counters = {} self.previous_counters = {} def update(self, key: str, duration_secs: float) -> None: count, cum_time = self.current_counters.get(key, (0, 0)) count += 1 cum_time += duration_secs self.current_counters[key] = (count, cum_time) def interval(self, interval_duration_secs: float, limit: int = 3) -> str: counters = [] for name, (count, cum_time) in self.current_counters.items(): prev_count, prev_time = self.previous_counters.get(name, (0, 0)) counters.append( ( (cum_time - prev_time) / interval_duration_secs, count - prev_count, name, ) ) self.previous_counters = dict(self.current_counters) counters.sort(reverse=True) top_n_counters = ", ".join( "%s(%d): %.3f%%" % (name, count, 100 * ratio) for ratio, count, name in counters[:limit] ) return top_n_counters class DatabasePool: """Wraps a single physical database and connection pool. A single database may be used by multiple data stores. """ _TXN_ID = 0 def __init__( self, hs, database_config: DatabaseConnectionConfig, engine: BaseDatabaseEngine ): self.hs = hs self._clock = hs.get_clock() self._database_config = database_config self._db_pool = make_pool(hs.get_reactor(), database_config, engine) self.updates = BackgroundUpdater(hs, self) self._previous_txn_total_time = 0.0 self._current_txn_total_time = 0.0 self._previous_loop_ts = 0.0 # TODO(paul): These can eventually be removed once the metrics code # is running in mainline, and we have some nice monitoring frontends # to watch it self._txn_perf_counters = PerformanceCounters() self.engine = engine # A set of tables that are not safe to use native upserts in. self._unsafe_to_upsert_tables = set(UNIQUE_INDEX_BACKGROUND_UPDATES.keys()) # We add the user_directory_search table to the blacklist on SQLite # because the existing search table does not have an index, making it # unsafe to use native upserts. if isinstance(self.engine, Sqlite3Engine): self._unsafe_to_upsert_tables.add("user_directory_search") if self.engine.can_native_upsert: # Check ASAP (and then later, every 1s) to see if we have finished # background updates of tables that aren't safe to update. self._clock.call_later( 0.0, run_as_background_process, "upsert_safety_check", self._check_safe_to_upsert, ) def is_running(self) -> bool: """Is the database pool currently running """ return self._db_pool.running async def _check_safe_to_upsert(self) -> None: """ Is it safe to use native UPSERT? If there are background updates, we will need to wait, as they may be the addition of indexes that set the UNIQUE constraint that we require. If the background updates have not completed, wait 15 sec and check again. """ updates = await self.simple_select_list( "background_updates", keyvalues=None, retcols=["update_name"], desc="check_background_updates", ) updates = [x["update_name"] for x in updates] for table, update_name in UNIQUE_INDEX_BACKGROUND_UPDATES.items(): if update_name not in updates: logger.debug("Now safe to upsert in %s", table) self._unsafe_to_upsert_tables.discard(table) # If there's any updates still running, reschedule to run. if updates: self._clock.call_later( 15.0, run_as_background_process, "upsert_safety_check", self._check_safe_to_upsert, ) def start_profiling(self) -> None: self._previous_loop_ts = monotonic_time() def loop(): curr = self._current_txn_total_time prev = self._previous_txn_total_time self._previous_txn_total_time = curr time_now = monotonic_time() time_then = self._previous_loop_ts self._previous_loop_ts = time_now duration = time_now - time_then ratio = (curr - prev) / duration top_three_counters = self._txn_perf_counters.interval(duration, limit=3) perf_logger.debug( "Total database time: %.3f%% {%s}", ratio * 100, top_three_counters ) self._clock.looping_call(loop, 10000) def new_transaction( self, conn: LoggingDatabaseConnection, desc: str, after_callbacks: List[_CallbackListEntry], exception_callbacks: List[_CallbackListEntry], func: "Callable[..., R]", *args: Any, **kwargs: Any ) -> R: """Start a new database transaction with the given connection. Note: The given func may be called multiple times under certain failure modes. This is normally fine when in a standard transaction, but care must be taken if the connection is in `autocommit` mode that the function will correctly handle being aborted and retried half way through its execution. Args: conn desc after_callbacks exception_callbacks func *args **kwargs """ start = monotonic_time() txn_id = self._TXN_ID # We don't really need these to be unique, so lets stop it from # growing really large. self._TXN_ID = (self._TXN_ID + 1) % (MAX_TXN_ID) name = "%s-%x" % (desc, txn_id) transaction_logger.debug("[TXN START] {%s}", name) try: i = 0 N = 5 while True: cursor = conn.cursor( txn_name=name, after_callbacks=after_callbacks, exception_callbacks=exception_callbacks, ) try: r = func(cursor, *args, **kwargs) conn.commit() return r except self.engine.module.OperationalError as e: # This can happen if the database disappears mid # transaction. transaction_logger.warning( "[TXN OPERROR] {%s} %s %d/%d", name, e, i, N, ) if i < N: i += 1 try: conn.rollback() except self.engine.module.Error as e1: transaction_logger.warning("[TXN EROLL] {%s} %s", name, e1) continue raise except self.engine.module.DatabaseError as e: if self.engine.is_deadlock(e): transaction_logger.warning( "[TXN DEADLOCK] {%s} %d/%d", name, i, N ) if i < N: i += 1 try: conn.rollback() except self.engine.module.Error as e1: transaction_logger.warning( "[TXN EROLL] {%s} %s", name, e1, ) continue raise finally: # we're either about to retry with