Agent Reprocesses Already-Processed Items in Batch — Wasted Compute
Symptom
- Batch job fails at item 8,000 of 10,000 — restart means 8,000 items re-processed
- Agent emails all users again because it restarted mid-batch
- Daily job takes 6 hours; any failure means 6 more hours to restart
- No way to resume from where the job left off
- Restarted job creates duplicate records for already-processed items
processed_countresets to 0 on every restart
Root Cause
Batch jobs without checkpointing are all-or-nothing: they succeed completely or restart from the beginning. Without persisting which items have been processed, a restart replays everything. This is expensive for long-running jobs and dangerous if processing has side effects (emails, charges, database writes).
Fix
Option 1: SQLite checkpoint — track processed items by ID
import sqlite3
from pathlib import Path
from datetime import datetime
class BatchCheckpoint:
"""
SQLite-based checkpoint for batch jobs.
Tracks which item IDs have been successfully processed.
Survives crashes, restarts, and partial failures.
"""
def __init__(self, job_name: str, db_path: str = "batch_checkpoints.db"):
self.job_name = job_name
self.db = sqlite3.connect(db_path, check_same_thread=False)
self.db.execute("""
CREATE TABLE IF NOT EXISTS processed_items (
job_name TEXT NOT NULL,
item_id TEXT NOT NULL,
processed_at TEXT NOT NULL,
status TEXT DEFAULT 'success',
PRIMARY KEY (job_name, item_id)
)
""")
self.db.commit()
def is_done(self, item_id: str) -> bool:
"""Check if this item was already successfully processed"""
row = self.db.execute(
"SELECT status FROM processed_items WHERE job_name=? AND item_id=? AND status='success'",
(self.job_name, str(item_id))
).fetchone()
return row is not None
def mark_done(self, item_id: str, status: str = "success"):
self.db.execute("""
INSERT OR REPLACE INTO processed_items (job_name, item_id, processed_at, status)
VALUES (?, ?, ?, ?)
""", (self.job_name, str(item_id), datetime.utcnow().isoformat(), status))
self.db.commit()
def stats(self) -> dict:
row = self.db.execute(
"SELECT COUNT(*), SUM(status='success'), SUM(status='failed') "
"FROM processed_items WHERE job_name=?",
(self.job_name,)
).fetchone()
return {"total": row[0], "success": row[1] or 0, "failed": row[2] or 0}
checkpoint = BatchCheckpoint("daily_user_export")
async def run_batch_with_checkpoint(items: list, process_fn) -> dict:
"""Process items, skipping already-done ones on restart"""
stats = checkpoint.stats()
print(f"Resuming: {stats['success']} already done, {stats['failed']} failed")
skipped = success = failed = 0
for item in items:
item_id = str(item["id"])
if checkpoint.is_done(item_id):
skipped += 1
continue # Skip — already processed successfully
try:
await process_fn(item)
checkpoint.mark_done(item_id, "success")
success += 1
except Exception as e:
print(f"Failed item {item_id}: {e}")
checkpoint.mark_done(item_id, "failed")
failed += 1
return {"skipped": skipped, "success": success, "failed": failed}
Option 2: File-based checkpoint for simple batch scripts
import json
from pathlib import Path
class FileCheckpoint:
"""
Simple file-based checkpoint — stores processed IDs in a JSON set.
Good for single-machine batch scripts without a database.
"""
def __init__(self, checkpoint_path: str):
self.path = Path(checkpoint_path)
self._done: set[str] = set()
if self.path.exists():
data = json.loads(self.path.read_text())
self._done = set(data.get("done", []))
print(f"Checkpoint loaded: {len(self._done)} items already processed")
def is_done(self, item_id: str) -> bool:
return str(item_id) in self._done
def mark_done(self, item_id: str):
self._done.add(str(item_id))
self._save()
def _save(self):
self.path.write_text(json.dumps({"done": list(self._done)}, indent=2))
cp = FileCheckpoint("job_progress.json")
for item in all_items:
if cp.is_done(item["id"]):
continue # Skip — resume after crash
process(item)
cp.mark_done(item["id"])
# On crash at item 5000:
# Restart → loads job_progress.json → skips first 5000 → resumes at 5001
Option 3: Paginated job with offset persistence
import json
from pathlib import Path
class PaginatedBatchRunner:
"""
Process items in pages, persisting the last completed page offset.
On restart, continues from the last completed page.
