Параллельное выполнение задач воздушного потока - ничего не планируется

Я только что прошел процесс настройки моей настройки Airflow для параллельной обработки, следуя эту статью и используя эта статья.

Кажется, все работает нормально в том смысле, что я смог запустить все эти команды из статей без каких-либо ошибок, предупреждений или исключений. Мне удалось запустить airflow webserver и airflow scheduler, и я могу перейти в пользовательский интерфейс и просмотреть все мои DAG, но теперь ни один из моих DAG не запускается, который ранее работал. У меня был этот базовый пример DAG, который работал, когда мой исполнитель был установлен на SequentialExecuter, но теперь, когда он установлен на LocalExecuter, он никогда не запускается. Все задачи в DAG окрашены в белый цвет на представлении графика с no status, когда первая должна находиться в состоянии running, пока она ожидает появления файла S3. Я уже очистил всю историю ПРОШЛОГО, БУДУЩЕГО, ИСПЫТАНИЯ в пользовательском интерфейсе, и у меня включен DAG, так что проблема не в этом. Кроме того, планировщик в настоящее время тоже работает.

Я пробовал использовать этот Stackoverflow Сообщение по той же теме, но безрезультатно.

Вот код, который у меня есть:

from airflow import DAG
from airflow.operators import SimpleHttpOperator, HttpSensor, EmailOperator, S3KeySensor
from datetime import datetime, timedelta
from airflow.operators.bash_operator import BashOperator

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2018, 5, 29),
    'email': ['[email protected]'],
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 5,
    'retry_delay': timedelta(minutes=5)
}

dag = DAG('myDag', default_args=default_args, schedule_interval= '@once')

t1 = BashOperator(
    task_id='my_t1_id',
    bash_command='echo "Dag Ran Successfully!" >> /home/ec2-user/output.txt',
    dag=dag)

sensor = S3KeySensor(
    task_id='my_sensor_id',
    bucket_key='*',
    wildcard_match=True,
    bucket_name='foobar',
    s3_conn_id='s3://foobar',
    timeout=18*60*60,
    poke_interval=120,
    dag=dag)

t1.set_upstream(sensor)

И, если нужно, вот мой файл airflow.cfg (обратите внимание, что я изменил только строки executor = LocalExecutor и sql_alchemy_conn = postgresql+psycopg2://postgres:password@localhost/airflow_meta_db

[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /home/ec2-user/airflow

# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /home/ec2-user/airflow/dags

# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /home/ec2-user/airflow/logs

# Airflow can store logs remotely in AWS S3 or Google Cloud Storage. Users
# must supply an Airflow connection id that provides access to the storage
# location.
remote_log_conn_id =
encrypt_s3_logs = False

# Logging level
logging_level = INFO

# Logging class
# Specify the class that will specify the logging configuration
# This class has to be on the python classpath
# logging_config_class = my.path.default_local_settings.LOGGING_CONFIG
logging_config_class =

# Log format
log_format = [%%(asctime)s] {%%(filename)s:%%(lineno)d} %%(levelname)s - %%(message)s
simple_log_format = %%(asctime)s %%(levelname)s - %%(message)s

# The executor class that airflow should use. Choices include
# SequentialExecutor, LocalExecutor, CeleryExecutor, DaskExecutor
#executor = SequentialExecutor
executor = LocalExecutor

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
#sql_alchemy_conn = sqlite:////home/ec2-user/airflow/airflow.db
sql_alchemy_conn = postgresql+psycopg2://postgres:password@localhost/airflow_meta_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 = True

# Where your Airflow plugins are stored
plugins_folder = /home/ec2-user/airflow/plugins

# Secret key to save connection passwords in the db
fernet_key = ibwZ5uSASmZGphBmwdJ4BIhd1-5WZXMTTgMF9u1_dGM=

# 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

# Name of handler to read task instance logs.
# Default to use file task handler.
task_log_reader = file.task

# Whether to enable pickling for xcom (note that this is insecure and allows for
# RCE exploits). This will be deprecated in Airflow 2.0 (be forced to False).
enable_xcom_pickling = True

# When a task is killed forcefully, this is the amount of time in seconds that
# it has to cleanup after it is sent a SIGTERM, before it is SIGKILLED
killed_task_cleanup_time = 60

[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:8080

[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: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 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 = 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

# 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

[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 = [email protected]


[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 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 listen on.
default_queue = default

# Import path for celery configuration options
celery_config_options = airflow.config_templates.default_celery.DEFAULT_CELERY_CONFIG

[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


[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

# 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/ec2-user/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 = True

# This changes the batch size of queries in the scheduling main loop.
# This depends on query length limits and how long you are willing to hold locks.
# 0 for no limit
max_tis_per_query = 0

# 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.
max_threads = 2

authenticate = 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

[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 = False

Выход планировщика воздушного потока:

[2018-05-31 21:15:12,056] {jobs.py:1504} INFO -
================================================================================
DAG File Processing Stats