a new cursor, or we're about to # release the connection. Once we release the connection, it could # get used for another query, which might do a conn.rollback(). # # In the latter case, even though that probably wouldn't affect the # results of this transaction, python's sqlite will reset all # statements on the connection [1], which will make our cursor # invalid [2]. # # In any case, continuing to read rows after commit()ing seems # dubious from the PoV of ACID transactional semantics # (sqlite explicitly says that once you commit, you may see rows # from subsequent updates.) # # In psycopg2, cursors are essentially a client-side fabrication - # all the data is transferred to the client side when the statement # finishes executing - so in theory we could go on streaming results # from the cursor, but attempting to do so would make us # incompatible with sqlite, so let's make sure we're not doing that # by closing the cursor. # # (*named* cursors in psycopg2 are different and are proper server- # side things, but (a) we don't use them and (b) they are implicitly # closed by ending the transaction anyway.) # # In short, if we haven't finished with the cursor yet, that's a # problem waiting to bite us. # # TL;DR: we're done with the cursor, so we can close it. # # [1]: https://github.com/python/cpython/blob/v3.8.0/Modules/_sqlite/connection.c#L465 # [2]: https://github.com/python/cpython/blob/v3.8.0/Modules/_sqlite/cursor.c#L236 cursor.close() except Exception as e: transaction_logger.debug("[TXN FAIL] {%s} %s", name, e) raise finally: end = monotonic_time() duration = end - start current_context().add_database_transaction(duration) transaction_logger.debug("[TXN END] {%s} %f sec", name, duration) self._current_txn_total_time += duration self._txn_perf_counters.update(desc, duration) sql_txn_timer.labels(desc).observe(duration) async def runInteraction( self, desc: str, func: "Callable[..., R]", *args: Any, db_autocommit: bool = False, **kwargs: Any ) -> R: """Starts a transaction on the database and runs a given function Arguments: desc: description of the transaction, for logging and metrics func: callback function, which will be called with a database transaction (twisted.enterprise.adbapi.Transaction) as its first argument, followed by `args` and `kwargs`. db_autocommit: Whether to run the function in "autocommit" mode, i.e. outside of a transaction. This is useful for transactions that are only a single query. Currently, this is only implemented for Postgres. SQLite will still run the function inside a transaction. WARNING: This means that if func fails half way through then the changes will *not* be rolled back. `func` may also get called multiple times if the transaction is retried, so must correctly handle that case. args: positional args to pass to `func` kwargs: named args to pass to `func` Returns: The result of func """ after_callbacks = [] # type: List[_CallbackListEntry] exception_callbacks = [] # type: List[_CallbackListEntry] if not current_context(): logger.warning("Starting db txn '%s' from sentinel context", desc) try: result = await self.runWithConnection( self.new_transaction, desc, after_callbacks, exception_callbacks, func, *args, db_autocommit=db_autocommit, **kwargs, ) for after_callback, after_args, after_kwargs in after_callbacks: after_callback(*after_args, **after_kwargs) except: # noqa: E722, as we reraise the exception this is fine. for after_callback, after_args, after_kwargs in exception_callbacks: after_callback(*after_args, **after_kwargs) raise return cast(R, result) async def runWithConnection( self, func: "Callable[..., R]", *args: Any, db_autocommit: bool = False, **kwargs: Any ) -> R: """Wraps the .runWithConnection() method on the underlying db_pool. Arguments: func: callback function, which will be called with a database connection (twisted.enterprise.adbapi.Connection) as its first argument, followed by `args` and `kwargs`. args: positional args to pass to `func` db_autocommit: Whether to run the function in "autocommit" mode, i.e. outside of a transaction. This is useful for transaction that are only a single query. Currently only affects postgres. kwargs: named args to pass to `func` Returns: The result of func """ curr_context = current_context() if not curr_context: logger.warning( "Starting db connection from sentinel context: metrics will be lost" ) parent_context = None else: assert isinstance(curr_context, LoggingContext) parent_context = curr_context start_time = monotonic_time() def inner_func(conn, *args, **kwargs): # We shouldn't be in a transaction. If we are then something # somewhere hasn't committed after doing work. (This is likely only # possible during startup, as `run*` will ensure changes are # committed/rolled back before putting the connection back in the # pool). assert not self.engine.in_transaction(conn) with LoggingContext("runWithConnection", parent_context) as context: sched_duration_sec = monotonic_time() - start_time sql_scheduling_timer.observe(sched_duration_sec) context.add_database_scheduled(sched_duration_sec) if self.engine.is_connection_closed(conn): logger.debug("Reconnecting closed database connection") conn.reconnect() try: if db_autocommit: self.engine.attempt_to_set_autocommit(conn, True) db_conn = LoggingDatabaseConnection( conn, self.engine, "runWithConnection" ) return func(db_conn, *args, **kwargs) finally: if db_autocommit: self.engine.attempt_to_set_autocommit(conn, False) return await make_deferred_yieldable( self._db_pool.runWithConnection(inner_func, *args, **kwargs) ) @staticmethod