to present my configuration I followed this tutorial until "Securing the application"
https://www.digitalocean.com/community/tutorials/how-to-serve-flask-applications-with-uswgi-and-nginx-on-ubuntu-18-04#step-4-%E2%80%94-configuring-uwsgi
So my .ini looks like this:
[uwsgi]
module = wsgi:app
master = true
processes = 5
socket = myproject.sock
chmod-socket = 660
vacuum = true
die-on-term = true
In flask i'm using SQLAlchemy with this configuration:
app.config['SQLALCHEMY_COMMIT_ON_TEARDOWN'] = True
app.config['SQLALCHEMY_POOL_RECYCLE'] = 299
app.config['SQLALCHEMY_POOL_TIMEOUT'] = 20
My api endpoints are like this with various themes, they receive parameters via get, process, and return a json
#app.route('/api/theme1/subtheme1')
#auth.login_required
def get_test1(): ...
#app.route('/api/theme1/subtheme2')
#auth.login_required
def get_test2(): ...
#app.route('/api/theme1/subtheme3')
#auth.login_required
def get_test3(): ...
Now my problem, when, for example I make 3 simultaneous calls (not other calls to the api) to those 3 endpoints procesor of the uwsgi process spikes to 25% per call.
Im using a tiny compute engine, just running this for now, the ram is for another tests. n1-highmem-2 (2 vCPUs, 13 GB memory)
I googled and search here and even tunning a little the configuration cannot reduce the processor usage, so I cannot improve the overall performance of the API.
Any idea of what can I be doing wrong? Why the spike in cpu usage?
Thank you!
Related
Have had airflow webserver -D deamon process (v1.10.7) running on machine (CentOS 7) for long time. Suddenly saw that the webserver could no longer be accessed and checking the airflow-webserver.log saw...
[airflow#airflowetl airflow]$ cat airflow-webserver.log
2020-10-23 00:57:15,648 ERROR - No response from gunicorn master within 120 seconds
2020-10-23 00:57:15,649 ERROR - Shutting down webserver
(nothing of note in airflow-webserver.err)
[airflow#airflowetl airflow]$ cat airflow-webserver.err
/home/airflow/.local/lib/python3.6/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
The airflow.cfg values for the webserver section looks like...
[webserver]
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is used in automated emails that
# airflow sends to point links to the right web server
#base_url = http://localhost:8080
base_url = http://airflowetl.co.local:8080
# The ip specified when starting the web server
web_server_host = 0.0.0.0
# The port on which to run the web server
web_server_port = 8080
# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_cert =
web_server_ssl_key =
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 120
# Number of seconds the gunicorn webserver waits before timing out on a worker
#web_server_worker_timeout = 120
web_server_worker_timeout = 300
# Number of workers to refresh at a time. When set to 0, worker refresh is
# disabled. When nonzero, airflow periodically refreshes webserver workers by
# bringing up new ones and killing old ones.
worker_refresh_batch_size = 1
# Number of seconds to wait before refreshing a batch of workers.
worker_refresh_interval = 30
# Secret key used to run your flask app
secret_key = my_key
# Number of workers to run the Gunicorn web server
workers = 4
# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent
worker_class = sync
Ultimately, just restarted the process as a daemon again (airflow webserver -D (should I have deleted the old airflow-webserer.log and .err files first?)), but not sure what would make this happen, since it had had no problems running for months before this.
Could anyone with more experience explain what could have happened after all this time and how I could prevent it in the future? Any issues with running dags or anything else that I should check for that this temporary unexpected shutdown of the websever may have caused?
I am experiencing the same issue, and it only started (very unfrequently) when I changed the following two config parameters in the webserver.
worker_refresh_interval = 120
workers = 2
However, my parameters are also set quite differently than yours, will share them here.
rbac = True
web_server_host = 0.0.0.0
web_server_port = 8080
web_server_master_timeout = 600
web_server_worker_timeout = 600
default_ui_timezone = Europe/Amsterdam
reload_on_plugin_change = True
After comparing the two, as your settings of the two I changed were set to the default (same as me before changing them), it seems that it is a combination of more parameters.
I have a problem deploying a pytorch model in production. For a demonstration, I build a simple model and a flask app. I put everything in a docker container (pytorch+flask+uwsgi) plus another container for nginx. Everything is running well, my app is rendered and I can navigate inside. However, well I navigate into the URL that launches a prediction of the model, the server hangs and does not seem to compute anything.
