In many cases, I want to allow Vertex Pipeline tasks to automatically retry on failures.
Is there anyway I can set the retry policy for the task?
Answer:
Pipeline task allows you to set the retry policy
set_retry(num_retries: int, backoff_duration: str | None = None, backoff_factor: float | None = None, backoff_max_duration: str | None = None) → PipelineTask
For example,
task.set_retry(2)
For a comprehensive document about the policy, see https://kubeflow-pipelines.readthedocs.io/en/master/source/dsl.html#kfp.dsl.PipelineTask.set_retry
Related
So, I am running Superset installed on an EC2 instance. In my config.py file, I had only made these changes:
FEATURE_FLAGS = {
"ALERT_REPORTS": True
}
EMAIL_NOTIFICATIONS = True
SMTP_HOST = "email-smtp.us-east-1.amazonaws.com"
SMTP_STARTTLS = True
SMTP_SSL = False
SMTP_USER = "***my user***"
SMTP_PORT = 25
SMTP_PASSWORD = "***my pass***"
SMTP_MAIL_FROM = "***an email ID***"
ENABLE_SCHEDULED_EMAIL_REPORTS = True
ENABLE_ALERTS = True
After setting these, I remembered to do superset init before launching the service.
Yet, after the scheduled time, the UI shows no value in the last run column and the logs gives the following message:
DEBUG:cron_descriptor.GetText:Failed to find locale en_US
INFO:werkzeug:127.0.0.1 - - [02/Apr/2021 10:56:51] "GET /api/v1/report/?q=(filters:!((col:type,opr:eq,value:Alert)),order_column:name,order_direction:desc,page:0,page_size:25) HTTP/1.1" 200 -
Here's a sreenshot of the UI:
As can be seen, there is nothing in the last run column, even after the scheduled time (I had also scheduled it to every 1 minute interval - but same results
Alerts/reports are executed in the workers, with celery beat scheduling the jobs and celery workers executing them. You need to configure celery beat and workers to run, check out this documentation for some info on how to set this up with docker-compose: https://superset.apache.org/docs/installation/alerts-reports
Everyone, I am using Airflow(v.1.10.12) for one of the project to schedule the Jobs daily. Wanted to execute multiple DAG’s(14) in parallel. Have updated the concurrency parameters in cfg file. However, have observed the behavior that: Scheduler starts the execution of tasks, assigns the tasks to queue but after certain amount of time it pauses and restart after 5 minutes. Due to this behavior the time taken by all Dag is huge and also some of tasks are getting failed. Can someone help me with the understanding why the Scheduler gets halt and if we should modify some parameter or shift to some other Airflow version?
Below is the configuration file
[core]
dags_folder = /home/airflow/dags
base_log_folder = /home/airflow/logs
remote_logging = False
remote_log_conn_id =
remote_base_log_folder =
encrypt_s3_logs = False
logging_level = INFO
fab_logging_level = WARN
logging_config_class =
colored_console_log = True
colored_log_format = [%%(blue)s%%(asctime)s%%(reset)s] {%%(blue)s%%(filename)s:%%(reset)s%%(lineno)d} %%(log_color)s%%(levelname)s%%(reset)s - %%(log_color)s%%(message)s%%(reset)s
colored_formatter_class = airflow.utils.log.colored_log.CustomTTYColoredFormatter
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s
log_filename_template = {{ ti.dag_id }}/{{ ti.task_id }}/{{ ts }}/{{ try_number }}.log
log_processor_filename_template = {{ filename }}.log
dag_processor_manager_log_location = /home/airflow/logs/dag_processor_manager/dag_processor_manager.log
