Refactor storage: remove Redis duplication, use proper data tiers
- Recipes: Now content-addressed only (cache + IPFS), removed Redis storage - Runs: Completed runs stored in PostgreSQL, Redis only for task_id mapping - Add list_runs_by_actor() to database.py for paginated run queries - Add list_by_type() to cache_manager for filtering by node_type - Fix upload endpoint to return size and filename fields - Fix recipe run endpoint with proper DAG input binding - Fix get_run_service() dependency to pass database module Storage architecture: - Redis: Ephemeral only (sessions, task mappings with TTL) - PostgreSQL: Permanent records (completed runs, metadata) - Cache: Content-addressed files (recipes, media, outputs) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
giles
@@ -1,147 +1,338 @@
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"""
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Run Service - business logic for run management.
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Runs are content-addressed (run_id computed from inputs + recipe).
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Completed runs are stored in PostgreSQL, not Redis.
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In-progress runs are tracked via Celery task state.
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"""
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from typing import Optional, List, Dict, Any
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import hashlib
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import json
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional, List, Dict, Any, Tuple
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def compute_run_id(input_hashes: list, recipe: str, recipe_hash: str = None) -> str:
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"""
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Compute a deterministic run_id from inputs and recipe.
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The run_id is a SHA3-256 hash of:
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- Sorted input content hashes
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- Recipe identifier (recipe_hash if provided, else "effect:{recipe}")
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This makes runs content-addressable: same inputs + recipe = same run_id.
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"""
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# Handle both list and dict inputs
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if isinstance(input_hashes, dict):
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sorted_inputs = sorted(input_hashes.values())
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else:
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sorted_inputs = sorted(input_hashes)
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data = {
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"inputs": sorted_inputs,
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"recipe": recipe_hash or f"effect:{recipe}",
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"version": "1",
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}
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json_str = json.dumps(data, sort_keys=True, separators=(",", ":"))
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return hashlib.sha3_256(json_str.encode()).hexdigest()
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def detect_media_type(cache_path: Path) -> str:
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"""Detect if file is image, video, or audio based on magic bytes."""
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try:
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with open(cache_path, "rb") as f:
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header = f.read(32)
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except Exception:
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return "unknown"
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# Video signatures
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if header[:4] == b'\x1a\x45\xdf\xa3': # WebM/MKV
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return "video"
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if len(header) > 8 and header[4:8] == b'ftyp': # MP4/MOV
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return "video"
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if header[:4] == b'RIFF' and len(header) > 12 and header[8:12] == b'AVI ': # AVI
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return "video"
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# Image signatures
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if header[:8] == b'\x89PNG\r\n\x1a\n': # PNG
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return "image"
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if header[:2] == b'\xff\xd8': # JPEG
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return "image"
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if header[:6] in (b'GIF87a', b'GIF89a'): # GIF
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return "image"
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if header[:4] == b'RIFF' and len(header) > 12 and header[8:12] == b'WEBP': # WebP
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return "image"
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return "unknown"
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class RunService:
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"""
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Service for managing recipe runs.
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Handles run lifecycle, plan loading, and result aggregation.
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Uses PostgreSQL for completed runs, Celery for task state.
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Redis is only used for task_id mapping (ephemeral).
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"""
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def __init__(self, database, redis, cache):
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self.db = database
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self.redis = redis
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self.redis = redis # Only for task_id mapping
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self.cache = cache
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self.run_prefix = "artdag:run:"
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self.task_key_prefix = "artdag:task:" # run_id -> task_id mapping only
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self.cache_dir = Path(os.environ.get("CACHE_DIR", "/tmp/artdag-cache"))
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async def get_run(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Get a run by ID."""
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data = self.redis.get(f"{self.run_prefix}{run_id}")
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if not data:
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return None
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return json.loads(data)
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"""Get a run by ID. Checks database first, then Celery task state."""
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# Check database for completed run
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cached = await self.db.get_run_cache(run_id)
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if cached:
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": cached.get("recipe"),
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"inputs": cached.get("inputs", []),
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"output_hash": cached.get("output_hash"),
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"ipfs_cid": cached.get("ipfs_cid"),
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"provenance_cid": cached.get("provenance_cid"),
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"actor_id": cached.get("actor_id"),
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"created_at": cached.get("created_at"),
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"completed_at": cached.get("created_at"),
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}
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# Check if there's a running task
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task_id = self.redis.get(f"{self.task_key_prefix}{run_id}")
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if task_id:
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if isinstance(task_id, bytes):
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task_id = task_id.decode()
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# Get task state from Celery
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from celery.result import AsyncResult
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from celery_app import app as celery_app
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result = AsyncResult(task_id, app=celery_app)
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status = result.status.lower()
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run_data = {
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"run_id": run_id,
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"status": status if status != "pending" else "pending",
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"celery_task_id": task_id,
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}
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# If task completed, get result
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if result.ready():
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if result.successful():
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run_data["status"] = "completed"
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task_result = result.result
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if isinstance(task_result, dict):
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run_data["output_hash"] = task_result.get("output_hash")
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else:
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run_data["status"] = "failed"
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run_data["error"] = str(result.result)
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return run_data
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return None
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async def list_runs(self, actor_id: str, offset: int = 0, limit: int = 20) -> list:
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"""List runs for a user with pagination."""
