I am using H2O DAI 1.9.0.6. I am tring to load custom recipe (BERT pretained model using custom recipe) on Expert settings. I am using local file to upload. However upload is not happning. No error, no progress nothing. After that activity I am not able to see this model under RECIPE tab.
Took Sample Recipe from below URL and Modified for my need. Thanks for the person who created this Recipe.
https://github.com/h2oai/driverlessai-recipes/blob/master/models/nlp/portuguese_bert.py
Custom Recipe
import os
import shutil
from urllib.parse import urlparse
import requests
from h2oaicore.models import TextBERTModel, CustomModel
from h2oaicore.systemutils import make_experiment_logger, temporary_files_path, atomic_move, loggerinfo
def is_url(url):
try:
result = urlparse(url)
return all([result.scheme, result.netloc, result.path])
except:
return False
def maybe_download_language_model(logger,
save_directory,
model_link,
config_link,
vocab_link):
model_name = "pytorch_model.bin"
if isinstance(model_link, str):
model_name = model_link.split('/')[-1]
if '.bin' not in model_name:
model_name = "pytorch_model.bin"
maybe_download(url=config_link,
dest=os.path.join(save_directory, "config.json"),
logger=logger)
maybe_download(url=vocab_link,
dest=os.path.join(save_directory, "vocab.txt"),
logger=logger)
maybe_download(url=model_link,
dest=os.path.join(save_directory, model_name),
logger=logger)
def maybe_download(url, dest, logger=None):
if not is_url(url):
loggerinfo(logger, f"{url} is not a valid URL.")
return
dest_tmp = dest + ".tmp"
if os.path.exists(dest):
loggerinfo(logger, f"already downloaded {url} -> {dest}")
return
if os.path.exists(dest_tmp):
loggerinfo(logger, f"Download has already started {url} -> {dest_tmp}. "
f"Delete {dest_tmp} to download the file once more.")
return
loggerinfo(logger, f"Downloading {url} -> {dest}")
url_data = requests.get(url, stream=True)
if url_data.status_code != requests.codes.ok:
msg = "Cannot get url %s, code: %s, reason: %s" % (
str(url), str(url_data.status_code), str(url_data.reason))
raise requests.exceptions.RequestException(msg)
url_data.raw.decode_content = True
if not os.path.isdir(os.path.dirname(dest)):
os.makedirs(os.path.dirname(dest), exist_ok=True)
with open(dest_tmp, 'wb') as f:
shutil.copyfileobj(url_data.raw, f)
atomic_move(dest_tmp, dest)
def check_correct_name(custom_name):
allowed_pretrained_models = ['bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', 'xlm-roberta',
'xlm', 'roberta', 'distilbert', 'camembert', 'ctrl', 'albert']
assert len([model_name for model_name in allowed_pretrained_models
if model_name in custom_name]), f"{custom_name} needs to contain the name" \
" of the pretrained model architecture (e.g. bert or xlnet) " \
"to be able to process the model correctly."
class CustomBertModel(TextBERTModel, CustomModel):
"""
Custom model class for using pretrained transformer models.
The class inherits :
- CustomModel that really is just a tag. It's there to make sure DAI knows it's a custom model.
- TextBERTModel so that the custom model inherits all the properties and methods.
Supported model architecture:
'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', 'xlm-roberta',
'xlm', 'roberta', 'distilbert', 'camembert', 'ctrl', 'albert'
How to use:
- You have already downloaded the weights, the vocab and the config file:
- Set _model_path as the folder where the weights, the vocab and the config file are stored.
- Set _model_name according to the pretrained architecture (e.g. bert-base-uncased).
- You want to to download the weights, the vocab and the config file:
- Set _model_link, _config_link and _vocab_link accordingly.
- _model_path is the folder where the weights, the vocab and the config file will be saved.
- Set _model_name according to the pretrained architecture (e.g. bert-base-uncased).
- Important:
_model_path needs to contain the name of the pretrained model architecture (e.g. bert or xlnet)
to be able to load the model correctly.