hostname_callable = socket:getfqdn
default_timezone = utc
executor = CeleryExecutor
sql_alchemy_conn = postgresql+psycopg2://devairflow:airflow#localhost:5432/pcfdb
sql_engine_encoding = utf-8
sql_alchemy_pool_enabled = True
sql_alchemy_pool_size = 0
sql_alchemy_max_overflow = -1
# 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. If the number of DB connections is ever exceeded,
# a lower config value will allow the system to recover faster.
sql_alchemy_pool_recycle = 1800
sql_alchemy_pool_pre_ping = True
sql_alchemy_schema =
parallelism = 50
dag_concurrency = 50
dags_are_paused_at_creation = True
max_active_runs_per_dag = 50
load_examples = False
load_default_connections = True
plugins_folder = /home/airflow/plugins
fernet_key = M4dpP6f2Hd5p3N--CxtIoUo9XaSDifA42MPLs1UR7-g=
donot_pickle = False
dagbag_import_timeout = 41460
dag_file_processor_timeout = 60
task_runner = StandardTaskRunner
default_impersonation =
security =
secure_mode = False
unit_test_mode = False
enable_xcom_pickling = True
killed_task_cleanup_time = 60
dag_run_conf_overrides_params = False
worker_precheck = False
dag_discovery_safe_mode = True
default_task_retries = 0
store_serialized_dags = False
min_serialized_dag_update_interval = 30
max_num_rendered_ti_fields_per_task = 100
# On each dagrun check against defined SLAs
check_slas = True
[secrets]
# Full class name of secrets backend to enable (will precede env vars and metastore in search path)
# Example: backend = airflow.contrib.secrets.aws_systems_manager.SystemsManagerParameterStoreBackend
backend =
# The backend_kwargs param is loaded into a dictionary and passed to __init__ of secrets backend class.
# See documentation for the secrets backend you are using. JSON is expected.
# Example for AWS Systems Manager ParameterStore:
# ``{"connections_prefix": "/airflow/connections", "profile_name": "default"}``
backend_kwargs =
[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
# If you set web_server_url_prefix, do NOT forget to append it here, ex:
# ``endpoint_url = http://localhost:8080/myroot``
# So api will look like: ``http://localhost:8080/myroot/api/experimental/...``
endpoint_url = http://localhost:8080
[debug]
# Used only with DebugExecutor. If set to True DAG will fail with first
# failed task. Helpful for debugging purposes.
fail_fast = False
[api]
# How to authenticate users of the API. See
# https://airflow.apache.org/docs/stable/security.html for possible values.
# ("airflow.api.auth.backend.default" allows all requests for historic reasons)
auth_backend = airflow.api.auth.backend.deny_all
[lineage]
# what lineage backend to use
backend =
[atlas]
sasl_enabled = False
host =
port = 21000
username =
password =
[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 = 1024
default_disk = 1024
default_gpus = 0
[hive]
