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Posted to commits@airflow.apache.org by GitBox <gi...@apache.org> on 2022/03/11 20:02:26 UTC
[GitHub] [airflow] dlesco opened a new issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
dlesco opened a new issue #22191:
URL: https://github.com/apache/airflow/issues/22191
### Apache Airflow version
2.1.4
### What happened
The problem also exists in the latest version of the Airflow code, but I experienced it in 2.1.4.
This is the root cause of problems experienced in [issue#13542](https://github.com/apache/airflow/issues/13542).
I'll provide a stack trace below. The problem is in the code of airflow/dag_processing/processor.py (and manager.py), all poll() and recv() calls to the multiprocessing communication channels need to be wrapped in exception handlers, handling OSError("handle is closed") exceptions. If one looks at the Python multiprocessing source code, it throws this exception when the channel's handle has been closed.
This occurs in Airflow when a DAG File Processor has been killed or terminated; the Airflow code closes the communication channel when it is killing or terminating a DAG File Processor process (for example, when a dag_file_processor_timeout occurs).This killing or terminating happens asynchronously (in another process) from the process calling the poll() or recv() on the communication channel. This is why an exception needs to be handled. A pre-check of the handle being open is not good enough, because the other process doing the kill or terminate may close the handle in between your pre-check and actually calling poll() or recv() (a race condition).
### What you expected to happen
Here is the stack trace of the occurence I saw:
```
[2022-03-08 17:41:06,101] {taskinstance.py:914} DEBUG - <TaskInstance: staq_report_daily.gs.wait_staq_csv_file 2022-03-06 17:15:00+00:00 [running]> dependency 'Not In Retry Period' PASSED: True, The context specified that being in a retry p
eriod was permitted.
[2022-03-08 17:41:06,101] {taskinstance.py:904} DEBUG - Dependencies all met for <TaskInstance: staq_report_daily.gs.wait_staq_csv_file 2022-03-06 17:15:00+00:00 [running]>
[2022-03-08 17:41:06,119] {scheduler_job.py:1196} DEBUG - Skipping SLA check for <DAG: gdai_gcs_sync> because no tasks in DAG have SLAs
[2022-03-08 17:41:06,119] {scheduler_job.py:1196} DEBUG - Skipping SLA check for <DAG: unity_creative_import_process> because no tasks in DAG have SLAs
[2022-03-08 17:41:06,119] {scheduler_job.py:1196} DEBUG - Skipping SLA check for <DAG: sales_dm_to_bq> because no tasks in DAG have SLAs
[2022-03-08 17:44:50,454] {settings.py:302} DEBUG - Disposing DB connection pool (PID 1902)
Process ForkProcess-1:
Traceback (most recent call last):
File "/opt/python3.8/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/opt/python3.8/lib/python3.8/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/dag_processing/manager.py", line 370, in _run_processor_manager
processor_manager.start()
File "/opt/python3.8/lib/python3.8/site-packages/airflow/dag_processing/manager.py", line 610, in start
return self._run_parsing_loop()
File "/opt/python3.8/lib/python3.8/site-packages/airflow/dag_processing/manager.py", line 671, in _run_parsing_loop
self._collect_results_from_processor(processor)
File "/opt/python3.8/lib/python3.8/site-packages/airflow/dag_processing/manager.py", line 981, in _collect_results_from_processor
if processor.result is not None:
File "/opt/python3.8/lib/python3.8/site-packages/airflow/dag_processing/processor.py", line 321, in result
if not self.done:
File "/opt/python3.8/lib/python3.8/site-packages/airflow/dag_processing/processor.py", line 286, in done
if self._parent_channel.poll():
File "/opt/python3.8/lib/python3.8/multiprocessing/connection.py", line 255, in poll
self._check_closed()
File "/opt/python3.8/lib/python3.8/multiprocessing/connection.py", line 136, in _check_closed
raise OSError("handle is closed")
OSError: handle is closed
```
This corresponded in time to the following log entries:
```
% kubectl logs airflow-scheduler-58c997dd98-n8xr8 -c airflow-scheduler --previous | egrep 'Ran scheduling loop in|[[]heartbeat[]]'
[2022-03-08 17:40:47,586] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.56 seconds
