Can't read S3 remote logs when using gevent/eventlent webserver workers.

Hey everyone. I’ve upgraded to 1.10.9. It appears that the logging changes broke the functionality for reading S3 remote logs in the Web UI (writing is ok). In the change log it mentions that Airflow’s logging mechanism has been refactored to uses Python’s builtin logging module:

[AIRFLOW-1611] Customize logging

I followed the directions in the changelog and created the following log config:

import os
import six

from airflow import AirflowException
from airflow.configuration import conf
from airflow.utils.file import mkdirs
from typing import Dict, Any

LOG_LEVEL = conf.get('core', 'LOGGING_LEVEL').upper()

FAB_LOG_LEVEL = conf.get('core', 'FAB_LOGGING_LEVEL').upper()

LOG_FORMAT = conf.get('core', 'LOG_FORMAT')

COLORED_LOG_FORMAT = conf.get('core', 'COLORED_LOG_FORMAT')

COLORED_LOG = conf.getboolean('core', 'COLORED_CONSOLE_LOG')

COLORED_FORMATTER_CLASS = conf.get('core', 'COLORED_FORMATTER_CLASS')

BASE_LOG_FOLDER = conf.get('core', 'BASE_LOG_FOLDER')

PROCESSOR_LOG_FOLDER = conf.get('scheduler', 'CHILD_PROCESS_LOG_DIRECTORY')

DAG_PROCESSOR_MANAGER_LOG_LOCATION = \
    conf.get('core', 'DAG_PROCESSOR_MANAGER_LOG_LOCATION')

FILENAME_TEMPLATE = conf.get('core', 'LOG_FILENAME_TEMPLATE')

PROCESSOR_FILENAME_TEMPLATE = conf.get('core', 'LOG_PROCESSOR_FILENAME_TEMPLATE')

FORMATTER_CLASS_KEY = '()' if six.PY2 else 'class'


#
# Getting this from environment because the changelog for 1.10.9 says to set
# the path of `REMOTE_BASE_LOG_FOLDER` explicitly in the config. The
# `REMOTE_BASE_LOG_FOLDER` key is not used anymore.
#
REMOTE_BASE_LOG_FOLDER = os.environ.get('AIRFLOW__CORE__REMOTE_BASE_LOG_FOLDER')

LOGGING_CONFIG = {
    'version': 1,
    'disable_existing_loggers': False,
    'formatters': {
        'airflow.task': {
            'format': LOG_FORMAT,
        },
        'airflow.processor': {
            'format': LOG_FORMAT,
        },
        'airflow_coloured': {
            'format': COLORED_LOG_FORMAT if COLORED_LOG else LOG_FORMAT,
            FORMATTER_CLASS_KEY: COLORED_FORMATTER_CLASS if COLORED_LOG else 'logging.Formatter'
        },
    },
    'handlers': {
        'console': {
            'class': 'logging.StreamHandler',
            'formatter': 'airflow.task',
            'stream': 'ext://sys.stdout'
        },
        'file.task': {
            'class': 'airflow.utils.log.file_task_handler.FileTaskHandler',
            'formatter': 'airflow.task',
            'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
            'filename_template': FILENAME_TEMPLATE,
        },
        'file.processor': {
            'class': 'airflow.utils.log.file_processor_handler.FileProcessorHandler',
            'formatter': 'airflow.processor',
            'base_log_folder': os.path.expanduser(PROCESSOR_LOG_FOLDER),
            'filename_template': PROCESSOR_FILENAME_TEMPLATE,
        },
        's3.task': {
            'class': 'airflow.utils.log.s3_task_handler.S3TaskHandler',
            'formatter': 'airflow.task',
            'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
            's3_log_folder': REMOTE_BASE_LOG_FOLDER,
            'filename_template': FILENAME_TEMPLATE,
        },
    },
    'loggers': {
        '': {
            'handlers': ['console'],
            'level': LOG_LEVEL
        },
        'airflow': {
            'handlers': ['console'],
            'level': LOG_LEVEL,
            'propagate': False,
        },
        'airflow.processor': {
            'handlers': ['file.processor'],
            'level': LOG_LEVEL,
            'propagate': True,
        },
        'airflow.task': {
            'handlers': ['s3.task'],
            'level': LOG_LEVEL,
            'propagate': False,
        },
        'airflow.task_runner': {
            'handlers': ['s3.task'],
            'level': LOG_LEVEL,
            'propagate': True,
        },
    }
}  # type: Dict[str, Any]