# Default mapreduce queue for HiveOperator tasks
default_hive_mapred_queue =
[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
# Default timezone to display all dates in the RBAC UI, can be UTC, system, or
# any IANA timezone string (e.g. Europe/Amsterdam). If left empty the
# default value of core/default_timezone will be used
# Example: default_ui_timezone = America/New_York
default_ui_timezone = UTC
# 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 =
# 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_key =
# Number of seconds the webserver waits before killing gunicorn master that doesn't respond
web_server_master_timeout = 41460
# Number of seconds the gunicorn webserver waits before timing out on a worker
web_server_worker_timeout = 41460
# 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
# If set to True, Airflow will track files in plugins_folder directory. When it detects changes,
# then reload the gunicorn.
reload_on_plugin_change = False
# Secret key used to run your flask app
# It should be as random as possible
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 = -
# Log files for the gunicorn webserver. '-' means log to stderr.
error_logfile = -
# Expose the configuration file in the web server
expose_config = True
# Expose hostname in the web server
expose_hostname = True
# Expose stacktrace in the web server
expose_stacktrace = True
# Set to true to turn on authentication:
# https://airflow.apache.org/security.html#web-authentication
authenticate = False
# Filter the list of dags by owner name (requires authentication to be enabled)
filter_by_owner = False
# 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 view. Valid values are:
# tree, graph, duration, gantt, landing_times
dag_default_view = tree
# "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 initial handshake
# while fetching logs from other worker machine
log_fetch_timeout_sec = 5
# Time interval (in secs) to wait before next log fetching.
log_fetch_delay_sec = 2
# Distance away from page bottom to enable auto tailing.
log_auto_tailing_offset = 30
# Animation speed for auto tailing log display.
log_animation_speed = 1000
# By default, the webserver shows paused DAGs. Flip this to hide paused
# DAGs by default
hide_paused_dags_by_default = False
# Consistent page size across all listing views in the UI
page_size = 100
# Use FAB-based webserver with RBAC feature
rbac = False
# Define the color of navigation bar
navbar_color = #007A87
# Default dagrun to show in UI
default_dag_run_display_number = 25
# Enable werkzeug ``ProxyFix`` middleware for reverse proxy
enable_proxy_fix = False
# Number of values to trust for ``X-Forwarded-For``.
# More info: https://werkzeug.palletsprojects.com/en/0.16.x/middleware/proxy_fix/
proxy_fix_x_for = 1
# Number of values to trust for ``X-Forwarded-Proto``
proxy_fix_x_proto = 1
# Number of values to trust for ``X-Forwarded-Host``
proxy_fix_x_host = 1
# Number of values to trust for ``X-Forwarded-Port``
proxy_fix_x_port = 1
# Number of values to trust for ``X-Forwarded-Prefix``
proxy_fix_x_prefix = 1
# Set secure flag on session cookie
cookie_secure = False
# Set samesite policy on session cookie
cookie_samesite =
# Default setting for wrap toggle on DAG code and TI log views.
default_wrap = False
# Allow the UI to be rendered in a frame
x_frame_enabled = True
# Send anonymous user activity to your analytics tool
# choose from google_analytics, segment, or metarouter
# analytics_tool =
# Unique ID of your account in the analytics tool
# analytics_id =
# Update FAB permissions and sync security manager roles
# on webserver startup
update_fab_perms = True
# Minutes of non-activity before logged out from UI
# 0 means never get forcibly logged out
force_log_out_after = 0
# The UI cookie lifetime in days
session_lifetime_days = 30
[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 Address
# 192.168.152.213
# SMTP Port
# 25
# User Name
# etf#csopasset.com
smtp_host = *.*.*.*
smtp_starttls = True
smtp_ssl = False
# smtp_user = etf#csopasset.com
# smtp_password = etfGen2013
smtp_port = 25
smtp_mail_from = etf#***.com
[sentry]
# Sentry (https://docs.sentry.io) integration
sentry_dsn =
[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 celery 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
worker_concurrency = 50
# The maximum and minimum concurrency that will be used when starting workers with the
# ``airflow celery worker`` command (always keep minimum processes, but grow
# to maximum if necessary). Note the value should be max_concurrency,min_concurrency
# Pick these numbers based on resources on worker box and the nature of the task.
# If autoscale option is available, worker_concurrency will be ignored.
# http://docs.celeryproject.org/en/latest/reference/celery.bin.worker.html#cmdoption-celery-worker-autoscale
# Example: worker_autoscale = 16,12
# worker_autoscale =
# 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.
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#broker-settings
#roker_url = sqla+mysql://airflow:airflow#localhost:3306/airflow
broker_url = amqp://guest:guest#localhost:5672//
# The Celery result_backend. When a job finishes, it needs to update the
# metadata of the job. Therefore it will post a message on a message bus,
# or insert it into a database (depending of the backend)
# This status is used by the scheduler to update the state of the task
# The use of a database is highly recommended
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#task-result-backend-settings
#result_backend = db+mysql://airflow:airflow#localhost:3306/airflow
result_backend = db+postgresql+psycopg2://devairflow:airflow#localhost:5432/pcfdb
