I want to use https://github.com/bazelbuild/rules_webtesting. I am using Bazel 5.2.0.
The whole project can be found here.
My WORKSPACE.bazel file looks like this:
load("#bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
http_archive(
name = "io_bazel_rules_webtesting",
sha256 = "3ef3bb22852546693c94e9b0b02c2570e74abab6f800fd58e0cbe79492e49c1b",
urls = [
"https://github.com/bazelbuild/rules_webtesting/archive/581b1557e382f93419da6a03b91a45c2ac9a9ec8/rules_webtesting.tar.gz",
],
)
load("#io_bazel_rules_webtesting//web:repositories.bzl", "web_test_repositories")
web_test_repositories()
My BUILD.bazel file looks like this:
load("#io_bazel_rules_webtesting//web:py.bzl", "py_web_test_suite")
py_web_test_suite(
name = "browser_test",
srcs = ["browser_test.py"],
browsers = [
"#io_bazel_rules_webtesting//browsers:chromium-local",
],
local = True,
deps = ["#io_bazel_rules_webtesting//testing/web"],
)
browser_test.py looks like this:
import unittest
from testing.web import webtest
class BrowserTest(unittest.TestCase):
def setUp(self):
self.driver = webtest.new_webdriver_session()
def tearDown(self):
try:
self.driver.quit()
finally:
self.driver = None
# Your tests here
if __name__ == "__main__":
unittest.main()
When I try to do a bazel build //... I get (under Ubuntu 20.04 and macOS):
INFO: Invocation ID: 74c03efd-9caa-4174-9fda-42f7ff37e38b
ERROR: error loading package '': Every .bzl file must have a corresponding package, but '#io_bazel_rules_webtesting//web:repositories.bzl' does not have one. Please create a BUILD file in the same or any parent directory. Note that this BUILD file does not need to do anything except exist.
INFO: Elapsed time: 0.038s
INFO: 0 processes.
FAILED: Build did NOT complete successfully (0 packages loaded)
The error message does not make sense to me, since there is a BUILD file in
https://github.com/bazelbuild/rules_webtesting/blob/581b1557e382f93419da6a03b91a45c2ac9a9ec8/BUILD.bazel
and https://github.com/bazelbuild/rules_webtesting/blob/581b1557e382f93419da6a03b91a45c2ac9a9ec8/web/BUILD.bazel.
I also tried a different version of Bazel - but with the same result.
Any ideas on how to get this working?
You need to add a strip_prefix = "rules_webtesting-581b1557e382f93419da6a03b91a45c2ac9a9ec8" in your http_archive call.
For debugging, you can look in the folder where Bazel extracts it: bazel-out/../../../external/io_bazel_rules_webtesting. #io_bazel_rules_webtesting//web translates to bazel-out/../../../external/io_bazel_rules_webtesting/web, so if that folder doesn't exist things won't work.
I'm using Hydra for training machine learning models. It's great for doing complex commands like python train.py data=MNIST batch_size=64 loss=l2. However, if I want to then run the trained model with the same parameters, I have to do something like python reconstruct.py --config_file path_to_previous_job/.hydra/config.yaml. I then use argparse to load in the previous yaml and use the compose API to initialize the Hydra environment. The path to the trained model is inferred from the path to Hydra's .yaml file. If I want to modify one of the parameters, I have to add additional argparse parameters and run something like python reconstruct.py --config_file path_to_previous_job/.hydra/config.yaml --batch_size 128. The code then manually overrides any Hydra parameters with those that were specified on the command line.
What's the right way of doing this?
My current code looks something like the following:
train.py:
import hydra
#hydra.main(config_name="config", config_path="conf")
def main(cfg):
# [training code using cfg.data, cfg.batch_size, cfg.loss etc.]
# [code outputs model checkpoint to job folder generated by Hydra]
main()
reconstruct.py:
import argparse
import os
from hydra.experimental import initialize, compose
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('hydra_config')
parser.add_argument('--batch_size', type=int)
# [other flags and parameters I may need to override]
args = parser.parse_args()
# Create the Hydra environment.
initialize()
cfg = compose(config_name=args.hydra_config)
# Since checkpoints are stored next to the .hydra, we manually generate the path.
checkpoint_dir = os.path.dirname(os.path.dirname(args.hydra_config))
# Manually override any parameters which can be changed on the command line.
batch_size = args.batch_size if args.batch_size else cfg.data.batch_size
# [code which uses checkpoint_dir to load the model]
# [code which uses both batch_size and params in cfg to set up the data etc.]
This is my first time posting, so let me know if I should clarify anything.
