AWS Big Data Blog

Optimize cost and performance for Amazon MWAA

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed service for Apache Airflow that allows you to orchestrate data pipelines and workflows at scale. With Amazon MWAA, you can design Directed Acyclic Graphs (DAGs) that describe your workflows without managing the operational burden of scaling the infrastructure. In this post, we provide guidance on how you can optimize performance and save cost by following best practices.

Amazon MWAA environments include four Airflow components hosted on groups of AWS compute resources: the scheduler that schedules the work, the workers that implement the work, the web server that provides the UI, and the metadata database that keeps track of state. For intermittent or varying workloads, optimizing costs while maintaining price and performance is crucial. This post outlines best practices to achieve cost optimization and efficient performance in Amazon MWAA environments, with detailed explanations and examples. It may not be necessary to apply all of these best practices for a given Amazon MWAA workload; you can selectively choose and implement relevant and applicable principles for your specific workloads.

Right-sizing your Amazon MWAA environment

Right-sizing your Amazon MWAA environment makes sure you have an environment that is able to concurrently scale across your different workloads to provide the best price-performance. The environment class you choose for your Amazon MWAA environment determines the size and the number of concurrent tasks supported by the worker nodes. In Amazon MWAA, you can choose from five different environment classes. In this section, we discuss the steps you can follow to right-size your Amazon MWAA environment.

Monitor resource utilization

The first step in right-sizing your Amazon MWAA environment is to monitor the resource utilization of your existing setup. You can monitor the underlying components of your environments using Amazon CloudWatch, which collects raw data and processes data into readable, near real-time metrics. With these environment metrics, you have greater visibility into key performance indicators to help you appropriately size your environments and debug issues with your workflows. Based on the concurrent tasks needed for your workload, you can adjust the environment size as well as the maximum and minimum workers needed. CloudWatch will provide CPU and memory utilization for all the underlying AWS services utilize by Amazon MWAA. Refer to Container, queue, and database metrics for Amazon MWAA for additional details on available metrics for Amazon MWAA. These metrics also include the number of base workers, additional workers, schedulers, and web servers.

Analyze your workload patterns

Next, take a deep dive into your workflow patterns. Examine DAG schedules, task concurrency, and task runtimes. Monitor CPU/memory usage during peak periods. Query CloudWatch metrics and Airflow logs. Identify long-running tasks, bottlenecks, and resource-intensive operations for optimal environment sizing. Understanding the resource demands of your workload will help you make informed decisions about the appropriate Amazon MWAA environment class to use.

Choose the right environment class

Match requirements to Amazon MWAA environment class specifications (mw1.small to mw1.2xlarge) that can handle your workload efficiently. You can vertically scale up or scale down an existing environment through an API, the AWS Command Line Interface (AWS CLI), or the AWS Management Console. Be aware that a change in the environment class requires a scheduled downtime.

Fine tune configuration parameters

Fine-tuning configuration parameters in Apache Airflow is crucial for optimizing workflow performance and cost reductions. It allows you to tune settings such as Auto scaling, parallelism, logging, and DAG code optimizations.

Auto scaling

Amazon MWAA supports worker auto scaling, which automatically adjusts the number of running worker and web server nodes based on your workload demands. You can specify the minimum and maximum number of Airflow workers that run in your environment. For worker node auto scaling, Amazon MWAA uses RunningTasks and QueuedTasks metrics, where (tasks running + tasks queued) / (tasks per worker) = (required workers). If the required number of workers is greater than the current number of running workers, Amazon MWAA will add additional worker instances using AWS Fargate, up to the maximum value specified by the maximum worker configuration.

Auto scaling in Amazon MWAA will gracefully downscale when there are more additional workers than required. For example, let’s assume a large Amazon MWAA environment with a minimum of 1 worker and a maximum of 10, where each large Amazon MWAA worker can support up to 20 tasks. Let’s say, each day at 8:00 AM, DAGs start up that use 190 concurrent tasks. Amazon MWAA will automatically scale to 10 workers, because the required workers = 190 requested tasks (some running, some queued) / 20 (tasks per worker) = 9.5 workers, rounded up to 10. At 10:00 AM, half of the tasks complete, leaving 85 running. Amazon MWAA will then downscale to 6 workers (95 tasks/20 tasks per worker = 5.25 workers, rounded up to 6). Any workers that are still running tasks remain protected during downscaling until they’re complete, and no tasks will be interrupted. As the queued and running tasks decrease, Amazon MWAA will remove workers without affecting running tasks, down to the minimum specified worker count.

