Заполнение обратной засыпки воздушного потока, несмотря на то, что тест работает нормально

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Я пытаюсь выполнить DAG только с одним PythonOperator. Когда я пытаюсь проверить, он отлично работает, а также когда я пробовал Airflow без CeleryExecutor и работал нормально.


Но с ошибкой не возникает описательной ошибки, когда я пытаюсь выполнить ее залив в Airflow, который работает с CeleryExecutor:


airflow@ip:/home/admin$ airflow backfill REDSHIFT3 -s 2017-05-10
[2017-05-22 14:41:14,373] {__init__.py:57} INFO - Using executor CeleryExecutor
[2017-05-22 14:41:14,432] {driver.py:120} INFO - Generating grammar tables from /usr/lib/python2.7/lib2to3/Grammar.txt
[2017-05-22 14:41:14,452] {driver.py:120} INFO - Generating grammar tables from /usr/lib/python2.7/lib2to3/PatternGrammar.txt
[2017-05-22 14:41:14,616] {models.py:167} INFO - Filling up the DagBag from /usr/local/airflow/dags
[2017-05-22 14:41:14,994] {models.py:1126} INFO - Dependencies all met for <TaskInstance: REDSHIFT3.get_data_redshift 2017-05-10 00:00:00 [scheduled]>
[2017-05-22 14:41:15,000] {base_executor.py:50} INFO - Adding to queue: airflow run REDSHIFT3 get_data_redshift 2017-05-10T00:00:00 --pickle 81 --local
[2017-05-22 14:41:19,893] {celery_executor.py:78} INFO - [celery] queuing (u'REDSHIFT3', u'get_data_redshift', datetime.datetime(2017, 5, 10, 0, 0)) through celery, queue=default
[2017-05-22 14:41:20,598] {models.py:4024} INFO - Updating state for <DagRun REDSHIFT3 @ 2017-05-10 00:00:00: backfill_2017-05-10T00:00:00, externally triggered: False> considering 1 task(s)
[2017-05-22 14:41:20,607] {jobs.py:1978} INFO - [backfill progress] | finished run 0 of 1 | tasks waiting: 0 | succeeded: 0 | kicked_off: 1 | failed: 0 | skipped: 0 | deadlocked: 0 | not ready: 0
[2017-05-22 14:41:24,954] {jobs.py:1725} ERROR - Executor reports task instance <TaskInstance: REDSHIFT3.get_data_redshift 2017-05-10 00:00:00 [queued]> finished (failed) although the task says its queued. Was the task killed externally?
[2017-05-22 14:41:24,954] {models.py:1417} ERROR - Executor reports task instance <TaskInstance: REDSHIFT3.get_data_redshift 2017-05-10 00:00:00 [queued]> finished (failed) although the task says its queued. Was the task killed externally?
None
[2017-05-22 14:41:24,954] {models.py:1441} INFO - Marking task as FAILED.
[2017-05-22 14:41:25,037] {models.py:1462} ERROR - Executor reports task instance <TaskInstance: REDSHIFT3.get_data_redshift 2017-05-10 00:00:00 [queued]> finished (failed) although the task says its queued. Was the task killed externally?
[2017-05-22 14:41:25,042] {jobs.py:1690} ERROR - Task instance <TaskInstance: REDSHIFT3.get_data_redshift 2017-05-10 00:00:00 [failed]> failed
[2017-05-22 14:41:25,044] {models.py:4024} INFO - Updating state for <DagRun REDSHIFT3 @ 2017-05-10 00:00:00: backfill_2017-05-10T00:00:00, externally triggered: False> considering 1 task(s)
[2017-05-22 14:41:25,047] {models.py:4064} INFO - Marking run <DagRun REDSHIFT3 @ 2017-05-10 00:00:00: backfill_2017-05-10T00:00:00, externally triggered: False> failed
[2017-05-22 14:41:25,087] {jobs.py:1978} INFO - [backfill progress] | finished run 1 of 1 | tasks waiting: 0 | succeeded: 0 | kicked_off: 0 | failed: 1 | skipped: 0 | deadlocked: 0 | not ready: 0
Traceback (most recent call last):
File "/usr/local/bin/airflow", line 28, in <module>
args.func(args)
File "/usr/local/lib/python2.7/dist-packages/airflow/bin/cli.py", line 167, in backfill
pool=args.pool)
File "/usr/local/lib/python2.7/dist-packages/airflow/models.py", line 3330, in run
job.run()
File "/usr/local/lib/python2.7/dist-packages/airflow/jobs.py", line 200, in run
self._execute()
File "/usr/local/lib/python2.7/dist-packages/airflow/jobs.py", line 2021, in _execute
raise AirflowException(err)
airflow.exceptions.AirflowException: ---------------------------------------------------
Some task instances failed:
set([(u'REDSHIFT3', u'get_data_redshift', datetime.datetime(2017, 5, 10, 0, 0))])

Вот DAG, который я пытаюсь выполнить:


from __future__ import print_function    
from builtins import range
import airflow
from pprint import pprint
from airflow.operators.bash_operator import BashOperator
from airflow.hooks.postgres_hook import PostgresHook
from airflow.operators.python_operator import PythonOperator
from airflow.models import DAG

import time
from pprint import pprint

args = {
'owner': 'airflow',
'start_date': airflow.utils.dates.days_ago(2)
}

dag = DAG(
dag_id='REDSHIFT3', default_args=args,
schedule_interval=None)

def get_data(ds, **kwargs):
pprint(kwargs)

run_this = PythonOperator(
task_id='get_data_redshift',
provide_context=True,
python_callable=get_data,
dag=dag)

спросил(а) 2017-05-22T17:59:00+03:00 3 года, 6 месяцев назад
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