The uWSGI is run like this:
/opt/conda/bin/uwsgi --ini /usr/src/web/uwsgi.ini
with uwsgi.ini
[uwsgi]
#application's base folder
chdir = /usr/src/web/
#python module to import
wsgi-file = /usr/src/web/wsgi.py
callable = app
#socket file's location
socket = /usr/src/web/uwsgi.sock
#permissions for the socket file
chmod-socket = 666
# Port to expose
http = :5000
# Cleanup the socket when process stops
vacuum = true
#Log directory
logto = /usr/src/web/app.log
# minimum number of workers to keep at all times
cheaper = 2
processes = 16
As said, the server hangs and I finally got a timeout. What is strange is when I run the flask application directly (also in the container) with
python /usr/src/web/manage.py runserver --host 0.0.0.0
I get my prediction in no time
I think this is related to
https://discuss.pytorch.org/t/basic-operations-do-not-work-in-1-1-0-with-uwsgi-flask/50257
Maybe try as mentioned there:
app = flask.Flask(__name__)
segmentator = None
#app.before_first_request
def load_segmentator():
global segmentator
segmentator = Segmentator()
where Segmentator is a class with pytorch’s nn.Module, which loads weights in __init__
FYI this solution worked for me with one app but not the other
The problem is pretty simple. I need to limit airflow web users to see and execute only certain DAGs and tasks.
If possible, I'd prefer not to use Kerberos nor OAuth.
The Multi-tenancy option seems like an option to go, but couldn't make it work the way I expect.
My current setup:
added airflow web users test and ikar via Web Authentication / Password
my unix username is ikar with a home in /home/ikar
no test unix user
airflow 1.8.2 is installed in /home/ikar/airflow
added two DAGs with one task:
one with owner set to ikar
one with owner set to test
cat airflow.cfg:
[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /home/ikar/airflow
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /home/ikar/airflow-test/dags
# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /home/ikar/airflow/logs
# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
# must supply a remote location URL (starting with either 's3://...' or
# 'gs://...') and an Airflow connection id that provides access to the storage
# location.
remote_base_log_folder =
remote_log_conn_id =
# Use server-side encryption for logs stored in S3
encrypt_s3_logs = False
# DEPRECATED option for remote log storage, use remote_base_log_folder instead!
s3_log_folder =
# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor
executor = SequentialExecutor
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
sql_alchemy_conn = sqlite:////home/ikar/airflow/airflow.db
# The SqlAlchemy pool size is the maximum number of database connections
# in the pool.
sql_alchemy_pool_size = 5
# The SqlAlchemy pool recycle is the number of seconds a connection
# can be idle in the pool before it is invalidated. This config does
# not apply to sqlite.
sql_alchemy_pool_recycle = 3600
# The amount of parallelism as a setting to the executor. This defines
# the max number of task instances that should run simultaneously
# on this airflow installation
parallelism = 32
# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 16
# Are DAGs paused by default at creation
dags_are_paused_at_creation = True
# When not using pools, tasks are run in the "default pool",
# whose size is guided by this config element
non_pooled_task_slot_count = 128
# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 16
# Whether to load the examples that ship with Airflow. It's good to
# get started, but you probably want to set this to False in a production
# environment
load_examples = False
# Where your Airflow plugins are stored
plugins_folder = /home/ikar/airflow/plugins
# Secret key to save connection passwords in the db
fernet_key = cryptography_not_found_storing_passwords_in_plain_text
# Whether to disable pickling dags
donot_pickle = False
# How long before timing out a python file import while filling the DagBag
dagbag_import_timeout = 30
# The class to use for running task instances in a subprocess
task_runner = BashTaskRunner
# If set, tasks without a `run_as_user` argument will be run with this user
# Can be used to de-elevate a sudo user running Airflow when executing tasks
default_impersonation =
# What security module to use (for example kerberos):
security =
# Turn unit test mode on (overwrites many configuration options with test
# values at runtime)
unit_test_mode = False
[cli]
# In what way should the cli access the API. The LocalClient will use the
# database directly, while the json_client will use the api running on the
# webserver
api_client = airflow.api.client.local_client
endpoint_url = http://localhost:8888
[api]
# How to authenticate users of the API
auth_backend = airflow.api.auth.backend.default
[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via `default_args`
default_owner = Airflow
default_cpus = 1
default_ram = 512
default_disk = 512
default_gpus = 0
[webserver]
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is used in automated emails that
