I have two machines. Machine1: airflow-webserver, airflow-scheduler. Machine2: airflow-worker on specific queue. I am using CeleryExecutor. Task on machine2 runs successfully (writing and deleting files on local drive), but in web UI on machine1 I didnt read log files.
*** Log file does not exist: /home/airflow/logs/delete_images_by_ttl/delete_images/2018-10-29T12:24:23.299741+00:00/1.log
*** Fetching from: http://localhost-int.localdomain:8793/log/delete_images_by_ttl/delete_images/2018-10-29T12:24:23.299741+00:00/1.log
*** Failed to fetch log file from worker. HTTPConnectionPool(host='localhost-int.localdomain', port=8793): Max retries exceeded with url: /log/delete_images_by_ttl/delete_images/2018-10-29T12:24:23.299741+00:00/1.log
To solve this problem edit your /etc/hosts. Add ip and dns-name for airflow webserver
HTTPConnectionPool means webserver is not able to communicate to the worker node.
Add worker node hostname on /etc/hosts file
Also verify below
base_log_folder = /home/airflow/logs/
sudo chmod -R 777 /home/airflow/logs/
Related
i have my airflow docker container running with celery worker nodes, the jobs are getting triggered correctly. However from the webserver UI section the it cannot reach the worker for log, showing as
*** Log file does not exist: /usr/local/airflow/airflow/logs/spark-submit/transform/2021-08-07T04:21:58.646836+00:00/1.log
*** Fetching from: http://localhost.localdomain:8793/log/spark-submit/transform/2021-08-07T04:21:58.646836+00:00/1.log
*** Failed to fetch log file from worker. [Errno 111] Connection refused
trying to diagnose why this happened? does it mean that the webserver is not able to resolve the worker ip address correctly? How can i configure this worker ip mapping somewhere?
I've tried set up the hostname in airflow.cfg as
hostname_callable = airflow.utils.net.get_host_ip_address
but doesn't help.
Appreciate any help! Thanks
Version:
airflow==2.1.2
celery[redis]==4.4.2
Composer is failing a task due to it not being able to read a log file, it's complaining about incorrect encoding.
Here's the log that appears in the UI:
*** Unable to read remote log from gs://bucket/logs/campaign_exceptions_0_0_1/merge_campaign_exceptions/2019-08-03T10:00:00+00:00/1.log
*** 'ascii' codec can't decode byte 0xc2 in position 6986: ordinal not in range(128)
*** Log file does not exist: /home/airflow/gcs/logs/campaign_exceptions_0_0_1/merge_campaign_exceptions/2019-08-03T10:00:00+00:00/1.log
*** Fetching from: http://airflow-worker-68dc66c9db-x945n:8793/log/campaign_exceptions_0_0_1/merge_campaign_exceptions/2019-08-03T10:00:00+00:00/1.log
*** Failed to fetch log file from worker. HTTPConnectionPool(host='airflow-worker-68dc66c9db-x945n', port=8793): Max retries exceeded with url: /log/campaign_exceptions_0_0_1/merge_campaign_exceptions/2019-08-03T10:00:00+00:00/1.log (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f1c9ff19d10>: Failed to establish a new connection: [Errno -2] Name or service not known',))
I try viewing the file in the google cloud console and it also throws an error:
Failed to load
Tracking Number: 8075820889980640204
But I am able to download the file via gsutil.
When I view the file, it seems to have text overriding other text.
