we have added ssh connection in airflow connections, while testing it we get error like
'Hook SSHHook doesn't implement or inherit test_connection method'
Test button in the UI works only with Hooks that implemented test_connection. This means that you can not test the connection with the UI and you will have to create some DAG to test it.
In newer airflow versions the button will be disabled for connections/hooks that doesn't support this functionality (See PR)
Related
I am using AWS MWAA (Managed Workflow for Apache Airflow) and setting up monitoring for the same.
Airflow Version: v2.2.2
Metric: dag_processing.import_errors
My airflow setup is being used by multiple teams to execute their DAGs and so I wanted to ensure that there are no breaking changes pushed to Airflow. This is the reason that I have setup an alert on the metric.
The metric gives me import errors intermittently (2-3 times a day). At that particular time there is no import error available on the UI nor any errors in dag_processing logs. The DAG themselves are running fine without any issue/s. I raised AWS support for the same and did not get anything fruitful as they said that they have no control on the metrics that are being emitted from the open source Airflow.
Any suggestions on why this could happen or even what steps could be done to debug it further would be really helpful. Without any error/s, I am essentially stuck.
I have setup airflow in my local machine. I am trying to access the below airflow link:
http://localhost:8080/api/experimental/test/
I am getting Airflow 404 = lots of circles
I have tried to set auth_backend to default, but no luck.
What changes do i need to make in airflow.cfg to be able to make REST API calls to airflow for triggering DAGs?
Experimental API is disabled by default in Airlfow 2. It was used in 1.10 but it has been deprecated and disabled by default in Airflow 2. Instead you should use the fully-fledged REST API which uses completely different URL scheme:
https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html
In Airflow UI you can even browse and try the API (just look at the menus of Airflow).
When you trigger an Airflow DAG either through the UI (see screenshot) or the API (https://airflow.apache.org/docs/stable/rest-api-ref.html), you have the option of submitting a JSON configuration. However the usefulness of this isn't clearly documented as far as I can tell. I have two basic questions:
Is this intended for free-form configuration settings at the application level, or is this only for Airflow configuration variables?
If this is for free-form configuration settings, how (in my code) can I access whatever configuration was passed when the DAG was triggered?
Here is the screenshot where you can provide configuration when triggering a DAG:
Yes it is intended for Application level configuration.
Example -
{"appConfig":"Test"}
To read it in your DAG
def read_app_configuration(**kwargs):
print("Read App Config - Task : Start")
dag_run = kwargs['dag_run']
region = kwargs['dag_run'].conf.get('appConfig')
I just started using Airflow to coordinate our ETL pipeline.
I encountered the pipe error when I run a dag.
I've seen a general stackoverflow discussion here.
My case is more on the Airflow side. According to the discussion in that post, the possible root cause is:
The broken pipe error usually occurs if your request is blocked or
takes too long and after request-side timeout, it'll close the
connection and then, when the respond-side (server) tries to write to
the socket, it will throw a pipe broken error.
This might be the real cause in my case, I have a pythonoperator that will start another job outside of Airflow, and that job could be very lengthy (i.e. 10+ hours), I wonder if what is the mechanism in place in Airflow that I can leverage to prevent this error.
Can anyone help?
UPDATE1 20190303-1:
Thanks to #y2k-shubham for the SSHOperator, I am able to use it to set up a SSH connection successfully and am able to run some simple commands on the remote site (indeed the default ssh connection has to be set to localhost because the job is on the localhost) and am able to see the correct result of hostname, pwd.
However, when I attempted to run the actual job, I received same error, again, the error is from the jpipeline ob instead of the Airflow dag/task.
UPDATE2: 20190303-2
I had a successful run (airflow test) with no error, and then followed another failed run (scheduler) with same error from pipeline.