# airflow sends to point links to the right web server
base_url = http://localhost:8888
# The ip specified when starting the web server
web_server_host = 0.0.0.0
# The port on which to run the web server
web_server_port = 8888
# Paths to the SSL certificate and key for the web server. When both are
# provided SSL will be enabled. This does not change the web server port.
web_server_ssl_cert =
web_server_ssl_key =
# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 120
# Number of workers to refresh at a time. When set to 0, worker refresh is
# disabled. When nonzero, airflow periodically refreshes webserver workers by
# bringing up new ones and killing old ones.
worker_refresh_batch_size = 1
# Number of seconds to wait before refreshing a batch of workers.
worker_refresh_interval = 30
# Secret key used to run your flask app
secret_key = temporary_key
# Number of workers to run the Gunicorn web server
workers = 4
# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent
worker_class = sync
# Log files for the gunicorn webserver. '-' means log to stderr.
access_logfile = -
error_logfile = -
# Expose the configuration file in the web server
expose_config = False
# Set to true to turn on authentication:
# http://pythonhosted.org/airflow/security.html#web-authentication
authenticate = True
auth_backend = airflow.contrib.auth.backends.password_auth
# Filter the list of dags by owner name (requires authentication to be enabled)
filter_by_owner = True
# Filtering mode. Choices include user (default) and ldapgroup.
# Ldap group filtering requires using the ldap backend
#
# Note that the ldap server needs the "memberOf" overlay to be set up
# in order to user the ldapgroup mode.
owner_mode = user
# Default DAG orientation. Valid values are:
# LR (Left->Right), TB (Top->Bottom), RL (Right->Left), BT (Bottom->Top)
dag_orientation = LR
# Puts the webserver in demonstration mode; blurs the names of Operators for
# privacy.
demo_mode = False
# The amount of time (in secs) webserver will wait for the initial handshake
# while fetching logs from another worker machine
log_fetch_timeout_sec = 5
# By default, the webserver shows paused DAGs. Flip this to hide paused
# DAGs by default
hide_paused_dags_by_default = False
[email]
email_backend = airflow.utils.email.send_email_smtp
[smtp]
# If you want airflow to send emails on retries, failure, and you want to use
# the airflow.utils.email.send_email_smtp function, you have to configure an
# smtp server here
smtp_host = localhost
smtp_starttls = True
smtp_ssl = False
# Uncomment and set the user/pass settings if you want to use SMTP AUTH
# smtp_user = airflow
# smtp_password = airflow
smtp_port = 25
smtp_mail_from = airflow#airflow.com
[celery]
# This section only applies if you are using the CeleryExecutor in
# [core] section above
# The app name that will be used by celery
celery_app_name = airflow.executors.celery_executor
# The concurrency that will be used when starting workers with the
# "airflow worker" command. This defines the number of task instances that
# a worker will take, so size up your workers based on the resources on
# your worker box and the nature of your tasks
celeryd_concurrency = 4
# When you start an airflow worker, airflow starts a tiny web server
# subprocess to serve the workers local log files to the airflow main
# web server, who then builds pages and sends them to users. This defines
# the port on which the logs are served. It needs to be unused, and open
# visible from the main web server to connect into the workers.
worker_log_server_port = 8793
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more
# information.
broker_url = sqla+mysql://airflow:airflow#localhost:3306/airflow
# Another key Celery setting
celery_result_backend = db+mysql://airflow:airflow#localhost:3306/airflow
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it `airflow flower`. This defines the IP that Celery Flower runs on
flower_host = 0.0.0.0
# This defines the port that Celery Flower runs on
flower_port = 5555
# Default queue that tasks get assigned to and that worker listens on.
default_queue = default
[scheduler]