File Path                                                         PID  Runtime    Last Runtime    Last Run
--------------------------------------------------------------  -----  ---------  --------------  -------------------
/home/ec2-user/airflow/dags/Test_Dag_Create_EMR.py                                1.00s           2018-05-31T21:15:12
/home/ec2-user/airflow/dags/s3_triggered_emr_cluster_dag.py     19214  0.01s      1.00s           2018-05-31T21:15:10
/home/ec2-user/airflow/dags/myDag.py                                              1.00s           2018-05-31T21:15:11
/home/ec2-user/airflow/dags/s3_sensor_connection_test.py                          1.01s           2018-05-31T21:15:11
/home/ec2-user/airflow/dags/three_s3_triggers_then_emr_work.py  19213  0.01s      1.01s           2018-05-31T21:15:10
================================================================================
[2018-05-31 21:15:12,112] {jobs.py:1742} INFO - Processing file /home/ec2-user/airflow/dags/three_s3_triggers_then_emr_work.py for tasks to queue
[2018-05-31 21:15:12,112] {models.py:189} INFO - Filling up the DagBag from /home/ec2-user/airflow/dags/three_s3_triggers_then_emr_work.py
[2018-05-31 21:15:12,118] {jobs.py:1742} INFO - Processing file /home/ec2-user/airflow/dags/s3_triggered_emr_cluster_dag.py for tasks to queue
[2018-05-31 21:15:12,118] {models.py:189} INFO - Filling up the DagBag from /home/ec2-user/airflow/dags/s3_triggered_emr_cluster_dag.py
[2018-05-31 21:15:12,173] {jobs.py:1754} INFO - DAG(s) dict_keys(['example_trigger_controller_dag', 'example_python_operator', 'example_skip_dag', 'test_utils', 'example_xcom', 'example_passing_params_via_test_command', 'latest_only', 'example_trigger_target_dag', 'example_branch_operator', 'example_http_operator', 'example_branch_dop_operator_v3', 'example_subdag_operator', 'example_subdag_operator.section-1', 'example_subdag_operator.section-2', 'latest_only_with_trigger', 'example_bash_operator', 'tutorial', 'example_short_circuit_operator', 's3_triggered_emr_cluster_dag']) retrieved from /home/ec2-user/airflow/dags/s3_triggered_emr_cluster_dag.py
[2018-05-31 21:15:12,173] {jobs.py:1754} INFO - DAG(s) dict_keys(['example_trigger_controller_dag', 'example_python_operator', 'example_skip_dag', 'test_utils', 'example_xcom', 'example_passing_params_via_test_command', 'latest_only', 'example_trigger_target_dag', 'example_branch_operator', 'example_http_operator', 'example_branch_dop_operator_v3', 'example_subdag_operator', 'example_subdag_operator.section-1', 'example_subdag_operator.section-2', 'latest_only_with_trigger', 'example_bash_operator', 'tutorial', 'example_short_circuit_operator', 'three_s3_triggers_then_emr_work']) retrieved from /home/ec2-user/airflow/dags/three_s3_triggers_then_emr_work.py
[2018-05-31 21:15:12,309] {models.py:341} INFO - Finding 'running' jobs without a recent heartbeat
[2018-05-31 21:15:12,309] {models.py:345} INFO - Failing jobs without heartbeat after 2018-05-31 21:10:12.309615
[2018-05-31 21:15:12,311] {models.py:341} INFO - Finding 'running' jobs without a recent heartbeat
[2018-05-31 21:15:12,311] {models.py:345} INFO - Failing jobs without heartbeat after 2018-05-31 21:10:12.311879
[2018-05-31 21:15:12,314] {jobs.py:375} INFO - Processing /home/ec2-user/airflow/dags/three_s3_triggers_then_emr_work.py took 0.267 seconds
[2018-05-31 21:15:12,316] {jobs.py:375} INFO - Processing /home/ec2-user/airflow/dags/s3_triggered_emr_cluster_dag.py took 0.265 seconds
[2018-05-31 21:15:13,057] {jobs.py:1627} INFO - Heartbeating the process manager
[2018-05-31 21:15:13,057] {dag_processing.py:468} INFO - Processor for /home/ec2-user/airflow/dags/three_s3_triggers_then_emr_work.py finished
[2018-05-31 21:15:13,057] {dag_processing.py:468} INFO - Processor for /home/ec2-user/airflow/dags/s3_triggered_emr_cluster_dag.py finished
[2018-05-31 21:15:13,060] {dag_processing.py:537} INFO - Started a process (PID: 19219) to generate tasks for /home/ec2-user/airflow/dags/s3_sensor_connection_test.py
[2018-05-31 21:15:13,062] {dag_processing.py:537} INFO - Started a process (PID: 19220) to generate tasks for /home/ec2-user/airflow/dags/myDag.py
[2018-05-31 21:15:13,063] {jobs.py:1662} INFO - Heartbeating the executor
