Amazon Managed Workflows for Apache Airflow (Amazon MWAA) has grow to be a cornerstone for organizations embracing data-driven decision-making. As a scalable answer for managing complicated knowledge pipelines, Amazon MWAA permits seamless orchestration throughout AWS companies and on-premises methods. Though AWS manages the underlying infrastructure, you will need to rigorously plan and execute your Amazon MWAA setting updates in accordance with the shared duty mannequin. Upgrading to the most recent Amazon MWAA model can present important benefits, together with enhanced safety by means of vital safety patches and potential enhancements in efficiency with quicker DAG parsing and decreased database load. You need to use superior options whereas sustaining ecosystem compatibility and receiving prioritized AWS assist. The important thing to profitable upgrades lies in choosing the proper answer and following a methodical implementation method.
On this submit, we discover greatest practices for upgrading your Amazon MWAA setting and supply a step-by-step information to seamlessly transition to the most recent model.
Answer overview
Amazon MWAA supplies two main improve options:
- In-place improve – This technique works greatest when you’ll be able to accommodate deliberate downtime. You deploy the brand new model straight in your present infrastructure. In-place model upgrades on Amazon MWAA are supported for environments working Apache Airflow model 2.x and later. Nonetheless, for those who’re working model 1.10.z or older variations, you will need to create a brand new setting and migrate your assets, as a result of these variations don’t assist in-place upgrades.
- Cutover improve – This technique helps reduce disruption to manufacturing environments. You create a brand new Amazon MWAA setting with the goal model after which transition out of your previous setting to the brand new one.
Every answer affords a unique method that will help you improve whereas working to take care of knowledge integrity and system reliability.
In-place improve
In-place upgrades work effectively for environments the place you’ll be able to schedule a upkeep window for the improve course of. Throughout this window, Amazon MWAA preserves your workflow historical past. This technique works greatest when you’ll be able to accommodate deliberate downtime. It helps preserve historic knowledge, supplies a simple improve course of, and contains rollback capabilities if points happen throughout provisioning. You additionally use fewer assets since you don’t have to create a brand new setting.
You possibly can carry out in-place upgrades by means of the AWS Administration Console with a single operation. This course of helps scale back operational overhead by managing many improve steps for you.
In the course of the improve course of, your setting can’t schedule or run new duties. Amazon MWAA helps handle the improve course of and implements security measures—if points happen throughout the provisioning part, the service makes an attempt to revert to the earlier steady model.
Earlier than you start an in-place improve, we advocate testing your DAGs for compatibility with the goal model, as a result of DAG compatibility points can have an effect on the improve course of. You need to use the Amazon MWAA native runner to check DAG compatibility earlier than you begin the improve. You can begin the improve utilizing both the console and specifying the brand new model or the AWS Command Line Interface (AWS CLI). The next is an instance Amazon MWAA improve command utilizing the AWS CLI:
The next diagram reveals the Amazon MWAA in-place improve workflow and states.
Check with Introducing in-place model upgrades with Amazon MWAA for extra particulars.
Cutover improve
A cutover improve supplies an alternate answer when it is advisable reduce downtime, although it requires extra guide steps and operational planning. With this method, you create a brand new Amazon MWAA setting, migrate your metadata, and handle the transition between environments. Though this technique affords extra management over the improve course of, it requires further planning and execution effort in comparison with an in-place improve.
This technique can work effectively for environments with complicated workflows, notably whenever you plan to make important modifications alongside the model improve. The method affords a number of advantages: you’ll be able to reduce manufacturing downtime, carry out complete testing earlier than switching environments, and preserve the power to return to your authentic setting if wanted. You may as well evaluation and replace your configurations throughout the transition.
Think about the next facets of the cutover method. While you run two environments concurrently, you pay for each environments. The pricing for every Amazon MWAA setting is determined by:
- Period of setting uptime (billed hourly with per-second decision)
- Setting measurement configuration
- Automated scaling capability for staff
- Scheduler capability
AWS calculates the price of further computerized scaled staff individually. You possibly can estimate prices on your particular configuration utilizing the AWS Pricing Calculator.
To assist stop knowledge duplication or corruption throughout parallel operation, we advocate implementing idempotent DAGs. The Airflow scheduler routinely populates some metadata tables (dag, dag_tag, and dag_code) in your new setting. Nonetheless, it is advisable plan the migration of the next further metadata elements:
- DAG historical past
- Variables
- Slot pool configurations
- SLA miss information
- XCom knowledge
- Job information
- Log tables
You possibly can select this method when your necessities prioritize minimal downtime and you may handle the extra operational complexity.
The cutover improve course of includes three primary steps: creating a brand new setting, restoring it with the prevailing knowledge, and performing the improve. The next diagram illustrates the complete workflow.
Within the following sections, we stroll by means of the important thing steps to carry out a cutover improve.