[2022-03-08 17:40:49,146] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.56 seconds
[2022-03-08 17:40:50,675] {base_job.py:227} DEBUG - [heartbeat]
[2022-03-08 17:40:50,687] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.54 seconds
[2022-03-08 17:40:52,144] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.46 seconds
[2022-03-08 17:40:53,620] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.47 seconds
[2022-03-08 17:40:55,085] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.46 seconds
[2022-03-08 17:40:56,169] {base_job.py:227} DEBUG - [heartbeat]
[2022-03-08 17:40:56,180] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.49 seconds
[2022-03-08 17:40:57,667] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.49 seconds
[2022-03-08 17:40:59,148] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.48 seconds
[2022-03-08 17:41:00,618] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.47 seconds
[2022-03-08 17:41:01,742] {base_job.py:227} DEBUG - [heartbeat]
[2022-03-08 17:41:01,757] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.58 seconds
[2022-03-08 17:41:03,133] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.55 seconds
[2022-03-08 17:41:04,664] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 0.53 seconds
[2022-03-08 17:44:50,649] {base_job.py:227} DEBUG - [heartbeat]
[2022-03-08 17:44:50,814] {scheduler_job.py:813} DEBUG - Ran scheduling loop in 225.15 seconds
```
You can see that when this exception occurred, there was a hang in the scheduler for almost 4 minutes, no scheduling loops, and no scheduler_job heartbeats.
This hang probably also caused stuck queued jobs as issue#13542 describes.
### How to reproduce
This is hard to reproduce because it is a race condition. But you might be able to reproduce by having in a dagfile top-level code that calls sleep, so that it takes longer to parse than core dag_file_processor_timeout setting. That would cause the parsing processes to be terminated, creating the conditions for this bug to occur.
### Operating System
NAME="Ubuntu" VERSION="18.04.6 LTS (Bionic Beaver)" ID=ubuntu ID_LIKE=debian PRETTY_NAME="Ubuntu 18.04.6 LTS" VERSION_ID="18.04" HOME_URL="https://www.ubuntu.com/" SUPPORT_URL="https://help.ubuntu.com/" BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/" PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy" VERSION_CODENAME=bionic UBUNTU_CODENAME=bionic
### Versions of Apache Airflow Providers
Not relevant, this is a core dag_processing issue.
### Deployment
Composer
### Deployment details
"composer-1.17.6-airflow-2.1.4"
In order to isolate the scheduler to a separate machine, so as to not have interference from other processes such as airflow-workers running on the same machine, we created an additional node-pool for the scheduler, and ran these k8s patches to move the scheduler to a separate machine.
New node pool definition:
```HCL
{
name = "scheduler-pool"
machine_type = "n1-highcpu-8"
autoscaling = false
node_count = 1
disk_type = "pd-balanced"
disk_size = 64
image_type = "COS"
auto_repair = true
auto_upgrade = true
max_pods_per_node = 32
},
```
patch.sh
```sh
#!/bin/bash
if [ $# -lt 1 ]; then
echo "Usage: $0 namespace"
echo "Description: Isolate airflow-scheduler onto it's own node-pool (scheduler-pool)."
echo "Options:"
echo " namespace: kubernetes namespace used by Composer"
exit 1
fi
namespace=$1
set -eu
set -o pipefail
scheduler_patch="$(cat airflow-scheduler-patch.yaml)"
fluentd_patch="$(cat composer-fluentd-daemon-patch.yaml)"
set -x
kubectl -n default patch daemonset composer-fluentd-daemon -p "${fluentd_patch}"
kubectl -n ${namespace} patch deployment airflow-scheduler -p "${scheduler_patch}"
```
composer-fluentd-daemon-patch.yaml
```yaml
spec:
template:
spec:
nodeSelector: null
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: cloud.google.com/gke-nodepool
operator: In
values:
- default-pool
- scheduler-pool
```
airflow-scheduler-patch.yaml
```yaml
spec:
template:
spec:
nodeSelector:
cloud.google.com/gke-nodepool: scheduler-pool
containers:
- name: gcs-syncd
resources:
limits:
memory: 2Gi
```
### Anything else
On the below checkbox of submitting a PR, I could submit one, but it'd be untested code, I don't really have the environment setup to test the patch.
