Airflow 1.9-无法将日志写入S3 [英] Airflow 1.9 - Cannot get logs to write to s3
问题描述
我正在用Kubernetes在aws中运行airflow 1.9。我希望日志能够转到s3，因为气流容器本身寿命不长。
I'm running airflow 1.9 in kubernetes in aws. I would like the logs to go to s3 as the airflow containers themselves are not long lived.
我已经阅读了描述该过程的各种线程和文档，但我仍然无法正常工作。首先进行一个测试，向我证明s3配置和权限有效。这是在我们的一个工作实例上运行的。
I've read the various threads and documents which describe the process but I still cannot get it working. First a test that demonstrates to me that the s3 configuration and permissions are valid. This is run on one of our worker instances.
使用气流写入s3文件
airflow@airflow-worker-847c66d478-lbcn2:~$ id
uid=1000(airflow) gid=1000(airflow) groups=1000(airflow)
airflow@airflow-worker-847c66d478-lbcn2:~$ env |grep s3
AIRFLOW__CONN__S3_LOGS=s3://vevo-dev-us-east-1-services-airflow/logs/
AIRFLOW__CORE__REMOTE_LOG_CONN_ID=s3_logs
AIRFLOW__CORE__REMOTE_BASE_LOG_FOLDER=s3://vevo-dev-us-east-1-services-airflow/logs/
airflow@airflow-worker-847c66d478-lbcn2:~$ python
Python 3.6.4 (default, Dec 21 2017, 01:37:56)
[GCC 4.9.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import airflow
>>> s3 = airflow.hooks.S3Hook('s3_logs')
/usr/local/lib/python3.6/site-packages/airflow/utils/helpers.py:351: DeprecationWarning: Importing S3Hook directly from <module 'airflow.hooks' from '/usr/local/lib/python3.6/site-packages/airflow/hooks/__init__.py'> has been deprecated. Please import from '<module 'airflow.hooks' from '/usr/local/lib/python3.6/site-packages/airflow/hooks/__init__.py'>.[operator_module]' instead. Support for direct imports will be dropped entirely in Airflow 2.0.
DeprecationWarning)
>>> s3.load_string('put this in s3 file', airflow.conf.get('core', 'remote_base_log_folder') + "/airflow-test")
[2018-02-23 18:43:58,437] {{base_hook.py:80}} INFO - Using connection to: vevo-dev-us-east-1-services-airflow
现在让我们从s3检索文件并查看内容。我们可以在这里看到一切看起来很好。
Now let's retrieve the file from s3 and look at the contents. We can see everything looks good here.
root@4f8171d4fe47:/# aws s3 cp s3://vevo-dev-us-east-1-services-airflow/logs//airflow-test .
download: s3://vevo-dev-us-east-1-services-airflow/logs//airflow-test to ./airflow-test
root@4f8171d4fe47:/# cat airflow-test
put this in s3 fileroot@4f8171d4fe47:/stringer#
所以好像是气流s3连接良好，除了气流作业不使用s3进行记录。这是我所拥有的设置，我认为它们是错误的或丢失了某些东西。
So it seems like the airflow s3 connection is good except airflow jobs do not use s3 for logging. Here are the settings I have which I figure something is either wrong or I am missing something.
正在运行的worker / scheduler / master实例的环境变量是
Env vars of running worker/scheduler/master instances are
airflow@airflow-worker-847c66d478-lbcn2:~$ env |grep -i s3
AIRFLOW__CONN__S3_LOGS=s3://vevo-dev-us-east-1-services-airflow/logs/
AIRFLOW__CORE__REMOTE_LOG_CONN_ID=s3_logs
AIRFLOW__CORE__REMOTE_BASE_LOG_FOLDER=s3://vevo-dev-us-east-1-services-airflow/logs/
S3_BUCKET=vevo-dev-us-east-1-services-airflow
这表明s3_logs连接气流中存在
This shows that the s3_logs connection exists in airflow
airflow@airflow-worker-847c66d478-lbcn2:~$ airflow connections -l|grep s3
│ 's3_logs' │ 's3' │ 'vevo-dev-
us-...vices-airflow' │ None │ False │ False │ None │
我将此文件放在 https://github.com/apache/incubator-airflow/blob/master/airflow/config_templates/airflow_local_settings.py 就地在我的docker映像中。您可以在我们的一位工人上看到一个示例
I put this file https://github.com/apache/incubator-airflow/blob/master/airflow/config_templates/airflow_local_settings.py in place in my docker image. You can see an example here on one of our workers
airflow@airflow-worker-847c66d478-lbcn2:~$ ls -al /usr/local/airflow/config/
total 32
drwxr-xr-x. 2 root root 4096 Feb 23 00:39 .
drwxr-xr-x. 1 airflow airflow 4096 Feb 23 00:53 ..