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# Get all runs and filter by actor
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# TODO: Use Redis index for efficient filtering
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all_runs = []
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cursor = 0
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"""List runs for a user. Returns completed runs from database."""
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# Get completed runs from database
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runs = await self.db.list_runs_by_actor(actor_id, offset=offset, limit=limit)
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# Also check for any pending tasks in Redis
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pending = []
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cursor = 0
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while True:
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cursor, keys = self.redis.scan(
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cursor=cursor,
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match=f"{self.run_prefix}*",
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match=f"{self.task_key_prefix}*",
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count=100
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)
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for key in keys:
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data = self.redis.get(key)
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if data:
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run = json.loads(data)
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if run.get("actor_id") == actor_id or run.get("username") == actor_id:
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all_runs.append(run)
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run_id = key.decode().replace(self.task_key_prefix, "") if isinstance(key, bytes) else key.replace(self.task_key_prefix, "")
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# Check if this run belongs to the user and isn't already in results
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if not any(r.get("run_id") == run_id for r in runs):
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run = await self.get_run(run_id)
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if run and run.get("status") in ("pending", "running"):
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pending.append(run)
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if cursor == 0:
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break
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# Sort by created_at descending
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# Combine and sort
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all_runs = pending + runs
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all_runs.sort(key=lambda r: r.get("created_at", ""), reverse=True)
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# Paginate
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return all_runs[offset:offset + limit]
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async def create_run(
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self,
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recipe: str,
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inputs: list,
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output_name: str = None,
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use_dag: bool = True,
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dag_json: str = None,
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actor_id: str = None,
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l2_server: str = None,
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) -> Tuple[Optional[Dict[str, Any]], Optional[str]]:
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"""
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Create a new rendering run. Checks cache before executing.
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Returns (run_dict, error_message).
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"""
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import httpx
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try:
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from legacy_tasks import render_effect, execute_dag, build_effect_dag
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except ImportError as e:
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return None, f"Celery tasks not available: {e}"
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# Handle both list and dict inputs
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if isinstance(inputs, dict):
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input_list = list(inputs.values())
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else:
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input_list = inputs
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# Compute content-addressable run_id
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run_id = compute_run_id(input_list, recipe)
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# Generate output name if not provided
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if not output_name:
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output_name = f"{recipe}-{run_id[:8]}"
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# Check database cache first (completed runs)
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cached_run = await self.db.get_run_cache(run_id)
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if cached_run:
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output_hash = cached_run.get("output_hash")
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if output_hash and self.cache.has_content(output_hash):
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": recipe,
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"inputs": input_list,
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"output_name": output_name,
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"output_hash": output_hash,
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"ipfs_cid": cached_run.get("ipfs_cid"),
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"provenance_cid": cached_run.get("provenance_cid"),
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"created_at": cached_run.get("created_at"),
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"completed_at": cached_run.get("created_at"),
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"actor_id": actor_id,
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}, None
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# Check L2 if not in local cache
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if l2_server:
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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l2_resp = await client.get(f"{l2_server}/assets/by-run-id/{run_id}")
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if l2_resp.status_code == 200:
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l2_data = l2_resp.json()
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output_hash = l2_data.get("output_hash")
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ipfs_cid = l2_data.get("ipfs_cid")
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if output_hash and ipfs_cid:
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# Pull from IPFS to local cache
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try:
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import ipfs_client
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legacy_dir = self.cache_dir / "legacy"
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legacy_dir.mkdir(parents=True, exist_ok=True)
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recovery_path = legacy_dir / output_hash
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if ipfs_client.get_file(ipfs_cid, str(recovery_path)):
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# Save to database cache
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await self.db.save_run_cache(
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run_id=run_id,
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output_hash=output_hash,
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recipe=recipe,
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inputs=input_list,
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ipfs_cid=ipfs_cid,
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provenance_cid=l2_data.get("provenance_cid"),
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actor_id=actor_id,
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)
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return {
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"run_id": run_id,
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"status": "completed",
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"recipe": recipe,
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"inputs": input_list,
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"output_hash": output_hash,
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"ipfs_cid": ipfs_cid,
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"provenance_cid": l2_data.get("provenance_cid"),
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"created_at": datetime.now(timezone.utc).isoformat(),
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"actor_id": actor_id,
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}, None
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except Exception:
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pass # IPFS recovery failed, continue to run
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except Exception:
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pass # L2 lookup failed, continue to run
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# Not cached - submit to Celery
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try:
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if use_dag or recipe == "dag":
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if dag_json:
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dag_data = dag_json
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else:
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dag = build_effect_dag(input_list, recipe)
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dag_data = dag.to_json()
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task = execute_dag.delay(dag_data, run_id)
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else:
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if len(input_list) != 1:
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return None, "Legacy mode only supports single-input recipes. Use use_dag=true for multi-input."