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
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[GitHub] [airflow] pcolladosoto commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
pcolladosoto commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1085746755
Hi! As seen above I've just opened a related PR: #22685
It would be great if we could continue the discussion over there and iterate over a few solutions until we arrive at the final thing.
Does the PR look good @potiuk or should I polish it up a bit more?
Thanks a ton for your time!
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[GitHub] [airflow] potiuk commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
potiuk commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1080290761
Cool. If you have any questions during the contribution process - happy to help - just "@" me. And even if you are not sure about some of the decisions, we can discuss it in the PR and iterate before we merge (and drag-in more of the minds here to make it really good)
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[GitHub] [airflow] uranusjr commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
uranusjr commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1065497621
Feel free to submit a pull request to handle the exception! We can figure out how to test the solution in the review process.
BTW I don’t know what your current fix looks like, but `OSError` has an `errno` attribute, checking that in the error handling code may be appropriate as well. (Not sure, I don’t even know what errno this error has right now.)
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[GitHub] [airflow] potiuk commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
potiuk commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1086009246
It does look good. I think it's the simples but also most efficient solution.
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[GitHub] [airflow] pcolladosoto commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
pcolladosoto commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1074273533
# Trying to explain things...
Our team has run into this issue time and time again. We have tried different combinations of both Airflow and Python versions to no avail.
## TL;DR
When a `DAGFileProcessor` hangs and is killed due to a timeout we believe the `self.waitables` and `self._processors` attributes of the `DAGFileProcessorManager` are not being updated as they should. This causes an unhandled exception when trying to receive data on a pipe end (i.e. file descriptor) which has already been closed.
## The long read...
We're running a decouple Airflow deployment within a k8s cluster. We are currently using a 3-container *pod* where one of them runs the *Web Server*, another one executes the *Scheduler* and the third one implements *Flower* (we're using the *CeleryExecutor*). The backbone of the deployment is implemented through a *StatefulSet* that runs the Celery executors themselves.
The trace we were seeing on the scheduler time and time again was:
```
Process ForkServerProcess-1:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/usr/local/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 370, in _run_processor_manager
processor_manager.start()
File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 610, in start
return self._run_parsing_loop()
File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 671, in _run_parsing_loop
self._collect_results_from_processor(processor)
File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/manager.py", line 981, in _collect_results_from_processor
if processor.result is not None:
File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/processor.py", line 321, in result
if not self.done:
File "/home/airflow/.local/lib/python3.7/site-packages/airflow/dag_processing/processor.py", line 286, in done
if self._parent_channel.poll():
File "/usr/local/lib/python3.7/multiprocessing/connection.py", line 255, in poll
self._check_closed()
File "/usr/local/lib/python3.7/multiprocessing/connection.py", line 136, in _check_closed
raise OSError("handle is closed")
OSError: handle is closed
```
This has been thrown by Airflow 2.1.3, but we've seen very similar (if not equal) variations with versions all the way up to Airflow 2.2.4.
Given we traced the problem down to the way multiprocessing synchronisation was being handled we played around with `multiprocessing`'s [*start method*](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) through the `mp_start_method` configuration parameter which wasn't included in the stock configuration example:
https://github.com/apache/airflow/blob/f309ea78f7d8b62383bc41eac217681a0916382b/airflow/utils/mixins.py#L27-L38
The containers we are using leverage `fork` as the default way of creating new process. After trying that one out we moved on to using `spawn` and ended up settling on `forkserver`. No matter the *start method* we leveraged, we ran into the same issue over and over again.
For a while we coped with this behaviour by just restarting the Airflow deployment on an hourly basis, but we decided to set some time apart today to delve a bit deeper into all this. The good news is after a thorough investigation we noticed a pattern the preceded the crash.