While I'd suggest you keep looking for a more graceful way of trying to achieve what you want, I'm putting up example usage as requested
First you've got to create an SSHHook. This can be done in two ways
The conventional way where you supply all requisite settings like host, user, password (if needed) etc from the client code where you are instantiating the hook. Im hereby citing an example from test_ssh_hook.py, but you must thoroughly go through SSHHook as well as its tests to understand all possible usages
ssh_hook = SSHHook(remote_host="remote_host",
port="port",
username="username",
timeout=10,
key_file="fake.file")
The Airflow way where you put all connection details inside a Connection object that can be managed from UI and only pass it's conn_id to instantiate your hook
ssh_hook = SSHHook(ssh_conn_id="my_ssh_conn_id")
Of course, if your'e relying on SSHOperator, then you can directly pass the ssh_conn_id to operator.
ssh_operator = SSHOperator(ssh_conn_id="my_ssh_conn_id")
Now if your'e planning to have a dedicated task for running a command over SSH, you can use SSHOperator. Again I'm citing an example from test_ssh_operator.py, but go through the sources for a better picture.
task = SSHOperator(task_id="test",
command="echo -n airflow",
dag=self.dag,
timeout=10,
ssh_conn_id="ssh_default")
But then you might want to run a command over SSH as a part of your bigger task. In that case, you don't want an SSHOperator, you can still use just the SSHHook. The get_conn() method of SSHHook provides you an instance of paramiko SSHClient. With this you can run a command using exec_command() call
my_command = "echo airflow"
stdin, stdout, stderr = ssh_client.exec_command(
command=my_command,
get_pty=my_command.startswith("sudo"),
timeout=10)
If you look at SSHOperator's execute() method, it is a rather complicated (but robust) piece of code trying to achieve a very simple thing. For my own usage, I had created some snippets that you might want to look at
For using SSHHook independently of SSHOperator, have a look at ssh_utils.py
For an operator that runs multiple commands over SSH (you can achieve the same thing by using bash's && operator), see MultiCmdSSHOperator
I have recently started using spark and I want to run spark job from Spring web application.
I have a situation where I am running web application in Tomcat server using Spring boot.My web application receives a REST web service request based on that It needs to trigger spark calculation job in Yarn cluster. Since my job can take longer to run and can access data from HDFS, so I want to run the spark job in yarn-cluster mode and I don't want to keep spark context alive in my web layer. One other reason for this is my application is multi tenant so each tenant can run it's own job, so in yarn-cluster mode each tenant's job can start it's own driver and run in it's own spark cluster. In web app JVM, I assume I can't run multiple spark context in one JVM.
I want to trigger spark jobs in yarn-cluster mode from java program in the my web application. what is the best way to achieve this. I am exploring various options and looking your guidance on which one is best
1) I can use spark-submit command line shell to submit my jobs. But to trigger it from my web application I need to use either Java ProcessBuilder api or some package built on java ProcessBuilder. This has 2 issues. First it doesn't sound like a clean way of doing it. I should have a programatic way of triggering my spark applications. Second problem will be I will loose the capability of monitoring the submitted application and getting it's status.. Only crude way of doing it is reading the output stream of spark-submit shell, which again doesn't sound like good approach.
2) I tried using Yarn client to submit the job from spring application. Following is the code that I use to submit spark job using Yarn Client:
Configuration config = new Configuration();
System.setProperty("SPARK_YARN_MODE", "true");
SparkConf conf = new SparkConf();
ClientArguments cArgs = new ClientArguments(sparkArgs, conf);
Client client = new Client(cArgs, config, conf);
client.run();
But when I run the above code, it tries to connect on localhost only. I get this error:
5/08/05 14:06:10 INFO Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 0 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS) 15/08/05 14:06:12 INFO Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 1 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)
So I don't think it can connect to remote machine.
Please suggest, what is best way of doing this with latest version of spark. Later I have plans to deploy this entire application in amazon EMR. So approach should work there also.
Thanks in advance
Spark JobServer might help:https://github.com/spark-jobserver/spark-jobserver, this project receives RESTful web requests and start a spark job. Results is returned as json response.
I also had similar issues trying to run Spark app that connects to YARN cluster - having no cluster config it was trying to connect to the local machine as for the main node of the cluster, which obviously failed.
It worked for me when I've placed core-site.xml and yarn-site.xml into the classpath (src/main/resources in typical sbt or Maven project structure) - application correctly connected to the cluster.
When using spark-submit location of those files is typically specified by HADOOP_CONF_DIR environment variable, but for stand-alone application it didn't have effect.