329 lines
20 KiB
Python
329 lines
20 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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from typing import Any
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import json
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from ..db import connect, default_user_id, utcnow
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from . import rtorrent
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from .preferences import active_profile
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from .workers import enqueue
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AUTOMATION_JOB_CHUNK_SIZE = 100
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def _loads(value: str | None, default: Any) -> Any:
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try: return json.loads(value or '')
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except Exception: return default
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def _ts(value: str | None) -> float:
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if not value: return 0.0
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try: return datetime.fromisoformat(str(value).replace('Z', '+00:00')).timestamp()
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except Exception: return 0.0
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def _now_ts() -> float:
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return datetime.now(timezone.utc).timestamp()
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def _label_names(value: str | None) -> list[str]:
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seen = []
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for part in str(value or '').replace(';', ',').replace('|', ',').split(','):
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item = part.strip()
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if item and item not in seen: seen.append(item)
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return seen
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def _label_value(labels: list[str]) -> str:
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out = []
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for label in labels:
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label = str(label or '').strip()
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if label and label not in out: out.append(label)
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return ', '.join(out)
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def _rule_row(row: dict[str, Any]) -> dict[str, Any]:
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item = dict(row)
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item['conditions'] = _loads(item.pop('conditions_json', '[]'), [])
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item['effects'] = _loads(item.pop('effects_json', '[]'), [])
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return item
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def list_rules(profile_id: int | None = None, user_id: int | None = None) -> list[dict[str, Any]]:
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user_id = user_id or default_user_id()
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if profile_id is None:
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profile = active_profile(); profile_id = int(profile['id']) if profile else None
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with connect() as conn:
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rows = conn.execute('SELECT * FROM automation_rules WHERE user_id=? AND (profile_id=? OR profile_id IS NULL) ORDER BY enabled DESC, name COLLATE NOCASE', (user_id, profile_id)).fetchall()
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return [_rule_row(r) for r in rows]
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def get_rule(rule_id: int, profile_id: int, user_id: int | None = None) -> dict[str, Any]:
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user_id = user_id or default_user_id()
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with connect() as conn:
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row = conn.execute('SELECT * FROM automation_rules WHERE id=? AND user_id=? AND profile_id=?', (rule_id, user_id, profile_id)).fetchone()
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if not row: raise ValueError('Rule not found')
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return _rule_row(row)
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def save_rule(profile_id: int, data: dict[str, Any], user_id: int | None = None) -> dict[str, Any]:
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user_id = user_id or default_user_id()
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name = str(data.get('name') or 'Automation rule').strip() or 'Automation rule'
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conditions = data.get('conditions') or []
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effects = data.get('effects') or []
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if not isinstance(conditions, list) or not conditions: raise ValueError('Rule needs at least one condition')
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if not isinstance(effects, list) or not effects: raise ValueError('Rule needs at least one effect')
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cooldown = max(0, int(data.get('cooldown_minutes') or 0))
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enabled = 1 if data.get('enabled', True) else 0
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now = utcnow(); rule_id = int(data.get('id') or 0)
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with connect() as conn:
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if rule_id:
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conn.execute('UPDATE automation_rules SET name=?, enabled=?, conditions_json=?, effects_json=?, cooldown_minutes=?, updated_at=? WHERE id=? AND user_id=? AND profile_id=?', (name, enabled, json.dumps(conditions), json.dumps(effects), cooldown, now, rule_id, user_id, profile_id))
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else:
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cur = conn.execute('INSERT INTO automation_rules(user_id,profile_id,name,enabled,conditions_json,effects_json,cooldown_minutes,created_at,updated_at) VALUES(?,?,?,?,?,?,?,?,?)', (user_id, profile_id, name, enabled, json.dumps(conditions), json.dumps(effects), cooldown, now, now))
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rule_id = int(cur.lastrowid)
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return get_rule(rule_id, profile_id, user_id)
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def delete_rule(rule_id: int, profile_id: int, user_id: int | None = None) -> None:
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user_id = user_id or default_user_id()
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with connect() as conn:
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conn.execute('DELETE FROM automation_rules WHERE id=? AND user_id=? AND profile_id=?', (rule_id, user_id, profile_id))
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conn.execute('DELETE FROM automation_rule_state WHERE rule_id=? AND profile_id=?', (rule_id, profile_id))
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def list_history(profile_id: int, user_id: int | None = None, limit: int = 30) -> list[dict[str, Any]]:
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user_id = user_id or default_user_id()
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with connect() as conn:
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return conn.execute('SELECT * FROM automation_history WHERE user_id=? AND profile_id=? ORDER BY created_at DESC LIMIT ?', (user_id, profile_id, max(1, min(int(limit or 30), 100)))).fetchall()
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def clear_history(profile_id: int, user_id: int | None = None) -> int:
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user_id = user_id or default_user_id()
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with connect() as conn:
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# Note: Manual automation log cleanup is scoped to the active profile and current user.