[2018-05-31 21:15:13,064] {jobs.py:368} INFO - Started process (PID=19219) to work on /home/ec2-user/airflow/dags/s3_sensor_connection_test.py
[2018-05-31 21:15:13,068] {jobs.py:368} INFO - Started process (PID=19220) to work on /home/ec2-user/airflow/dags/myDag.py
[2018-05-31 21:15:13,130] {jobs.py:1742} INFO - Processing file /home/ec2-user/airflow/dags/s3_sensor_connection_test.py for tasks to queue
[2018-05-31 21:15:13,130] {models.py:189} INFO - Filling up the DagBag from /home/ec2-user/airflow/dags/s3_sensor_connection_test.py
[2018-05-31 21:15:13,134] {jobs.py:1742} INFO - Processing file /home/ec2-user/airflow/dags/myDag.py for tasks to queue
[2018-05-31 21:15:13,134] {models.py:189} INFO - Filling up the DagBag from /home/ec2-user/airflow/dags/myDag.py
[2018-05-31 21:15:13,189] {jobs.py:1754} INFO - DAG(s) dict_keys(['example_trigger_controller_dag', 'example_python_operator', 'example_skip_dag', 'test_utils', 'example_xcom', 'example_passing_params_via_test_command', 'latest_only', 'example_trigger_target_dag', 'example_branch_operator', 'example_http_operator', 'example_branch_dop_operator_v3', 'example_subdag_operator', 'example_subdag_operator.section-1', 'example_subdag_operator.section-2', 'latest_only_with_trigger', 'example_bash_operator', 'tutorial', 'example_short_circuit_operator', 'myDag']) retrieved from /home/ec2-user/airflow/dags/myDag.py
[2018-05-31 21:15:13,315] {models.py:341} INFO - Finding 'running' jobs without a recent heartbeat
[2018-05-31 21:15:13,316] {models.py:345} INFO - Failing jobs without heartbeat after 2018-05-31 21:10:13.316206
[2018-05-31 21:15:13,321] {jobs.py:375} INFO - Processing /home/ec2-user/airflow/dags/s3_sensor_connection_test.py took 0.257 seconds
[2018-05-31 21:15:13,333] {models.py:341} INFO - Finding 'running' jobs without a recent heartbeat
[2018-05-31 21:15:13,334] {models.py:345} INFO - Failing jobs without heartbeat after 2018-05-31 21:10:13.334021
[2018-05-31 21:15:13,338] {jobs.py:375} INFO - Processing /home/ec2-user/airflow/dags/myDag.py took 0.270 seconds
[2018-05-31 21:15:14,065] {jobs.py:1627} INFO - Heartbeating the process manager
[2018-05-31 21:15:14,066] {dag_processing.py:468} INFO - Processor for /home/ec2-user/airflow/dags/s3_sensor_connection_test.py finished
[2018-05-31 21:15:14,066] {dag_processing.py:468} INFO - Processor for /home/ec2-user/airflow/dags/myDag.py finished
[2018-05-31 21:15:14,068] {dag_processing.py:537} INFO - Started a process (PID: 19225) to generate tasks for /home/ec2-user/airflow/dags/Test_Dag_Create_EMR.py
[2018-05-31 21:15:14,069] {jobs.py:1662} INFO - Heartbeating the executor
[2018-05-31 21:15:14,072] {jobs.py:368} INFO - Started process (PID=19225) to work on /home/ec2-user/airflow/dags/Test_Dag_Create_EMR.py
[2018-05-31 21:15:14,187] {jobs.py:1742} INFO - Processing file /home/ec2-user/airflow/dags/Test_Dag_Create_EMR.py for tasks to queue
[2018-05-31 21:15:14,188] {models.py:189} INFO - Filling up the DagBag from /home/ec2-user/airflow/dags/Test_Dag_Create_EMR.py
[2018-05-31 21:15:14,239] {jobs.py:1754} INFO - DAG(s) dict_keys(['example_trigger_controller_dag', 'example_python_operator', 'example_skip_dag', 'test_utils', 'example_xcom', 'example_passing_params_via_test_command', 'latest_only', 'example_trigger_target_dag', 'example_branch_operator', 'example_http_operator', 'example_branch_dop_operator_v3', 'example_subdag_operator', 'example_subdag_operator.section-1', 'example_subdag_operator.section-2', 'latest_only_with_trigger', 'example_bash_operator', 'tutorial', 'example_short_circuit_operator', 'kyles_dag']) retrieved from /home/ec2-user/airflow/dags/Test_Dag_Create_EMR.py
[2018-05-31 21:15:14,366] {models.py:341} INFO - Finding 'running' jobs without a recent heartbeat
[2018-05-31 21:15:14,366] {models.py:345} INFO - Failing jobs without heartbeat after 2018-05-31 21:10:14.366593
[2018-05-31 21:15:14,371] {jobs.py:375} INFO - Processing /home/ec2-user/airflow/dags/Test_Dag_Create_EMR.py took 0.299 seconds
[2018-05-31 21:15:15,071] {jobs.py:1627} INFO - Heartbeating the process manager

Примечание: я не думаю, что это очень актуально для этого вопроса, но я использую Airflow на Amazon EC2-Instance.