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task = render_effect.delay(input_list[0], recipe, output_name)
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# Store task_id mapping in Redis (ephemeral)
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self.redis.setex(
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f"{self.task_key_prefix}{run_id}",
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3600 * 24, # 24 hour TTL
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task.id
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)
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return {
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"run_id": run_id,
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"status": "running",
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"recipe": recipe,
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"inputs": input_list,
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"output_name": output_name,
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"celery_task_id": task.id,
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"created_at": datetime.now(timezone.utc).isoformat(),
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"actor_id": actor_id,
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}, None
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except Exception as e:
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return None, f"Failed to submit task: {e}"
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async def discard_run(
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self,
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run_id: str,
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recipe_id: str,
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inputs: Dict[str, str],
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actor_id: str,
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) -> Dict[str, Any]:
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"""Create a new run."""
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from datetime import datetime
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username: str,
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) -> Tuple[bool, Optional[str]]:
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"""
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Discard (delete) a run record.
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run = {
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"run_id": run_id,
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"recipe": f"recipe:{recipe_id}",
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"inputs": inputs,
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"actor_id": actor_id,
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"status": "pending",
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"created_at": datetime.utcnow().isoformat(),
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}
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self.redis.set(f"{self.run_prefix}{run_id}", json.dumps(run))
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return run
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async def update_run(self, run_id: str, updates: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""Update a run's fields."""
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Note: This removes the run record but not the output content.
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"""
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run = await self.get_run(run_id)
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if not run:
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return None
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return False, f"Run {run_id} not found"
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run.update(updates)
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self.redis.set(f"{self.run_prefix}{run_id}", json.dumps(run))
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return run
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# Check ownership
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run_owner = run.get("actor_id")
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if run_owner and run_owner not in (username, actor_id):
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return False, "Access denied"
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async def delete_run(self, run_id: str) -> bool:
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"""Delete a run."""
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return self.redis.delete(f"{self.run_prefix}{run_id}") > 0
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# Remove task_id mapping from Redis
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self.redis.delete(f"{self.task_key_prefix}{run_id}")
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async def load_plan(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Load execution plan for a run."""
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from pathlib import Path
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import os
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# Note: We don't delete from run_cache as that's a permanent record
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# of completed work. The content itself remains in cache.
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# Try plan cache directory
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cache_dir = Path(os.environ.get("CACHE_DIR", "/tmp/artdag-cache"))
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plan_path = cache_dir / "plans" / f"{run_id}.json"
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return True, None
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async def get_run_plan(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Get execution plan for a run."""
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plan_path = self.cache_dir / "plans" / f"{run_id}.json"
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if plan_path.exists():
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with open(plan_path) as f:
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return json.load(f)
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# Also check for plan_id in run data
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run = await self.get_run(run_id)
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if run and run.get("plan_id"):
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plan_path = cache_dir / "plans" / f"{run['plan_id']}.json"
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if plan_path.exists():
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with open(plan_path) as f:
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return json.load(f)
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return None
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async def get_run_plan(self, run_id: str) -> Optional[Dict[str, Any]]:
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"""Get execution plan with step results merged in."""
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run = await self.get_run(run_id)
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if not run:
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return None
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plan = await self.load_plan(run_id)
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# If no stored plan, try to reconstruct from run data
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if not plan and run.get("step_results"):
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plan = {
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"plan_id": run.get("plan_id"),
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"recipe": run.get("recipe"),
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"steps": [],
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}
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if plan and run.get("step_results"):
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# Merge step results into plan
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step_results = run.get("step_results", {})
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for step in plan.get("steps", []):
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step_id = step.get("id") or step.get("name")
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if step_id and step_id in step_results:
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result = step_results[step_id]
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step["cache_id"] = result.get("cache_id") or result.get("output_cache_id")
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step["status"] = result.get("status", "completed")
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step["cached"] = result.get("cached", False)
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step["outputs"] = result.get("outputs", [])
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return plan
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async def get_run_artifacts(self, run_id: str) -> List[Dict[str, Any]]:
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"""Get all artifacts (inputs + outputs) for a run."""
