167 lines
6.4 KiB
Python
167 lines
6.4 KiB
Python
"""The materialization walk (Feature 07 core).
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:func:`materialize` turns the schedule rules + custody presence of the
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active quest definitions into concrete ``quest_instances`` rows over a
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date range. It is the pure walk over definitions x dates x assignees x
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windows; HA scheduling (day-rollover triggers) and skip-completed
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generation are separate later tasks and live elsewhere.
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The walk is deterministic and idempotent:
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- All inputs — the active definitions with their rules, assignees and
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windows, AND the assignee children's presence schedules and scoped
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overrides — are read inside ONE locked transaction (see
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:func:`~.dao_rules.load_materialization_input`), so the walk sees a
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single coherent snapshot rather than a definition state from one
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instant and a presence state from another.
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- Presence is snapshotted into an immutable
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:class:`~.presence.PresenceEngine` from that same coherent read.
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- For each date in the range, each definition whose decoded rule
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:func:`~.recurrence.occurs_on` on that date, each assignee who is
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``is_present`` on that date and each declared window, one instance is
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written through :class:`~.dao_instances.QuestInstancesDao.upsert_if_valid`
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with a ``generated_at`` stamp shared by the whole batch (one coherent
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time per run).
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A config change that lands AFTER the snapshot is read but BEFORE a
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tuple's write (an assignment removed, a definition or child deactivated,
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a window removed) makes that tuple SKIP rather than abort the batch:
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:meth:`~.dao_instances.QuestInstancesDao.upsert_if_valid` re-validates
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every materialization precondition (active definition, active child,
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live assignment, declared window, and its CURRENT ``due_time``) inside
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the SAME transaction as the INSERT, so the check and the write cannot be
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split by a racing config edit.
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The upsert is idempotent on (definition_id, child_id, due_date, window),
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so re-running the materialization over the same range never duplicates a
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row; skipped-completed handling is deliberately NOT done here.
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"""
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from __future__ import annotations
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import datetime
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from .dao_instances import QuestInstancesDao
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from .dao_rules import (
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_validate_date,
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load_materialization_input,
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schedule_rule_from_storage,
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schedule_rule_storage_from_record,
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)
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from .db import NestQuestDatabase
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from .presence import PresenceEngine, PresenceOverride, PresenceSchedule
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from .recurrence import ScheduleRule, occurs_on
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def _now_stamp() -> str:
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"""Return the batch's shared UTC second-precision generation stamp."""
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return datetime.datetime.now(datetime.timezone.utc).isoformat(
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timespec="seconds"
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)
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def _decode_rule(snapshot) -> ScheduleRule:
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"""Decode a snapshot's rule record back into a ScheduleRule.
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The record carried by the snapshot is the one the definition
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referenced at read time, so decoding it here (rather than re-reading
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through ``ScheduleRulesDao``) preserves the coherence the snapshot
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guarantees.
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"""
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return schedule_rule_from_storage(
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schedule_rule_storage_from_record(snapshot.rule)
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)
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def _build_engine(
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schedules_records,
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overrides_records,
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) -> PresenceEngine:
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"""Convert raw presence records into an immutable PresenceEngine."""
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schedules: dict[int, PresenceSchedule] = {}
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for child_id, record in schedules_records.items():
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schedules[child_id] = PresenceSchedule.decode(
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child_id, record.anchor_date, record.pattern
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)
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overrides: dict[int, list[PresenceOverride]] = {}
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for child_id, records in overrides_records.items():
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overrides[child_id] = [
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PresenceOverride(
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record.child_id,
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record.start_date,
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record.end_date,
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record.is_present,
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record.note,
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)
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for record in records
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]
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return PresenceEngine(schedules, overrides)
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async def materialize(
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database: NestQuestDatabase,
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start_date: str,
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end_date: str,
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) -> int:
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"""Generate quest instances for the closed range [start_date, end_date].
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``start_date`` and ``end_date`` must be strict ISO calendar dates
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(YYYY-MM-DD) with ``end_date`` on or after ``start_date``; anything
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else raises ValueError naming the offending field. Returns the number
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of instances upserted for the range (idempotent — a re-run refreshes
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the same rows, never duplicates them).
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All inputs are read in ONE locked transaction; each tuple's write then
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goes through the atomic :meth:`~.dao_instances.QuestInstancesDao.upsert_if_valid`,
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which re-validates the tuple and its current ``due_time`` in the same
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transaction as the INSERT, so a config change that arrives mid-batch
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skips the affected tuples rather than aborting the batch or leaving a
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partial result.
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"""
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_validate_date(start_date, "start_date")
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_validate_date(end_date, "end_date")
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if end_date < start_date:
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raise ValueError(
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f"end_date must be on or after start_date, got "
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f"{end_date!r} < {start_date!r}"
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)
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snapshots, schedules_records, overrides_records = await (
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load_materialization_input(database, start_date, end_date)
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)
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engine = _build_engine(schedules_records, overrides_records)
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definitions = [
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(snapshot.definition.id, _decode_rule(snapshot),
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snapshot.assignees, snapshot.windows)
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for snapshot in snapshots
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]
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generated_at = _now_stamp()
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instances = QuestInstancesDao(database)
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start = datetime.datetime.strptime(start_date, "%Y-%m-%d").date()
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end = datetime.datetime.strptime(end_date, "%Y-%m-%d").date()
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count = 0
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cursor = start
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while cursor <= end:
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iso = cursor.isoformat()
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for definition_id, rule, assignees, windows in definitions:
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if not occurs_on(rule, cursor):
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continue
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for child in assignees:
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if not engine.is_present(child.id, cursor):
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continue
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for window in windows:
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written = await instances.upsert_if_valid(
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definition_id,
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child.id,
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iso,
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generated_at,
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window=window.window,
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due_time=window.due_time,
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)
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if written is not None:
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count += 1
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cursor += datetime.timedelta(days=1)
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return count |