AirFlow常见问题汇总

airflow常见问题的排查记录如下:

1，airflow怎么批量`unpause`大量的dag任务

普通少量任务可以通过命令airflow unpause dag_id命令来启动，或者在web界面点击启动按钮实现，但是当任务过多的时候，一个个任务去启动就比较麻烦。其实dag信息是存储在数据库中的，可以通过批量修改数据库信息来达到批量启动dag任务的效果。假如是用mysql作为sql_alchemy_conn，那么只需要登录airflow数据库，然后更新表dag的is_paused字段为0即可启动dag任务。

示例: update dag set is_paused = 0 where dag_id like "benchmark%";

2，airflow的scheduler进程在执行一个任务后就挂起进入假死状态

出现这个情况的一般原因是scheduler调度器生成了任务，但是无法发布出去。而日志中又没有什么错误信息。

可能原因是Borker连接依赖库没安装：

如果是redis作为broker则执行pip install apache‐airflow[redis]

如果是rabbitmq作为broker则执行pip install apache-airflow[rabbitmq]

还有要排查scheduler节点是否能正常访问rabbitmq。

3，当定义的`dag`文件过多的时候，airflow的scheduler节点运行效率缓慢

airflow的scheduler默认是起两个线程，可以通过修改配置文件airflow.cfg改进:

[scheduler]

# The scheduler can run multiple threads in parallel to schedule dags.

# This defines how many threads will run.

#默认是2这里改为100

max_threads = 100

4，airflow日志级别更改

$ vi airflow.cfg

[core]

#logging_level = INFO

logging_level = WARNING

NOTSET < DEBUG < INFO < WARNING < ERROR < CRITICAL

如果把log的级别设置为INFO，那么小于INFO级别的日志都不输出，大于等于INFO级别的日志都输出。也就是说，日志级别越高，打印的日志越不详细。默认日志级别为WARNING。

注意: 如果将logging_level改为WARNING或以上级别，则不仅仅是日志，命令行输出明细也会同样受到影响，也只会输出大于等于指定级别的信息，所以如果命令行输出信息不全且系统无错误日志输出，那么说明是日志级别过高导致的。

5，AirFlow: jinja2.exceptions.TemplateNotFound

这是由于airflow使用了jinja2作为模板引擎导致的一个陷阱，当使用bash命令的时候，尾部必须加一个空格:

Described here : see below. You need to add a space after the script name in cases where you are directly calling a bash scripts in the bash_command attribute of BashOperator - this is because the Airflow tries to apply a Jinja template to it, which will fail.

t2 = BashOperator(

task_id='sleep',

bash_command="/home/batcher/test.sh", // This fails with `Jinja template not found` error

#bash_command="/home/batcher/test.sh ", // This works (has a space after)

dag=dag)

参考链接:

https://stackoverflow.com/questions/42147514/templatenotfound-error-when-running-simple-airflow-bashoperator

https://cwiki.apache.org/confluence/display/AIRFLOW/Common+Pitfalls

6，AirFlow: Task is not able to be run

任务执行一段时间后突然无法执行，后台worker日志显示如下提示:

[2018-05-25 17:22:05,068] {jobs.py:2508} INFO - Task is not able to be run

查看任务对应的执行日志:

cat /home/py/airflow-home/logs/testBashOperator/print_date/2018-05-25T00:00:00/6.log

...

[2018-05-25 17:22:05,067] {models.py:1190} INFO - Dependencies not met for <TaskInstance: testBashOperator.print_date 2018-05-25 00:00:00 [success]>,

dependency 'Task Instance State' FAILED: Task is in the 'success' state which is not a valid state for execution. The task must be cleared in order to be run.

根据错误提示，说明依赖任务状态失败，针对这种情况有两种解决办法:

使用airflow run运行task的时候指定忽略依赖task:
```
$ airflow run -A dag_id task_id execution_date
```
使用命令airflow clear dag_id进行任务清理:
```
$ airflow clear -u testBashOperator
```

7，CELERY: PRECONDITION_FAILED - inequivalent arg 'x-expires' for queue 'celery@xxxx.celery.pidbox' in vhost ''

在升级celery 4.x以后使用rabbitmq为broker运行任务抛出如下异常:

[2018-06-29 09:32:14,622: CRITICAL/MainProcess] Unrecoverable error: PreconditionFailed(406, "PRECONDITION_FAILED - inequivalent arg 'x-expires' for queue 'celery@PQ

SZ-L01395.celery.pidbox' in vhost '/': received the value '10000' of type 'signedint' but current is none", (50, 10), 'Queue.declare')

Traceback (most recent call last):

  File "c:\programdata\anaconda3\lib\site-packages\celery\worker\worker.py", line 205, in start

    self.blueprint.start(self)

.......