# Task instances listen for external kill signal (when you clear tasks
# from the CLI or the UI), this defines the frequency at which they should
# listen (in seconds).
job_heartbeat_sec = 5
# The schedule constantly tries to trigger new tasks (look at the
# scheduler section in the docs for more information). This defines
# how often the scheduler should run (in seconds).
scheduler_heartbeat_sec = 5
# after how much time should the scheduler terminate in seconds
# -1 indicates to run continuously (see also num_runs)
run_duration = -1
# after how much time a new DAGs should be picked up from the filesystem
min_file_process_interval = 0
dag_dir_list_interval = 300
# How often should stats be printed to the logs
print_stats_interval = 30
child_process_log_directory = /home/ikar/airflow/logs/scheduler
# Local task jobs periodically heartbeat to the DB. If the job has
# not heartbeat in this many seconds, the scheduler will mark the
# associated task instance as failed and will re-schedule the task.
scheduler_zombie_task_threshold = 300
# Turn off scheduler catchup by setting this to False.
# Default behavior is unchanged and
# Command Line Backfills still work, but the scheduler
# will not do scheduler catchup if this is False,
# however it can be set on a per DAG basis in the
# DAG definition (catchup)
catchup_by_default = False
# Statsd (https://github.com/etsy/statsd) integration settings
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow
# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run. However, airflow will never
# use more threads than the number of cpu cores available.
max_threads = 2
authenticate = False
[mesos]
# Mesos master address which MesosExecutor will connect to.
master = localhost:5050
# The framework name which Airflow scheduler will register itself as on mesos
framework_name = Airflow
# Number of cpu cores required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_cpu = 1
# Memory in MB required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_memory = 256
# Enable framework checkpointing for mesos
# See http://mesos.apache.org/documentation/latest/slave-recovery/
checkpoint = False
# Failover timeout in milliseconds.
# When checkpointing is enabled and this option is set, Mesos waits
# until the configured timeout for
# the MesosExecutor framework to re-register after a failover. Mesos
# shuts down running tasks if the
# MesosExecutor framework fails to re-register within this timeframe.
# failover_timeout = 604800
# Enable framework authentication for mesos
# See http://mesos.apache.org/documentation/latest/configuration/
authenticate = False
# Mesos credentials, if authentication is enabled
# default_principal = admin
# default_secret = admin
[kerberos]
ccache = /tmp/airflow_krb5_ccache
# gets augmented with fqdn
principal = airflow
reinit_frequency = 3600
kinit_path = kinit
keytab = airflow.keytab
[github_enterprise]
api_rev = v3
[admin]
# UI to hide sensitive variable fields when set to True
hide_sensitive_variable_fields = True
I'd expect that test user will only see DAG with the owner set to test but both users can see and execute both DAGs.
Couldn't find any detailed documentation on how to setup the user restrictions for airflow DAGs.
Can anyone help? Am I missing something?
this error is solved in airflow 1.10.5, you can check the changelog
https://github.com/apache/airflow/blob/6fb821713fe0185b4af3a17d008a04a7669a3a64/CHANGELOG.txt#L932
I opened up airflow and checked the connections, and found out there are too many connections running behind it.
Any ideas to how to kill those which I don't use, or I'd love to know the minimum conn_id to run it.
Architecture
LocalExecutor (Nothing like any other brokers)
Postgres as the metadb
However it lists 17 connections.
Here are the connection lists.
This is the airflow.cfg.
[core]