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cur = conn.execute('DELETE FROM automation_history WHERE user_id=? AND profile_id=?', (user_id, profile_id))
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return int(cur.rowcount or 0)
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def _condition_true(t: dict[str, Any], cond: dict[str, Any]) -> bool:
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typ = str(cond.get('type') or '')
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if typ == 'completed': return bool(int(t.get('complete') or 0))
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if typ == 'no_seeds': return int(t.get('seeds') or 0) <= int(cond.get('seeds') or 0)
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if typ == 'ratio_gte': return float(t.get('ratio') or 0) >= float(cond.get('ratio') or 0)
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if typ == 'label_missing': return str(cond.get('label') or '').strip() not in _label_names(t.get('label'))
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if typ == 'label_has': return str(cond.get('label') or '').strip() in _label_names(t.get('label'))
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if typ == 'status': return str(t.get('status') or '').lower() == str(cond.get('status') or '').lower()
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if typ == 'path_contains': return str(cond.get('text') or '').lower() in str(t.get('path') or '').lower()
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return False
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def _conditions_match(conn, rule: dict[str, Any], profile_id: int, t: dict[str, Any]) -> bool:
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h = str(t.get('hash') or '')
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if not h: return False
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immediate_ok = True; delayed_ok = True; now = utcnow(); now_ts = _now_ts()
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for cond in rule.get('conditions') or []:
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raw_ok = _condition_true(t, cond)
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negated = bool(cond.get('negate'))
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# Note: Negation is applied in the backend, so UI and API only store the condition flag.
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ok = (not raw_ok) if negated else raw_ok
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if cond.get('type') == 'no_seeds' and int(cond.get('minutes') or 0) > 0 and not negated:
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row = conn.execute('SELECT condition_since_at FROM automation_rule_state WHERE rule_id=? AND profile_id=? AND torrent_hash=?', (rule['id'], profile_id, h)).fetchone()
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if ok:
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since = row['condition_since_at'] if row and row.get('condition_since_at') else now
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conn.execute('INSERT INTO automation_rule_state(rule_id,profile_id,torrent_hash,condition_since_at,last_matched_at,updated_at) VALUES(?,?,?,?,?,?) ON CONFLICT(rule_id,profile_id,torrent_hash) DO UPDATE SET condition_since_at=COALESCE(automation_rule_state.condition_since_at, excluded.condition_since_at), last_matched_at=excluded.last_matched_at, updated_at=excluded.updated_at', (rule['id'], profile_id, h, since, now, now))
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delayed_ok = delayed_ok and (now_ts - _ts(since) >= int(cond.get('minutes') or 0) * 60)
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else:
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conn.execute('UPDATE automation_rule_state SET condition_since_at=NULL, updated_at=? WHERE rule_id=? AND profile_id=? AND torrent_hash=?', (now, rule['id'], profile_id, h)); delayed_ok = False
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else:
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immediate_ok = immediate_ok and ok
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return immediate_ok and delayed_ok
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def _cooldown_ok(conn, rule: dict[str, Any], profile_id: int, torrent_hash: str = '__rule__') -> bool:
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cooldown = int(rule.get('cooldown_minutes') or 0)
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if cooldown <= 0: return True
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row = conn.execute('SELECT last_applied_at FROM automation_rule_state WHERE rule_id=? AND profile_id=? AND torrent_hash=?', (rule['id'], profile_id, torrent_hash)).fetchone()
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if not row or not row.get('last_applied_at'): return True
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return _now_ts() - _ts(row['last_applied_at']) >= cooldown * 60
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def _mark_rule_cooldown(conn, rule: dict[str, Any], profile_id: int, now: str) -> None:
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# Note: Cooldown is rule-level, so one batch execution blocks the whole automation until the cooldown expires.