If you want to load the previous config as is and not change it, use OmegaConf.load(file_path).
If you want to re-compose the config (and it sounds like you do, because you added that you want override things), I recommend that you use the Compose API and pass in parameters from the overrides file in the job output directory (next to the stored config.yaml), but concatenate the current run parameters.
This script seems to be doing the job:
import os
from dataclasses import dataclass
from os.path import join
from typing import Optional
from omegaconf import OmegaConf
import hydra
from hydra import compose
from hydra.core.config_store import ConfigStore
from hydra.core.hydra_config import HydraConfig
from hydra.utils import to_absolute_path
# You can also use a yaml config file instead of this Structured Config
#dataclass
class Config:
load_checkpoint: Optional[str] = None
batch_size: int = 16
loss: str = "l2"
cs = ConfigStore.instance()
cs.store(name="config", node=Config)
#hydra.main(config_path=".", config_name="config")
def my_app(cfg: Config) -> None:
if cfg.load_checkpoint is not None:
output_dir = to_absolute_path(cfg.load_checkpoint)
original_overrides = OmegaConf.load(join(output_dir, ".hydra/overrides.yaml"))
current_overrides = HydraConfig.get().overrides.task
hydra_config = OmegaConf.load(join(output_dir, ".hydra/hydra.yaml"))
# getting the config name from the previous job.
config_name = hydra_config.hydra.job.config_name
# concatenating the original overrides with the current overrides
overrides = original_overrides + current_overrides
# compose a new config from scratch
cfg = compose(config_name, overrides=overrides)
# train
print("Running in ", os.getcwd())
print(OmegaConf.to_yaml(cfg))
if __name__ == "__main__":
my_app()
~/tmp$ python train.py
Running in /home/omry/tmp/outputs/2021-04-19/21-23-13
load_checkpoint: null
batch_size: 16
loss: l2
~/tmp$ python train.py load_checkpoint=/home/omry/tmp/outputs/2021-04-19/21-23-13
Running in /home/omry/tmp/outputs/2021-04-19/21-23-22
load_checkpoint: /home/omry/tmp/outputs/2021-04-19/21-23-13
batch_size: 16
loss: l2
~/tmp$ python train.py load_checkpoint=/home/omry/tmp/outputs/2021-04-19/21-23-13 batch_size=32
Running in /home/omry/tmp/outputs/2021-04-19/21-23-28
load_checkpoint: /home/omry/tmp/outputs/2021-04-19/21-23-13
batch_size: 32
loss: l2
I'm running 5 DAG's which have generated a total of about 6GB of log data in the base_log_folder over a months period. I just added a remote_base_log_folder but it seems it does not exclude logging to the base_log_folder.
Is there anyway to automatically remove old log files, rotate them or force airflow to not log on disk (base_log_folder) only in remote storage?
Please refer https://github.com/teamclairvoyant/airflow-maintenance-dags
This plugin has DAGs that can kill halted tasks and log-cleanups.
You can grab the concepts and can come up with a new DAG that can cleanup as per your requirement.
We remove the Task logs by implementing our own FileTaskHandler, and then pointing to it in the airflow.cfg. So, we overwrite the default LogHandler to keep only N task logs, without scheduling additional DAGs.
We are using Airflow==1.10.1.