- Disable genetic algorithm in the expert setting.
"""
# _model_path is the full path to the directory where the weights, vocab and the config will be saved.
_model_name = NotImplemented # Will be used to create the MOJO
_model_path = NotImplemented
_model_link = NotImplemented
_config_link = NotImplemented
_vocab_link = NotImplemented
_booster_str = "pytorch-custom"
# Requirements for MOJO creation:
# _model_name needs to be one of
# bert-base-uncased, bert-base-multilingual-cased, xlnet-base-cased, roberta-base, distilbert-base-uncased
# vocab.txt needs to be the same as vocab.txt used in _model_name (no custom vocabulary yet).
_mojo = False
#staticmethod
def is_enabled():
return False # Abstract Base model should not show up in models.
def _set_model_name(self, language_detected):
self.model_path = self.__class__._model_path
self.model_name = self.__class__._model_name
check_correct_name(self.model_path)
check_correct_name(self.model_name)
def fit(self, X, y, sample_weight=None, eval_set=None, sample_weight_eval_set=None, **kwargs):
logger = None
if self.context and self.context.experiment_id:
logger = make_experiment_logger(experiment_id=self.context.experiment_id, tmp_dir=self.context.tmp_dir,
experiment_tmp_dir=self.context.experiment_tmp_dir)
maybe_download_language_model(logger,
save_directory=self.__class__._model_path,
model_link=self.__class__._model_link,
config_link=self.__class__._config_link,
vocab_link=self.__class__._vocab_link)
super().fit(X, y, sample_weight, eval_set, sample_weight_eval_set, **kwargs)
class GermanBertModel(CustomBertModel):
_model_name = "bert-base-german-dbmdz-uncased"
_model_path = os.path.join(temporary_files_path, "german_bert_language_model/")
_model_link = "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/pytorch_model.bin"
_config_link = "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json"
_vocab_link = "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
_mojo = True
#staticmethod
def is_enabled():
return True
Check that your custom recipe has is_enabled() returning True.
def is_enabled():
return True
I'm trying to send data from my dronekit.io vehicle using flask-socket.io. Unfortunately, I got this log:
Starting copter simulator (SITL)
SITL already Downloaded and Extracted.
Ready to boot.
Connecting to vehicle on: tcp:127.0.0.1:5760
>>> APM:Copter V3.3 (d6053245)
>>> Frame: QUAD
>>> Calibrating barometer
>>> Initialising APM...
>>> barometer calibration complete
>>> GROUND START
* Restarting with stat
latitude -35.363261
>>> Exception in attribute handler for location.global_relative_frame
>>> Working outside of request context.
This typically means that you attempted to use functionality that needed
an active HTTP request. Consult the documentation on testing for
information about how to avoid this problem.
longitude 149.1652299
>>> Exception in attribute handler for location.global_relative_frame
>>> Working outside of request context.
This typically means that you attempted to use functionality that needed
an active HTTP request. Consult the documentation on testing for
information about how to avoid this problem.
Here is my code:
sample.py
from dronekit import connect, VehicleMode
from flask import Flask
from flask_socketio import SocketIO, emit
import dronekit_sitl
import time
sitl = dronekit_sitl.start_default()
connection_string = sitl.connection_string()
print("Connecting to vehicle on: %s" % (connection_string,))
vehicle = connect(connection_string, wait_ready=True)
def arm_and_takeoff(aTargetAltitude):
print "Basic pre-arm checks"
while not vehicle.is_armable:
print " Waiting for vehicle to initialise..."
time.sleep(1)
print "Arming motors"
vehicle.mode = VehicleMode("GUIDED")
vehicle.armed = True
while not vehicle.armed:
print " Waiting for arming..."
time.sleep(1)
print "Taking off!"
