Troubleshooting: DAGs, Operators, Connections, and other issues in Apache Airflow v1

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

Add the following package to your requirements.txt.

boto

Explore ways to specify Python dependencies in a requirements.txt file, see Managing Python dependencies in requirements.txt.

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

Add the following to your requirements.txt with your Apache Airflow version. For example:

apache-airflow[postgres]==1.10.12

Explore ways to specify Python dependencies in a requirements.txt file, see Managing Python dependencies in requirements.txt.

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

Add the following package to your requirements.txt and specify your Apache Airflow version. For example:

apache-airflow[slack]==1.10.12

Explore ways to specify Python dependencies in a requirements.txt file, see Managing Python dependencies in requirements.txt.

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

Add the following package to your requirements.txt.

grpcio==1.27.2
cython==0.29.21
pandas-gbq==0.13.3
cryptography==3.3.2
apache-airflow-backport-providers-amazon[google]

If you’re not using backport providers, you can use:

grpcio==1.27.2
cython==0.29.21
pandas-gbq==0.13.3
cryptography==3.3.2
apache-airflow[gcp]==1.10.12

Explore ways to specify Python dependencies in a requirements.txt file, see Managing Python dependencies in requirements.txt.

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

Add the following package to your requirements.txt.

cython==0.29.21

Cython libraries have various required pip dependency versions. For example, using awswrangler==2.4.0 requires pyarrow<3.1.0,>=2.0.0, so pip3 tries to install pyarrow==3.0.0 which causes a Broken DAG error. We recommend specifying the oldest acceptible version explicity. For example, if you specify the minimum value pyarrow==2.0.0 before awswrangler==2.4.0 then the error goes away, and the requirements.txt installs correctly. The final requirements should look like this:

cython==0.29.21
pyarrow==2.0.0
awswrangler==2.4.0

Explore ways to specify Python dependencies in a requirements.txt file, see Managing Python dependencies in requirements.txt.

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

A workaround is to override the extension by adding a line in the DAG to set <operator name>.operator_extra_links = None after importing the problem operators. For example:

from airflow.contrib.operators.bigquery_operator import BigQueryOperator
BigQueryOperator.operator_extra_links = None

You can use this approach for all DAGs by adding the above to a plugin. For an example, see Creating a custom plugin for Apache Airflow PythonVirtualenvOperator.

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

Add the following entries to the requirements.txt for your environment.

asn1crypto == 0.24.0
snowflake-connector-python == 1.7.2

Add the following imports to your DAG:

from airflow.contrib.hooks.snowflake_hook import SnowflakeHook
from airflow.contrib.operators.snowflake_operator import SnowflakeOperator

Conn Id: snowflake_conn

Conn Type: Snowflake

Host: <my account>.<my region if not us-west-2>.snowflakecomputing.com

Schema: <my schema>

Login: <my user name>

Password: ********

Port: <port, if any>

Extra:

{
    "account": "<my account>",
    "warehouse": "<my warehouse>",
    "database": "<my database>",
    "region": "<my region if not using us-west-2 otherwise omit this line>"
}

Learn how to create secret keys for your Apache Airflow connection and variables in Configuring an Apache Airflow connection using a Amazon Secrets Manager secret.

Learn how to use the secret key for an Apache Airflow variable (test-variable) in Using a secret key in Amazon Secrets Manager for an Apache Airflow variable.

Learn how to use the secret key for an Apache Airflow connection (myconn) in Using a secret key in Amazon Secrets Manager for an Apache Airflow connection.

Modify the RDS security group to allow all traffic from Amazon MWAA's VPC security group.

Modify Amazon MWAA's VPC security group to allow all traffic from the RDS security group.

Rerun your tasks again and verify whether the SQL query succeeded by checking Apache Airflow logs in CloudWatch Logs.

Apache Airflow operators such as the BigQueryOperator and QuboleOperator that contain operator_extra_links could cause your Apache Airflow web server to crash. These operators attempt to load code to your web server, which is not permitted for security reasons. We recommend patching the operators in your DAG by adding the following code after your import statements:

BigQueryOperator.operator_extra_links = None

Test your DAGs, custom plugins, and Python dependencies locally using the aws-mwaa-local-runner on GitHub.