In order to pin it down down ran [`ps(1)`](https://www.man7.org/linux/man-pages/man1/ps.1.html) on the scheduler container. We also monitored the *DAG Processor Manager* log (which we have at `/opt/airflow/logs/dag_processor_manager/dag_processor_manager.log` given our Airflow home is `/opt/airflow`) and we took a look at the scheduler's log through `kubectl logs` given it's sent to *stdout/stderr*. The pattern itself goes something like:
1. A `DAGFileProcessor` get's stuck for longer than `dag_file_processor_timeout` as seen on `ps`' output.
2. As soon as the timeout is exceeded, the `DAGFileProcessorManager` kills the stuck `DAGFileProcessor`.
3. When the `DAGFileProcessorManager` tries to collect results back from the different `DAGFileProcessor`s it crashes.
The above led us to believe something was a bit off in the way the `DAGFileProcessor`s were being killed. Given our Docker-based deployment allowed for it, we retrieved a copy of the stock [`manager.py`](https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/manager.py) and [`processor.py`](https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/processor.py) files and added a bit of logging through `self.log.debug()`. The following appeared on out `DAGFileProcessorManager` log:
```
[2022-03-21 13:01:00,747] {manager.py:1163} DEBUG - FOO - Looking for DAG processors to terminate due to timeouts!
[2022-03-21 13:01:00,748] {manager.py:1172} ERROR - Processor for /opt/airflow/dags/dags-airflow/spark_streaming.py with PID 965 started at 2022-03-21T13:00:10.536124+00:00 has timed out, killing it.
[2022-03-21 13:01:00,748] {manager.py:1178} DEBUG - FOO - # of waitables BEFORE killing timed out processor: 2
[2022-03-21 13:01:00,748] {manager.py:1180} DEBUG - FOO - # of waitables AFTER killing timed out processor: 2
[2022-03-21 13:01:00,748] {manager.py:1013} DEBUG - 1/1 DAG parsing processes running
[2022-03-21 13:01:00,748] {manager.py:1015} DEBUG - 2 file paths queued for processing
[2022-03-21 13:01:00,758] {manager.py:978} DEBUG - Processor for /opt/airflow/dags/dags-airflow/spark_streaming.py finished
[2022-03-21 13:01:00,758] {manager.py:982} DEBUG - FOO - Trying to get result for processor with PID: 965
```
Can you see how the number of `waitables` (more on that later) doesn't change even though we're killing a `DAGFileProcessor`? We believe that's what's causing the trouble...
Note we added the `- FOO -` token to the logging entries we added to easily `grep` for them. These entries were generated with calls to `self.log.debug()` within the `_kill_timed_out_processors()` function. The 'stock' version is:
https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/manager.py#L1159-L1175
After we added additional logging it looked like:
```python
def _kill_timed_out_processors(self):
"""Kill any file processors that timeout to defend against process hangs."""
self.log.debug("FOO - Looking for DAG processors to terminate due to timeouts!")
now = timezone.utcnow()
processors_to_remove = []
for file_path, processor in self._processors.items():
duration = now - processor.start_time
if duration > self._processor_timeout:
self.log.error(
"Processor for %s with PID %s started at %s has timed out, killing it.",
file_path,
processor.pid,
processor.start_time.isoformat(),
)
Stats.decr('dag_processing.processes')
Stats.incr('dag_processing.processor_timeouts')
# TODO: Remove after Airflow 2.0
Stats.incr('dag_file_processor_timeouts')
self.log.debug(f"FOO - # of waitables BEFORE killing timed out processor: {len(self.waitables)}")
processor.kill()
self.log.debug(f"FOO - # of waitables AFTER killing timed out processor: {len(self.waitables)}")
```
You can see how we call the `kill()` (which basically wraps the processor's `_kill_process()` method) method on the timed out processor. We believe the key of all this resides on `line 246` of:
https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/processor.py#L238-L246
Notice how the end of the communicating pipe opened on `line 187` is being closed here:
https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/processor.py#L187
That's exactly the same pipe end (i.e. file descriptor) the `DAGFileProcessorManager` tries to read from later on! If we look at the traceback we included before it's `line 286` which ultimately triggers the `OSError`:
https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/processor.py#L286
What it's trying to do is `poll()` a closed pipe. If we take a look at `multiprocessing`'s implementation we'll check how, as shown on the traceback, it calls `_check_closed()` on the pipe's file descriptor (i.e. handle) before proceeding: this, as seen before, triggers the `OSError`.