Web server auto scaling in Amazon MWAA allows you to automatically scale the number of web servers based on CPU utilization and active connection count. Amazon MWAA makes sure your Airflow environment can seamlessly accommodate increased demand, whether from REST API requests, AWS CLI usage, or more concurrent Airflow UI users. You can specify the maximum and minimum web server count while configuring your Amazon MWAA environment.

Logging and metrics

In this section, we discuss the steps to select and set the appropriate log configurations and CloudWatch metrics.

Choose the right log levels

If enabled, Amazon MWAA will send Airflow logs to CloudWatch. You can view the logs to determine Airflow task delays or workflow errors without the need for additional third-party tools. You need to enable logging to view Airflow DAG processing, tasks, scheduler, web server, and worker logs. You can enable Airflow logs at the INFO, WARNING, ERROR, or CRITICAL level. When you choose a log level, Amazon MWAA sends logs for that level and higher levels of severity. Standard CloudWatch logs charges apply, so reducing log levels where possible can reduce overall costs. Use the most appropriate log level based on environment, such as INFO for dev and UAT, and ERROR for production.

Set appropriate log retention policy

By default, logs are kept indefinitely and never expire. To reduce CloudWatch cost, you can adjust the retention policy for each log group.

Choose required CloudWatch metrics

You can choose which Airflow metrics are sent to CloudWatch by using the Amazon MWAA configuration option metrics.statsd_allow_list. Refer to the complete list of available metrics. Some metrics such as schedule_delay and duration_success are published per DAG, whereas others such as ti.finish are published per task per DAG.

Therefore, the cumulative number of DAGs and tasks directly influence your CloudWatch metric ingestion costs. To control CloudWatch costs, choose to publish selective metrics. For example, the following will only publish metrics that start with scheduler and executor:

metrics.statsd_allow_list = scheduler,executor

We recommend using metrics.statsd_allow_list with metrics.metrics_use_pattern_match.

An effective practice is to utilize regular expression (regex) pattern matching against the entire metric name instead of only matching the prefix at the beginning of the name.

Monitor CloudWatch dashboards and set up alarms

Create a custom dashboard in CloudWatch and add alarms for a particular metric to monitor the health status of your Amazon MWAA environment. Configuring alarms allows you to proactively monitor the health of the environment.

Optimize AWS Secrets Manager invocations

Airflow has a mechanism to store secrets such as variables and connection information. By default, these secrets are stored in the Airflow meta database. Airflow users can optionally configure a centrally managed location for secrets, such as AWS Secrets Manager. When specified, Airflow will first check this alternate secrets backend when a connection or variable is requested. If the alternate backend contains the needed value, it is returned; if not, Airflow will check the meta database for the value and return that instead. One of the factors affecting the cost to use Secrets Manager is the number of API calls made to it.

On the Amazon MWAA console, you can configure the backend Secrets Manager path for the connections and variables that will be used by Airflow. By default, Airflow searches for all connections and variables in the configured backend. To reduce the number of API calls Amazon MWAA makes to Secrets Manager on your behalf, configure it to use a lookup pattern. By specifying a pattern, you narrow the possible paths that Airflow will look at. This will help in lowering your costs when using Secrets Manager with Amazon MWAA.

To use a secrets cache, enable AIRFLOW_SECRETS_USE_CACHE with TTL to help to reduce the Secrets Manager API calls.

For example, if you want to only look up a specific subset of connections, variables, or config in Secrets Manager, set the relevant *_lookup_pattern parameter. This parameter takes a regex as a string as value. To lookup connections starting with m in Secrets Manager, your configuration file should look like the following code:

[secrets]
backend = airflow.providers.amazon.aws.secrets.secrets_manager.SecretsManagerBackend
backend_kwargs =

{
  "connections_prefix": "airflow/connections",
  "connections_lookup_pattern": "^m",
  "profile_name": "default"
}

DAG code optimization

Schedulers and workers are two components that are involved in parsing the DAG. After the scheduler parses the DAG and places it in a queue, the worker picks up the DAG from the queue. At the point, all the worker knows is the DAG_id and the Python file, along with some other info. The worker has to parse the Python file in order to run the task.

