Multiple Agents Write to the Same File Simultaneously — Corrupted Output
Symptom
- Output CSV has rows from two different runs interleaved at random positions
- JSON file is invalid because two agents wrote partial JSON simultaneously
- Log file contains interleaved lines from multiple workers
- One agent’s output is silently missing — overwritten by another agent
- File appears complete but data from one agent is partially or fully lost
- Report generated by 5 parallel workers has inconsistent row counts
Root Cause
File writes are not atomic at the OS level for concurrent processes/threads unless explicitly synchronized. When two agents call open(path, "a").write(data) simultaneously, the OS can interleave their writes at any byte boundary. The result is corrupted data that looks complete but contains garbage. This is a classic race condition on a shared mutable resource.
Fix
Option 1: File locking with fcntl (Unix) or portalocker (cross-platform)
import fcntl
import time
def safe_append(filepath: str, data: str, retries: int = 10) -> None:
"""
Append to a file with exclusive lock — prevents concurrent write corruption.
"""
for attempt in range(retries):
try:
with open(filepath, "a") as f:
fcntl.flock(f.fileno(), fcntl.LOCK_EX) # Exclusive lock
try:
f.write(data)
f.flush()
os.fsync(f.fileno()) # Ensure write to disk
finally:
fcntl.flock(f.fileno(), fcntl.LOCK_UN) # Always unlock
return
except BlockingIOError:
time.sleep(0.01 * (2 ** attempt)) # Backoff on contention
raise RuntimeError(f"Could not acquire file lock after {retries} attempts: {filepath}")
# Cross-platform version:
def safe_append_portable(filepath: str, data: str) -> None:
"""Using portalocker — works on Windows, Linux, macOS"""
import portalocker
with portalocker.Lock(filepath, "a", timeout=30) as f:
f.write(data)
Option 2: Atomic write — write to temp file, then rename
import tempfile
import os
from pathlib import Path
def atomic_write(filepath: str, content: str, mode: str = "w") -> None:
"""
Write content atomically: write to temp file, then rename.
Rename is atomic on POSIX — prevents partial/corrupted reads.
Not for append — for full file overwrites.
"""
path = Path(filepath)
# Write to temp file in same directory (same filesystem for atomic rename)
fd, tmp_path = tempfile.mkstemp(dir=path.parent, prefix=".tmp_")
try:
with os.fdopen(fd, mode) as f:
f.write(content)
f.flush()
os.fsync(f.fileno())
# Atomic rename — either fully succeeds or fails, never partial
os.replace(tmp_path, filepath)
except Exception:
os.unlink(tmp_path) # Clean up temp file on failure
raise
# For parallel agents writing separate outputs that get merged:
def worker_output_path(worker_id: int, base_path: str) -> str:
"""Each worker writes its own file — merge at the end"""
stem = Path(base_path).stem
suffix = Path(base_path).suffix
return str(Path(base_path).parent / f"{stem}.worker{worker_id}{suffix}")
def merge_worker_outputs(worker_paths: list[str], final_path: str) -> None:
"""Merge all worker outputs into final file — sequential, no conflict"""
with open(final_path, "w") as out:
for path in worker_paths:
if Path(path).exists():
out.write(Path(path).read_text())
Path(path).unlink() # Clean up worker file
Option 3: Per-worker output files, merge at completion
import asyncio
from pathlib import Path
import csv
async def parallel_process_with_merge(
items: list,
worker_count: int = 5,
output_path: str = "results.csv"
) -> int:
"""
Each worker writes to its own file.
No locking needed — files don't overlap.
Merge all files at the end.
"""
output_dir = Path(output_path).parent
stem = Path(output_path).stem
suffix = Path(output_path).suffix
# Split items across workers
chunks = [items[i::worker_count] for i in range(worker_count)]
async def worker(worker_id: int, chunk: list) -> str:
worker_path = output_dir / f"{stem}.w{worker_id}{suffix}"
rows_written = 0
with open(worker_path, "w", newline="") as f:
writer = csv.writer(f)
for item in chunk:
result = await process_item(item)
writer.writerow([item, result])
rows_written += 1
return str(worker_path)
# Run all workers in parallel
worker_paths = await asyncio.gather(*[
worker(i, chunk) for i, chunk in enumerate(chunks)
])
# Merge into final file (sequential — no conflict)
total_rows = 0
with open(output_path, "w", newline="") as final:
writer = csv.writer(final)
writer.writerow(["item", "result"]) # Header once
for path in worker_paths:
p = Path(path)
if p.exists():
with open(path, newline="") as f:
for row in csv.reader(f):
writer.writerow(row)
total_rows += 1
p.unlink()
return total_rows
Option 4: asyncio.Lock for in-process concurrent writes
import asyncio
from pathlib import Path
class LockedFileWriter:
"""
Thread-safe/coroutine-safe file writer using asyncio.Lock.