[core]
logging_config_class = log_config.LOGGING_CONFIG
log_config.LOGGING_CONFIG
BASE_LOG_FOLDER = conf.get('core', 'BASE_LOG_FOLDER')
FOLDER_TASK_TEMPLATE = '{{ ti.dag_id }}/{{ ti.task_id }}'
FILENAME_TEMPLATE = '{{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log'
LOGGING_CONFIG = {
'formatters': {},
'handlers': {
'...': {},
'task': {
'class': 'file_task_handler.FileTaskRotationHandler',
'formatter': 'airflow.job',
'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
'filename_template': FILENAME_TEMPLATE,
'folder_task_template': FOLDER_TASK_TEMPLATE,
'retention': 20
},
'...': {}
},
'loggers': {
'airflow.task': {
'handlers': ['task'],
'level': JOB_LOG_LEVEL,
'propagate': False,
},
'airflow.task_runner': {
'handlers': ['task'],
'level': LOG_LEVEL,
'propagate': True,
},
'...': {}
}
}
file_task_handler.FileTaskRotationHandler
import os
import shutil
from airflow.utils.helpers import parse_template_string
from airflow.utils.log.file_task_handler import FileTaskHandler
class FileTaskRotationHandler(FileTaskHandler):
def __init__(self, base_log_folder, filename_template, folder_task_template, retention):
"""
:param base_log_folder: Base log folder to place logs.
:param filename_template: template filename string.
:param folder_task_template: template folder task path.
:param retention: Number of folder logs to keep
"""
super(FileTaskRotationHandler, self).__init__(base_log_folder, filename_template)
self.retention = retention
self.folder_task_template, self.folder_task_template_jinja_template = \
parse_template_string(folder_task_template)
#staticmethod
def _get_directories(path='.'):
return next(os.walk(path))[1]
def _render_folder_task_path(self, ti):
if self.folder_task_template_jinja_template:
jinja_context = ti.get_template_context()
return self.folder_task_template_jinja_template.render(**jinja_context)
return self.folder_task_template.format(dag_id=ti.dag_id, task_id=ti.task_id)
def _init_file(self, ti):
relative_path = self._render_folder_task_path(ti)
folder_task_path = os.path.join(self.local_base, relative_path)
subfolders = self._get_directories(folder_task_path)
to_remove = set(subfolders) - set(subfolders[-self.retention:])
for dir_to_remove in to_remove:
full_dir_to_remove = os.path.join(folder_task_path, dir_to_remove)
print('Removing', full_dir_to_remove)
shutil.rmtree(full_dir_to_remove)
return FileTaskHandler._init_file(self, ti)
Airflow maintainers don't think truncating logs is a part of airflow core logic, to see this, and then in this issue, maintainers suggest to change LOG_LEVEL avoid too many log data.
And in this PR, we can learn how to change log level in airflow.cfg.
good luck.
I know it sounds savage, but have you tried pointing base_log_folder to /dev/null? I use Airflow as a part of a container, so I don't care about the files either, as long as the logger pipe to STDOUT as well.
Not sure how well this plays with S3 though.
For your concrete problems, I have some suggestions.
For those, you would always need a specialized logging config as described in this answer: https://stackoverflow.com/a/54195537/2668430
automatically remove old log files and rotate them
I don't have any practical experience with the TimedRotatingFileHandler from the Python standard library yet, but you might give it a try:
https://docs.python.org/3/library/logging.handlers.html#timedrotatingfilehandler
It not only offers to rotate your files based on a time interval, but if you specify the backupCount parameter, it even deletes your old log files:
If backupCount is nonzero, at most backupCount files will be kept, and if more would be created when rollover occurs, the oldest one is deleted. The deletion logic uses the interval to determine which files to delete, so changing the interval may leave old files lying around.
Which sounds pretty much like the best solution for your first problem.
force airflow to not log on disk (base_log_folder), but only in remote storage?
In this case you should specify the logging config in such a way that you do not have any logging handlers that write to a file, i.e. remove all FileHandlers.
Rather, try to find logging handlers that send the output directly to a remote address.
E.g. CMRESHandler which logs directly to ElasticSearch but needs some extra fields in the log calls.
Alternatively, write your own handler class and let it inherit from the Python standard library's HTTPHandler.
A final suggestion would be to combine both the TimedRotatingFileHandler and setup ElasticSearch together with FileBeat, so you would be able to store your logs inside ElasticSearch (i.e. remote), but you wouldn't store a huge amount of logs on your Airflow disk since they will be removed by the backupCount retention policy of your TimedRotatingFileHandler.
Usually apache airflow grab the disk space due to 3 reasons
1. airflow scheduler logs files
2. mysql binaly logs [Major]
3. xcom table records.
To make it clean up on regular basis I have set up a dag which run on daily basis and cleans the binary logs and truncate the xcom table to make the disk space free
You also might need to install [pip install mysql-connector-python].
