Celery Celery worker closes unexpectedly (Fix)

The Fix

pip install celery==4.4.0rc5

Based on closed celery/celery issue #5013 · PR/commit linked

Production note: Most teams hit this during upgrades or environment changes. Roll out with a canary and smoke critical endpoints (health, OpenAPI/docs) before 100%.

Jump to Verify Open PR/Commit

@@ -9,6 +9,7 @@
 
 import datetime
+import inspect
 import sys
 import time

repro.py

$ python --version
Python 3.6.5

$ python -c 'import django; print(django.__version__)'
2.1

$ celery --version
4.2.1 (windowlicker)

$ supervisord --version
4.0.0.dev0

$ redis-cli --version
redis-cli 4.0.11 (git:230489b2)

verify

Re-run the minimal reproduction on your broken version, then apply the fix and re-run.

fix.md

Option A — Upgrade to fixed release\npip install celery==4.4.0rc5\nWhen NOT to use: Do not use if it changes public behavior or if the failure cannot be reproduced.\n\n

Why This Fix Works in Production

Trigger: /path/to/project_name/py36/lib64/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected…
Mechanism: Celery did not respect exception types when using serializers, causing unexpected worker terminations
Why the fix works: Fixed an issue where Celery would not respect exception types when using serializers, which could lead to unexpected worker terminations. (first fixed release: 4.4.0rc5).

Production impact:

If left unfixed, the same config can fail only in production (env differences), causing startup failures or partial feature outages.

Why This Breaks in Prod

Shows up under Python 3.6 in real deployments (not just unit tests).
Celery did not respect exception types when using serializers, causing unexpected worker terminations
Production symptom (often without a traceback): /path/to/project_name/py36/lib64/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88

Proof / Evidence

GitHub issue: #5013
Fix PR: https://github.com/celery/celery/pull/5074
First fixed release: 4.4.0rc5
Reproduced locally: No (not executed)
Last verified: 2026-02-09
Confidence: 0.95
Did this fix it?: Yes (upstream fix exists)
Own content ratio: 0.74

Discussion

High-signal excerpts from the issue thread (symptoms, repros, edge-cases).

Jump to Sources Open on GitHub

“Maybe it's because of serializer="pickle" ? See #5035”

@jedie · 2018-09-05 · source

“Possibly @jedie -- but the other side of things is the data type limitations of json.”

@bsolomon1124 · 2018-09-05 · source

“https://github.com/celery/celery/pull/5074 is this pr related? anyone check?”

@auvipy · 2018-09-26 · source

“Had the same issue when tasks were created from another project using send_task()”

@ychouinard · 2019-01-21 · source

Failure Signature (Search String)

/path/to/project_name/py36/lib64/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
.> master exchange=master(direct) key=master

Copy-friendly signature

signature.txt

Failure Signature
-----------------
/path/to/project_name/py36/lib64/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
.> master           exchange=master(direct) key=master

Error Message

Signature-only (no traceback captured)

error.txt

Error Message
-------------
/path/to/project_name/py36/lib64/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
.> master           exchange=master(direct) key=master

Minimal Reproduction

repro.py

$ python --version
Python 3.6.5

$ python -c 'import django; print(django.__version__)'
2.1

$ celery --version
4.2.1 (windowlicker)

$ supervisord --version
4.0.0.dev0

$ redis-cli --version
redis-cli 4.0.11 (git:230489b2)

Environment

Python: 3.6

What Broke

Workers terminate unexpectedly without meaningful log messages, disrupting task processing.

Why It Broke

Celery did not respect exception types when using serializers, causing unexpected worker terminations

Fix Options (Details)

Option A — Upgrade to fixed release Safe default (recommended)

pip install celery==4.4.0rc5

When NOT to use: Do not use if it changes public behavior or if the failure cannot be reproduced.

Use when you can deploy the upstream fix. It is usually lower-risk than long-lived workarounds.

Option D — Guard side-effects with OnceOnly Guardrail for side-effects

Mitigate duplicate external side-effects under retries/timeouts/agent loops by gating the operation before calling external systems.

Place OnceOnly between your code/agent and real side-effects (Stripe, emails, CRM, APIs).
Use a stable key per side-effect (e.g., customer_id + action + idempotency_key).
Fail-safe: configure fail-open vs fail-closed based on blast radius and spend risk.

Show example snippet (optional)

onceonly.py

from onceonly import OnceOnly
import os

once = OnceOnly(api_key=os.environ["ONCEONLY_API_KEY"], fail_open=True)

# Stable idempotency key per real side-effect.
# Use a request id / job id / webhook delivery id / Stripe event id, etc.
event_id = "evt_..."  # replace
key = f"stripe:webhook:{event_id}"

res = once.check_lock(key=key, ttl=3600)
if res.duplicate:
    return {"status": "already_processed"}

# Safe to execute the side-effect exactly once.
handle_event(event_id)

See OnceOnly SDK

When NOT to use: Do not use this to hide logic bugs or data corruption. Use it to block duplicate external side-effects and enforce tool permissions/spend caps.

Fix reference: https://github.com/celery/celery/pull/5074

First fixed release: 4.4.0rc5

Last verified: 2026-02-09. Validate in your environment.

When NOT to Use This Fix

Do not use if it changes public behavior or if the failure cannot be reproduced.
Do not use this to hide logic bugs or data corruption. Use it to block duplicate external side-effects and enforce tool permissions/spend caps.

Verify Fix

verify

Re-run the minimal reproduction on your broken version, then apply the fix and re-run.

Did This Fix Work in Your Case?

Quick signal helps us prioritize which fixes to verify and improve.

Prevention

Capture the exact failing error string in logs and tests so you can reproduce via a minimal script.
Pin production dependencies and upgrade only with a reproducible test that hits the failing path.

Version Compatibility Table

Version	Status
4.4.0rc5	Fixed

Related Issues

No related fixes found.

Cluster: celery:configuration-error Celery hub Celery best practices All hubs All clusters

Related clusters: Data consistency Duplicates Duplicate processing

Sources

We don’t republish the full GitHub discussion text. Use the links above for context.