# Thee home folder for airflow, default is ~/airflow
airflow_home = /usr/src/app
# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
dags_folder = /usr/src/app/dags
# The folder where airflow should store its log files. This location
base_log_folder = /usr/src/app/logs
# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
# must supply a remote location URL (starting with either 's3://...' or
# 'gs://...') and an Airflow connection id that provides access to the storage
# location.
remote_base_log_folder =
remote_log_conn_id =
# Use server-side encryption for logs stored in S3
encrypt_s3_logs = False
# deprecated option for remote log storage, use remote_base_log_folder instead!
# s3_log_folder =
# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor
executor = LocalExecutor
# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
sql_alchemy_conn = postgresql+psycopg2://airflow:airflow#db/airflow
# The SqlAlchemy pool size is the maximum number of database connections
# in the pool.
sql_alchemy_pool_size = 5
# The SqlAlchemy pool recycle is the number of seconds a connection
# can be idle in the pool before it is invalidated. This config does
# not apply to sqlite.
sql_alchemy_pool_recycle = 3600
# The amount of parallelism as a setting to the executor. This defines
# the max number of task instances that should run simultaneously
# on this airflow installation
parallelism = 32
# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 16
# Are DAGs paused by default at creation
dags_are_paused_at_creation = True
# When not using pools, tasks are run in the "default pool",
# whose size is guided by this config element
non_pooled_task_slot_count = 128
# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 16
# Whether to load the examples that ship with Airflow. It's good to
# get started, but you probably want to set this to False in a production
# environment
load_examples = False
# Where your Airflow plugins are stored
plugins_folder = /usr/src/app/plugins
# Secret key to save connection passwords in the db
fernet_key = cryptography_not_found_storing_passwords_in_plain_text
# Whether to disable pickling dags
donot_pickle = False
# How long before timing out a python file import while filling the DagBag
dagbag_import_timeout = 30
[operators]
# The default owner assigned to each new operator, unless
# provided explicitly or passed via `default_args`
default_owner = Airflow
[webserver]
# The base url of your website as airflow cannot guess what domain or
# cname you are using. This is used in automated emails that
# airflow sends to point links to the right web server
base_url = http://localhost:8080
# The ip specified when starting the web server
web_server_host = 0.0.0.0
# The port on which to run the web server
web_server_port = 8080
# The time the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 120
# Secret key used to run your flask app
secret_key = temporary_key
# Number of workers to run the Gunicorn web server
workers = 4
# The worker class gunicorn should use. Choices include
# sync (default), eventlet, gevent
worker_class = sync
# Expose the configuration file in the web server
expose_config = true
# Set to true to turn on authentication:
# http://pythonhosted.org/airflow/installation.html#web-authentication
authenticate = False
# Filter the list of dags by owner name (requires authentication to be enabled)
filter_by_owner = False
[email]
email_backend = airflow.utils.email.send_email_smtp
[smtp]
# If you want airflow to send emails on retries, failure, and you want to use
# the airflow.utils.email.send_email_smtp function, you have to configure an smtp
# server here
smtp_host = localhost
smtp_starttls = True
smtp_ssl = False
smtp_user = airflow
smtp_port = 25
smtp_password = airflow
smtp_mail_from = airflow#airflow.com
[celery]
# This section only applies if you are using the CeleryExecutor in
# [core] section above
# The app name that will be used by celery
celery_app_name = airflow.executors.celery_executor
# The concurrency that will be used when starting workers with the
# "airflow worker" command. This defines the number of task instances that
# a worker will take, so size up your workers based on the resources on
# your worker box and the nature of your tasks
celeryd_concurrency = 16
# When you start an airflow worker, airflow starts a tiny web server
# subprocess to serve the workers local log files to the airflow main
# web server, who then builds pages and sends them to users. This defines
# the port on which the logs are served. It needs to be unused, and open
# visible from the main web server to connect into the workers.
worker_log_server_port = 8793
# The Celery broker URL. Celery supports RabbitMQ, Redis and experimentally
# a sqlalchemy database. Refer to the Celery documentation for more
# information.
broker_url = sqla+mysql://airflow:airflow#localhost:3306/airflow
# Another key Celery setting
celery_result_backend = db+mysql://airflow:airflow#localhost:3306/airflow
# Celery Flower is a sweet UI for Celery. Airflow has a shortcut to start
# it `airflow flower`. This defines the port that Celery Flower runs on
flower_port = 5555
# Default queue that tasks get assigned to and that worker listen on.
default_queue = default
[scheduler]
# Task instances listen for external kill signal (when you clear tasks
# from the CLI or the UI), this defines the frequency at which they should
# listen (in seconds).
job_heartbeat_sec = 5
# The scheduler constantly tries to trigger new tasks (look at the
# scheduler section in the docs for more information). This defines
# how often the scheduler should run (in seconds).
scheduler_heartbeat_sec = 5
# Statsd (https://github.com/etsy/statsd) integration settings
# statsd_on = False
# statsd_host = localhost
# statsd_port = 8125
# statsd_prefix = airflow
# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run. However airflow will never
# use more threads than the amount of cpu cores available.
max_threads = 2
[mesos]
# Mesos master address which MesosExecutor will connect to.
master = localhost:5050
# The framework name which Airflow scheduler will register itself as on mesos
framework_name = Airflow
# Number of cpu cores required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_cpu = 1
# Memory in MB required for running one task instance using
# 'airflow run <dag_id> <task_id> <execution_date> --local -p <pickle_id>'
# command on a mesos slave
task_memory = 256
# Enable framework checkpointing for mesos
# See http://mesos.apache.org/documentation/latest/slave-recovery/
checkpoint = False
# Failover timeout in milliseconds.
# When checkpointing is enabled and this option is set, Mesos waits
# until the configured timeout for
# the MesosExecutor framework to re-register after a failover. Mesos
# shuts down running tasks if the
# MesosExecutor framework fails to re-register within this timeframe.
# failover_timeout = 604800
# Enable framework authentication for mesos
# See http://mesos.apache.org/documentation/latest/configuration/
authenticate = False
# Mesos credentials, if authentication is enabled
# default_principal = admin
# default_secret = admin
These are default connections. They are not "running", they are just configuration records in your settings. You can delete them manually.