To clean up scheduler log files I do delete them manually two times in a week to avoid the risk of logs deleted which needs to be required for some reasons.
I clean the logs files by [sudo rm -rd airflow/logs/] command.
Below is my python code for reference
'
"""Example DAG demonstrating the usage of the PythonOperator."""
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta
from airflow.utils.dates import days_ago
from airflow.operators.bash import BashOperator
from airflow.providers.postgres.operators.postgres import PostgresOperator
args = {
'owner': 'airflow',
'email_on_failure':True,
'retries': 1,
'email':['Your Email Id'],
'retry_delay': timedelta(minutes=5)
}
dag = DAG(
dag_id='airflow_logs_cleanup',
default_args=args,
schedule_interval='#daily',
start_date=days_ago(0),
catchup=False,
max_active_runs=1,
tags=['airflow_maintenance'],
)
def truncate_table():
import mysql.connector
connection = mysql.connector.connect(host='localhost',
database='db_name',
user='username',
password='your password',
auth_plugin='mysql_native_password')
cursor = connection.cursor()
sql_select_query = """TRUNCATE TABLE xcom"""
cursor.execute(sql_select_query)
connection.commit()
connection.close()
print("XCOM Table truncated successfully")
def delete_binary_logs():
import mysql.connector
from datetime import datetime
date = datetime.today().strftime('%Y-%m-%d')
connection = mysql.connector.connect(host='localhost',
database='db_name',
user='username',
password='your_password',
auth_plugin='mysql_native_password')
cursor = connection.cursor()
query = 'PURGE BINARY LOGS BEFORE ' + "'" + str(date) + "'"
sql_select_query = query
cursor.execute(sql_select_query)
connection.commit()
connection.close()
print("Binary logs deleted successfully")
t1 = PythonOperator(
task_id='truncate_table',
python_callable=truncate_table, dag=dag
)
t2 = PythonOperator(
task_id='delete_binary_logs',
python_callable=delete_binary_logs, dag=dag
)
t2 << t1
'
I am surprized but it worked for me. Update your config as below:
base_log_folder=""
It is test in minio and in s3.
Our solution looks a lot like Franzi's:
Running on Airflow 2.0.1 (py3.8)
Override default logging configuration
Since we use a helm chart for airflow deployment it was easiest to push an env there, but it can also be done in the airflow.cfg or using ENV in dockerfile.
# Set custom logging configuration to enable log rotation for task logging
AIRFLOW__LOGGING__LOGGING_CONFIG_CLASS: "airflow_plugins.settings.airflow_local_settings.DEFAULT_LOGGING_CONFIG"
Then we added the logging configuration together with the custom log handler to a python module we build and install in the docker image. As described here: https://airflow.apache.org/docs/apache-airflow/stable/modules_management.html
Logging configuration snippet
This is only a copy on the default from the airflow codebase, but then the task logger gets a different handler.
DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'airflow': {'format': LOG_FORMAT},
'airflow_coloured': {
'format': COLORED_LOG_FORMAT if COLORED_LOG else LOG_FORMAT,
'class': COLORED_FORMATTER_CLASS if COLORED_LOG else 'logging.Formatter',
},
},
'handlers': {
'console': {
'class': 'airflow.utils.log.logging_mixin.RedirectStdHandler',
'formatter': 'airflow_coloured',
'stream': 'sys.stdout',
},
'task': {
'class': 'airflow_plugins.log.rotating_file_task_handler.RotatingFileTaskHandler',
'formatter': 'airflow',
'base_log_folder': os.path.expanduser(BASE_LOG_FOLDER),
'filename_template': FILENAME_TEMPLATE,
'maxBytes': 10485760, # 10MB
'backupCount': 6,
},
...
RotatingFileTaskHandler
And finally the custom handler which is just a merge of the logging.handlers.RotatingFileHandler and the FileTaskHandler.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
"""File logging handler for tasks."""
import logging
import os
from pathlib import Path
from typing import TYPE_CHECKING, Optional
import requests
from airflow.configuration import AirflowConfigException, conf
from airflow.utils.helpers import parse_template_string
if TYPE_CHECKING:
from airflow.models import TaskInstance
class RotatingFileTaskHandler(logging.Handler):
"""
FileTaskHandler is a python log handler that handles and reads
task instance logs. It creates and delegates log handling
to `logging.FileHandler` after receiving task instance context.
