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anonymousland-synapse/synapse/storage/database.py
Jonathan de Jong 4c3827f2c1
Enable addtional flake8-bugbear linting checks. (#9659)
2021-03-24 09:34:30 -04:00

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# -*- 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) -> None:
self.conn.rollback()
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 fetchone(self) -> Optional[Tuple]:
return self.txn.fetchone()
def fetchmany(self, size: Optional[int] = None) -> List[Tuple]:
return self.txn.fetchmany(size=size)
def fetchall(self) -> List[Tuple]:
return self.txn.fetchall()
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:
"""Similar to `executemany`, except `txn.rowcount` will not be correct
afterwards.
More efficient than `executemany` on PostgreSQL
"""
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:
self.executemany(sql, args)
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 Exception:
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.
"""
assert cursor.description is not None, "cursor.description was None"
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.execute_batch(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
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