Use for multiple coroutines in the same process writing to one file.
"""
def __init__(self, filepath: str, mode: str = "a"):
self.filepath = filepath
self.mode = mode
self._lock = asyncio.Lock()
self._buffer: list[str] = []
self._flush_threshold = 100 # Batch writes
async def write(self, data: str) -> None:
async with self._lock:
self._buffer.append(data)
if len(self._buffer) >= self._flush_threshold:
await self._flush()
async def _flush(self) -> None:
"""Must be called with lock held"""
if not self._buffer:
return
with open(self.filepath, self.mode) as f:
f.writelines(self._buffer)
f.flush()
self._buffer.clear()
async def flush(self) -> None:
async with self._lock:
await self._flush()
async def __aenter__(self):
return self
async def __aexit__(self, *args):
await self.flush()
# Usage: all coroutines share one writer
async def main():
async with LockedFileWriter("output.jsonl") as writer:
tasks = [process_and_write(item, writer) for item in items]
await asyncio.gather(*tasks)
async def process_and_write(item: dict, writer: LockedFileWriter):
result = await process(item)
await writer.write(json.dumps(result) + "\n")
Option 5: Database as shared write target (eliminates file conflicts)
import sqlite3
import threading
class ThreadSafeResultStore:
"""
Use SQLite instead of flat files for concurrent agent output.
SQLite WAL mode supports concurrent writes safely.
"""
def __init__(self, db_path: str):
self.db_path = db_path
self._local = threading.local()
self._init_db()
def _get_conn(self) -> sqlite3.Connection:
"""Per-thread connection (SQLite connections are not thread-safe)"""
if not hasattr(self._local, "conn"):
self._local.conn = sqlite3.connect(self.db_path, timeout=30)
self._local.conn.execute("PRAGMA journal_mode=WAL")
self._local.conn.execute("PRAGMA synchronous=NORMAL")
return self._local.conn
def _init_db(self):
conn = self._get_conn()
conn.execute("""
CREATE TABLE IF NOT EXISTS results (
id INTEGER PRIMARY KEY AUTOINCREMENT,
worker_id TEXT,
item_id TEXT,
result TEXT,
created_at TEXT DEFAULT (datetime('now'))
)
""")
conn.commit()
def save_result(self, worker_id: str, item_id: str, result: dict) -> None:
"""Thread-safe result storage — no file conflicts possible"""
conn = self._get_conn()
conn.execute(
"INSERT INTO results (worker_id, item_id, result) VALUES (?, ?, ?)",
(worker_id, item_id, json.dumps(result))
)
conn.commit()
def export_csv(self, output_path: str) -> int:
"""Export all results to CSV after all workers complete"""
conn = self._get_conn()
rows = conn.execute("SELECT item_id, result FROM results ORDER BY id").fetchall()
with open(output_path, "w", newline="") as f:
writer = csv.writer(f)
for row in rows:
writer.writerow(row)
return len(rows)
store = ThreadSafeResultStore("agent_results.db")
# Each worker calls store.save_result() — no corruption possible
Concurrent Write Strategy Comparison
| Strategy | Scope | Complexity | Best for |
|---|---|---|---|
fcntl file lock |
Multi-process | Low | Simple append workloads |
| Per-worker files + merge | Multi-process | Low | Batch processing pipelines |
| Atomic rename | Single-writer | Low | Full-file overwrites |
asyncio.Lock |
Single-process | Low | Async coroutines |
| SQLite WAL | Multi-process | Medium | Structured result storage |
| Message queue to single writer | Multi-process | Medium | High-throughput streams |
Expected Token Savings
Debugging corrupted output file + data recovery: ~30,000 tokens File locking or per-worker files prevents corruption entirely: 0 wasted
Environment
- Any parallel agent pipeline where multiple workers write to shared output files
- Source: direct experience; concurrent file corruption is a silent data loss bug that is hard to detect without checksums
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