It reads logs from task instance's host machine.
:param base_log_folder: Base log folder to place logs.
:param filename_template: template filename string
"""
def __init__(self, base_log_folder: str, filename_template: str, maxBytes=0, backupCount=0):
self.max_bytes = maxBytes
self.backup_count = backupCount
super().__init__()
self.handler = None # type: Optional[logging.FileHandler]
self.local_base = base_log_folder
self.filename_template, self.filename_jinja_template = parse_template_string(filename_template)
def set_context(self, ti: "TaskInstance"):
"""
Provide task_instance context to airflow task handler.
:param ti: task instance object
"""
local_loc = self._init_file(ti)
self.handler = logging.handlers.RotatingFileHandler(
filename=local_loc,
mode='a',
maxBytes=self.max_bytes,
backupCount=self.backup_count,
encoding='utf-8',
delay=False,
)
if self.formatter:
self.handler.setFormatter(self.formatter)
self.handler.setLevel(self.level)
def emit(self, record):
if self.handler:
self.handler.emit(record)
def flush(self):
if self.handler:
self.handler.flush()
def close(self):
if self.handler:
self.handler.close()
def _render_filename(self, ti, try_number):
if self.filename_jinja_template:
if hasattr(ti, 'task'):
jinja_context = ti.get_template_context()
jinja_context['try_number'] = try_number
else:
jinja_context = {
'ti': ti,
'ts': ti.execution_date.isoformat(),
'try_number': try_number,
}
return self.filename_jinja_template.render(**jinja_context)
return self.filename_template.format(
dag_id=ti.dag_id,
task_id=ti.task_id,
execution_date=ti.execution_date.isoformat(),
try_number=try_number,
)
def _read_grouped_logs(self):
return False
def _read(self, ti, try_number, metadata=None): # pylint: disable=unused-argument
"""
Template method that contains custom logic of reading
logs given the try_number.
:param ti: task instance record
:param try_number: current try_number to read log from
:param metadata: log metadata,
can be used for steaming log reading and auto-tailing.
:return: log message as a string and metadata.
"""
# Task instance here might be different from task instance when
# initializing the handler. Thus explicitly getting log location
# is needed to get correct log path.
log_relative_path = self._render_filename(ti, try_number)
location = os.path.join(self.local_base, log_relative_path)
log = ""
if os.path.exists(location):
try:
with open(location) as file:
log += f"*** Reading local file: {location}\n"
log += "".join(file.readlines())
except Exception as e: # pylint: disable=broad-except
log = f"*** Failed to load local log file: {location}\n"
log += "*** {}\n".format(str(e))
elif conf.get('core', 'executor') == 'KubernetesExecutor': # pylint: disable=too-many-nested-blocks
try:
from airflow.kubernetes.kube_client import get_kube_client
kube_client = get_kube_client()
if len(ti.hostname) >= 63:
# Kubernetes takes the pod name and truncates it for the hostname. This truncated hostname
# is returned for the fqdn to comply with the 63 character limit imposed by DNS standards
# on any label of a FQDN.
pod_list = kube_client.list_namespaced_pod(conf.get('kubernetes', 'namespace'))
matches = [
pod.metadata.name
for pod in pod_list.items
if pod.metadata.name.startswith(ti.hostname)
]
if len(matches) == 1:
if len(matches[0]) > len(ti.hostname):
ti.hostname = matches[0]
log += '*** Trying to get logs (last 100 lines) from worker pod {} ***\n\n'.format(
ti.hostname
)
res = kube_client.read_namespaced_pod_log(
name=ti.hostname,
namespace=conf.get('kubernetes', 'namespace'),
container='base',
follow=False,
tail_lines=100,
_preload_content=False,
)
for line in res:
log += line.decode()
except Exception as f: # pylint: disable=broad-except
log += '*** Unable to fetch logs from worker pod {} ***\n{}\n\n'.format(ti.hostname, str(f))
else:
url = os.path.join("http://{ti.hostname}:{worker_log_server_port}/log", log_relative_path).format(
ti=ti, worker_log_server_port=conf.get('celery', 'WORKER_LOG_SERVER_PORT')
)
log += f"*** Log file does not exist: {location}\n"
log += f"*** Fetching from: {url}\n"
try:
timeout = None # No timeout
try:
timeout = conf.getint('webserver', 'log_fetch_timeout_sec')
except (AirflowConfigException, ValueError):
pass
response = requests.get(url, timeout=timeout)
response.encoding = "utf-8"
# Check if the resource was properly fetched
response.raise_for_status()
log += '\n' + response.text
except Exception as e: # pylint: disable=broad-except
log += "*** Failed to fetch log file from worker. {}\n".format(str(e))
return log, {'end_of_log': True}
def read(self, task_instance, try_number=None, metadata=None):
"""
Read logs of given task instance from local machine.
