import datetime import traceback from typing import ClassVar from asgiref.sync import async_to_sync, iscoroutinefunction 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_next_attempt", "state_locked_until"], name=f"ix_{sender.__name__.lower()[:11]}_state_next", ), ] if not sender._meta.indexes: # Meta.indexes needs to not be None to trigger Django behaviors sender.Meta.indexes = [] 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. """ CLEAN_BATCH_SIZE = 1000 DELETE_BATCH_SIZE = 500 state: StateField # When the state last actually changed, or the date of instance creation state_changed = models.DateTimeField(auto_now_add=True) # When the next state change should be attempted (null means immediately) state_next_attempt = 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, db_index=True) # Collection of subclasses of us subclasses: ClassVar[list[type["StatorModel"]]] = [] class Meta: abstract = True 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 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(): # Query for `number` rows that: # - Have a next_attempt that's either null or in the past # - Have one of the states we care about # Then, sort them by next_attempt NULLS FIRST, so that we handle the # rows in a roughly FIFO order. selected = list( cls.objects.filter( models.Q(state_next_attempt__isnull=True) | models.Q(state_next_attempt__lte=timezone.now()), state__in=cls.state_graph.automatic_states, state_locked_until__isnull=True, )[:number].select_for_update() ) cls.objects.filter(pk__in=[i.pk for i in selected]).update( state_locked_until=lock_expiry ) return selected @classmethod def transition_delete_due(cls) -> int | None: """ Finds instances of this model that need to be deleted and deletes them in small batches. Returns how many were deleted. """ if cls.state_graph.deletion_states: constraints = models.Q() for state in cls.state_graph.deletion_states: constraints |= models.Q( state=state, state_changed__lte=( timezone.now() - datetime.timedelta(seconds=state.delete_after) ), ) select_query = cls.objects.filter( models.Q(state_next_attempt__isnull=True) | models.Q(state_next_attempt__lte=timezone.now()), constraints, )[: cls.DELETE_BATCH_SIZE] return cls.objects.filter(pk__in=select_query).delete()[0] return None @classmethod def transition_ready_count(cls) -> int: """ Returns how many instances are "queued" """ return cls.objects.filter( models.Q(state_next_attempt__isnull=True) | models.Q(state_next_attempt__lte=timezone.now()), state_locked_until__isnull=True, state__in=cls.state_graph.automatic_states, ).count() @classmethod def transition_clean_locks(cls): """ Deletes stale locks (in batches, to avoid a giant query) """ select_query = cls.objects.filter(state_locked_until__lte=timezone.now())[ : cls.CLEAN_BATCH_SIZE ] cls.objects.filter(pk__in=select_query).update(state_locked_until=None) def transition_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 running its handler function try: if iscoroutinefunction(current_state.handler): next_state = async_to_sync(current_state.handler)(self) else: next_state = current_state.handler(self) except TryAgainLater: pass except BaseException as e: exceptions.capture_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" ) self.transition_perform(next_state) return next_state # See if it timed out since its last state change if ( current_state.timeout_value and current_state.timeout_value <= (timezone.now() - self.state_changed).total_seconds() ): self.transition_perform(current_state.timeout_state) # type: ignore return current_state.timeout_state # Nothing happened, set next execution and unlock it self.__class__.objects.filter(pk=self.pk).update( state_next_attempt=( timezone.now() + datetime.timedelta(seconds=current_state.try_interval) # type: ignore ), state_locked_until=None, ) 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, ) @classmethod def transition_perform_queryset( cls, queryset: models.QuerySet, state: State | str, ): """ Transitions every instance in the queryset to the given state name, forcibly. """ # Really ensure we have the right state object if isinstance(state, State): state_obj = cls.state_graph.states[state.name] else: state_obj = cls.state_graph.states[state] # See if it's ready immediately (if not, delay until first try_interval) if state_obj.attempt_immediately or state_obj.try_interval is None: queryset.update( state=state_obj, state_changed=timezone.now(), state_next_attempt=None, state_locked_until=None, ) else: queryset.update( state=state_obj, state_changed=timezone.now(), state_next_attempt=( timezone.now() + datetime.timedelta(seconds=state_obj.try_interval) ), state_locked_until=None, ) 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 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), )