takahe/stator/models.py
2023-07-07 15:14:06 -06:00

400 lines
14 KiB
Python

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),
)