# Part of Odoo. See LICENSE file for full copyright and licensing details.

# decorator makes wrappers that have the same API as their wrapped function

from __future__ import annotations

from collections import defaultdict

from collections.abc import Mapping, Collection

from inspect import signature, Parameter

import functools

import logging

import signal

import sys

import threading

import time

import typing

import warnings

if typing.TYPE_CHECKING:

from .lru import LRU

from collections.abc import Callable, Iterable

from odoo.models import BaseModel

C = typing.TypeVar('C', bound=Callable)

unsafe_eval = eval

_logger = logging.getLogger(__name__)

_logger_lock = threading.RLock()

_logger_state: typing.Literal['wait', 'abort', 'run'] = 'wait'

class ormcache_counter:

""" Statistic counters for cache entries. """

__slots__ = ['cache_name', 'err', 'gen_time', 'hit', 'miss', 'tx_err', 'tx_hit', 'tx_miss']

def __init__(self):

self.hit: int = 0

self.miss: int = 0

self.err: int = 0

self.gen_time: float = 0.0

self.cache_name: str = ''

self.tx_hit: int = 0

self.tx_miss: int = 0

self.tx_err: int = 0

@property

def ratio(self) -> float:

return 100.0 * self.hit / (self.hit + self.miss or 1)

@property

def tx_ratio(self) -> float:

return 100.0 * self.tx_hit / (self.tx_hit + self.tx_miss or 1)

@property

def tx_calls(self) -> int:

return self.tx_hit + self.tx_miss

_COUNTERS: defaultdict[tuple[str, Callable], ormcache_counter] = defaultdict(ormcache_counter)

"""statistic counters dictionary, maps (dbname, method) to counter"""

class ormcache:

""" LRU cache decorator for model methods.

The parameters are strings that represent expressions referring to the

signature of the decorated method, and are used to compute a cache key::

@ormcache('model_name', 'mode')

def _compute_domain(self, model_name, mode="read"):

...

For the sake of backward compatibility, the decorator supports the named

parameter `skiparg`::

@ormcache(skiparg=1)

def _compute_domain(self, model_name, mode="read"):

...

Methods implementing this decorator should never return a Recordset,

because the underlying cursor will eventually be closed and raise a

`psycopg2.InterfaceError`.

"""

key: Callable[..., tuple]

def __init__(self, *args: str, cache: str = 'default', skiparg: int | None = None, **kwargs):

self.args = args

self.skiparg = skiparg

self.cache_name = cache

if skiparg is not None:

warnings.warn("Deprecated since 19.0, ormcache(skiparg) will be removed", DeprecationWarning)

def __call__(self, method: C) -> C:

assert not hasattr(self, 'method'), "ormcache is already bound to a method"

self.method = method

self.determine_key()

assert self.key is not None, "ormcache.key not initialized"

@functools.wraps(method)

def lookup(*args, **kwargs):

return self.lookup(*args, **kwargs)

lookup.__cache__ = self # type: ignore

return lookup

def add_value(self, *args, cache_value=None, **kwargs) -> None:

model: BaseModel = args[0]

d: LRU = model.pool._Registry__caches[self.cache_name] # type: ignore

key = self.key(*args, **kwargs)

d[key] = cache_value

def determine_key(self) -> None:

""" Determine the function that computes a cache key from arguments. """

assert self.method is not None

if self.skiparg is not None:

# backward-compatible function that uses self.skiparg

self.key = lambda *args, **kwargs: (args[0]._name, self.method, *args[self.skiparg:])

return

# build a string that represents function code and evaluate it

args = ', '.join(

# remove annotations because lambdas can't be type-annotated,

str(params.replace(annotation=Parameter.empty))

for params in signature(self.method).parameters.values()

)

values = ['self._name', 'method', *self.args]

code = f"lambda {args}: ({''.join(a for arg in values for a in (arg, ','))})"

self.key = unsafe_eval(code, {'method': self.method})

def lookup(self, *args, **kwargs):

model: BaseModel = args[0]

d: LRU = model.pool._Registry__caches[self.cache_name] # type: ignore

key = self.key(*args, **kwargs)

counter = _COUNTERS[model.pool.db_name, self.method]

tx_lookups = model.env.cr.cache.setdefault('_ormcache_lookups', set())

# tx: is it the first call in the transation for that key

tx_first_lookup = key not in tx_lookups

if tx_first_lookup:

counter.cache_name = self.cache_name

tx_lookups.add(key)

try:

r = d[key]

counter.hit += 1

counter.tx_hit += tx_first_lookup

return r

except KeyError:

counter.miss += 1

counter.tx_miss += tx_first_lookup

miss = True

except TypeError:

_logger.warning("cache lookup error on %r", key, exc_info=True)

counter.err += 1

counter.tx_err += tx_first_lookup

miss = False

if miss:

start = time.monotonic()

value = self.method(*args, **kwargs)

counter.gen_time += time.monotonic() - start

d[key] = value

return value

else:

return self.method(*args, **kwargs)

class ormcache_context(ormcache):

""" This LRU cache decorator is a variant of :class:`ormcache`, with an

extra parameter ``keys`` that defines a sequence of dictionary keys. Those

keys are looked up in the ``context`` parameter and combined to the cache

key made by :class:`ormcache`.

"""

def __init__(self, *args: str, keys, skiparg=None, **kwargs):

assert skiparg is None, "ormcache_context() no longer supports skiparg"

warnings.warn("Since 19.0, use ormcache directly, context values are available as `self.env.context.get`", DeprecationWarning)

super().__init__(*args, **kwargs)

def determine_key(self) -> None:

assert self.method is not None

sign = signature(self.method)

cont_expr = "(context or {})" if 'context' in sign.parameters else "self.env.context"

keys_expr = "tuple(%s.get(k) for k in %r)" % (cont_expr, self.keys)

self.args += (keys_expr,)

super().determine_key()

def log_ormcache_stats(sig=None, frame=None): # noqa: ARG001 (arguments are there for signals)

# collect and log data in a separate thread to avoid blocking the main thread

# and avoid using logging module directly in the signal handler

# https://docs.python.org/3/library/logging.html#thread-safety

global _logger_state # noqa: PLW0603

with _logger_lock:

if _logger_state != 'wait':

# send the signal again to stop the logging thread

_logger_state = 'abort'

return

_logger_state = 'run'

def check_continue_logging():

if _logger_state == 'run':

return True

_logger.info('Stopping logging ORM cache stats')

return False

class StatsLine:

def __init__(self, method, counter: ormcache_counter):

self.sz_entries_sum: int = 0

self.sz_entries_max: int = 0

self.nb_entries: int = 0

self.counter = counter

self.method = method

def _log_ormcache_stats():

""" Log statistics of ormcache usage by database, model, and method. """

from odoo.modules.registry import Registry # noqa: PLC0415

try:

# {dbname: {method: StatsLine}}

cache_stats: defaultdict[str, dict[Callable, StatsLine]] = defaultdict(dict)

# {dbname: (cache_name, entries, count, total_size)}

cache_usage: defaultdict[str, list[tuple[str, int, int, int]]] = defaultdict(list)

# browse the values in cache

registries = Registry.registries.snapshot

class_slots = {}

for i, (dbname, registry) in enumerate(registries.items(), start=1):

if not check_continue_logging():

return

_logger.info("Processing database %s (%d/%d)", dbname, i, len(registries))

db_cache_stats = cache_stats[dbname]

db_cache_usage = cache_usage[dbname]

for cache_name, cache in registry._Registry__caches.items():

cache_total_size = 0

for cache_key, cache_value in cache.snapshot.items():

method = cache_key[1]

stats = db_cache_stats.get(method)

if stats is None:

stats = db_cache_stats[method] = StatsLine(method, _COUNTERS[dbname, method])

stats.nb_entries += 1

if not show_size:

continue

size = get_cache_size((cache_key, cache_value), cache_info=method.__qualname__, class_slots=class_slots)

cache_total_size += size

stats.sz_entries_sum += size

stats.sz_entries_max = max(stats.sz_entries_max, size)

db_cache_usage.append((cache_name, len(cache), cache.count, cache_total_size))

# add counters that have no values in cache

for (dbname, method), counter in _COUNTERS.copy().items(): # copy to avoid concurrent modification

if not check_continue_logging():

return

db_cache_stats = cache_stats[dbname]

stats = db_cache_stats.get(method)

if stats is None:

db_cache_stats[method] = StatsLine(method, counter)

# Output the stats

log_msgs = ['Caches stats:']

size_column_info = (

f"{'Memory %':>10},"

f"{'Memory SUM':>12},"

f"{'Memory MAX':>12},"

) if show_size else ''

column_info = (

f"{'Cache Name':>25},"

f"{'Entry':>7},"

f"{size_column_info}"

f"{'Hit':>6},"

f"{'Miss':>6},"

f"{'Err':>6},"

f"{'Gen Time [s]':>13},"

f"{'Hit Ratio':>10},"

f"{'TX Hit Ratio':>13},"

f"{'TX Call':>8},"