vehicle.simple_takeoff(aTargetAltitude)
while True:
if vehicle.location.global_relative_frame.alt>=aTargetAltitude*0.95:
print "Reached target altitude"
break
time.sleep(1)
last_latitude = 0.0
last_longitude = 0.0
last_altitude = 0.0
#vehicle.on_attribute('location.global_relative_frame')
def location_callback(self, attr_name, value):
global last_latitude
global last_longitude
global last_altitude
if round(value.lat, 6) != round(last_latitude, 6):
last_latitude = value.lat
print "latitude ", value.lat, "\n"
emit("latitude", value.lat)
if round(value.lon, 6) != round(last_longitude, 6):
last_longitude = value.lon
print "longitude ", value.lon, "\n"
emit("longitude", value.lon)
if round(value.alt) != round(last_altitude):
last_altitude = value.alt
print "altitude ", value.alt, "\n"
emit("altitude", value.alt)
app = Flask(__name__)
socketio = SocketIO(app)
if __name__ == '__main__':
socketio.run(app, host='0.0.0.0', port=5000, debug=True)
arm_and_takeoff(20)
I know because of the logs that I should not do any HTTP request inside "vehicle.on_attribute" decorator method and I should search for information on how to solve this problem but I didn't found any info about the error.
Hope you could help me.
Thank you very much,
Raniel
The emit() function by default returns an event back to the active client. If you call this function outside of a request context there is no concept of active client, so you get this error.
You have a couple of options:
indicate the recipient of the event and the namespace that you are using, so that there is no need to look them up in the context. You can do this by adding room and namespace arguments. Use '/' for the namespace if you are using the default namespace.
emit to all clients by adding broadcast=True as an argument, plus the namespace as indicated in #1.
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've been trying to create a server-process that receives an input file path and an output path from client processes asynchronously. The server does some database-reliant transformations, but for the sake of simplicity let's assume it merely puts everything to the upper case. Here is a toy example of the server:
import asyncio
import aiofiles as aiof
import logging
import sys
ADDRESS = ("localhost", 10000)
logging.basicConfig(level=logging.DEBUG,
format="%(name)s: %(message)s",
stream=sys.stderr)
log = logging.getLogger("main")
loop = asyncio.get_event_loop()
async def server(reader, writer):
log = logging.getLogger("process at {}:{}".format(*ADDRESS))
paths = await reader.read()
in_fp, out_fp = paths.splitlines()
log.debug("connection accepted")
log.debug("processing file {!r}, writing output to {!r}".format(in_fp, out_fp))
async with aiof.open(in_fp, loop=loop) as inp, aiof.open(out_fp, "w", loop=loop) as out:
async for line in inp:
out.write(line.upper())
out.flush()
writer.write(b"done")
await writer.drain()
log.debug("closing")
writer.close()
return
factory = asyncio.start_server(server, *ADDRESS)
server = loop.run_until_complete(factory)
log.debug("starting up on {} port {}".format(*ADDRESS))
try:
loop.run_forever()
except KeyboardInterrupt:
pass
finally:
log.debug("closing server")
server.close()
loop.run_until_complete(server.wait_closed())
log.debug("closing event loop")
loop.close()
The client:
import asyncio
import logging
import sys
import random
ADDRESS = ("localhost", 10000)
MESSAGES = ["/path/to/a/big/file.txt\n",
"/output/file_{}.txt\n".format(random.randint(0, 99999))]
logging.basicConfig(level=logging.DEBUG,
format="%(name)s: %(message)s",
stream=sys.stderr)
log = logging.getLogger("main")
loop = asyncio.get_event_loop()
async def client(address, messages):
log = logging.getLogger("client")
log.debug("connecting to {} port {}".format(*address))
reader, writer = await asyncio.open_connection(*address)
writer.writelines([bytes(line, "utf8") for line in messages])
if writer.can_write_eof():
writer.write_eof()
await writer.drain()
log.debug("waiting for response")
response = await reader.read()
log.debug("received {!r}".format(response))
writer.close()
return
try:
loop.run_until_complete(client(ADDRESS, MESSAGES))
finally:
log.debug("closing event loop")
loop.close()
I activated the server and several clients at once. The server's logs:
asyncio: Using selector: KqueueSelector
main: starting up on localhost port 10000
process at localhost:10000: connection accepted
process at localhost:10000: processing file b'/path/to/a/big/file.txt', writing output to b'/output/file_79609.txt'
process at localhost:10000: connection accepted
process at localhost:10000: processing file b'/path/to/a/big/file.txt', writing output to b'/output/file_68917.txt'
process at localhost:10000: connection accepted
process at localhost:10000: processing file b'/path/to/a/big/file.txt', writing output to b'/output/file_2439.txt'
process at localhost:10000: closing
process at localhost:10000: closing
process at localhost:10000: closing
All clients print this:
asyncio: Using selector: KqueueSelector
client: connecting to localhost port 10000
client: waiting for response
client: received b'done'
main: closing event loop
The output files are created, but they remain empty. I believe they are not being flushed. Any way I can fix it?