# 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
# The root URL for Flower
# Example: flower_url_prefix = /flower
flower_url_prefix =
# This defines the port that Celery Flower runs on
flower_port = 5555
# Securing Flower with Basic Authentication
# Accepts user:password pairs separated by a comma
# Example: flower_basic_auth = user1:password1,user2:password2
flower_basic_auth =
# Default queue that tasks get assigned to and that worker listen on.
default_queue = default
# How many processes CeleryExecutor uses to sync task state.
# 0 means to use max(1, number of cores - 1) processes.
sync_parallelism = 0
# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG
# In case of using SSL
ssl_active = False
ssl_key =
ssl_cert =
ssl_cacert =
# Celery Pool implementation.
# Choices include: prefork (default), eventlet, gevent or solo.
# See:
# https://docs.celeryproject.org/en/latest/userguide/workers.html#concurrency
# https://docs.celeryproject.org/en/latest/userguide/concurrency/eventlet.html
pool = prefork
# The number of seconds to wait before timing out ``send_task_to_executor`` or
# ``fetch_celery_task_state`` operations.
operation_timeout = 50
[celery_broker_transport_options]
# This section is for specifying options which can be passed to the
# underlying celery broker transport. See:
# http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-broker_transport_options
# The visibility timeout defines the number of seconds to wait for the worker
# to acknowledge the task before the message is redelivered to another worker.
# Make sure to increase the visibility timeout to match the time of the longest
# ETA you're planning to use.
# visibility_timeout is only supported for Redis and SQS celery brokers.
# See:
# http://docs.celeryproject.org/en/master/userguide/configuration.html#std:setting-broker_transport_options
# Example: visibility_timeout = 21600
# visibility_timeout =
[dask]
# This section only applies if you are using the DaskExecutor in
# [core] section above
# The IP address and port of the Dask cluster's scheduler.
cluster_address = 127.0.0.1:8786
# TLS/ SSL settings to access a secured Dask scheduler.
tls_ca =
tls_cert =
tls_key =
[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
# After how much time should the scheduler terminate in seconds
# -1 indicates to run continuously (see also num_runs)
run_duration = -1
# The number of times to try to schedule each DAG file
# -1 indicates unlimited number
num_runs = -1
# The number of seconds to wait between consecutive DAG file processing
processor_poll_interval = 1
# after how much time (seconds) a new DAGs should be picked up from the filesystem
min_file_process_interval = 0
# How often (in seconds) to scan the DAGs directory for new files. Default to 5 minutes.
dag_dir_list_interval = 300
# How often should stats be printed to the logs. Setting to 0 will disable printing stats
print_stats_interval = 30
# If the last scheduler heartbeat happened more than scheduler_health_check_threshold
# ago (in seconds), scheduler is considered unhealthy.
# This is used by the health check in the "/health" endpoint
scheduler_health_check_threshold = 30
child_process_log_directory = /home/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 = 1800
# 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 = True
# This changes the batch size of queries in the scheduling main loop.
# If this is too high, SQL query performance may be impacted by one
# or more of the following:
# - reversion to full table scan
# - complexity of query predicate
# - excessive locking
# Additionally, you may hit the maximum allowable query length for your db.
# Set this to 0 for no limit (not advised)
max_tis_per_query = 512
# Statsd (https://github.com/etsy/statsd) integration settings
statsd_on = False
statsd_host = localhost
statsd_port = 8125
statsd_prefix = airflow
# If you want to avoid send all the available metrics to StatsD,
# you can configure an allow list of prefixes to send only the metrics that
# start with the elements of the list (e.g: scheduler,executor,dagrun)
statsd_allow_list =
# The scheduler can run multiple threads in parallel to schedule dags.
# This defines how many threads will run.
max_threads = 2
authenticate = False
# Turn off scheduler use of cron intervals by setting this to False.
# DAGs submitted manually in the web UI or with trigger_dag will still run.
use_job_schedule = True
# Allow externally triggered DagRuns for Execution Dates in the future
# Only has effect if schedule_interval is set to None in DAG
allow_trigger_in_future = False
[ldap]
# set this to ldaps://<your.ldap.server>:<port>
uri =
user_filter = objectClass=*
user_name_attr = uid
group_member_attr = memberOf
superuser_filter =
data_profiler_filter =
bind_user = cn=Manager,dc=example,dc=com
bind_password = insecure
basedn = dc=example,dc=com
cacert = /etc/ca/ldap_ca.crt
search_scope = LEVEL
# This setting allows the use of LDAP servers that either return a
# broken schema, or do not return a schema.
ignore_malformed_schema = 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.
