import datetime import traceback from typing import ClassVar, cast from asgiref.sync import sync_to_async from django.db import models, transaction from django.db.models.signals import class_prepared from django.utils import timezone from django.utils.functional import classproperty from core import exceptions from stator.exceptions import TryAgainLater from stator.graph import State, StateGraph class StateField(models.CharField): """ A special field that automatically gets choices from a state graph """ def __init__(self, graph: type[StateGraph], **kwargs): # Sensible default for state length kwargs.setdefault("max_length", 100) # Add choices and initial self.graph = graph kwargs["choices"] = self.graph.choices kwargs["default"] = self.graph.initial_state.name super().__init__(**kwargs) def deconstruct(self): name, path, args, kwargs = super().deconstruct() kwargs["graph"] = self.graph return name, path, args, kwargs def get_prep_value(self, value): if isinstance(value, State): return value.name return value def add_stator_indexes(sender, **kwargs): """ Inject Indexes used by StatorModel in to any subclasses. This sidesteps the current Django inability to inherit indexes when the Model subclass defines its own indexes. """ if issubclass(sender, StatorModel): indexes = [ models.Index( fields=["state", "state_attempted"], name=f"ix_{sender.__name__.lower()[:11]}_state_attempted", ), models.Index( fields=["state_locked_until", "state"], condition=models.Q(state_locked_until__isnull=False), name=f"ix_{sender.__name__.lower()[:11]}_state_locked", ), ] if not sender._meta.indexes: # Meta.indexes needs to not be None to trigger Django behaviors sender.Meta.indexes = [] for idx in indexes: sender._meta.indexes.append(idx) # class_prepared might become deprecated [1]. If it's removed, the named Index # injection would need to happen in a metaclass subclass of ModelBase's _prepare() # # [1] https://code.djangoproject.com/ticket/24313 class_prepared.connect(add_stator_indexes) class StatorModel(models.Model): """ A model base class that has a state machine backing it, with tasks to work out when to move the state to the next one. You need to provide a "state" field as an instance of StateField on the concrete model yourself. """ state: StateField # If this row is up for transition attempts (which it always is on creation!) state_ready = models.BooleanField(default=True) # When the state last actually changed, or the date of instance creation state_changed = models.DateTimeField(auto_now_add=True) # When the last state change for the current state was attempted # (and not successful, as this is cleared on transition) state_attempted = models.DateTimeField(blank=True, null=True) # If a lock is out on this row, when it is locked until # (we don't identify the lock owner, as there's no heartbeats) state_locked_until = models.DateTimeField(null=True, blank=True) # Collection of subclasses of us subclasses: ClassVar[list[type["StatorModel"]]] = [] class Meta: abstract = True # Need this empty indexes to ensure child Models have a Meta.indexes # that will look to add indexes (that we inject with class_prepared) indexes: list = [] def __init_subclass__(cls) -> None: if cls is not StatorModel: cls.subclasses.append(cls) @classproperty def state_graph(cls) -> type[StateGraph]: return cls._meta.get_field("state").graph @property def state_age(self) -> float: return (timezone.now() - self.state_changed).total_seconds() @classmethod async def atransition_schedule_due(cls, now=None): """ Finds instances of this model that need to run and schedule them. """ q = models.Q() for state in cls.state_graph.states.values(): state = cast(State, state) if not state.externally_progressed: q = q | models.Q( ( models.Q( state_attempted__lte=timezone.now() - datetime.timedelta( seconds=cast(float, state.try_interval) ) ) | models.Q(state_attempted__isnull=True) ), state=state.name, ) await cls.objects.filter(q).aupdate(state_ready=True) @classmethod def transition_get_with_lock( cls, number: int, lock_expiry: datetime.datetime ) -> list["StatorModel"]: """ Returns up to `number` tasks for execution, having locked them. """ with transaction.atomic(): selected = list( cls.objects.filter( state_locked_until__isnull=True, state_ready=True, state__in=cls.state_graph.automatic_states, )[:number].select_for_update() ) cls.objects.filter(pk__in=[i.pk for i in selected]).update( state_locked_until=lock_expiry ) return selected @classmethod async def atransition_get_with_lock( cls, number: int, lock_expiry: datetime.datetime ) -> list["StatorModel"]: return await sync_to_async(cls.transition_get_with_lock)(number, lock_expiry) @classmethod async def atransition_ready_count(cls) -> int: """ Returns how many instances are "queued" """ return await ( cls.objects.filter( state_locked_until__isnull=True, state_ready=True, state__in=cls.state_graph.automatic_states, ).acount() ) @classmethod async def atransition_clean_locks(cls): await cls.objects.filter(state_locked_until__lte=timezone.now()).aupdate( state_locked_until=None ) def transition_schedule(self): """ Adds this instance to the queue to get its state transition attempted. The scheduler will call this, but you can also call it directly if you know it'll be ready and want to lower latency. """ self.state_ready = True self.save() async def atransition_attempt(self) -> State | None: """ Attempts to transition the current state by running its handler(s). """ current_state: State = self.state_graph.states[self.state] # If it's a manual progression state don't even try # We shouldn't really be here in this case, but it could be a race condition if