DAG parsing is run twice, once by the scheduler and then by the worker. Because the workers are also parsing the DAG, the amount of time it takes for the code to parse dictates the number of workers needed, which adds cost of running those workers.

For example, for a total of 200 DAGs having 10 tasks each, taking 60 seconds per task to parse, we can calculate the following:

  • Total tasks across all DAGs = 2,000
  • Time per task = 60 seconds + 20 seconds (parse DAG)
  • Total time = 2000 * 80 = 160,000 seconds
  • Total time per worker = 72,000 seconds
  • Number of workers needs = Total time/Total time per worker = 160,000/72,000 = ~3

Now, let’s increase the time taken to parse the DAGs to 100 seconds:

  • Total tasks across all DAGs = 2,000
  • Time per task = 60 seconds + 100 seconds
  • Total time = 2,000 *160 = 320,000 seconds
  • Total time per worker = 72,000 seconds
  • Number of workers needs = Total time/Total time per worker = 320,000/72,000 = ~5

As you can see, when the DAG parsing time increased from 20 seconds to 100 seconds, the number of worker nodes needed increased from 3 to 5, thereby adding compute cost.

To reduce the time it takes for parsing the code, follow the best practices in the subsequent sections.

Remove top-level imports

Code imports will run every time the DAG is parsed. If you don’t need the libraries being imported to create the DAG objects, move the import to the task level instead of defining it at the top. After it’s defined in the task, the import will be called only when the task is run.

Avoid multiple calls to databases like the meta database or external system database. Variables are used within the DAG that are defined in the meta database or a backend system like Secrets Manager. Use templating (Jinja) wherein calls to populate the variables are only made at task runtime and not at task parsing time.

For example, see the following code:

import pendulum
from airflow import DAG
from airflow.decorators import task
import numpy as np  # <-- DON'T DO THAT!

with DAG(
    dag_id="example_python_operator",
    schedule=None,
    start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
    catchup=False,
    tags=["example"],
) as dag:

    @task()
    def print_array():
        """Print Numpy array."""
        import numpy as np  # <-- INSTEAD DO THIS!
        a = np.arange(15).reshape(3, 5)
        print(a)
        return a
    print_array()

The following code is another example:

# Bad example
from airflow.models import Variable

foo_var = Variable.get("foo")  # DON'T DO THAT

bash_use_variable_bad_1 = BashOperator(
    task_id="bash_use_variable_bad_1", bash_command="echo variable foo=${foo_env}", env={"foo_env": foo_var}
)

bash_use_variable_bad_2 = BashOperator(
    task_id="bash_use_variable_bad_2",
    bash_command=f"echo variable foo=${Variable.get('foo')}",  # DON'T DO THAT
)

bash_use_variable_bad_3 = BashOperator(
    task_id="bash_use_variable_bad_3",
    bash_command="echo variable foo=${foo_env}",
    env={"foo_env": Variable.get("foo")},  # DON'T DO THAT
)

# Good example
bash_use_variable_good = BashOperator(
    task_id="bash_use_variable_good",
    bash_command="echo variable foo=${foo_env}",
    env={"foo_env": "{{ var.value.get('foo') }}"},
)

@task
def my_task():
    var = Variable.get("foo")  # this is fine, because func my_task called only run task, not scan DAGs.
print(var)

Writing DAGs

Complex DAGs with a large number of tasks and dependencies between them can impact performance of scheduling. One way to keep your Airflow instance performant and well utilized is to simplify and optimize your DAGs.

For example, a DAG that has simple linear structure A → B → C will experience less delays in task scheduling than a DAG that has a deeply nested tree structure with an exponentially growing number of dependent tasks.

Dynamic DAGs

In the following example, a DAG is defined with hardcoded table names from a database. A developer has to define N number of DAGs for N number of tables in a database.