def cursor_to_dict(cursor: Cursor) -> List[Dict[str, Any]]: """Converts a SQL cursor into an list of dicts. Args: cursor: The DBAPI cursor which has executed a query. Returns: A list of dicts where the key is the column header. """ col_headers = [intern(str(column[0])) for column in cursor.description] results = [dict(zip(col_headers, row)) for row in cursor] return results @overload async def execute( self, desc: str, decoder: Literal[None], query: str, *args: Any ) -> List[Tuple[Any, ...]]: ... @overload async def execute( self, desc: str, decoder: Callable[[Cursor], R], query: str, *args: Any ) -> R: ... async def execute( self, desc: str, decoder: Optional[Callable[[Cursor], R]], query: str, *args: Any ) -> R: """Runs a single query for a result set. Args: desc: description of the transaction, for logging and metrics decoder - The function which can resolve the cursor results to something meaningful. query - The query string to execute *args - Query args. Returns: The result of decoder(results) """ def interaction(txn): txn.execute(query, args) if decoder: return decoder(txn) else: return txn.fetchall() return await self.runInteraction(desc, interaction) # "Simple" SQL API methods that operate on a single table with no JOINs, # no complex WHERE clauses, just a dict of values for columns. async def simple_insert( self, table: str, values: Dict[str, Any], or_ignore: bool = False, desc: str = "simple_insert", ) -> bool: """Executes an INSERT query on the named table. Args: table: string giving the table name values: dict of new column names and values for them or_ignore: bool stating whether an exception should be raised when a conflicting row already exists. If True, False will be returned by the function instead desc: description of the transaction, for logging and metrics Returns: Whether the row was inserted or not. Only useful when `or_ignore` is True """ try: await self.runInteraction(desc, self.simple_insert_txn, table, values) except self.engine.module.IntegrityError: # We have to do or_ignore flag at this layer, since we can't reuse # a cursor after we receive an error from the db. if not or_ignore: raise return False return True @staticmethod def simple_insert_txn( txn: LoggingTransaction, table: str, values: Dict[str, Any] ) -> None: keys, vals = zip(*values.items()) sql = "INSERT INTO %s (%s) VALUES(%s)" % ( table, ", ".join(k for k in keys), ", ".join("?" for _ in keys), ) txn.execute(sql, vals) async def simple_insert_many( self, table: str, values: List[Dict[str, Any]], desc: str ) -> None: """Executes an INSERT query on the named table. Args: table: string giving the table name values: dict of new column names and values for them desc: description of the transaction, for logging and metrics """ await self.runInteraction(desc, self.simple_insert_many_txn, table, values) @staticmethod def simple_insert_many_txn( txn: LoggingTransaction, table: str, values: List[Dict[str, Any]] ) -> None: """Executes an INSERT query on the named table. Args: txn: The transaction to use. table: string giving the table name values: dict of new column names and values for them """ if not values: return # This is a *slight* abomination to get a list of tuples of key names # and a list of tuples of value names. # # i.e. [{"a": 1, "b": 2}, {"c": 3, "d": 4}] # => [("a", "b",), ("c", "d",)] and [(1, 2,), (3, 4,)] # # The sort is to ensure that we don't rely on dictionary iteration # order. keys, vals = zip( *[zip(*(sorted(i.items(), key=lambda kv: kv[0]))) for i in values if i] ) for k in keys: if k != keys[0]: raise RuntimeError("All items must have the same keys") sql = "INSERT INTO %s (%s) VALUES(%s)" % ( table, ", ".join(k for k in keys[0]), ", ".join("?" for _ in keys[0]), ) txn.executemany(sql, vals) async def simple_upsert( self, table: str, keyvalues: Dict[str, Any], values: Dict[str, Any], insertion_values: Dict[str, Any] = {}, desc: str = "simple_upsert", lock: bool = True, ) -> Optional[bool]: """ `lock` should generally be set to True (the default), but can be set to False if either of the following are true: * there is a UNIQUE INDEX on the key columns. In this case a conflict will cause an IntegrityError in which case this function will retry the update. * we somehow know that we are the only thread which will be updating this table. Args: table: The table to upsert into keyvalues: The unique key columns and their new values values: The nonunique columns and their new values insertion_values: additional key/values to use only when inserting desc: description of the transaction, for logging and metrics lock: True to lock the table when doing the upsert. Returns: Native upserts always return None. Emulated upserts return True if a new entry was created, False if an existing one was updated. """ attempts = 0 while True: try: # We can autocommit if we are going to use native upserts autocommit = ( self.engine.can_native_upsert and table not in self._unsafe_to_upsert_tables ) return await self.runInteraction( desc, self.simple_upsert_txn, table, keyvalues, values, insertion_values, lock=lock, db_autocommit=autocommit, ) except self.engine.module.IntegrityError as e: attempts += 1 if attempts >= 5: # don't retry forever, because things other than races # can cause IntegrityErrors raise # presumably we raced with another transaction: let's retry. logger.warning( "IntegrityError when upserting into %s; retrying: %s", table, e ) def simple_upsert_txn( self, txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], values: Dict[str, Any], insertion_values: Dict[str, Any] = {}, lock: bool = True, ) -> Optional[bool]: """ Pick the UPSERT method which works best on the platform. Either the native one (Pg9.5+, recent SQLites), or fall back to an emulated method. Args: txn: The transaction to use. table: The table to upsert into keyvalues: The unique key tables and their new values values: The nonunique columns and their new values insertion_values: additional key/values to use only when inserting lock: True to lock the table when doing the upsert. Returns: Native upserts always return None. Emulated upserts return True if a new entry was created, False if an existing one was updated. """ if self.engine.can_native_upsert and table not in self._unsafe_to_upsert_tables: self.simple_upsert_txn_native_upsert( txn, table, keyvalues, values, insertion_values=insertion_values ) return None else: return self.simple_upsert_txn_emulated( txn, table, keyvalues, values, insertion_values=insertion_values, lock=lock, ) def simple_upsert_txn_emulated( self, txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], values: Dict[str, Any], insertion_values: Dict[str, Any] = {}, lock: bool = True, ) -> bool: """ Args: table: The table to upsert into keyvalues: The unique key tables and their new values values: The nonunique columns and their new values insertion_values: additional key/values to use only when inserting lock: True to lock the table when doing the upsert. Returns: Returns True if a new entry was created, False if an existing one was updated. """ # We need to lock the table :(, unless we're *really* careful if lock: self.engine.lock_table(txn, table) def _getwhere(key): # If the value we're passing in is None (aka NULL), we need to use # IS, not =, as NULL = NULL equals NULL (False). if keyvalues[key] is None: return "%s IS ?" % (key,) else: return "%s = ?" % (key,) if not values: # If `values` is empty, then all of the values we care about are in # the unique key, so there is nothing to UPDATE. We can just do a # SELECT instead to see if it exists. sql = "SELECT 1 FROM %s WHERE %s" % ( table, " AND ".join(_getwhere(k) for k in keyvalues), ) sqlargs = list(keyvalues.values()) txn.execute(sql, sqlargs) if txn.fetchall(): # We have an existing record. return False else: # First try to update. sql = "UPDATE %s SET %s WHERE %s" % ( table, ", ".join("%s = ?" % (k,) for k in values), " AND ".join(_getwhere(k) for k in keyvalues), ) sqlargs = list(values.values()) + list(keyvalues.values()) txn.execute(sql, sqlargs) if txn.rowcount > 0: # successfully updated at least one row. return False # We didn't find any existing rows, so insert a new one allvalues = {} # type: Dict[str, Any] allvalues.update(keyvalues) allvalues.update(values) allvalues.update(insertion_values) sql = "INSERT INTO %s (%s) VALUES (%s)" % ( table, ", ".join(k for k in allvalues), ", ".join("?" for _ in allvalues), ) txn.execute(sql, list(allvalues.values())) # successfully inserted return True def simple_upsert_txn_native_upsert( self, txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], values: Dict[str, Any], insertion_values: Dict[str, Any] = {}, ) -> None: """ Use the native UPSERT functionality in recent PostgreSQL versions. Args: table: The table to upsert into keyvalues: The unique key tables and their new values values: The nonunique columns and their new values insertion_values: additional key/values to use only when inserting """ allvalues = {} # type: Dict[str, Any] allvalues.update(keyvalues) allvalues.update(insertion_values) if not values: latter = "NOTHING" else: allvalues.update(values) latter = "UPDATE SET " + ", ".join(k + "=EXCLUDED." + k for k in values) sql = ("INSERT INTO %s (%s) VALUES (%s) ON CONFLICT (%s) DO %s") % ( table, ", ".join(k for k in allvalues), ", ".join("?" for _ in allvalues), ", ".join(k for k in keyvalues), latter, ) txn.execute(sql, list(allvalues.values())) async def simple_upsert_many( self, table: str, key_names: Collection[str], key_values: Collection[Iterable[Any]], value_names: Collection[str], value_values: Iterable[Iterable[Any]], desc: str, ) -> None: """ Upsert, many times. Args: table: The table to upsert into key_names: The key column names. key_values: A list of each row's key column values. value_names: The value column names value_values: A list of each row's value column values. Ignored if value_names is empty. """ # We can autocommit if we are going to use native upserts autocommit = ( self.engine.can_native_upsert and table not in self._unsafe_to_upsert_tables ) return await self.runInteraction( desc, self.simple_upsert_many_txn, table, key_names, key_values, value_names, value_values, db_autocommit=autocommit, ) def simple_upsert_many_txn( self, txn: LoggingTransaction, table: str, key_names: Collection[str], key_values: Collection[Iterable[Any]], value_names: Collection[str], value_values: Iterable[Iterable[Any]], ) -> None: """ Upsert, many times. Args: table: The table to upsert into key_names: The key column names. key_values: A list of each row's key column values. value_names: The value column names value_values: A list of each row's value column values. Ignored if value_names is empty. """ if self.engine.can_native_upsert and table not in self._unsafe_to_upsert_tables: return self.simple_upsert_many_txn_native_upsert( txn, table, key_names, key_values, value_names, value_values ) else: return self.simple_upsert_many_txn_emulated( txn, table, key_names, key_values, value_names, value_values ) def simple_upsert_many_txn_emulated( self, txn: LoggingTransaction, table: str, key_names: Iterable[str], key_values: Collection[Iterable[Any]], value_names: Collection[str], value_values: Iterable[Iterable[Any]], ) -> None: """ Upsert, many times, but without native UPSERT support or batching. Args: table: The table to upsert into key_names: The key column names. key_values: A list of each row's key column values. value_names: The value column names value_values: A list of each row's value column values. Ignored if value_names is empty. """ # No value columns, therefore make a blank list so that the following # zip() works correctly. if not value_names: value_values = [() for x in range(len(key_values))] for keyv, valv in zip(key_values, value_values): _keys = {x: y for x, y in zip(key_names, keyv)} _vals = {x: y for x, y in zip(value_names, valv)} self.simple_upsert_txn_emulated(txn, table, _keys, _vals) def simple_upsert_many_txn_native_upsert( self, txn: LoggingTransaction, table: str, key_names: Collection[str], key_values: Collection[Iterable[Any]], value_names: Collection[str], value_values: Iterable[Iterable[Any]], ) -> None: """ Upsert, many times, using batching where possible. Args: table: The table to upsert into key_names: The key column names. key_values: A list of each row's key column values. value_names: The value column names value_values: A list of each row's value column values. Ignored if value_names is empty. """ allnames = [] # type: List[str] allnames.extend(key_names) allnames.extend(value_names) if not value_names: # No value columns, therefore make a blank list so that the # following zip() works correctly. latter = "NOTHING" value_values = [() for x in range(len(key_values))] else: latter = "UPDATE SET " + ", ".join( k + "=EXCLUDED." + k for k in value_names ) sql = "INSERT INTO %s (%s) VALUES (%s) ON CONFLICT (%s) DO %s" % ( table, ", ".join(k for k in allnames), ", ".join("?" for _ in allnames), ", ".join(key_names), latter, ) args = [] for x, y in zip(key_values, value_values): args.append(tuple(x) + tuple(y)) return txn.execute_batch(sql, args) @overload async def simple_select_one( self, table: str, keyvalues: Dict[str, Any], retcols: Iterable[str], allow_none: Literal[False] = False, desc: str = "simple_select_one", ) -> Dict[str, Any]: ... @overload async def simple_select_one( self, table: str, keyvalues: Dict[str, Any], retcols: Iterable[str], allow_none: Literal[True] = True, desc: str = "simple_select_one", ) -> Optional[Dict[str, Any]]: ... async def simple_select_one( self, table: str, keyvalues: Dict[str, Any], retcols: Iterable[str], allow_none: bool = False, desc: str = "simple_select_one", ) -> Optional[Dict[str, Any]]: """Executes a SELECT query on the named table, which is expected to return a single row, returning multiple columns from it. Args: table: string giving the table name keyvalues: dict of column names and values to select the row with retcols: list of strings giving the names of the columns to return allow_none: If true, return None instead of failing if the SELECT statement returns no rows desc: description of the transaction, for logging and metrics """ return await self.runInteraction( desc, self.simple_select_one_txn, table, keyvalues, retcols, allow_none, db_autocommit=True, ) @overload async def simple_select_one_onecol( self, table: str, keyvalues: Dict[str, Any], retcol: str, allow_none: Literal[False] = False, desc: str = "simple_select_one_onecol", ) -> Any: ... @overload async def simple_select_one_onecol( self, table: str, keyvalues: Dict[str, Any], retcol: str, allow_none: Literal[True] = True, desc: str = "simple_select_one_onecol", ) -> Optional[Any]: ... async def simple_select_one_onecol( self, table: str, keyvalues: Dict[str, Any], retcol: str, allow_none: bool = False, desc: str = "simple_select_one_onecol", ) -> Optional[Any]: """Executes a SELECT query on the named table, which is expected to return a single row, returning a single column from it. Args: table: string giving the table name keyvalues: dict of column names and values to select the row with retcol: string giving the name of the column to return allow_none: If true, return None instead of failing if the SELECT statement returns no rows desc: description of the transaction, for logging and metrics """ return await self.runInteraction( desc, self.simple_select_one_onecol_txn, table, keyvalues, retcol, allow_none=allow_none, db_autocommit=True, ) @overload @classmethod def simple_select_one_onecol_txn( cls, txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], retcol: str, allow_none: Literal[False] = False, ) -> Any: ... @overload @classmethod def simple_select_one_onecol_txn( cls, txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], retcol: str, allow_none: Literal[True] = True, ) -> Optional[Any]: ... @classmethod def simple_select_one_onecol_txn( cls, txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], retcol: str, allow_none: bool = False, ) -> Optional[Any]: ret = cls.simple_select_onecol_txn( txn, table=table, keyvalues=keyvalues, retcol=retcol ) if ret: return ret[0] else: if allow_none: return None else: raise StoreError(404, "No row found") @staticmethod def simple_select_onecol_txn( txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], retcol: str, ) -> List[Any]: sql = ("SELECT %(retcol)s FROM %(table)s") % {"retcol": retcol, "table": table} if keyvalues: sql += " WHERE %s" % " AND ".join("%s = ?" % k for k in keyvalues.keys()) txn.execute(sql, list(keyvalues.values())) else: txn.execute(sql) return [r[0] for r in txn] async def simple_select_onecol( self, table: str, keyvalues: Optional[Dict[str, Any]], retcol: str, desc: str = "simple_select_onecol", ) -> List[Any]: """Executes a SELECT query on the named table, which returns a list comprising of the values of the named column from the selected rows. Args: table: table name keyvalues: column names and values to select the rows with retcol: column whos value we wish to retrieve. desc: description of the transaction, for logging and metrics Returns: Results in a list """ return await self.runInteraction( desc, self.simple_select_onecol_txn, table, keyvalues, retcol, db_autocommit=True, ) async def simple_select_list( self, table: str, keyvalues: Optional[Dict[str, Any]], retcols: Iterable[str], desc: str = "simple_select_list", ) -> List[Dict[str, Any]]: """Executes a SELECT query on the named table, which may return zero or more rows, returning the result as a list of dicts. Args: table: the table name keyvalues: column names and values to select the rows with, or None to not apply a WHERE clause. retcols: the names of the columns to return desc: description of the transaction, for logging and metrics Returns: A list of dictionaries. """ return await self.runInteraction( desc, self.simple_select_list_txn, table, keyvalues, retcols, db_autocommit=True, ) @classmethod def simple_select_list_txn( cls, txn: LoggingTransaction, table: str, keyvalues: Optional[Dict[str, Any]], retcols: Iterable[str], ) -> List[Dict[str, Any]]: """Executes a SELECT query on the named table, which may return zero or more rows, returning the result as a list of dicts. Args: txn: Transaction object table: the table name keyvalues: column names and values to select the rows with, or None to not apply a WHERE clause. retcols: the names of the columns to return """ if keyvalues: sql = "SELECT %s FROM %s WHERE %s" % ( ", ".join(retcols), table, " AND ".join("%s = ?" % (k,) for k in keyvalues), ) txn.execute(sql, list(keyvalues.values())) else: sql = "SELECT %s FROM %s" % (", ".join(retcols), table) txn.execute(sql) return cls.cursor_to_dict(txn) async def simple_select_many_batch( self, table: str, column: str, iterable: Iterable[Any], retcols: Iterable[str], keyvalues: Dict[str, Any] = {}, desc: str = "simple_select_many_batch", batch_size: int = 100, ) -> List[Any]: """Executes a SELECT query on the named table, which may return zero or more rows, returning the result as a list of dicts. Filters rows by whether the value of `column` is in `iterable`. Args: table: string giving the table name column: column name to test for inclusion against `iterable` iterable: list retcols: list of strings giving the names of the columns to return keyvalues: dict of column names and values to select the rows with desc: description of the transaction, for logging and metrics batch_size: the number of rows for each select query """ results = [] # type: List[Dict[str, Any]] if not iterable: return results # iterables can not be sliced, so convert it to a list first it_list = list(iterable) chunks = [ it_list[i : i + batch_size] for i in range(0, len(it_list), batch_size) ] for chunk in chunks: rows = await self.runInteraction( desc, self.simple_select_many_txn, table, column, chunk, keyvalues, retcols, db_autocommit=True, ) results.extend(rows) return results @classmethod def simple_select_many_txn( cls, txn: LoggingTransaction, table: str, column: str, iterable: Iterable[Any], keyvalues: Dict[str, Any], retcols: Iterable[str], ) -> List[Dict[str, Any]]: """Executes a SELECT query on the named table, which may return zero or more rows, returning the result as a list of dicts. Filters rows by whether the value of `column` is in `iterable`. Args: txn: Transaction object table: string giving the table name column: column name to test for inclusion against `iterable` iterable: list keyvalues: dict of column names and values to select the rows with retcols: list of strings giving the names of the columns to return """ if not iterable: return [] clause, values = make_in_list_sql_clause(txn.database_engine, column, iterable) clauses = [clause] for key, value in keyvalues.items(): clauses.append("%s = ?" % (key,)) values.append(value) sql = "SELECT %s FROM %s WHERE %s" % ( ", ".join(retcols), table, " AND ".join(clauses), ) txn.execute(sql, values) return cls.cursor_to_dict(txn) async def simple_update( self, table: str, keyvalues: Dict[str, Any], updatevalues: Dict[str, Any], desc: str, ) -> int: return await self.runInteraction( desc, self.simple_update_txn, table, keyvalues, updatevalues ) @staticmethod def simple_update_txn( txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], updatevalues: Dict[str, Any], ) -> int: if keyvalues: where = "WHERE %s" % " AND ".join("%s = ?" % k for k in keyvalues.keys()) else: where = "" update_sql = "UPDATE %s SET %s %s" % ( table, ", ".join("%s = ?" % (k,) for k in updatevalues), where, ) txn.execute(update_sql, list(updatevalues.values()) + list(keyvalues.values())) return txn.rowcount async def simple_update_one( self, table: str, keyvalues: Dict[str, Any], updatevalues: Dict[str, Any], desc: str = "simple_update_one", ) -> None: """Executes an UPDATE query on the named table, setting new values for columns in a row matching the key values. Args: table: string giving the table name keyvalues: dict of column names and values to select the row with updatevalues: dict giving column names and values to update desc: description of the transaction, for logging and metrics """ await self.runInteraction( desc, self.simple_update_one_txn, table, keyvalues, updatevalues, db_autocommit=True, ) @classmethod