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conn.execute('INSERT INTO automation_rule_state(rule_id,profile_id,torrent_hash,last_applied_at,updated_at) VALUES(?,?,?,?,?) ON CONFLICT(rule_id,profile_id,torrent_hash) DO UPDATE SET last_applied_at=excluded.last_applied_at, updated_at=excluded.updated_at', (rule['id'], profile_id, '__rule__', now, now))
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def _chunk_hashes(hashes: list[str], size: int = AUTOMATION_JOB_CHUNK_SIZE) -> list[list[str]]:
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# Note: Automation jobs use the same small-batch idea as manual bulk jobs, so long move/remove/actions remain visible and recoverable.
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safe_size = max(1, int(size or AUTOMATION_JOB_CHUNK_SIZE))
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return [hashes[index:index + safe_size] for index in range(0, len(hashes), safe_size)]
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def _job_context(rule: dict[str, Any], eff_type: str, hashes: list[str], torrents_by_hash: dict[str, dict[str, Any]], extra: dict[str, Any] | None = None) -> dict[str, Any]:
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# Note: Job context marks jobs created by automations, making the Jobs log explain what rule queued the work.
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ctx = {
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'source': 'automation',
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'rule_id': rule.get('id'),
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'rule_name': str(rule.get('name') or ''),
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'effect': eff_type,
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'bulk': len(hashes) > 1,
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'hash_count': len(hashes),
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'requested_at': utcnow(),
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'items': [
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{
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'hash': h,
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'name': str((torrents_by_hash.get(h) or {}).get('name') or ''),
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'path': str((torrents_by_hash.get(h) or {}).get('path') or ''),
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}
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for h in hashes
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],
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}
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if extra:
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ctx.update(extra)
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return ctx
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def _enqueue_automation_job(profile: dict[str, Any], rule: dict[str, Any], action_name: str, hashes: list[str], payload: dict[str, Any], torrents_by_hash: dict[str, dict[str, Any]], user_id: int | None = None, context_extra: dict[str, Any] | None = None) -> list[str]:
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# Note: Every automation side effect is queued as a normal job instead of running inline, so it appears in Jobs and uses worker retries/ordering.
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job_ids: list[str] = []
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chunks = _chunk_hashes(hashes)
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for index, chunk in enumerate(chunks, start=1):
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part_payload = dict(payload or {})
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part_payload['hashes'] = chunk
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part_payload['automation_ordered'] = True
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extra = dict(context_extra or {})
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if len(chunks) > 1:
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extra.update({'bulk_label': f'automation-{index}', 'bulk_part': index, 'bulk_parts': len(chunks), 'parent_hash_count': len(hashes)})
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if action_name == 'move':
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extra.update({'target_path': str(part_payload.get('path') or ''), 'move_data': bool(part_payload.get('move_data'))})
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if action_name == 'remove':
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extra.update({'remove_data': bool(part_payload.get('remove_data'))})
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part_payload['job_context'] = _job_context(rule, str(context_extra.get('effect_type') if context_extra else action_name), chunk, torrents_by_hash, extra)
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job_ids.append(enqueue(action_name, int(profile['id']), part_payload, user_id=user_id))
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return job_ids
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def _apply_effects_bulk(c: Any, profile: dict[str, Any], torrents: list[dict[str, Any]], effects: list[dict[str, Any]], rule: dict[str, Any], user_id: int | None = None) -> list[dict[str, Any]]:
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hashes = [str(t.get('hash') or '') for t in torrents if str(t.get('hash') or '')]
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torrents_by_hash = {str(t.get('hash') or ''): t for t in torrents if str(t.get('hash') or '')}
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labels_by_hash = {str(t.get('hash') or ''): _label_names(t.get('label')) for t in torrents}
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applied: list[dict[str, Any]] = []
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if not hashes: return applied
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for eff in effects:
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typ = str(eff.get('type') or '')
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if typ == 'move':
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path = str(eff.get('path') or '').strip() or rtorrent.default_download_path(profile)
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payload = {
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'path': path,
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'move_data': bool(eff.get('move_data')),
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'recheck': bool(eff.get('recheck', eff.get('move_data'))),
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'keep_seeding': bool(eff.get('keep_seeding')),
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}
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job_ids = _enqueue_automation_job(profile, rule, 'move', hashes, payload, torrents_by_hash, user_id, {'effect_type': 'move'})
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applied.append({'type': 'move', 'path': path, 'count': len(hashes), 'target_hashes': hashes, 'move_data': payload['move_data'], 'recheck': payload['recheck'], 'keep_seeding': payload['keep_seeding'], 'job_ids': job_ids})
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elif typ == 'add_label':
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label = str(eff.get('label') or '').strip()
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if label:
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# Note: Add-label automations are idempotent and queue only torrents that need a changed label value.