So... why are we trying to collect results from a `DAGFileProcessor` we killed due to a timeout? in order to answer that we took a walk around `_run_parsing_loop()`:
https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/manager.py#L612-L734
It basically runs an infinite (unless we specify a maximum number of runs) loop that calls [`multiprocessing.connection.wait`](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.connection.wait) based on the contents of `self.waitables`. This attribute is a dictionary containing a reference to the different `DAGFileProcessor`'s spawned by the `DAGFileProcessorManager`. Entries are added on `line 1034` within `start_new_process()`:
https://github.com/apache/airflow/blob/v2-1-stable/airflow/dag_processing/manager.py#L1015-L1034
However, this dictionary **is not** updated when a processor is killed due to a timeout. You can check that out on the snippet we included above. Thus, after the timed out `DAGFileProcessor` is killed, the infinite loop on `_run_parsing_loop()` will assume the underlying process is ready (it's done, as we've effectively terminated it) and will try to read from the pipe end we closed on `_kill_process()`, thus triggering the exception and bringing everything down. In other words, we believe the `self.waitables` attribute is not being updated as it should when `DAGFileProcessor`s are terminated due to timeouts. The same is true for the `self._processors` attribute on the `DAGFileProcessorManager`. After all, `_kill_timed_out_processors()` iterates over its contents... If we don't update it too we'll see how we try to kill an already terminated process over and over again.
After some testing we arrived at the following implementation of `_kill_timed_out_processors()`:
```python
def _kill_timed_out_processors(self):
"""Kill any file processors that timeout to defend against process hangs."""
self.log.debug("FOO - ** Entering _kill_timed_out_processors() **")
# We'll get a clear picture of what these two attributes look like before
# killing anything.
self.log.debug(f"FOO - We'll iterate over: {self._processors}")
self.log.debug(f"FOO - Current waitables dir: {self.waitables}")
now = timezone.utcnow()
processors_to_remove = []
for file_path, processor in self._processors.items():
duration = now - processor.start_time
if duration > self._processor_timeout:
self.log.error(
"Processor for %s with PID %s started at %s has timed out, killing it.",
file_path,
processor.pid,
processor.start_time.isoformat(),
)
Stats.decr('dag_processing.processes')
Stats.incr('dag_processing.processor_timeouts')
# TODO: Remove after Airflow 2.0
Stats.incr('dag_file_processor_timeouts')
# Add some logging to check stuff out
self.log.debug(f"FOO - # of waitables BEFORE killing timed out processor: {len(self.waitables)}")
self.log.debug(f"FOO - We'll iterate over: {self._processors}")
self.log.debug(f"FOO - Current waitables dir: {self.waitables}")
# Kill the hanged processor
processor.kill()
# Update self.waitables to avoid asking for results later on
self.waitables.pop(processor.waitable_handle)
# Make a note of the file_paths we are to remove later on: we feel a bit uneasy about modifying the
# container we're currently iterating over...
processors_to_remove.append(file_path)
# Add some logging to check how stuff is doing...
self.log.debug(f"FOO - # of waitables AFTER killing timed out processor: {len(self.waitables)}")
self.log.debug(f"FOO - We'll iterate over: {self._processors}")
self.log.debug(f"FOO - Current waitables dir: {self.waitables}")
# Clean up `self._processors` too!
for proc in processors_to_remove:
self._processors.pop(proc)
# And after we're done take a look at the final state
self.log.debug(f"FOO - Processors after cleanup: {self._processors}")
self.log.debug(f"FOO - Waitables after cleanup: {self.waitables}")
self.log.debug(f"FOO - ** Leaving _kill_timed_out_processors() with **")
```
We know the above can surely be written in a more succinct/better way: we're by no means good programmers!
Against all odds, the code above seems to prevent the crash! :tada: It does, however, spawn zombies when we kill the `DAGFileProcessor`: it's not being `wait()`ed for...