-rw-r--r--. 1 root root 4471 Feb 23 00:25 airflow_local_settings.py
-rw-r--r--. 1 root root 0 Feb 16 21:35 __init__.py
我们已编辑文件以定义REMOTE_BASE_LOG_FOLDER变量。这是我们版本与上游版本之间的区别
We have edited the file to define the REMOTE_BASE_LOG_FOLDER variable. Here is the diff between our version and the upstream version
index 899e815..897d2fd 100644
--- a/var/tmp/file
+++ b/config/airflow_local_settings.py
@@ -35,7 +35,8 @@ PROCESSOR_FILENAME_TEMPLATE = '{{ filename }}.log'
# Storage bucket url for remote logging
# s3 buckets should start with "s3://"
# gcs buckets should start with "gs://"
-REMOTE_BASE_LOG_FOLDER = ''
+REMOTE_BASE_LOG_FOLDER = conf.get('core', 'remote_base_log_folder')
+
DEFAULT_LOGGING_CONFIG = {
'version': 1,
在这里您可以看到我们其中一位工人的设置正确。
Here you can see that the setting is correct on one of our workers.
>>> import airflow
>>> airflow.conf.get('core', 'remote_base_log_folder')
's3://vevo-dev-us-east-1-services-airflow/logs/'
基于REMOTE_BASE_LOG_FOLDER以 s3开头且REMOTE_LOGGING为真的事实
Based on the fact that REMOTE_BASE_LOG_FOLDER starts with 's3' and REMOTE_LOGGING is True
>>> airflow.conf.get('core', 'remote_logging')
'True'
我希望这个块 https：/ /github.com/apache/incubator-airflow/blob/master/airflow/config_templates/airflow_local_settings.py#L122-L123 评估为true并将日志转到s3。
I would expect this block https://github.com/apache/incubator-airflow/blob/master/airflow/config_templates/airflow_local_settings.py#L122-L123 to evaluate to true and make the logs go to s3.
请在1.9上使用s3日志记录的人指出我所缺少的内容吗？我想向上游项目提交PR来更新文档，因为这似乎是一个非常普遍的问题，并且我可以告诉上游文档无效或经常被误解。
Please can anyone who has s3 logging working on 1.9 point out what I am missing? I would like to submit a PR to the upstream project to update the docs as this seems to be a pretty common problem and as near as I can tell the upstream documents are not valid or somehow get misinterpreted frequently.
谢谢！ G。
推荐答案
是的，我也很难仅基于文档进行设置。我必须仔细检查一下气流的代码才能解决。有很多事情您可能做不到。
Yea, I also had trouble setting it up based just on the docs. I had to go over airflow's code to figure it out. There are multiple things you could have not done.
要检查的一些事情：
1.确保您拥有log_config.py文件及其在正确的目录中：./config/log_config.py。还请确保您没有忘记该目录中的__init__.py文件。
2.确保已定义s3.task处理程序并将其格式化程序设置为airflow.task
3.确保将airflow.task和airflow.task_runner处理程序设置为s3.task
Some things to check:
1. Make sure you have the log_config.py file and it is in the correct dir: ./config/log_config.py. Also make sure you didn't forget the __init__.py file in that dir.
2. Make sure you defined the s3.task handler and set its formatter to airflow.task
3. Make sure you set airflow.task and airflow.task_runner handlers to s3.task
这是一个对我有用的log_config.py文件：
Here is a log_config.py file that works for me:
# -*- coding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from airflow import configuration as conf
# TO DO: Logging format and level should be configured
# in this file instead of from airflow.cfg. Currently
# there are other log format and level configurations in
# settings.py and cli.py. Please see AIRFLOW-1455.
LOG_LEVEL = conf.get('core', 'LOGGING_LEVEL').upper()
LOG_FORMAT = conf.get('core', 'log_format')
BASE_LOG_FOLDER = conf.get('core', 'BASE_LOG_FOLDER')
PROCESSOR_LOG_FOLDER = conf.get('scheduler', 'child_process_log_directory')
FILENAME_TEMPLATE = '{{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log'
PROCESSOR_FILENAME_TEMPLATE = '{{ filename }}.log'
S3_LOG_FOLDER = 's3://your_path_to_airflow_logs'
LOGGING_CONFIG = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'airflow.task': {
'format': LOG_FORMAT,
},
'airflow.processor': {
'format': LOG_FORMAT,
},
},
'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,
},
# When using s3 or gcs, provide a customized LOGGING_CONFIG
# in airflow_local_settings within your PYTHONPATH, see UPDATING.md
# for details
'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': S3_LOG_FOLDER,
'filename_template': FILENAME_TEMPLATE,
},
# 'gcs.task': {
# 'class': 'airflow.utils.log.gcs_task_handler.GCSTaskHandler',
# 'formatter': 'airflow.task',
# 'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
# 'gcs_log_folder': GCS_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,
},
}
}
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