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grouped: dict[str, list[str]] = {}
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for h in hashes:
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labels = labels_by_hash.get(h, [])
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if label in labels:
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continue
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new_labels = list(labels) + [label]
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value = _label_value(new_labels)
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labels_by_hash[h] = _label_names(value)
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grouped.setdefault(value, []).append(h)
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target_hashes = [h for group in grouped.values() for h in group]
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job_ids: list[str] = []
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for value, group_hashes in grouped.items():
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job_ids.extend(_enqueue_automation_job(profile, rule, 'set_label', group_hashes, {'label': value}, torrents_by_hash, user_id, {'effect_type': 'add_label', 'label': label}))
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if target_hashes:
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applied.append({'type': 'add_label', 'label': label, 'count': len(target_hashes), 'target_hashes': target_hashes, 'job_ids': job_ids})
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elif typ == 'remove_label':
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label = str(eff.get('label') or '').strip()
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if label:
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# Note: Remove-label automations are queued only for torrents where the requested label exists.
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grouped: dict[str, list[str]] = {}
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for h in hashes:
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labels = labels_by_hash.get(h, [])
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if label not in labels:
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continue
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value = _label_value([x for x in labels if x != label])
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labels_by_hash[h] = _label_names(value)
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grouped.setdefault(value, []).append(h)
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target_hashes = [h for group in grouped.values() for h in group]
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job_ids: list[str] = []
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for value, group_hashes in grouped.items():
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job_ids.extend(_enqueue_automation_job(profile, rule, 'set_label', group_hashes, {'label': value}, torrents_by_hash, user_id, {'effect_type': 'remove_label', 'label': label}))
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if target_hashes:
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applied.append({'type': 'remove_label', 'label': label, 'count': len(target_hashes), 'target_hashes': target_hashes, 'job_ids': job_ids})
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elif typ == 'set_labels':
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value = _label_value(_label_names(eff.get('labels')))
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target_labels = _label_names(value)
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# Note: Set-labels queues a job only if the current labels differ from the requested exact list.
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target_hashes = [h for h in hashes if labels_by_hash.get(h, []) != target_labels]
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for h in target_hashes:
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labels_by_hash[h] = list(target_labels)
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if target_hashes:
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job_ids = _enqueue_automation_job(profile, rule, 'set_label', target_hashes, {'label': value}, torrents_by_hash, user_id, {'effect_type': 'set_labels', 'labels': value})
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applied.append({'type': 'set_labels', 'labels': value, 'count': len(target_hashes), 'target_hashes': target_hashes, 'job_ids': job_ids})
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elif typ in {'pause', 'stop', 'start', 'resume', 'recheck', 'reannounce'}:
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# Note: Runtime actions are queued as jobs too, so automation activity is visible in the Jobs panel.
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job_ids = _enqueue_automation_job(profile, rule, typ, hashes, {}, torrents_by_hash, user_id, {'effect_type': typ})
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applied.append({'type': typ, 'count': len(hashes), 'target_hashes': hashes, 'job_ids': job_ids})
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elif typ == 'remove':
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# Note: Remove is supported for automation payloads and still goes through ordered worker jobs.