current_state.externally_progressed: print( f"Warning: trying to progress externally progressed state {self.state}!" ) return None try: next_state = await current_state.handler(self) # type: ignore except TryAgainLater: pass except BaseException as e: await exceptions.acapture_exception(e) traceback.print_exc() else: if next_state: # Ensure it's a State object if isinstance(next_state, str): next_state = self.state_graph.states[next_state] # Ensure it's a child if next_state not in current_state.children: raise ValueError( f"Cannot transition from {current_state} to {next_state} - not a declared transition" ) await self.atransition_perform(next_state) return next_state # See if it timed out if ( current_state.timeout_value and current_state.timeout_value <= (timezone.now() - self.state_changed).total_seconds() ): await self.atransition_perform(current_state.timeout_state) return current_state.timeout_state await self.__class__.objects.filter(pk=self.pk).aupdate( state_attempted=timezone.now(), state_locked_until=None, state_ready=False, ) return None def transition_perform(self, state: State | str): """ Transitions the instance to the given state name, forcibly. """ self.transition_perform_queryset( self.__class__.objects.filter(pk=self.pk), state, ) atransition_perform = sync_to_async(transition_perform) @classmethod def transition_perform_queryset( cls, queryset: models.QuerySet, state: State | str, ): """ Transitions every instance in the queryset to the given state name, forcibly. """ if isinstance(state, State): state = state.name if state not in cls.state_graph.states: raise ValueError(f"Invalid state {state}") # See if it's ready immediately (if not, delay until first try_interval) if cls.state_graph.states[state].attempt_immediately: queryset.update( state=state, state_changed=timezone.now(), state_attempted=None, state_locked_until=None, state_ready=True, ) else: queryset.update( state=state, state_changed=timezone.now(), state_attempted=timezone.now(), state_locked_until=None, state_ready=False, ) class Stats(models.Model): """ Tracks summary statistics of each model over time. """ # appname.modelname (lowercased) label for the model this represents model_label = models.CharField(max_length=200, primary_key=True) statistics = models.JSONField() created = models.DateTimeField(auto_now_add=True) updated = models.DateTimeField(auto_now=True) class Meta: verbose_name_plural = "Stats" @classmethod def get_for_model(cls, model: type[StatorModel]) -> "Stats": instance = cls.objects.filter(model_label=model._meta.label_lower).first() if instance is None: instance = cls(model_label=model._meta.label_lower) if not instance.statistics: instance.statistics = {} # Ensure there are the right keys for key in ["queued", "hourly", "daily", "monthly"]: if key not in instance.statistics: instance.statistics[key] = {} return instance @classmethod async def aget_for_model(cls, model: type[StatorModel]) -> "Stats": return await sync_to_async(cls.get_for_model)(model) def set_queued(self, number: int): """ Sets the current queued amount. The queue is an instantaneous value (a "gauge") rather than a sum ("counter"). It's mostly used for reporting what things are right now, but basic trend analysis is also used to see if we think the queue is backing up. """ self.statistics["queued"][ int(timezone.now().replace(second=0, microsecond=0).timestamp()) ] = number def add_handled(self, number: int): """ Adds the "handled" number to the current stats. """ hour = timezone.now().replace(minute=0, second=0, microsecond=0) day = hour.replace(hour=0) hour_timestamp = str(int(hour.timestamp())) day_timestamp = str(int(day.timestamp())) month_timestamp = str(int(day.replace(day=1).timestamp())) self.statistics["hourly"][hour_timestamp] = ( self.statistics["hourly"].get(hour_timestamp, 0) + number ) self.statistics["daily"][day_timestamp] = ( self.statistics["daily"].get(day_timestamp, 0) + number ) self.statistics["monthly"][month_timestamp] = ( self.statistics["monthly"].get(month_timestamp, 0) + number ) def trim_data(self): """ Removes excessively old data from the field """ queued_horizon = int((timezone.now() - datetime.timedelta(hours=2)).timestamp()) hourly_horizon = int( (timezone.now() - datetime.timedelta(hours=50)).timestamp() ) daily_horizon = int((timezone.now() - datetime.timedelta(days=62)).timestamp()) monthly_horizon = int( (timezone.now() - datetime.timedelta(days=3653)).timestamp() ) self.statistics["queued"] = { ts: v for ts, v in self.statistics["queued"].items() if int(ts) >= queued_horizon } self.statistics["hourly"] = { ts: v for ts, v in self.statistics["hourly"].items() if int(ts) >= hourly_horizon } self.statistics["daily"] = { ts: v for ts, v in self.statistics["daily"].items() if int(ts) >= daily_horizon } self.statistics["monthly"] = { ts: v for ts, v in self.statistics["monthly"].items() if int(ts) >= monthly_horizon } def most_recent_queued(self) -> int: """ Returns the most recent number of how many were queued """ queued = [(int(ts), v) for ts, v in self.statistics["queued"].items()] queued.sort(reverse=True) if queued: return queued[0][1] else: return 0 def most_recent_handled(self) -> tuple[int, int, int]: """ Returns the current handling numbers for hour, day, month """ hour = timezone.now().replace(minute=0, second=0, microsecond=0) day = hour.replace(hour=0) hour_timestamp = str(int(hour.timestamp())) day_timestamp = str(int(day.timestamp())) month_timestamp = str(int(day.replace(day=1).timestamp())) return ( self.statistics["hourly"].get(hour_timestamp, 0), self.statistics["daily"].get(day_timestamp, 0), self.statistics["monthly"].get(month_timestamp, 0), )