Conditions
Earlier than you start the improve course of, full the next steps:
Create a brand new setting
Full the next steps to create a brand new setting:
- Generate a template on your new setting configuration utilizing the AWS CLI:
- Modify the generated JSON file:
- Copy configurations out of your backup file
.json to .json. - Replace the setting identify.
- Preserve the AirflowVersion parameter worth out of your present setting.
- Evaluation and replace different configuration parameters as wanted.
- Copy configurations out of your backup file
- Create your new setting:
Restore the brand new setting
Full the next steps to revive the brand new setting:
- Use the mwaa-dr PyPI bundle to create and run the restore DAG.
- This course of copies metadata out of your S3 backup bucket to the brand new setting.
- Confirm that your new setting incorporates the anticipated metadata out of your authentic setting.
Carry out the model improve
Full the next steps to carry out the model improve:
- Improve your setting:
- Monitor the improve:
- Observe the setting standing on the console.
- Look ahead to error messages or warnings.
- Confirm the setting reaches the AVAILABLE
Plan your transition timing rigorously. When your authentic setting continues to course of workflows throughout this improve, the metadata between environments can change.
Clear up
After you confirm the soundness of your upgraded setting by means of monitoring, you’ll be able to start the cleanup course of:
- Take away your authentic Amazon MWAA setting utilizing the AWS CLI command:
- Clear up your related assets by eradicating unused backup knowledge from S3 buckets, deleting momentary AWS Identification and Entry Administration (IAM) roles and insurance policies created for the improve, and updating your DNS or routing configurations.
Earlier than eradicating any assets, be sure you comply with your group’s backup retention insurance policies, preserve obligatory backup knowledge on your compliance necessities, and doc configuration modifications made throughout the improve.
This method helps you carry out a managed improve with alternatives for testing and the power to return to your authentic setting if wanted.
Monitoring and validation
You possibly can observe your improve progress utilizing Amazon CloudWatch metrics, with a deal with DAG processing metrics and scheduler heartbeat. Your setting transitions by means of a number of states throughout the improve course of, together with UPDATING and CREATING. When your setting reveals the AVAILABLE state, you’ll be able to start validation testing. We advocate checking system accessibility, testing vital workflow operations, and verifying exterior connections. For detailed monitoring steering, see Monitoring and metrics for Amazon Managed Workflows for Apache Airflow.
Key issues
Think about using infrastructure as code (IaC) practices to assist preserve constant setting administration and assist repeatable deployments. Schedule metadata backups utilizing mwaa-dr during times of low exercise to assist defend your knowledge. When designing your workflows, implement idempotent pipelines to assist handle potential interruptions, and preserve documentation of your configurations and dependencies.
Conclusion
A profitable Amazon MWAA improve begins with deciding on an method that aligns together with your operational necessities. Whether or not you select an in-place or cutover improve, thorough preparation and testing assist assist a managed transition. Utilizing accessible instruments, monitoring capabilities, and really helpful practices can assist you improve to the most recent Amazon MWAA options whereas working to take care of your workflow operations.
For extra particulars and code examples on Amazon MWAA, discuss with the Amazon MWAA Person Information and Amazon MWAA examples GitHub repo.
Apache, Apache Airflow, and Airflow are both registered emblems or emblems of the Apache Software program Basis in the US and/or different international locations.
In regards to the Authors
Anurag Srivastava works as a Senior Huge Information Cloud Engineer at Amazon Internet Providers (AWS), specializing in Amazon MWAA. He’s keen about serving to clients construct scalable knowledge pipelines and workflow automation options on AWS.
Sriharsh Adari is a Senior Options Architect at Amazon Internet Providers (AWS), the place he helps clients work backwards from enterprise outcomes to develop progressive options on AWS. Through the years, he has helped a number of clients on knowledge platform transformations throughout trade verticals. His core space of experience embody Know-how Technique, Information Analytics, and Information Science. In his spare time, he enjoys enjoying sports activities, binge-watching TV reveals, and enjoying Tabla.
Venu Thangalapally is a Senior Options Architect at AWS, primarily based in Chicago, with deep experience in cloud structure, knowledge and analytics, containers, and utility modernization. He companions with Monetary Providers trade clients to translate enterprise targets into safe, scalable, and compliant cloud options that ship measurable worth. Venu is keen about leveraging know-how to drive innovation and operational excellence. Exterior of labor, he enjoys spending time along with his household, studying, and taking lengthy walks.
Chandan Rupakheti is a Senior Options Architect at AWS. His primary focus at AWS lies within the intersection of analytics, serverless, and AdTech companies. He’s a passionate technical chief, researcher, and mentor with a knack for constructing progressive options within the cloud. Exterior of his skilled life, he loves spending time along with his household and pals, and listening to and enjoying music.