# Bad example
dag_params = getData()
no_of_dags = int(dag_params["no_of_dags"]['N'])
# build a dag for each number in no_of_dags
for n in range(no_of_dags):
    dag_id = 'dynperf_t1_{}'.format(str(n))
default_args = {'owner': 'airflow','start_date': datetime(2022, 2, 2, 12, n)}

To reduce verbose and error-prone work, use dynamic DAGs. The following definition of the DAG is created after querying a database catalog, and creates as many DAGs dynamically as there are tables in the database. This achieves the same objective with less code.

def getData():
    client = boto3.client('dynamodb’)
    response = client.get_item(
        TableName="mwaa-dag-creation",
        Key={'key': {'S': 'mwaa’}}
    )
    return response["Item"]

Stagger DAG schedules

Running all DAGs simultaneously or within a short interval in your environment can result in a higher number of worker nodes required to process the tasks, thereby increasing compute costs. For business scenarios where the workload is not time-sensitive, consider spreading the schedule of DAG runs in a way that maximizes the utilization of available worker resources.

DAG folder parsing

Simpler DAGs are usually only in a single Python file; more complex DAGs might be spread across multiple files and have dependencies that should be shipped with them. You can either do this all inside of the DAG_FOLDER , with a standard filesystem layout, or you can package the DAG and all of its Python files up as a single .zip file. Airflow will look into all the directories and files in the DAG_FOLDER. Using the .airflowignore file specifies which directories or files Airflow should intentionally ignore. This will increase the efficiency of finding a DAG within a directory, improving parsing times.

Deferrable operators

You can run deferrable operators on Amazon MWAA. Deferrable operators have the ability to suspend themselves and free up the worker slot. No tasks in the worker means fewer required worker resources, which can lower the worker cost.

For example, let’s assume you’re using a large number of sensors that wait for something to occur and occupy worker node slots. By making the sensors deferrable and using worker auto scaling improvements to aggressively downscale workers, you will immediately see an impact where fewer worker nodes are needed, saving on worker node costs.

Dynamic Task Mapping

Dynamic Task Mapping allows a way for a workflow to create a number of tasks at runtime based on current data, rather than the DAG author having to know in advance how many tasks would be needed. This is similar to defining your tasks in a for loop, but instead of having the DAG file fetch the data and do that itself, the scheduler can do this based on the output of a previous task. Right before a mapped task is run, the scheduler will create N copies of the task, one for each input.

Stop and start the environment

You can stop and start your Amazon MWAA environment based on your workload requirements, which will result in cost savings. You can perform the action manually or automate stopping and starting Amazon MWAA environments. Refer to Automating stopping and starting Amazon MWAA environments to reduce cost to learn how to automate the stop and start of your Amazon MWAA environment retaining metadata.

Conclusion

In conclusion, implementing performance optimization best practices for Amazon MWAA can significantly reduce overall costs while maintaining optimal performance and reliability. Key strategies include right-sizing environment classes based on CloudWatch metrics, managing logging and monitoring costs, using lookup patterns with Secrets Manager, optimizing DAG code, and selectively stopping and starting environments based on workload demands. Continuously monitoring and adjusting these settings as workloads evolve can maximize your cost-efficiency.

About the Authors

Sriharsh Adari is a Senior Solutions Architect at AWS, where he helps customers work backward from business outcomes to develop innovative solutions on AWS. Over the years, he has helped multiple customers on data platform transformations across industry verticals. His core area of expertise includes technology strategy, data analytics, and data science. In his spare time, he enjoys playing sports, binge-watching TV shows, and playing Tabla.

Retina Satish is a Solutions Architect at AWS, bringing her expertise in data analytics and generative AI. She collaborates with customers to understand business challenges and architect innovative, data-driven solutions using cutting-edge technologies. She is dedicated to delivering secure, scalable, and cost-effective solutions that drive digital transformation.

Jeetendra Vaidya is a Senior Solutions Architect at AWS, bringing his expertise to the realms of AI/ML, serverless, and data analytics domains. He is passionate about assisting customers in architecting secure, scalable, reliable, and cost-effective solutions.