I can't show the entire file but it looks like this:
--------------------------------------------------------------------------------
Starting attempt 1 of 1
--------------------------------------------------------------------------------
#-#{"task-id": "merge_campaign_exceptions", "execution-date": "2019-08-03T10:00:00+00:00", "workflow": "__campaign_exceptions_0_0_1"}
[2019-08-04 10:01:23,313] {models.py:1569} INFO - Executing <Task(BigQueryOperator): merge_campaign_exceptions> on 2019-08-03T10:00:00+00:00#-#{"task-id": "merge_campaign_exceptions", "execution-date": "2019-08-03T10:00:00+00:00", "workflow": "__campaign_exceptions_0_0_1"}
[2019-08-04 10:01:23,314] {base_task_runner.py:124} INFO - Running: ['bash', '-c', u'airflow run __campaign_exceptions_0_0_1 merge_campaign_exceptions 2019-08-03T10:00:00+00:00 --job_id 22767 --pool _bq_pool --raw -sd DAGS_FOLDER//-campaign-exceptions.py --cfg_path /tmp/tmpyBIVgT']#-#{"task-id": "merge_campaign_exceptions", "execution-date": "2019-08-03T10:00:00+00:00", "workflow": "__campaign_exceptions_0_0_1"}
[2019-08-04 10:01:24,658] {base_task_runner.py:107} INFO - Job 22767: Subtask merge_campaign_exceptions [2019-08-04 10:01:24,658] {settings.py:176} INFO - setting.configure_orm(): Using pool settings. pool_size=5, pool_recycle=1800#-#{"task-id": "merge_campaign_exceptions", "execution-date": "2019-08-03T10:00:00+00:00", "workflow": "__campaign_exceptions_0_0_1"}
Where the #-#{} pieces seems to be "on top of" the typical log.
I faced the same problem. In my case the problem was that I removed the google_gcloud_default connection that was being used to retrieve the logs.
Check the configuration and look for the connection name.
[core]
remote_log_conn_id = google_cloud_default
Then check the credentials used for that connection name has the right permissions to access the GCS bucket.
I'm having a similar problem with viewing logs in GCP Cloud Composer. It doesn't appear to be preventing the failing DAG task from running though. What it looks like is a permissions error between the GKE and Storage Bucket where the log files are kept.
You can still view the logs by going into your cluster's storage bucket in the same directory as your /dags folder where you should also see a logs/ folder.
Your helm chart should setup global env:
- name: AIRFLOW_CONN_GOOGLE_CLOUD_DEFAULT
value: "google-cloud-platform://"
Then, you should deploy a Dockerfile with root account only (not airflow account), additionaly, you set up your helm uid, gid as:
uid: 50000 #airflow user
gid: 50000 #airflow group
Then upgrade helm chart with new config
*** Unable to read remote log from gs://bucket
1)Found the solution after assigning the roles to the service account
2)The SA key(json or txt) to be added and configured to the connection in the
remote_log_conn_id = google_cloud_default
3)restart the scheduler and webserver of the airflow
4)restart the dags on the airflow
you can find the logs on the GCS bucket where its configured
I am running Airflowv1.9 with Celery Executor. I have 5 Airflow workers running in 5 different machines. Airflow scheduler is also running in one of these machines. I have copied the same airflow.cfg file across these 5 machines.
I have daily workflows setup in different queues like DEV, QA etc. (each worker runs with an individual queue name) which are running fine.
While scheduling a DAG in one of the worker (no other DAG have been setup for this worker/machine previously), I am seeing the error in the 1st task and as a result downstream tasks are failing:
*** Log file isn't local.
*** Fetching here: http://<worker hostname>:8793/log/PDI_Incr_20190407_v2/checkBCWatermarkDt/2019-04-07T17:00:00/1.log
*** Failed to fetch log file from worker. 404 Client Error: NOT FOUND for url: http://<worker hostname>:8793/log/PDI_Incr_20190407_v2/checkBCWatermarkDt/2019-04-07T17:00:00/1.log
I have configured MySQL for storing the DAG metadata. When I checked task_instance table, I see proper hostnames are populated against the task.
I also checked the log location and found that the log is getting created.
airflow.cfg snippet:
base_log_folder = /var/log/airflow
base_url = http://<webserver ip>:8082
worker_log_server_port = 8793
api_client = airflow.api.client.local_client
endpoint_url = http://localhost:8080
What am I missing here? What configurations do I need to check additionally for resolving this issue?
Looks like the worker's hostname is not being correctly resolved.