|
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run = await self.get_run(run_id)
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@@ -150,31 +341,16 @@ class RunService:
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||||
|
||||
artifacts = []
|
||||
|
||||
def get_artifact_info(content_hash: str, role: str, step_name: str) -> Optional[Dict]:
|
||||
"""Get artifact info using cache manager."""
|
||||
def get_artifact_info(content_hash: str, role: str, name: str) -> Optional[Dict]:
|
||||
if self.cache.has_content(content_hash):
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||||
path = self.cache.get_path(content_hash)
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path = self.cache.get_by_content_hash(content_hash)
|
||||
if path and path.exists():
|
||||
# Detect media type
|
||||
media_type = "file"
|
||||
try:
|
||||
with open(path, "rb") as f:
|
||||
header = f.read(12)
|
||||
if header[:4] == b'\x1a\x45\xdf\xa3' or header[4:8] == b'ftyp':
|
||||
media_type = "video"
|
||||
elif header[:8] == b'\x89PNG\r\n\x1a\n' or header[:2] == b'\xff\xd8':
|
||||
media_type = "image"
|
||||
elif header[:4] == b'RIFF' and header[8:12] == b'WAVE':
|
||||
media_type = "audio"
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return {
|
||||
"hash": content_hash,
|
||||
"size_bytes": path.stat().st_size,
|
||||
"media_type": media_type,
|
||||
"media_type": detect_media_type(path),
|
||||
"role": role,
|
||||
"step_name": step_name,
|
||||
"step_name": name,
|
||||
}
|
||||
return None
|
||||
|
||||
@@ -182,50 +358,28 @@ class RunService:
|
||||
inputs = run.get("inputs", [])
|
||||
if isinstance(inputs, dict):
|
||||
inputs = list(inputs.values())
|
||||
for i, content_hash in enumerate(inputs):
|
||||
info = get_artifact_info(content_hash, "input", f"Input {i + 1}")
|
||||
for i, h in enumerate(inputs):
|
||||
info = get_artifact_info(h, "input", f"Input {i + 1}")
|
||||
if info:
|
||||
artifacts.append(info)
|
||||
|
||||
# Add step outputs from step_results
|
||||
step_results = run.get("step_results", {})
|
||||
for step_id, result in step_results.items():
|
||||
cache_id = result.get("cache_id") or result.get("output_cache_id")
|
||||
if cache_id:
|
||||
info = get_artifact_info(cache_id, "step_output", step_id)
|
||||
if info:
|
||||
artifacts.append(info)
|
||||
# Also add any additional outputs
|
||||
for output in result.get("outputs", []):
|
||||
if output and output != cache_id:
|
||||
info = get_artifact_info(output, "step_output", step_id)
|
||||
if info:
|
||||
artifacts.append(info)
|
||||
|
||||
# Add final output
|
||||
# Add output
|
||||
if run.get("output_hash"):
|
||||
output_hash = run["output_hash"]
|
||||
# Avoid duplicates
|
||||
if not any(a["hash"] == output_hash for a in artifacts):
|
||||
info = get_artifact_info(output_hash, "output", "Final Output")
|
||||
if info:
|
||||
artifacts.append(info)
|
||||
info = get_artifact_info(run["output_hash"], "output", "Output")
|
||||
if info:
|
||||
artifacts.append(info)
|
||||
|
||||
return artifacts
|
||||
|
||||
async def get_run_analysis(self, run_id: str) -> List[Dict[str, Any]]:
|
||||
"""Get analysis data for each input in a run."""
|
||||
from pathlib import Path
|
||||
import os
|
||||
|
||||
run = await self.get_run(run_id)
|
||||
if not run:
|
||||
return []
|
||||
|
||||
cache_dir = Path(os.environ.get("CACHE_DIR", "/tmp/artdag-cache"))
|
||||
analysis_dir = cache_dir / "analysis"
|
||||
|
||||
analysis_dir = self.cache_dir / "analysis"
|
||||
results = []
|
||||
|
||||
inputs = run.get("inputs", [])
|
||||
if isinstance(inputs, dict):
|
||||
inputs = list(inputs.values())
|
||||
@@ -247,8 +401,11 @@ class RunService:
|
||||
"has_analysis": analysis_data is not None,
|
||||
"tempo": analysis_data.get("tempo") if analysis_data else None,
|
||||
"beat_times": analysis_data.get("beat_times", []) if analysis_data else [],
|
||||
"energy": analysis_data.get("energy") if analysis_data else None,
|
||||
"raw": analysis_data,
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
def detect_media_type(self, path: Path) -> str:
|
||||
"""Detect media type for a file path."""
|
||||
return detect_media_type(path)
|
||||
|
||||
Reference in New Issue
Block a user