# Example: failover_timeout = 604800
# failover_timeout =
# Enable framework authentication for mesos
# See http://mesos.apache.org/documentation/latest/configuration/
authenticate = False
# Mesos credentials, if authentication is enabled
# Example: default_principal = admin
# default_principal =
# Example: default_secret = admin
# default_secret =
# Optional Docker Image to run on slave before running the command
# This image should be accessible from mesos slave i.e mesos slave
# should be able to pull this docker image before executing the command.
# Example: docker_image_slave = puckel/docker-airflow
# docker_image_slave =
[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
[elasticsearch]
# Elasticsearch host
host =
# Format of the log_id, which is used to query for a given tasks logs
log_id_template = {dag_id}-{task_id}-{execution_date}-{try_number}
# Used to mark the end of a log stream for a task
end_of_log_mark = end_of_log
# Qualified URL for an elasticsearch frontend (like Kibana) with a template argument for log_id
# Code will construct log_id using the log_id template from the argument above.
# NOTE: The code will prefix the https:// automatically, don't include that here.
frontend =
# Write the task logs to the stdout of the worker, rather than the default files
write_stdout = False
# Instead of the default log formatter, write the log lines as JSON
json_format = False
# Log fields to also attach to the json output, if enabled
json_fields = asctime, filename, lineno, levelname, message
[elasticsearch_configs]
use_ssl = False
verify_certs = True
Below are the Worker logs:
Worker logs File
Airflow 2.0 has massive scheduler improvements. I you should upgrade to 2.0 and enjoy the new scheduler.
If the last scheduler heartbeat happened more than scheduler_health_check_threshold ago (in seconds), scheduler is considered unhealthy. This is used by the health check in the “/health” endpoint
Reference: Airflow Documentation
Make sure scheduler health check thresh hold is higher than scheduler heartbeat sec.
scheduler_health_check_threshold = 300
scheduler_heartbeat_sec = 60
I am trying to visualize the availability of a windows service by using the System event log of a machine, which is stored into Azure Log analytics. When the service is running, I consider it as a 1 and when it is stopped, I consider it as a 0, so that I can graph it out
| where EventLog == "System" and Source == "Service Control Manager" and EventID == 7036 and Computer =="COMPNAME"
| parse kind=relaxed EventData with * '<Data Name="param1">' Windows_Service_Name '</Data><Data Name="param2">' Windows_Service_State '</Data>' *
| where Windows_Service_Name == "Svc_Name"
| extend status = iif(Windows_Service_State == "running", "1", "0")
| summarize min(status) by bin(TimeGenerated , 1m)
| order by TimeGenerated, min_status desc
However, I am not getting any charts by selecting the charts on Azure Log analytics window. How can I produce a graph of availabilty using this query ?
Regards,
So there is two issues in this approach, the first is the actual syntax causing your issue. You are treating the status as strings and not numbers. If you remove the quotation marks hugging the numbers the chart will work.
The second part is that these logs are event based, which means that it will just log 1 event when its stopped and 1 event when its started. So looking at a specific time is impossible. You will in your graph just see last event change and after the retention time for the log passes and there has been no changes there will be no events at all.
Event
| where EventLog == "System" and Source == "Service Control Manager" and EventID == 7036 and Computer =="COMPNAME"
| parse kind=relaxed EventData with * '<Data Name="param1">' Windows_Service_Name '</Data><Data Name="param2">' Windows_Service_State '</Data>' *
| where Windows_Service_Name == "Svc_Name"
| extend status = iif(Windows_Service_State == "running", 1, 0)
| summarize min(status) by bin(TimeGenerated , 1m)
| order by TimeGenerated, min_status desc
I have a DAG that has 30 (or more) dynamically created parallel tasks.
I have concurrency option set on that DAG so that I only have single DAG Run running, when catching up the history.
When I run it on my server only 16 tasks actually run in parallel, while the rest 14 just wait being queued.
Which setting should I alter so that I have only 1 DAG Run running, but with all 30+ tasks running in parallel?
According to this FAQ, it seems like it's one of the dag_concurrency or max_active_runs_per_dag, but the former seem to be overdriven by concurrency setting already, while the latter seemed to have no effect (or I effectively messed up my setup).
