Paymahn Moghadasian created AIRFLOW-2105:
--------------------------------------------

             Summary: Exception on known event creation
                 Key: AIRFLOW-2105
                 URL: https://issues.apache.org/jira/browse/AIRFLOW-2105
             Project: Apache Airflow
          Issue Type: Bug
    Affects Versions: 1.9.0
            Reporter: Paymahn Moghadasian


I tried to create a known event through the UI and was shown the following 
error:


{noformat}
-------------------------------------------------------------------------------
Node: PaymahnSolvvy.local
-------------------------------------------------------------------------------
Traceback (most recent call last):
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/app.py",
 line 1988, in wsgi_app
    response = self.full_dispatch_request()
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/app.py",
 line 1641, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/app.py",
 line 1544, in handle_user_exception
    reraise(exc_type, exc_value, tb)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/_compat.py",
 line 33, in reraise
    raise value
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/app.py",
 line 1639, in full_dispatch_request
    rv = self.dispatch_request()
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/app.py",
 line 1625, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/base.py",
 line 69, in inner
    return self._run_view(f, *args, **kwargs)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/base.py",
 line 368, in _run_view
    return fn(self, *args, **kwargs)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/model/base.py",
 line 1947, in create_view
    return_url=return_url)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/base.py",
 line 308, in render
    return render_template(template, **kwargs)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/templating.py",
 line 134, in render_template
    context, ctx.app)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask/templating.py",
 line 116, in _render
    rv = template.render(context)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/jinja2/environment.py",
 line 989, in render
    return self.environment.handle_exception(exc_info, True)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/jinja2/environment.py",
 line 754, in handle_exception
    reraise(exc_type, exc_value, tb)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/jinja2/_compat.py",
 line 37, in reraise
    raise value.with_traceback(tb)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/airflow/www/templates/airflow/model_create.html",
 line 18, in top-level template code
    {% extends 'admin/model/create.html' %}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/model/create.html",
 line 3, in top-level template code
    {% from 'admin/lib.html' import extra with context %} {# backward 
compatible #}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/airflow/www/templates/admin/master.html",
 line 18, in top-level template code
    {% extends 'admin/base.html' %}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/base.html",
 line 30, in top-level template code
    {% block page_body %}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/airflow/www/templates/admin/master.html",
 line 104, in block "page_body"
    {% block body %}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/airflow/www/templates/airflow/model_create.html",
 line 28, in block "body"
    {{ super() }}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/model/create.html",
 line 22, in block "body"
    {% block create_form %}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/model/create.html",
 line 23, in block "create_form"
    {{ lib.render_form(form, return_url, extra(), form_opts) }}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/lib.html",
 line 202, in template
    {% call form_tag(action=action) %}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/lib.html",
 line 182, in template
    {{ caller() }}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/lib.html",
 line 203, in template
    {{ render_form_fields(form, form_opts=form_opts) }}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/lib.html",
 line 175, in template
    {{ render_field(form, f, kwargs) }}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/templates/bootstrap3/admin/lib.html",
 line 130, in template
    {{ field(**kwargs)|safe }}
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/wtforms/fields/core.py",
 line 153, in __call__
    return self.meta.render_field(self, kwargs)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/wtforms/meta.py",
 line 56, in render_field
    return field.widget(field, **render_kw)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/form/widgets.py",
 line 28, in __call__
    return super(Select2Widget, self).__call__(field, **kwargs)
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/wtforms/widgets/core.py",
 line 287, in __call__
    for val, label, selected in field.iter_choices():
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/contrib/sqla/fields.py",
 line 110, in iter_choices
    for pk, obj in self._get_object_list():
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/contrib/sqla/fields.py",
 line 103, in _get_object_list
    self._object_list = [(text_type(get_pk(obj)), obj) for obj in query]
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/contrib/sqla/fields.py",
 line 103, in <listcomp>
    self._object_list = [(text_type(get_pk(obj)), obj) for obj in query]
  File 
"/Users/paymahn/solvvy/scheduler/venv/lib/python3.6/site-packages/flask_admin/contrib/sqla/fields.py",
 line 300, in get_pk_from_identity
    cls, key = identity_key(instance=obj)
ValueError: too many values to unpack (expected 2)
{noformat}

My virtualenv looks like:

{noformat}
alembic==0.8.10
apache-airflow==1.9.0
asn1crypto==0.24.0
bleach==2.1.2
certifi==2018.1.18
cffi==1.11.4
chardet==3.0.4
click==6.7
configparser==3.5.0
croniter==0.3.20
cryptography==2.1.4
dill==0.2.7.1
docutils==0.14
fernet==1.0.1
Flask==0.11.1
Flask-Admin==1.4.1
Flask-Cache==0.13.1
Flask-Login==0.2.11
flask-swagger==0.2.13
Flask-WTF==0.14
funcsigs==1.0.0
future==0.16.0
gitdb2==2.0.3
GitPython==2.1.8
gunicorn==19.7.1
html5lib==1.0.1
idna==2.6
itsdangerous==0.24
Jinja2==2.8.1
lockfile==0.12.2
lxml==3.8.0
Mako==1.0.7
Markdown==2.6.11
MarkupSafe==1.0
numpy==1.14.0
ordereddict==1.1
pandas==0.22.0
psutil==4.4.2
psycopg2==2.7.4
psycopg2-binary==2.7.4
pyaes==1.6.1
pycparser==2.18
Pygments==2.2.0
python-daemon==2.1.2
python-dateutil==2.6.1
python-editor==1.0.3
python-nvd3==0.14.2
python-slugify==1.1.4
pytz==2018.3
PyYAML==3.12
requests==2.18.4
setproctitle==1.1.10
six==1.11.0
smmap2==2.0.3
SQLAlchemy==1.2.2
tabulate==0.7.7
thrift==0.11.0
Unidecode==1.0.22
urllib3==1.22
webencodings==0.5.1
Werkzeug==0.14.1
WTForms==2.1
zope.deprecation==4.3.0
{noformat}

My airflow.cfg looks like:

{noformat}
[core]
# The home folder for airflow, default is ~/airflow
airflow_home = /Users/paymahn/solvvy/scheduler/airflow_home

# The folder where your airflow pipelines live, most likely a
# subfolder in a code repository
# This path must be absolute
dags_folder = /Users/paymahn/solvvy/scheduler/airflow_home/dags

# The folder where airflow should store its log files
# This path must be absolute
base_log_folder = /Users/paymahn/solvvy/scheduler/airflow_home/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 = LocalExecutor

# The SqlAlchemy connection string to the metadata database.
# SqlAlchemy supports many different database engine, more information
# their website
# sql_alchemy_conn = 
sqlite:////Users/paymahn/solvvy/scheduler/airflow_home/airflow.db
sql_alchemy_conn = postgresql+psycopg2://airflow:airflow@localhost/postgres

# 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 = /Users/paymahn/solvvy/scheduler/airflow_home/plugins

# Secret key to save connection passwords in the db
fernet_key = pvHY8FTnk9VcN-LF8nKzuAr2PVclfQwKm4fhKQo_66k=

# 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 = airf...@example.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 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 = 
/Users/paymahn/solvvy/scheduler/airflow_home/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 = True

{noformat}



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