:param task_instance: task instance object
:param try_number: task instance try_number to read logs from. If None
it returns all logs separated by try_number
:param metadata: log metadata,
can be used for steaming log reading and auto-tailing.
:return: a list of listed tuples which order log string by host
"""
# Task instance increments its try number when it starts to run.
# So the log for a particular task try will only show up when
# try number gets incremented in DB, i.e logs produced the time
# after cli run and before try_number + 1 in DB will not be displayed.
if try_number is None:
next_try = task_instance.next_try_number
try_numbers = list(range(1, next_try))
elif try_number < 1:
logs = [
[('default_host', f'Error fetching the logs. Try number {try_number} is invalid.')],
]
return logs, [{'end_of_log': True}]
else:
try_numbers = [try_number]
logs = [''] * len(try_numbers)
metadata_array = [{}] * len(try_numbers)
for i, try_number_element in enumerate(try_numbers):
log, metadata = self._read(task_instance, try_number_element, metadata)
# es_task_handler return logs grouped by host. wrap other handler returning log string
# with default/ empty host so that UI can render the response in the same way
logs[i] = log if self._read_grouped_logs() else [(task_instance.hostname, log)]
metadata_array[i] = metadata
return logs, metadata_array
def _init_file(self, ti):
"""
Create log directory and give it correct permissions.
:param ti: task instance object
:return: relative log path of the given task instance
"""
# To handle log writing when tasks are impersonated, the log files need to
# be writable by the user that runs the Airflow command and the user
# that is impersonated. This is mainly to handle corner cases with the
# SubDagOperator. When the SubDagOperator is run, all of the operators
# run under the impersonated user and create appropriate log files
# as the impersonated user. However, if the user manually runs tasks
# of the SubDagOperator through the UI, then the log files are created
# by the user that runs the Airflow command. For example, the Airflow
# run command may be run by the `airflow_sudoable` user, but the Airflow
# tasks may be run by the `airflow` user. If the log files are not
# writable by both users, then it's possible that re-running a task
# via the UI (or vice versa) results in a permission error as the task
# tries to write to a log file created by the other user.
relative_path = self._render_filename(ti, ti.try_number)
full_path = os.path.join(self.local_base, relative_path)
directory = os.path.dirname(full_path)
# Create the log file and give it group writable permissions
# TODO(aoen): Make log dirs and logs globally readable for now since the SubDag
# operator is not compatible with impersonation (e.g. if a Celery executor is used
# for a SubDag operator and the SubDag operator has a different owner than the
# parent DAG)
Path(directory).mkdir(mode=0o777, parents=True, exist_ok=True)
if not os.path.exists(full_path):
open(full_path, "a").close()
# TODO: Investigate using 444 instead of 666.
os.chmod(full_path, 0o666)
return full_path
Maybe a final note; the links in the airflow UI to the logging will now only open the latest logfile, not the older rotated files which are only accessible by means of SSH or any other interface to access the airflow logging path.
I don't think that there is a rotation mechanism but you can store them in S3 or google cloud storage as describe here : https://airflow.incubator.apache.org/configuration.html#logs
I'm trying to get the external IP that Tor uses, as mentioned here. When using something like myip.dnsomatic.com, this is very slow. I tried what was suggested in the aforementioned link (python + stem to control tor through the control port), but all you get is circuit's IPs with no assurance of which one is the one on the exitnode, and, sometimes the real IP is not even among the results.
Any help would be appreciated.