Here's the sample code:
import datetime as dt
import logging
from airflow.operators.dummy_operator import DummyOperator
import config
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
default_args = {
'owner': 'airflow',
'depends_on_past': True,
'wait_for_downstream': True,
'concurrency': 1,
'retries': 0,
}
def print_operators(ds, **kwargs):
logging.info(f"Task {kwargs.get('task_instance_key_str', 'unknown_task_instance')}")
dag = DAG(
dag_id='test_parallelism_dag',
start_date=dt.datetime(2019, 1, 1),
default_args=default_args,
schedule_interval='#daily',
catchup=True,
template_searchpath=[config.DAGS_PATH],
params={'schema': config.SCHEMA_DB},
max_active_runs=1,
)
print_operators = [PythonOperator(
task_id=f'test_parallelism_dag.print_operator_{i}',
python_callable=print_operators,
provide_context=True,
dag=dag
) for i in range(60)]
dummy_operator_start = DummyOperator(
task_id=f'test_parallelism_dag.dummy_operator_start',
)
dummy_operator_end = DummyOperator(
task_id=f'test_parallelism_dag.dummy_operator_end',
)
dummy_operator_start >> print_operators >> dummy_operator_end
EDIT 1:
My current airflow.cfg contains:
executor = SequentialExecutor
parallelism = 32
dag_concurrency = 24
max_active_runs_per_dag = 26
My env variables are as following (set all of them different to easily spot which one helps):
AIRFLOW__CORE__EXECUTOR=LocalExecutor
AIRFLOW__CORE__DAG_CONCURRENCY=18
AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG=20
AIRFLOW__CORE__WORKER_CONCURRENCY=22
And with that I have following Gantt diagram:
Which kind of gives me a hint that setting DAG_CONCURRENCY env variable works.
The actual parameter to change was dag_concurrency in airflow.cfg or override it with AIRFLOW__CORE__DAG_CONCURRENCY env variable.
As per docs I referred to in my question:
concurrency: The Airflow scheduler will run no more than $concurrency
task instances for your DAG at any given time. Concurrency is defined
in your Airflow DAG. If you do not set the concurrency on your DAG,
the scheduler will use the default value from the dag_concurrency
entry in your airflow.cfg.
Which means following simplified code:
default_args = {
'owner': 'airflow',
'depends_on_past': True,
'wait_for_downstream': True,
'concurrency': 1,
}
dag = DAG(
dag_id='test_parallelism_dag',
default_args=default_args,
max_active_runs=1,
)
should be rewritten to:
default_args = {
'owner': 'airflow',
'depends_on_past': True,
'wait_for_downstream': True,
}
dag = DAG(
dag_id='test_parallelism_dag',
default_args=default_args,
max_active_runs=1,
concurrency=30
)
My code actually has wrong assumption that default_args at some point substitute actual kwargs to DAG constructor. I don't know what lead me to that conclusion back then, but I guess setting concurrency to 1 there is some draft leftover, which never actually affected anything and actual DAG concurrency was set from config default, which is 16.
Update the concurrency config as well in your airflow.cfg file. If it is 16, increase it to 32.
If you are using Celery Executor, change worker_concurrency to 32.
I've got a system that uses Akka 2.2.4 which creates a bunch of local actors and sets them as the routees of a Broadcast Router. Each worker handles some segment of the total work, according to some hash range we pass it. It works great.
Now, I've got to cluster this application for failover. Based on the requirement that only one worker per hash range exist/be triggered on the cluster, it seems to me that setting up each one as a ClusterSingletonManager would make sense..however I'm having trouble getting it working. The actor system starts up, it creates the ClusterSingletonManager, it adds the path in the code cited below to a Broadcast Router, but it never instantiates my actual worker actor to handle my messages for some reason. All I get is a log message: "unhandled event ${my message} in state Start". What am I doing wrong? Is there something else I need to do to start up this single instance cluster? Am I sending the wrong actor a message?