However, the task log reader is always defaulting to using the FileTaskHandler. This should not occur because I have the following settings in airflow.cfg:

remote_logging = True
remote_base_log_folder = s3://my-bucket-name
remote_log_conn_id = aws_default
task_log_reader = s3.task

The s3.task handler passed to the task_log_reader setting should be creating an instance of the S3TaskHandler class to read the task logs to from S3. This occurs when rendering the get_logs_with_metadata view in www/views.py.

---

Apache Airflow version: 1.10.9 Kubernetes version: 1.15

Environment:

• Cloud provider or hardware configuration: AWS
• OS (e.g. from /etc/os-release): python:3.7.6-buster docker image

What happened: Logs did not appear in the Airflow Web UI. The FileTaskHandler tries to fetch the file locally or from the worker on port 8793. However, the logs do not exist in either location since we are using the Kubernetes Executor. This produces the following errors messages:

*** Log file does not exist: /usr/local/airflow/logs/MY_DAG_NAME/MY_TASK_NAME/2020-04-07T20:59:19.312402+00:00/6.log
*** Fetching from: http://MY_DAG_NAME-0dde5ff5a786437cb14234:8793/log/MY_DAG_NAME/MY_TASK_NAME/2020-04-07T20:59:19.312402+00:00/6.log
*** Failed to fetch log file from worker. HTTPConnectionPool(host='MY_DAG_NAME-0dde5ff5a786437cb14234', port=8793): Max retries exceeded with url: /log/MY_DAG_NAME/MY_TASK_NAME/2020-04-07T20:59:19.312402+00:00/6.log (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f708332fc90>: Failed to establish a new connection: [Errno -2] Name or service not known'))

What you expected to happen:

The logs should be rendered in the Web UI using the S3TaskHandler class.

[danielpoonwj]

Update - new solution is adding gevent monkey patching to top of config_templates/airflow_local_settings.py. It works.

Previously I had to manually patch botocore to fix this by removing bypassing of patched SSLContext, but it broke in Airflow 1.10.12.

I ran into IOErrors on the scheduler when using monkey.patch_all() similar to this and the suggested fix monkey.patch_all(thread=False, socket=False) caused warnings on threading.

In the end I figured the issue was only with monkey-patching SSL as somehow it’s imported earlier than gunicorn expected as seen from the warning in the webserver logs.

Sep 15 02:10:45 ubuntu-xenial pipenv[22660]: /etc/airflow/.local/share/virtualenvs/airflow-bTdwlyD1/lib/python3.8/site-packages/gunicorn/workers/ggevent.py:53: MonkeyPatchWarning: Monkey-patching ssl after ssl has already been imported may lead to errors, including RecursionError on Python 3.6. It may also silently lead to incorrect behaviour on Python 3.7. Please monkey-patch earlier. See https://github.com/gevent/gevent/issues/1016. Modules that had direct imports (NOT patched): ['urllib3.util (/etc/airflow/.local/share/virtualenvs/airflow-bTdwlyD1/lib/python3.8/site-packages/urllib3/util/__init__.py)', 'urllib3.util.ssl_ (/etc/airflow/.local/share/virtualenvs/airflow-bTdwlyD1/lib/python3.8/site-packages/urllib3/util/ssl_.py)'].
Sep 15 02:10:45 ubuntu-xenial pipenv[22660]:   monkey.patch_all()

In the end adding this to the top of airflow_local_settings.py worked for me.

from gevent import monkey
monkey.patch_ssl()

[dimon222]

If anyone pops on this issue, I wanted to mention that as of 2.1.3 the issue is still present, and solution that still works is prepending this:

from gevent import monkey; monkey.patch_ssl()

to very top line of file airflow/config_templates/airflow_local_settings.py (or your respective actual file if you have made one) of Airflow Webserver component.

Make sure to not touch any other airflow components in your cluster like Airflow Worker or Airflow Scheduler (so if you have single node setup, this may not work for you), as those are not going to behave correctly with monkeypatches for SSL.