Also, from here, at the bottom, Amine suggests a way to renew the identity in Tor. There is an instruction, controller.get_newnym_wait(), which he uses to wait until the new connection is ready (controller is from Control in steam.control), isn't there any thing like that in Steam (sorry, I checked and double/triple checked and couldn't find nothing) that tells you that Tor is changing its identity?
You can get the exit node ip without calling a geoip site.
This is however on a different stackexchange site here - https://tor.stackexchange.com/questions/3253/how-do-i-trap-circuit-id-none-errors-in-the-stem-script-exit-used-py
As posted by #mirimir his code below essentially attaches a stream event listener function, which is then used to get the circuit id, circuit fingerprint, then finally the exit ip address -
#!/usr/bin/python
import functools
import time
from stem import StreamStatus
from stem.control import EventType, Controller
def main():
print "Tracking requests for tor exits. Press 'enter' to end."
print
with Controller.from_port() as controller:
controller.authenticate()
stream_listener = functools.partial(stream_event, controller)
controller.add_event_listener(stream_listener, EventType.STREAM)
raw_input() # wait for user to press enter
def stream_event(controller, event):
if event.status == StreamStatus.SUCCEEDED and event.circ_id:
circ = controller.get_circuit(event.circ_id)
exit_fingerprint = circ.path[-1][0]
exit_relay = controller.get_network_status(exit_fingerprint)
t = time.localtime()
print "datetime|%d-%02d-%02d %02d:%02d:%02d % (t.tm_year, t.tm_mon, t.tm_mday, t.tm_hour, t.tm_min, t.tm_sec)
print "website|%s" % (event.target)
print "exitip|%s" % (exit_relay.address)
print "exitport|%i" % (exit_relay.or_port)
print "fingerprint|%s" % exit_relay.fingerprint
print "nickname|%s" % exit_relay.nickname
print "locale|%s" % controller.get_info("ip-to-country/%s" % exit_relay.address, 'unknown')
print
You can use this code for check current IP (change SOCKS_PORT value to yours):
import re
import stem.process
import requesocks
SOCKS_PORT = 9053
tor_process = stem.process.launch_tor()
proxy_address = 'socks5://127.0.0.1:{}'.format(SOCKS_PORT)
proxies = {
'http': proxy_address,
'https': proxy_address
}
response = requesocks.get("http://httpbin.org/ip", proxies=proxies)
print re.findall(r'[\d.-]+', response.text)[0]
tor_process.kill()
If you want to use socks you should do:
pip install requests[socks]
Then you can do:
import requests
import json
import stem.process
import stem
SOCKS_PORT = "9999"
tor = stem.process.launch_tor_with_config(
config={
'SocksPort': SOCKS_PORT,
},
tor_cmd= 'absolute_path/to/tor.exe',
)
r = requests.Session()
proxies = {
'http': 'socks5://localhost:' + SOCKS_PORT,
'https': 'socks5://localhost:' + SOCKS_PORT
}
response = r.get("http://httpbin.org/ip", proxies=proxies)
self.current_ip = response.json()['origin']
I'm creating a fork of my Plone site (which has not been forked for a long time). This site has a special catalog object for user profiles (a special Archetypes-based object type) which is called portal_user_catalog:
$ bin/instance debug
>>> portal = app.Plone
>>> print [d for d in portal.objectMap() if d['meta_type'] == 'Plone Catalog Tool']
[{'meta_type': 'Plone Catalog Tool', 'id': 'portal_catalog'},
{'meta_type': 'Plone Catalog Tool', 'id': 'portal_user_catalog'}]
This looks reasonable because the user profiles don't have most of the indexes of the "normal" objects, but have a small set of own indexes.
Since I found no way how to create this object from scratch, I exported it from the old site (as portal_user_catalog.zexp) and imported it in the new site. This seemed to work, but I can't add objects to the imported catalog, not even by explicitly calling the catalog_object method. Instead, the user profiles are added to the standard portal_catalog.
Now I found a module in my product which seems to serve the purpose (Products/myproduct/exportimport/catalog.py):
"""Catalog tool setup handlers.
$Id: catalog.py 77004 2007-06-24 08:57:54Z yuppie $
"""
from Products.GenericSetup.utils import exportObjects
from Products.GenericSetup.utils import importObjects
from Products.CMFCore.utils import getToolByName
from zope.component import queryMultiAdapter
from Products.GenericSetup.interfaces import IBody
def importCatalogTool(context):
"""Import catalog tool.