def simple_update_one_txn( cls, txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], updatevalues: Dict[str, Any], ) -> None: rowcount = cls.simple_update_txn(txn, table, keyvalues, updatevalues) if rowcount == 0: raise StoreError(404, "No row found (%s)" % (table,)) if rowcount > 1: raise StoreError(500, "More than one row matched (%s)" % (table,)) # Ideally we could use the overload decorator here to specify that the # return type is only optional if allow_none is True, but this does not work # when you call a static method from an instance. # See https://github.com/python/mypy/issues/7781 @staticmethod def simple_select_one_txn( txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any], retcols: Iterable[str], allow_none: bool = False, ) -> Optional[Dict[str, Any]]: select_sql = "SELECT %s FROM %s WHERE %s" % ( ", ".join(retcols), table, " AND ".join("%s = ?" % (k,) for k in keyvalues), ) txn.execute(select_sql, list(keyvalues.values())) row = txn.fetchone() if not row: if allow_none: return None raise StoreError(404, "No row found (%s)" % (table,)) if txn.rowcount > 1: raise StoreError(500, "More than one row matched (%s)" % (table,)) return dict(zip(retcols, row)) async def simple_delete_one( self, table: str, keyvalues: Dict[str, Any], desc: str = "simple_delete_one" ) -> None: """Executes a DELETE query on the named table, expecting to delete a single row. Args: table: string giving the table name keyvalues: dict of column names and values to select the row with desc: description of the transaction, for logging and metrics """ await self.runInteraction( desc, self.simple_delete_one_txn, table, keyvalues, db_autocommit=True, ) @staticmethod def simple_delete_one_txn( txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any] ) -> None: """Executes a DELETE query on the named table, expecting to delete a single row. Args: table: string giving the table name keyvalues: dict of column names and values to select the row with """ sql = "DELETE FROM %s WHERE %s" % ( table, " AND ".join("%s = ?" % (k,) for k in keyvalues), ) txn.execute(sql, list(keyvalues.values())) if txn.rowcount == 0: raise StoreError(404, "No row found (%s)" % (table,)) if txn.rowcount > 1: raise StoreError(500, "More than one row matched (%s)" % (table,)) async def simple_delete( self, table: str, keyvalues: Dict[str, Any], desc: str ) -> int: """Executes a DELETE query on the named table. Filters rows by the key-value pairs. Args: table: string giving the table name keyvalues: dict of column names and values to select the row with desc: description of the transaction, for logging and metrics Returns: The number of deleted rows. """ return await self.runInteraction( desc, self.simple_delete_txn, table, keyvalues, db_autocommit=True ) @staticmethod def simple_delete_txn( txn: LoggingTransaction, table: str, keyvalues: Dict[str, Any] ) -> int: """Executes a DELETE query on the named table. Filters rows by the key-value pairs. Args: table: string giving the table name keyvalues: dict of column names and values to select the row with Returns: The number of deleted rows. """ sql = "DELETE FROM %s WHERE %s" % ( table, " AND ".join("%s = ?" % (k,) for k in keyvalues), ) txn.execute(sql, list(keyvalues.values())) return txn.rowcount async def simple_delete_many( self, table: str, column: str, iterable: Iterable[Any], keyvalues: Dict[str, Any], desc: str, ) -> int: """Executes a DELETE query on the named table. Filters rows by if value of `column` is in `iterable`. Args: table: string giving the table name column: column name to test for inclusion against `iterable` iterable: list keyvalues: dict of column names and values to select the rows with desc: description of the transaction, for logging and metrics Returns: Number rows deleted """ return await self.runInteraction( desc, self.simple_delete_many_txn, table, column, iterable, keyvalues, db_autocommit=True, ) @staticmethod def simple_delete_many_txn( txn: LoggingTransaction, table: str, column: str, iterable: Iterable[Any], keyvalues: Dict[str, Any], ) -> int: """Executes a DELETE query on the named table. Filters rows by if value of `column` is in `iterable`. Args: txn: Transaction object table: string giving the table name column: column name to test for inclusion against `iterable` iterable: list keyvalues: dict of column names and values to select the rows with Returns: Number rows deleted """ if not iterable: return 0 sql = "DELETE FROM %s" % table clause, values = make_in_list_sql_clause(txn.database_engine, column, iterable) clauses = [clause] for key, value in keyvalues.items(): clauses.append("%s = ?" % (key,)) values.append(value) if clauses: sql = "%s WHERE %s" % (sql, " AND ".join(clauses)) txn.execute(sql, values) return txn.rowcount def get_cache_dict( self, db_conn: LoggingDatabaseConnection, table: str, entity_column: str, stream_column: str, max_value: int, limit: int = 100000, ) -> Tuple[Dict[Any, int], int]: # Fetch a mapping of room_id -> max stream position for "recent" rooms. # It doesn't really matter how many we get, the StreamChangeCache will # do the right thing to ensure it respects the max size of cache. sql = ( "SELECT %(entity)s, MAX(%(stream)s) FROM %(table)s" " WHERE %(stream)s > ? - %(limit)s" " GROUP BY %(entity)s" ) % { "table": table, "entity": entity_column, "stream": stream_column, "limit": limit, } txn = db_conn.cursor(txn_name="get_cache_dict") txn.execute(sql, (int(max_value),)) cache = {row[0]: int(row[1]) for row in txn} txn.close() if cache: min_val = min(cache.values()) else: min_val = max_value return cache, min_val @classmethod def simple_select_list_paginate_txn( cls, txn: LoggingTransaction, table: str, orderby: str, start: int, limit: int, retcols: Iterable[str], filters: Optional[Dict[str, Any]] = None, keyvalues: Optional[Dict[str, Any]] = None, order_direction: str = "ASC", ) -> List[Dict[str, Any]]: """ Executes a SELECT query on the named table with start and limit, of row numbers, which may return zero or number of rows from start to limit, returning the result as a list of dicts. Use `filters` to search attributes using SQL wildcards and/or `keyvalues` to select attributes with exact matches. All constraints are joined together using 'AND'. Args: txn: Transaction object table: the table name orderby: Column to order the results by. start: Index to begin the query at. limit: Number of results to return. retcols: the names of the columns to return filters: column names and values to filter the rows with, or None to not apply a WHERE ? LIKE ? clause. keyvalues: column names and values to select the rows with, or None to not apply a WHERE clause. order_direction: Whether the results should be ordered "ASC" or "DESC". Returns: The result as a list of dictionaries. """ if order_direction not in ["ASC", "DESC"]: raise ValueError("order_direction must be one of 'ASC' or 'DESC'.") where_clause = "WHERE " if filters or keyvalues else "" arg_list = [] # type: List[Any] if filters: where_clause += " AND ".join("%s LIKE ?" % (k,) for k in filters) arg_list += list(filters.values()) where_clause += " AND " if filters and keyvalues else "" if keyvalues: where_clause += " AND ".join("%s = ?" % (k,) for k in keyvalues) arg_list += list(keyvalues.values()) sql = "SELECT %s FROM %s %s ORDER BY %s %s LIMIT ? OFFSET ?" % ( ", ".join(retcols), table, where_clause, orderby, order_direction, ) txn.execute(sql, arg_list + [limit, start]) return cls.cursor_to_dict(txn) async def simple_search_list( self, table: str, term: Optional[str], col: str, retcols: Iterable[str], desc="simple_search_list", ) -> Optional[List[Dict[str, Any]]]: """Executes a SELECT query on the named table, which may return zero or more rows, returning the result as a list of dicts. Args: table: the table name term: term for searching the table matched to a column. col: column to query term should be matched to retcols: the names of the columns to return Returns: A list of dictionaries or None. """ return await self.runInteraction( desc, self.simple_search_list_txn, table, term, col, retcols, db_autocommit=True, ) @classmethod def simple_search_list_txn( cls, txn: LoggingTransaction, table: str, term: Optional[str], col: str, retcols: Iterable[str], ) -> Optional[List[Dict[str, Any]]]: """Executes a SELECT query on the named table, which may return zero or more rows, returning the result as a list of dicts. Args: txn: Transaction object table: the table name term: term for searching the table matched to a column. col: column to query term should be matched to retcols: the names of the columns to return Returns: None if no term is given, otherwise a list of dictionaries. """ if term: sql = "SELECT %s FROM %s WHERE %s LIKE ?" % (", ".join(retcols), table, col) termvalues = ["%%" + term + "%%"] txn.execute(sql, termvalues) else: return None return cls.cursor_to_dict(txn) def make_in_list_sql_clause( database_engine: BaseDatabaseEngine, column: str, iterable: Iterable ) -> Tuple[str, list]: """Returns an SQL clause that checks the given column is in the iterable. On SQLite this expands to `column IN (?, ?, ...)`, whereas on Postgres it expands to `column = ANY(?)`. While both DBs support the `IN` form, using the `ANY` form on postgres means that it views queries with different length iterables as the same, helping the query stats. Args: database_engine column: Name of the column iterable: The values to check the column against. Returns: A tuple of SQL query and the args """ if database_engine.supports_using_any_list: # This should hopefully be faster, but also makes postgres query # stats easier to understand. return "%s = ANY(?)" % (column,), [list(iterable)] else: return "%s IN (%s)" % (column, ",".join("?" for _ in iterable)), list(iterable) KV = TypeVar("KV") def make_tuple_comparison_clause( database_engine: BaseDatabaseEngine, keys: List[Tuple[str, KV]] ) -> Tuple[str, List[KV]]: """Returns a tuple comparison SQL clause Depending what the SQL engine supports, builds a SQL clause that looks like either "(a, b) > (?, ?)", or "(a > ?) OR (a == ? AND b > ?)". Args: database_engine keys: A set of (column, value) pairs to be compared. Returns: A tuple of SQL query and the args """ if database_engine.supports_tuple_comparison: return ( "(%s) > (%s)" % (",".join(k[0] for k in keys), ",".join("?" for _ in keys)), [k[1] for k in keys], ) # we want to build a clause # (a > ?) OR # (a == ? AND b > ?) OR # (a == ? AND b == ? AND c > ?) # ... # (a == ? AND b == ? AND ... AND z > ?) # # or, equivalently: # # (a > ? OR (a == ? AND # (b > ? OR (b == ? AND # ... # (y > ? OR (y == ? AND # z > ? # )) # ... # )) # )) # # which itself is equivalent to (and apparently easier for the query optimiser): # # (a >= ? AND (a > ? OR # (b >= ? AND (b > ? OR # ... # (y >= ? AND (y > ? OR # z > ? # )) # ... # )) # )) # # clause = "" args = [] # type: List[KV] for k, v in keys[:-1]: clause = clause + "(%s >= ? AND (%s > ? OR " % (k, k) args.extend([v, v]) (k, v) = keys[-1] clause += "%s > ?" % (k,) args.append(v) clause += "))" * (len(keys) - 1) return clause, args