here's my akka config(I use the default config as a fallback):
akka{
cluster{
roles=["workerSystem"]
min-nr-of-members = 1
role {
workerSystem.min-nr-of-members = 1
}
}
daemonic = true
remote {
enabled-transports = ["akka.remote.netty.tcp"]
netty.tcp {
hostname = "127.0.0.1"
port = ${akkaPort}
}
}
actor{
provider = akka.cluster.ClusterActorRefProvider
single-message-bound-mailbox {
# FQCN of the MailboxType. The Class of the FQCN must have a public
# constructor with
# (akka.actor.ActorSystem.Settings, com.typesafe.config.Config) parameters.
mailbox-type = "akka.dispatch.BoundedMailbox"
# If the mailbox is bounded then it uses this setting to determine its
# capacity. The provided value must be positive.
# NOTICE:
# Up to version 2.1 the mailbox type was determined based on this setting;
# this is no longer the case, the type must explicitly be a bounded mailbox.
mailbox-capacity = 1
# If the mailbox is bounded then this is the timeout for enqueueing
# in case the mailbox is full. Negative values signify infinite
# timeout, which should be avoided as it bears the risk of dead-lock.
mailbox-push-timeout-time = 1
}
worker-dispatcher{
type = PinnedDispatcher
executor = "thread-pool-executor"
# Throughput defines the number of messages that are processed in a batch
# before the thread is returned to the pool. Set to 1 for as fair as possible.
throughput = 500
thread-pool-executor {
# Keep alive time for threads
keep-alive-time = 60s
# Min number of threads to cap factor-based core number to
core-pool-size-min = ${workerCount}
# The core pool size factor is used to determine thread pool core size
# using the following formula: ceil(available processors * factor).
# Resulting size is then bounded by the core-pool-size-min and
# core-pool-size-max values.
core-pool-size-factor = 3.0
# Max number of threads to cap factor-based number to
core-pool-size-max = 64
# Minimum number of threads to cap factor-based max number to
# (if using a bounded task queue)
max-pool-size-min = ${workerCount}
# Max no of threads (if using a bounded task queue) is determined by
# calculating: ceil(available processors * factor)
max-pool-size-factor = 3.0
# Max number of threads to cap factor-based max number to
# (if using a bounded task queue)
max-pool-size-max = 64
# Specifies the bounded capacity of the task queue (< 1 == unbounded)
task-queue-size = -1
# Specifies which type of task queue will be used, can be "array" or
# "linked" (default)
task-queue-type = "linked"
# Allow core threads to time out
allow-core-timeout = on
}
fork-join-executor {
# Min number of threads to cap factor-based parallelism number to
parallelism-min = 1
# The parallelism factor is used to determine thread pool size using the
# following formula: ceil(available processors * factor). Resulting size
# is then bounded by the parallelism-min and parallelism-max values.
parallelism-factor = 3.0
# Max number of threads to cap factor-based parallelism number to
parallelism-max = 1
}
}
}
}
Here's where I create my Actors(its' written in Groovy):
Props clusteredProps = ClusterSingletonManager.defaultProps("worker".toString(), PoisonPill.getInstance(), "workerSystem",
new ClusterSingletonPropsFactory(){
#Override
Props create(Object handOverData) {
log.info("called in ClusterSingetonManager")
Props.create(WorkerActorCreator.create(applicationContext, it.start, it.end)).withDispatcher("akka.actor.worker-dispatcher").withMailbox("akka.actor.single-message-bound-mailbox")
}
} )
ActorRef manager = system.actorOf(clusteredProps, "worker-${it.start}-${it.end}".toString())
String path = manager.path().child("worker").toString()
path
when I try to send a message to the actual worker actor, should the path above resolve? Currently it does not.
What am I doing wrong? Also, these actors live within a Spring application, and the worker actors are set up with some #Autowired dependencies. While this Spring integration worked well in a non-clustered environment, are there any gotchyas in a clustered environment I should be looking out for?
thank you
FYI:I've also posted this in the akka-user google group. Here's the link.
The path in your code is to the ClusterSingletonManager actor that you start on each node with role "workerSystem". It will create a child actor (WorkerActor) with name "worker-${it.start}-${it.end}" on the oldest node in the cluster, i.e. singleton within the cluster.
You should also define the name of the ClusterSingletonManager, e.g. system.actorOf(clusteredProps, "workerSingletonManager").
You can't send the messages to the ClusterSingletonManager. You must send them to the path of the active worker, i.e. including the address of the oldest node. That is illustrated by the ConsumerProxy in the documentation.
I'm not sure you should use a singleton at all for this. All workers will be running on the same node, the oldest. I would prefer to discuss alternative solutions to your problem at the akka-user google group.