"""
site = context.getSite()
obj = getToolByName(site, 'portal_user_catalog')
parent_path=''
if obj and not obj():
importer = queryMultiAdapter((obj, context), IBody)
path = '%s%s' % (parent_path, obj.getId().replace(' ', '_'))
__traceback_info__ = path
print [importer]
if importer:
print importer.name
if importer.name:
path = '%s%s' % (parent_path, 'usercatalog')
print path
filename = '%s%s' % (path, importer.suffix)
print filename
body = context.readDataFile(filename)
if body is not None:
importer.filename = filename # for error reporting
importer.body = body
if getattr(obj, 'objectValues', False):
for sub in obj.objectValues():
importObjects(sub, path+'/', context)
def exportCatalogTool(context):
"""Export catalog tool.
"""
site = context.getSite()
obj = getToolByName(site, 'portal_user_catalog', None)
if tool is None:
logger = context.getLogger('catalog')
logger.info('Nothing to export.')
return
parent_path=''
exporter = queryMultiAdapter((obj, context), IBody)
path = '%s%s' % (parent_path, obj.getId().replace(' ', '_'))
if exporter:
if exporter.name:
path = '%s%s' % (parent_path, 'usercatalog')
filename = '%s%s' % (path, exporter.suffix)
body = exporter.body
if body is not None:
context.writeDataFile(filename, body, exporter.mime_type)
if getattr(obj, 'objectValues', False):
for sub in obj.objectValues():
exportObjects(sub, path+'/', context)
I tried to use it, but I have no idea how it is supposed to be done;
I can't call it TTW (should I try to publish the methods?!).
I tried it in a debug session:
$ bin/instance debug
>>> portal = app.Plone
>>> from Products.myproduct.exportimport.catalog import exportCatalogTool
>>> exportCatalogTool(portal)
Traceback (most recent call last):
File "<console>", line 1, in <module>
File ".../Products/myproduct/exportimport/catalog.py", line 58, in exportCatalogTool
site = context.getSite()
AttributeError: getSite
So, if this is the way to go, it looks like I need a "real" context.
Update: To get this context, I tried an External Method:
# -*- coding: utf-8 -*-
from Products.myproduct.exportimport.catalog import exportCatalogTool
from pdb import set_trace
def p(dt, dd):
print '%-16s%s' % (dt+':', dd)
def main(self):
"""
Export the portal_user_catalog
"""
g = globals()
print '#' * 79
for a in ('__package__', '__module__'):
if a in g:
p(a, g[a])
p('self', self)
set_trace()
exportCatalogTool(self)
However, wenn I called it, I got the same <PloneSite at /Plone> object as the argument to the main function, which didn't have the getSite attribute. Perhaps my site doesn't call such External Methods correctly?
Or would I need to mention this module somehow in my configure.zcml, but how? I searched my directory tree (especially below Products/myproduct/profiles) for exportimport, the module name, and several other strings, but I couldn't find anything; perhaps there has been an integration once but was broken ...
So how do I make this portal_user_catalog work?
Thank you!
Update: Another debug session suggests the source of the problem to be some transaction matter:
>>> portal = app.Plone
>>> puc = portal.portal_user_catalog
>>> puc._catalog()
[]
>>> profiles_folder = portal.some_folder_with_profiles
>>> for o in profiles_folder.objectValues():
... puc.catalog_object(o)
...
>>> puc._catalog()
[<Products.ZCatalog.Catalog.mybrains object at 0x69ff8d8>, ...]
This population of the portal_user_catalog doesn't persist; after termination of the debug session and starting fg, the brains are gone.
It looks like the problem was indeed related with transactions.
I had
import transaction
...
class Browser(BrowserView):
...
def processNewUser(self):
....
transaction.commit()
before, but apparently this was not good enough (and/or perhaps not done correctly).
Now I start the transaction explicitly with transaction.begin(), save intermediate results with transaction.savepoint(), abort the transaction explicitly with transaction.abort() in case of errors (try / except), and have exactly one transaction.commit() at the end, in the case of success. Everything seems to work.
Of course, Plone still doesn't take this non-standard catalog into account; when I "clear and rebuild" it, it is empty afterwards. But for my application it works well enough.