  File "c:\programdata\anaconda3\lib\site-packages\amqp\channel.py", line 277, in _on_close

    reply_code, reply_text, (class_id, method_id), ChannelError,

amqp.exceptions.PreconditionFailed: Queue.declare: (406) PRECONDITION_FAILED - inequivalent arg 'x-expires' for queue 'celery@PQSZ-L01395.celery.pidbox' in vhost '/'

: received the value '10000' of type 'signedint' but current is none

出现该错误的原因一般是因为rabbitmq的客户端和服务端参数不一致导致的，将其参数保持一致即可。

比如这里提示是x-expires 对应的celery中的配置是control_queue_expires。因此只需要在配置文件中加上control_queue_expires = None即可。

在celery 3.x中是没有这两项配置的，在4.x中必须保证这两项配置的一致性，不然就会抛出如上的异常。

我这里遇到的了两个rabbitmq的配置与celery配置的映射关系如下表：

rabbitmq	celery4.x
x-expires	control_queue_expires
x-message-ttl	control_queue_ttl

8，CELERY: The AMQP result backend is scheduled for deprecation in version 4.0 and removal in version v5.0.Please use RPC backend or a persistent backend

celery升级到4.x之后运行抛出如下异常:

/anaconda/anaconda3/lib/python3.6/site-packages/celery/backends/amqp.py:67: CPendingDeprecationWarning:

    The AMQP result backend is scheduled for deprecation in     version 4.0 and removal in version v5.0.     Please use RPC backend or a persistent backend.

  alternative='Please use RPC backend or a persistent backend.')

原因解析:

在celery 4.0中 rabbitmq 配置result_backbend方式变了:

以前是跟broker一样:

result_backend = 'amqp://guest:guest@localhost:5672//'

现在对应的是rpc配置:

result_backend = 'rpc://'

参考链接:

http://docs.celeryproject.org/en/latest/userguide/configuration.html#std:setting-event_queue_prefix

9，CELERY: ValueError('not enough values to unpack (expected 3, got 0)',)

windows上运行celery 4.x抛出以下错误:

[2018-07-02 10:54:17,516: ERROR/MainProcess] Task handler raised error: ValueError('not enough values to unpack (expected 3, got 0)',)

Traceback (most recent call last):

	......

    tasks, accept, hostname = _loc

ValueError: not enough values to unpack (expected 3, got 0)

celery 4.x暂时不支持windows平台，如果为了调试目的的话，可以通过替换celery的线程池实现以达到在windows平台上运行的目的:

pip install eventlet

celery -A <module> worker -l info -P eventlet

参考链接:

https://stackoverflow.com/questions/45744992/celery-raises-valueerror-not-enough-values-to-unpack

https://blog.csdn.net/qq_30242609/article/details/79047660

10，Airflow: ERROR - 'DisabledBackend' object has no attribute '_get_task_meta_for'

airflow运行中抛出以下异常:

Traceback (most recent call last):

  File "/anaconda/anaconda3/lib/python3.6/site-packages/airflow/executors/celery_executor.py", line 83, in sync

......

    return self._maybe_set_cache(self.backend.get_task_meta(self.id))

  File "/anaconda/anaconda3/lib/python3.6/site-packages/celery/backends/base.py", line 307, in get_task_meta

    meta = self._get_task_meta_for(task_id)

AttributeError: 'DisabledBackend' object has no attribute '_get_task_meta_for'

[2018-07-04 10:52:14,746] {celery_executor.py:101} ERROR - Error syncing the celery executor, ignoring it:

[2018-07-04 10:52:14,746] {celery_executor.py:102} ERROR - 'DisabledBackend' object has no attribute '_get_task_meta_for'

这种错误有两种可能原因:

CELERY_RESULT_BACKEND属性没有配置或者配置错误;
celery版本太低，比如airflow 1.9.0要使用celery4.x，所以检查celery版本，保持版本兼容;

11，airflow.exceptions.AirflowException dag_id could not be found xxxx. Either the dag did not exist or it failed to parse

查看worker日志 airflow-worker.err

airflow.exceptions.AirflowException: dag_id could not be found: bmhttp. Either the dag did not exist or it failed to parse.

[2018-07-31 17:37:34,191: ERROR/ForkPoolWorker-6] Task airflow.executors.celery_executor.execute_command[181c78d0-242c-4265-aabe-11d04887f44a] raised unexpected: AirflowException('Celery command failed',)

Traceback (most recent call last):

  File "/anaconda/anaconda3/lib/python3.6/site-packages/airflow/executors/celery_executor.py", line 52, in execute_command

    subprocess.check_call(command, shell=True)

  File "/anaconda/anaconda3/lib/python3.6/subprocess.py", line 291, in check_call

    raise CalledProcessError(retcode, cmd)

subprocess.CalledProcessError: Command 'airflow run bmhttp get_op1 2018-07-26T06:28:00 --local -sd /home/ignite/airflow/dags/BenchMark01.py' returned non-zero exit status 1.

通过异常日志中的Command信息得知, 调度节点在生成任务消息的时候同时也指定了要执行的脚本的路径(通过ds参数指定)，也就是说调度节点(scheduler)和工作节点(worker)相应的dag脚本文件必须置于相同的路径下面，不然就会出现以上错误。

参考链接:

https://stackoverflow.com/questions/43235130/airflow-dag-id-could-not-be-found

12，airlfow 的 REST API调用返回 `Airflow 404 = lots of circles`

出现这个错误的原因是因为URL中未提供origin参数，这个参数用于重定向，例如调用airflow的/run接口，可用示例如下所示:

http://localhost:8080/admin/airflow/run?dag_id=example_hello_world_dag&task_id=sleep_task&execution_date=20180807&ignore_all_deps=true&origin=/admin