Add a file hostname_resolver.py:
import os
import socket
import requests
def resolve():
"""
Resolves Airflow external hostname for accessing logs on a worker
"""
if 'AWS_REGION' in os.environ:
# Return EC2 instance hostname:
return requests.get(
'http://169.254.169.254/latest/meta-data/local-ipv4').text
# Use DNS request for finding out what's our external IP:
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(('1.1.1.1', 53))
external_ip = s.getsockname()[0]
s.close()
return external_ip
And export: AIRFLOW__CORE__HOSTNAME_CALLABLE=airflow.hostname_resolver:resolve
The web program of the master needs to go to the worker to fetch the log and display it on the front-end page. This process is to find the host name of the worker. Obviously, the host name cannot be found,Therefore, add the host name to IP mapping on the master's vim /etc/hosts
If this happens as part of a Docker Compose Airflow setup, the hostname resolution needs to be passed to the container hosting the webserver, e.g. through extra_hosts:
# docker-compose.yml
version: "3.9"
services:
webserver:
extra_hosts:
- "worker_hostname_0:192.168.xxx.yyy"
- "worker_hostname_1:192.168.xxx.zzz"
...
...
More details here.
I upload a dag file to the web page and when I click 'Graph View' -> ${my_dag} -> 'View Log', it shows:
*** Log file isn't local.
*** Fetching here: http://:8793/log/demo_dag/hello_task/2018-11-14T15:06:00
*** Failed to fetch log file from worker.
*** Reading remote logs...
*** Unsupported remote log location.
I have checked the airflow.cfg and find these config info:
worker_log_server_port = 8793
base_log_folder = /root/airflow/logs
My question is:
How to setup IP address for log service (Only port is setup)?
I have setup directory for log service, why does it still go to /log/.. ?
Any help is appreciated.
This can happen when the task status was manually changed (likely through the "Mark Success" option) and the task never receives a hostname value on the record.
The webserver is attempting to reach out to a server, with no name, to get logs for a task that never ran.
PS: Be careful running processes as the root user.
I've been getting this error, fix it by correcting the socket volume path:
WARNING - OSError while attempting to symlink the latest log directory
In windows the volume will go with a double bar like this:
volumes:
- //var/run/docker.sock:/var/run/docker.sock
Bind to docker socket on Windows
Setting up Airflow to run with Docker Swarm’s orchestration
I have an Airflow installation (on Kubernetes). My setup uses DaskExecutor. I also configured remote logging to S3. However when the task is running I cannot see the log, and I get this error instead:
*** Log file does not exist: /airflow/logs/dbt/run_dbt/2018-11-01T06:00:00+00:00/3.log
*** Fetching from: http://airflow-worker-74d75ccd98-6g9h5:8793/log/dbt/run_dbt/2018-11-01T06:00:00+00:00/3.log
*** Failed to fetch log file from worker. HTTPConnectionPool(host='airflow-worker-74d75ccd98-6g9h5', port=8793): Max retries exceeded with url: /log/dbt/run_dbt/2018-11-01T06:00:00+00:00/3.log (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f7d0668ae80>: Failed to establish a new connection: [Errno -2] Name or service not known',))
Once the task is done, the log is shown correctly.
I believe what Airflow is doing is:
for finished tasks read logs from s3
for running tasks, connect to executor's log server endpoint and show that.
Looks like Airflow is using celery.worker_log_server_port to connect to my dask executor to fetch logs from there.
How to configure DaskExecutor to expose log server endpoint?
my configuration:
core remote_logging True
core remote_base_log_folder s3://some-s3-path
core executor DaskExecutor
dask cluster_address 127.0.0.1:8786
celery worker_log_server_port 8793
what i verified:
- verified that the log file exists and is being written to on the executor while the task is running
- called netstat -tunlp on executor container, but did not find any extra port exposed, where logs could be served from.
UPDATE
have a look at serve_logs airflow cli command - I believe it does exactly the same.
We solved the problem by simply starting a python HTTP handler on a worker.
Dockerfile:
RUN mkdir -p $AIRFLOW_HOME/serve
RUN ln -s $AIRFLOW_HOME/logs $AIRFLOW_HOME/serve/log
worker.sh (run by Docker CMD):
#!/usr/bin/env bash
cd $AIRFLOW_HOME/serve
python3 -m http.server 8793 &
cd -
dask-worker $#