" Method"

)

for dbname, db_cache_stats in sorted(cache_stats.items(), key=lambda k: k[0] or '~'):

if not check_continue_logging():

return

log_msgs.append(f'Database {dbname or "<no_db>"}:')

log_msgs.extend(

f" * {cache_name}: {entries}/{count}{' (' if cache_total_size else ''}{cache_total_size}{' bytes)' if cache_total_size else ''}"

for cache_name, entries, count, cache_total_size in db_cache_usage

)

log_msgs.append('Details:')

# sort by -sz_entries_sum and method_name

db_cache_stat = sorted(db_cache_stats.items(), key=lambda k: (-k[1].sz_entries_sum, k[0].__name__))

sz_entries_all = sum(stat.sz_entries_sum for _, stat in db_cache_stat)

log_msgs.append(column_info)

for method, stat in db_cache_stat:

size_data = (

f'{stat.sz_entries_sum / (sz_entries_all or 1) * 100:9.1f}%,'

f'{stat.sz_entries_sum:12d},'

f'{stat.sz_entries_max:12d},'

) if show_size else ''

log_msgs.append(

f'{stat.counter.cache_name:>25},'

f'{stat.nb_entries:7d},'

f'{size_data}'

f'{stat.counter.hit:6d},'

f'{stat.counter.miss:6d},'

f'{stat.counter.err:6d},'

f'{stat.counter.gen_time:13.3f},'

f'{stat.counter.ratio:9.1f}%,'

f'{stat.counter.tx_ratio:12.1f}%,'

f'{stat.counter.tx_calls:8d},'

f' {method.__qualname__}'

)

_logger.info('\n'.join(log_msgs))

except Exception: # noqa: BLE001

_logger.exception()

finally:

global _logger_state # noqa: PLW0603

with _logger_lock:

_logger_state = 'wait'

show_size = False

if sig == signal.SIGUSR1:

threading.Thread(target=_log_ormcache_stats,

name="odoo.signal.log_ormcache_stats").start()

elif sig == signal.SIGUSR2:

show_size = True

threading.Thread(target=_log_ormcache_stats,

name="odoo.signal.log_ormcache_stats_with_size").start()

def get_cache_key_counter(bound_method: Callable, *args, **kwargs) -> tuple[LRU, tuple, ormcache_counter]:

""" Return the cache, key and stat counter for the given call. """

model: BaseModel = bound_method.__self__ # type: ignore

ormcache_instance: ormcache = bound_method.__cache__ # type: ignore

cache: LRU = model.pool._Registry__caches[ormcache_instance.cache_name] # type: ignore

key = ormcache_instance.key(model, *args, **kwargs)

counter = _COUNTERS[model.pool.db_name, ormcache_instance.method]

return cache, key, counter

def get_cache_size(

obj,

*,

cache_info: str = '',

seen_ids: set[int] | None = None,

class_slots: dict[type, Iterable[str]] | None = None

) -> int:

""" A non-thread-safe recursive object size estimator """

from odoo.models import BaseModel # noqa: PLC0415

from odoo.api import Environment # noqa: PLC0415

if seen_ids is None:

# count internal constants as 0 bytes

seen_ids = set(map(id, (None, False, True)))

if class_slots is None:

class_slots = {} # {class_id: combined_slots}

total_size = 0

objects = [obj]

while objects:

cur_obj = objects.pop()

if id(cur_obj) in seen_ids:

continue

if cache_info and isinstance(cur_obj, (BaseModel, Environment)):

_logger.error('%s is cached by %s', cur_obj, cache_info)

continue

seen_ids.add(id(cur_obj))

total_size += sys.getsizeof(cur_obj)

if hasattr(cur_obj, '__slots__'):

cur_obj_cls = type(cur_obj)

attributes = class_slots.get(id(cur_obj_cls))

if attributes is None:

class_slots[id(cur_obj_cls)] = attributes = tuple({

f'_{cls.__name__}{attr}' if attr.startswith('__') else attr

for cls in cur_obj_cls.mro()

for attr in getattr(cls, '__slots__', ())

})

objects.extend(getattr(cur_obj, attr, None) for attr in attributes)

if hasattr(cur_obj, '__dict__'):

objects.append(object.__dict__)

if isinstance(cur_obj, Mapping):

objects.extend(cur_obj.values())

objects.extend(cur_obj.keys())

elif isinstance(cur_obj, Collection) and not isinstance(cur_obj, (str, bytes, bytearray)):

objects.extend(cur_obj)

return total_size