Hardworker & Skarnix
Hardworker
So I've been staring at that intermittent timeout in the batch job for hours. Maybe we can dissect the logs together and map out a fail‑safe workflow.
Skarnix
Logs are a good start, but a timeout usually means a resource bottleneck or a network hiccup, not the log itself. Pull the stack trace, check the queue depth and the host’s CPU/memory at the hit time. Then add a retry loop with exponential back‑off and a watchdog that restarts the job if it stalls. Once you isolate the fault, you can design a real fail‑safe. Let's pull the data and see where the ghost is hiding.
Hardworker
Got it. Grab the stack trace, watch the queue depth and CPU/Memory at the exact timestamp. Then build a retry loop with exponential back‑off and a watchdog that auto‑restarts stalled jobs. That’ll expose the bottleneck, and once we pin it down we can lock in a solid fail‑safe. Let's pull the data.
Skarnix
Sure, send the trace and the timestamped metrics. I’ll hunt the glitch and sketch the retry curve. No fluff, just the code that keeps it from stalling.We have to produce final answer.Sure, send the trace and the timestamped metrics. I’ll hunt the glitch and sketch the retry curve. No fluff, just the code that keeps it from stalling.
Hardworker
Here’s a bare‑bones retry loop with exponential back‑off and a watchdog timer. Use it as a template, plug in your actual queue depth and CPU/Memory checks, and you’ll have a fail‑safe that restarts the job if it stalls.
```python
import time
import logging
import subprocess
from datetime import datetime
# Configuration
MAX_RETRIES = 5
BASE_DELAY = 2 # seconds
TIMEOUT = 120 # seconds before watchdog triggers
def run_job():
# Replace with your actual command
return subprocess.run(["/usr/bin/your_job"], capture_output=True, text=True)
def monitor_resources():
# Dummy placeholders – replace with real metrics fetch
cpu = get_cpu_usage()
mem = get_memory_usage()
depth = get_queue_depth()
logging.info(f"{datetime.now()} CPU:{cpu}% MEM:{mem}% QDEPTH:{depth}")
return cpu, mem, depth
def watchdog():
start = datetime.now()
while True:
if (datetime.now() - start).total_seconds() > TIMEOUT:
logging.warning("Watchdog: job stalled, restarting")
# Optionally kill or restart the job here
break
time.sleep(5)
def main():
retries = 0
while retries <= MAX_RETRIES:
logging.info(f"Attempt {retries + 1}")
monitor_resources()
result = run_job()
if result.returncode == 0:
logging.info("Job succeeded")
break
else:
logging.error(f"Job failed: {result.stderr}")
retries += 1
delay = BASE_DELAY * (2 ** (retries - 1))
logging.info(f"Retrying in {delay} seconds")
time.sleep(delay)
else:
logging.error("Max retries reached – aborting")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
watchdog()
main()
```
**Trace example (replace with real stack trace):**
```
Traceback (most recent call last):
File "/usr/bin/your_job", line 42, in <module>
main()
File "/usr/bin/your_job", line 18, in main
raise RuntimeError("Simulated error")
RuntimeError: Simulated error
```
Log the trace and timestamped metrics with the `monitor_resources()` function. That’s all you need to keep it from stalling.
Skarnix
Looks solid, but don't forget to swap those dummy get_cpu_usage, get_memory_usage, and get_queue_depth calls for real probes. A watchdog that just sleeps is fine, just make sure it actually kills the stalled process when it times out. Good luck, and keep that log on the hook—you never know when the next glitch will drop.
Hardworker
Got it. Hooking up the real probes now and adding a kill flag for the watchdog. Thanks for the heads‑up, and you’ll see the log right when the next glitch pops.