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payload = {'remove_data': bool(eff.get('remove_data'))}
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job_ids = _enqueue_automation_job(profile, rule, 'remove', hashes, payload, torrents_by_hash, user_id, {'effect_type': 'remove'})
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applied.append({'type': 'remove', 'count': len(hashes), 'target_hashes': hashes, 'remove_data': payload['remove_data'], 'job_ids': job_ids})
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return applied
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def check(profile: dict | None = None, user_id: int | None = None, force: bool = False) -> dict[str, Any]:
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profile = profile or active_profile()
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if not profile: return {'ok': False, 'error': 'No active rTorrent profile'}
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user_id = user_id or default_user_id(); profile_id = int(profile['id'])
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rules = [r for r in list_rules(profile_id, user_id) if force or int(r.get('enabled') or 0)]
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if not rules: return {'ok': True, 'checked': 0, 'applied': [], 'batches': [], 'rules': 0}
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torrents = rtorrent.list_torrents(profile); applied = []; batches = []; now = utcnow()
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planned: list[dict[str, Any]] = []
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with connect() as conn:
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for rule in rules:
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# Note: This pass only matches rules and updates condition timers; job creation is intentionally delayed until after this DB transaction commits.
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if not force and not _cooldown_ok(conn, rule, profile_id):
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continue
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matched = [t for t in torrents if _conditions_match(conn, rule, profile_id, t)]
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if not matched:
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continue
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hashes = [str(t.get('hash') or '') for t in matched if str(t.get('hash') or '')]
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if hashes:
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planned.append({'rule': rule, 'matched': matched, 'hashes': hashes})
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for item in planned:
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rule = item['rule']
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matched = item['matched']
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hashes = item['hashes']
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# Note: Automation jobs are enqueued outside the rule-state transaction, preventing SQLite self-locks when enqueue() writes to jobs.
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try:
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actions = _apply_effects_bulk(None, profile, matched, rule.get('effects') or [], rule, user_id)
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except Exception as exc:
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actions = [{'error': str(exc), 'count': len(hashes), 'target_hashes': hashes}]
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changed_hashes = sorted({h for a in actions for h in (a.get('target_hashes') or [])})
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if not actions or not changed_hashes:
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# Note: Matching torrents with no real action are not logged and do not restart the cooldown.
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continue
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history_actions = [{k: v for k, v in a.items() if k != 'target_hashes'} for a in actions]
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matched_by_hash = {str(t.get('hash') or ''): t for t in matched}
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with connect() as conn:
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# Note: State/history writes happen after enqueue succeeds, so failed job creation does not create misleading automation history.
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for h in changed_hashes:
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t = matched_by_hash.get(h, {})
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conn.execute('INSERT INTO automation_rule_state(rule_id,profile_id,torrent_hash,last_matched_at,last_applied_at,updated_at) VALUES(?,?,?,?,?,?) ON CONFLICT(rule_id,profile_id,torrent_hash) DO UPDATE SET last_matched_at=excluded.last_matched_at, last_applied_at=excluded.last_applied_at, updated_at=excluded.updated_at', (rule['id'], profile_id, h, now, now, now))
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applied.append({'rule_id': rule['id'], 'rule_name': rule.get('name'), 'hash': h, 'name': t.get('name'), 'actions': [{'type': a.get('type', 'error'), 'count': a.get('count', len(changed_hashes))} for a in actions]})
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_mark_rule_cooldown(conn, rule, profile_id, now)
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torrent_name = str(matched_by_hash.get(changed_hashes[0], {}).get('name') or '') if len(changed_hashes) == 1 else f'{len(changed_hashes)} torrents'
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torrent_hash = changed_hashes[0] if len(changed_hashes) == 1 else f'batch:{rule["id"]}:{now}'
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conn.execute('INSERT INTO automation_history(user_id,profile_id,rule_id,torrent_hash,torrent_name,rule_name,actions_json,created_at) VALUES(?,?,?,?,?,?,?,?)', (user_id, profile_id, rule['id'], torrent_hash, torrent_name, str(rule.get('name') or ''), json.dumps(history_actions), now))
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batches.append({'rule_id': rule['id'], 'rule_name': rule.get('name'), 'count': len(changed_hashes), 'actions': history_actions})
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return {'ok': True, 'checked': len(torrents), 'rules': len(rules), 'applied': applied, 'batches': batches}
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