We decided to also play around with the `DAGFileProcessor`'s `_kill_process()` method a bit in the name of science to try and prevent that zombie from spawning:
```python
def _kill_process(self) -> None:
if self._process is None:
raise AirflowException("Tried to kill process before starting!")
if self._process.is_alive() and self._process.pid:
self.log.warning("Killing DAGFileProcessorProcess (PID=%d)", self._process.pid)
os.kill(self._process.pid, signal.SIGKILL)
# Reap the resulting zombie! Note the call to `waitpid()` blocks unless we
# leverage the `WNOHANG` (https://docs.python.org/3/library/os.html#os.WNOHANG)
# option. Given we were just playing around we decided not to bother with that...
self.warning(f"FOO - Waiting to reap the zombie spawned from PID {self._process.pid}")
os.waitpid(self._process.pid)
self.warning(f"FOO - Reaped the zombie spawned from PID {self._process.pid}")
if self._parent_channel:
self._parent_channel.close()
```
From what we could see, the above reaped the zombie like we initially expected it to.
So, after all this nonsense we just wanted to end up by saying that we believe it's the way `DAGFileManagerProcessor`'s attributes are being cleaned up that crashes Airflow for us. In our experience this is triggered by a `DAGFileProcessor` being forcefully terminated after a timeout.
We would also like to thank everybody making Airflow possible: it's one heck of a tool!
Feel free to ask for more details and, if we got anything wrong (it wouldn't be the first time), please do let us know!
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[GitHub] [airflow] pcolladosoto commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
pcolladosoto commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1085691641
That's one if the issues I wanted to bring up on the PR... As you say, `SIGKILL` *should* terminate the process in any circumstance. However, I do lean towards adding an optional (maybe hardcoded?) timeout too. Even though forcefully waiting the process should not be a problem in a normal scenario it can indeed become an indefinitely blocking operation in the event of the kernel misbehaving.
The other key aspect is thinking about how we want that to be handled. Should we halt it all with an exception? Should we just cope with the (hopefully) few zombies that could be spawned and just show something on the log? I'm going to open a PR right now to try and move this discussion there. That way we can iterate over the available choices and decided what's best.
Thanks a ton for the input!
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[GitHub] [airflow] potiuk commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
potiuk commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1079934904
@pcolladosoto - I :heart: your detailed description and explanation. It reads like a good crime story :dagger:
Fantastic investigation and outcome.
How about you clean it up a bit and submit a PR fixing ti?
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[GitHub] [airflow] boring-cyborg[bot] commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
boring-cyborg[bot] commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1065461379
Thanks for opening your first issue here! Be sure to follow the issue template!
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[GitHub] [airflow] dlesco commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
dlesco commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1069278603
I plan to submit a PR within the next two weeks.
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[GitHub] [airflow] pcolladosoto commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
pcolladosoto commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1080033434
Hi @potiuk! I'm glad you found our investigation useful and that you had fun reading through it. Reading so may Agatha Christie books has to pay off at some point 😜
I would be more than happy to polish it all up and open a Pull Request so that the changes are incorporated into Airflow itself. I'll do my best to find some time to do it throughout the week.
And thanks a ton for the kind words! I really appreciate it 😋
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[GitHub] [airflow] dlesco commented on issue #22191: dag_processing code needs to handle OSError("handle is closed") in poll() and recv() calls
Posted by GitBox <gi...@apache.org>.
dlesco commented on issue #22191:
URL: https://github.com/apache/airflow/issues/22191#issuecomment-1083342338
This looks like a better approach than what I was thinking.
One comment on the `_kill_process` change. self._process is an instance of multiprocessing.Process. An alternative to using `os.waitpid` is to use [self._process.join(optional_timeout)](https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process.join), which is multiprocessing's higher-level interface to the OS waitpid code. (Python versions >=3.7 also support a kill() method on the multiprocessing Process, but probably os.kill was used in order to support older versions of Python).
The question is whether to use a timeout or not for the join. Because SIGKILL was used, doing a join afterwards *should* always work, because KILL should always work (unlike SIGTERM, SIGTERM might not end the process). I don't know if the community wants to be extra cautious, and have a timeout on the join, and if a timeout occurs, abnormally end the whole process with an exception, because the OS did something unexpected. Abending would be better than hanging forever.
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