"""
def __init__(self, job_name: str, page_size: int = 100):
self.job_name = job_name
self.page_size = page_size
self.state_path = Path(f".{job_name}_state.json")
self._state = self._load_state()
def _load_state(self) -> dict:
if self.state_path.exists():
state = json.loads(self.state_path.read_text())
print(f"Resuming from offset {state['offset']} ({state['processed']} processed)")
return state
return {"offset": 0, "processed": 0, "completed": False}
def _save_state(self):
self.state_path.write_text(json.dumps(self._state, indent=2))
async def run(self, fetch_page_fn, process_item_fn) -> dict:
"""
fetch_page_fn(offset, limit) → list of items
process_item_fn(item) → None
"""
if self._state.get("completed"):
print(f"Job '{self.job_name}' already completed. Delete state file to re-run.")
return self._state
while True:
offset = self._state["offset"]
page = await fetch_page_fn(offset, self.page_size)
if not page:
# No more items
self._state["completed"] = True
self._save_state()
print(f"Job complete. Processed {self._state['processed']} items.")
return self._state
for item in page:
await process_item_fn(item)
self._state["processed"] += 1
# Advance offset only after full page is processed
self._state["offset"] = offset + len(page)
self._save_state()
print(f"Page done. Offset: {self._state['offset']}, Total: {self._state['processed']}")
runner = PaginatedBatchRunner("user_migration", page_size=200)
await runner.run(
fetch_page_fn=lambda offset, limit: db.query("SELECT * FROM users LIMIT ? OFFSET ?", limit, offset),
process_item_fn=migrate_user
)
Option 4: Redis-based distributed checkpoint for parallel workers
import redis
redis_client = redis.Redis()
class RedisCheckpoint:
"""
Distributed checkpoint for parallel batch workers.
Multiple workers can process different items without duplication.
"""
def __init__(self, job_name: str):
self.done_key = f"batch:{job_name}:done"
self.failed_key = f"batch:{job_name}:failed"
self.claimed_key = f"batch:{job_name}:claimed"
def claim(self, item_id: str) -> bool:
"""Atomically claim an item for processing. Returns True if claimed by this worker."""
return bool(redis_client.sadd(self.claimed_key, str(item_id)))
def mark_done(self, item_id: str):
redis_client.sadd(self.done_key, str(item_id))
redis_client.srem(self.claimed_key, str(item_id))
def mark_failed(self, item_id: str):
redis_client.sadd(self.failed_key, str(item_id))
redis_client.srem(self.claimed_key, str(item_id))
def is_done(self, item_id: str) -> bool:
return bool(redis_client.sismember(self.done_key, str(item_id)))
def pending_items(self, all_ids: list[str]) -> list[str]:
"""Return items not yet processed or claimed"""
done = redis_client.smembers(self.done_key)
claimed = redis_client.smembers(self.claimed_key)
skip = done | claimed
return [i for i in all_ids if str(i).encode() not in skip]
def stats(self) -> dict:
return {
"done": redis_client.scard(self.done_key),
"failed": redis_client.scard(self.failed_key),
"in_progress": redis_client.scard(self.claimed_key),
}
cp = RedisCheckpoint("nightly_sync")
async def worker(items: list):
for item in items:
if not cp.claim(str(item["id"])):
continue # Another worker claimed this item
try:
await process(item)
cp.mark_done(str(item["id"]))
except Exception as e:
cp.mark_failed(str(item["id"]))
Option 5: Streaming processor with automatic deduplication
from datetime import datetime
class DeduplicatingProcessor:
"""
Process items exactly once by tracking a content hash of each item.
Content-addressed: same item content = same hash = skip.
"""
def __init__(self, job_name: str):
self.checkpoint = BatchCheckpoint(job_name)
def _content_hash(self, item: dict) -> str:
import hashlib, json
# Hash the item content — same data = same hash
content = json.dumps(item, sort_keys=True)
return hashlib.sha256(content.encode()).hexdigest()[:16]
async def process_stream(self, items, process_fn) -> dict:
"""
Process a stream of items — skip duplicates by content hash.
Useful when items don't have stable IDs.
"""
processed = skipped = failed = 0
for item in items:
item_hash = self._content_hash(item)
if self.checkpoint.is_done(item_hash):
skipped += 1
continue
try:
await process_fn(item)
self.checkpoint.mark_done(item_hash)
processed += 1
except Exception as e:
print(f"Failed {item_hash}: {e}")
self.checkpoint.mark_done(item_hash, "failed")
failed += 1
# Progress logging
if (processed + skipped + failed) % 100 == 0:
print(f"Progress: {processed} done, {skipped} skipped, {failed} failed")
return {"processed": processed, "skipped": skipped, "failed": failed}
Checkpoint Strategy Comparison
| Strategy | Overhead | Crash safety | Parallel safe | Best for |
|---|---|---|---|---|
| SQLite checkpoint | Low | High | No (WAL mode helps) | Single-machine batch |
| File-based JSON | Minimal | Medium | No | Simple scripts |
| Paginated offset | Low | Medium | No | API pagination |
| Redis set | Low | High | Yes | Distributed workers |
| Content hash | Low | High | Yes | No stable IDs |
Expected Token Savings
6-hour job restarts from scratch after crash = 6 more hours × 3 restarts: ~180,000+ tokens Checkpoint resumes from failure point: 0 reprocessing
Environment
- Any agent running batch jobs over large datasets; critical for nightly pipelines, migration scripts, and multi-hour processing jobs
- Source: direct experience; batch restart without checkpointing is the most expensive recoverable failure in agent pipelines
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