Check Apache Airflow configuration options. Verify that the key-value pairs you specified as an Apache Airflow configuration option, such as Amazon Secrets Manager, were configured correctly. To learn more, see I can't connect to Secrets Manager.

Check the requirements.txt. Verify the Airflow "extras" package and other libraries listed in your requirements.txt are compatible with your Apache Airflow version.

Explore ways to specify Python dependencies in a requirements.txt file, see Managing Python dependencies in requirements.txt.

Confirm that your VPC security group allows inbound access to port 5432. This port is needed to connect to the Amazon Aurora PostgreSQL metadata database for your environment. After this rule is added, give Amazon MWAA a few minutes, and the error should disappear. To learn more, see Security in your VPC on Amazon MWAA.

If the scheduler is not running, it might be due to a number of factors such as dependency installation failures, or an overloaded scheduler. Confirm that your DAGs, plugins, and requirements are working correctly by viewing the corresponding log groups in CloudWatch Logs. To learn more, see Monitoring and metrics for Amazon Managed Workflows for Apache Airflow.

There may be a large number of DAGs defined. Reduce the number of DAGs and perform an update of the environment (such as changing a log level) to force a reset.

1. Airflow parses DAGs whether they are enabled or not. If you're using greater than 50% of your environment's capacity you may start overwhelming the Apache Airflow Scheduler. This leads to large Total Parse Time in CloudWatch Metrics or long DAG processing times in CloudWatch Logs. There are other ways to optimize Apache Airflow configurations which are outside the scope of this guide.

2. To learn more about the best practices we recommend to tune the performance of your environment, see Performance tuning for Apache Airflow on Amazon MWAA.

There may be a large number of tasks in the queue. This often appears as a large—and growing—number of tasks in the "None" state, or as a large number in Queued Tasks and/or Tasks Pending in CloudWatch. This can occur for the following reasons:

1. If there are more tasks to run than the environment has the capacity to run, and/or a large number of tasks that were queued before autoscaling has time to detect the tasks and deploy additional Workers.

2. If there are more tasks to run than an environment has the capacity to run, we recommend reducing the number of tasks that your DAGs run concurrently, and/or increasing the minimum Apache Airflow Workers.

3. If there are a large number of tasks that were queued before autoscaling has had time to detect and deploy additional workers, we recommend staggering task deployment and/or increasing the minimum Apache Airflow Workers.

4. You can use the update-environment command in the Amazon Command Line Interface (Amazon CLI) to change the minimum or maximum number of Workers that run on your environment.

   aws mwaa update-environment --name MyEnvironmentName --min-workers 2 --max-workers 10

5. To learn more about the best practices we recommend to tune the performance of your environment, see Performance tuning for Apache Airflow on Amazon MWAA.

There may be tasks being deleted mid-execution that appear as task logs which stop with no further indication in Apache Airflow. This can occur for the following reasons:

1. If there is a brief moment where 1) the current tasks exceed current environment capacity, followed by 2) a few minutes of no tasks executing or being queued, then 3) new tasks being queued.

2. Amazon MWAA autoscaling reacts to the first scenario by adding additional workers. In the second scenario, it removes the additional workers. Some of the tasks being queued may result with the workers in the process of being removed, and will end when the container is deleted.

3. We recommend increasing the minimum number of workers on your environment. Another option is to adjust the timing of your DAGs and tasks to ensure that that these scenarios don't occur.

4. You can also set the minimum workers equal to the maximum workers on your environment, effectively disabling autoscaling. Use the update-environment command in the Amazon Command Line Interface (Amazon CLI) to disable autoscaling by setting the minimum and maximum number of workers to be the same.

   aws mwaa update-environment --name MyEnvironmentName --min-workers 5 --max-workers 5

5. To learn more about the best practices we recommend to tune the performance of your environment, see Performance tuning for Apache Airflow on Amazon MWAA.

If your tasks are stuck in the "running" state, you can also clear the tasks or mark them as succeeded or failed. This allows the autoscaling component for your environment to scale down the number of workers running on your environment. The following image shows an example of a stranded task.

1. Choose the circle for the stranded task, and then select Clear (as shown). This allows Amazon MWAA to scale down workers; otherwise, Amazon MWAA can't determine which DAGs are enabled or disabled, and can't scale down, if there are still queued tasks.

   

Learn more about the Apache Airflow task lifecycle at Concepts in the Apache Airflow reference guide.