Airflow 1.10.10 now has a configuration to not create default connections.
Simply set load_default_connections to False in your airflow.cfg file.
https://github.com/apache/airflow/pull/7629
Here is an ugly little bash script that deletes the defaults (as of airflow v1.10):
#!/bin/bash
declare -a DEFAULT_CONNS=(
"cassandra_default"
"azure_cosmos_default"
"azure_data_lake_default"
"segment_default"
"qubole_default"
"databricks_default"
"emr_default"
"sqoop_default"
"redis_default"
"druid_ingest_default"
"druid_broker_default"
"spark_default"
"aws_default"
"fs_default"
"sftp_default"
"ssh_default"
"webhdfs_default"
"wasb_default"
"vertica_default"
"mssql_default"
"http_default"
"sqlite_default"
"postgres_default"
"mysql_default"
"mongo_default"
"metastore_default"
"hiveserver2_default"
"hive_cli_default"
"google_cloud_default"
"presto_default"
"bigquery_default"
"beeline_default"
)
for CONN in "${DEFAULT_CONNS[#]}"
do
airflow connections --delete --conn_id $CONN
done
Also, you can delete all connections using a bash script like this
airflow connections -l | awk -F"'" '{if ($2) print$2}' | xargs -I {} airflow connections -d --conn_id {}
I'm trying to setup a stress/load test using the WCAT toolkit included in the IIS Resources.
Using LogParser, I've processed a UBR file with configuration. It looks something like this:
[Configuration]
NumClientMachines: 1 # number of distinct client machines to use
NumClientThreads: 100 # number of threads per machine
AsynchronousWait: TRUE # asynchronous wait for think and delay
Duration: 5m # length of experiment (m = minutes, s = seconds)
MaxRecvBuffer: 8192K # suggested maximum received buffer
ThinkTime: 0s # maximum think-time before next request
WarmupTime: 5s # time to warm up before taking statistics
CooldownTime: 6s # time to cool down at the end of the experiment
[Performance]
[Script]
SET RequestHeader = "Accept: */*\r\n"
APP RequestHeader = "Accept-Language: en-us\r\n"
APP RequestHeader = "User-Agent: Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.2; .NET CLR 1.0.3705)\r\n"
APP RequestHeader = "Host: %HOST%\r\n"
NEW TRANSACTION
classId = 1
NEW REQUEST HTTP
ResponseStatusCode = 200
Weight = 45117
verb = "GET"
URL = "http://Url1.com"
NEW TRANSACTION
classId = 3
NEW REQUEST HTTP
ResponseStatusCode = 200
Weight = 13662
verb = "GET"
URL = "http://Url1.com/test.aspx"
Does it look OK?
I execute the controller with this command: wcctl -z StressTest.ubr -a localhost
The Client(s) is executed like this: wcclient localhost
When the client is executed, I get this error: main client thread Connect Attempt 0 Failed. Error = 10061
Has anyone in this world ever used WCAT?
I'd look at updating to WCat 6.3 - available here for x86 and here for x64
They've changed the settings/scenario file strucutures, which is a little painful, but should suit your needs.
I've just started evaluating wcat 6.3 and I'm afraid my experience has been a bit disapointing in terms of online support/community.
There is also a major bug in the wcat.wsf script - see:
http://forums.iis.net/t/1153312.aspx
I'm now struggling with getting performance counter measurement working.
I've had good success with WCAT, though I'm struggling with simulating NTLM connections.
I'm using 6.3, so my config files look very different from yours. Some gotchas I noted along the way:
+ Make sure you've got your firewall turned off, or holes punched through for WMI.
+ Each thing you set in the request header has a tremendous impact on throughput. Apples to apples must have the same request headers.
+ Remote calls with multiple clients work only after correcting the bug identified by sthorogood.
Once I crossed those hurdles, I got great results from WCAT. It tests quickly, repeatably, and aggressively.
Best of luck,
Kevin
I don't have an answer for you, but have you considered using other tools for your testing? The WCAT tools seems pretty limited and complicated to use.
OpenSTA and JMeter are good open source tools for load/stress/performance testing.
OpenSTA and JMeter looks very Apache like. I'm running IIS on Windows Server 2003.
Have you looked at the Microsoft Web Application Stress Tool?
for performance counter you can define -p .prf in the same command run for controller as:
wcctl -c config.txt -d distribution.txt -s script.txt -a localhost - p performance.prf