You are missing an await before out.write() and out.flush():
import asyncio
from pathlib import Path
import aiofiles as aiof
FILENAME = "foo.txt"
async def bad():
async with aiof.open(FILENAME, "w") as out:
out.write("hello world")
out.flush()
print("done")
async def good():
async with aiof.open(FILENAME, "w") as out:
await out.write("hello world")
await out.flush()
print("done")
loop = asyncio.get_event_loop()
server = loop.run_until_complete(bad())
print(Path(FILENAME).stat().st_size) # prints 0
server = loop.run_until_complete(good())
print(Path(FILENAME).stat().st_size) # prints 11
However, I would strongly recommend trying to skip aiofiles and use regular, synchronized disk I/O, and keep asyncio for network activity:
with open(file, "w") as out: # regular file I/O
async for s in network_request(): # asyncio for slow network work. measure it!
out.write(s) # should be really quick, measure it!
I have a setup such that an nginx server passes control off to uWsgi, which launches a pylons app using the following in my xml configuration file:
<ini-paste>...</ini-paste>
Everything is working nicely, and I was able to set it to debug mode using the following in the associated ini file, like:
debug = true
Except debug mode only prints out errors, and doesn't reload the code everytime a file has been touched. If I was running directly through paste, I could use the --reload option, but going through uWsgi complicates things.
Does anybody know of a way to tell uWsgi to tell paste to set the --reload option, or to do this directly in the paste .ini file?
I used something like the following code to solve this, the monitorFiles(...) method is called on application initialization, and it monitors the files, sending the TERM signal when it sees a change.
I'd still much prefer a solution using paster's --reload argument, as I imagine this solution has bugs:
import os
import time
import signal
from deepthought.system import deployment
from multiprocessing.process import Process
def monitorFiles():
if deployment.getDeployment().dev and not FileMonitor.isRunning:
monitor = FileMonitor(os.getpid())
try: monitor.start()
except: print "Something went wrong..."
class FileMonitor(Process):
isRunning = False
def __init__(self, masterPid):
self.updates = {}
self.rootDir = deployment.rootDir() + "/src/python"
self.skip = len(self.rootDir)
self.masterPid = masterPid
FileMonitor.isRunning = True
Process.__init__(self)
def run(self):
while True:
self._loop()
time.sleep(5)
def _loop(self):
for root, _, files in os.walk(self.rootDir):
for file in files:
if file.endswith(".py"):
self._monitorFile(root, file)
def _monitorFile(self, root, file):
mtime = os.path.getmtime("%s/%s" % (root, file))
moduleName = "%s/%s" % (root[self.skip+1:], file[:-3])
moduleName = moduleName.replace("/",".")
if not moduleName in self.updates:
self.updates[moduleName] = mtime
elif self.updates[moduleName] < mtime:
print "Change detected in %s" % moduleName
self._restartWorker()
self.updates[moduleName] = mtime
def _restartWorker(self):
os.kill(self.masterPid, signal.SIGTERM)
Use the signal framework in 0.9.7 tree
http://projects.unbit.it/uwsgi/wiki/SignalFramework
An example of auto-reloading:
import uwsgi
uwsgi.register_signal(1, "", uwsgi.reload)
uwsgi.add_file_monitor(1, 'myfile.py')
def application(env, start_response):
...