Amazon Managed Workflows for Apache Airflow (Amazon MWAA) has develop into a cornerstone for organizations embracing data-driven decision-making. As a scalable resolution for managing advanced knowledge pipelines, Amazon MWAA allows seamless orchestration throughout AWS companies and on-premises programs. Though AWS manages the underlying infrastructure, you should rigorously plan and execute your Amazon MWAA surroundings updates in accordance with the shared accountability mannequin. Upgrading to the most recent Amazon MWAA model can present vital benefits, together with enhanced safety via vital safety patches and potential enhancements in efficiency with quicker DAG parsing and diminished database load. You should utilize superior options whereas sustaining ecosystem compatibility and receiving prioritized AWS assist. The important thing to profitable upgrades lies in choosing the proper resolution and following a methodical implementation strategy.
On this put up, we discover finest practices for upgrading your Amazon MWAA surroundings and supply a step-by-step information to seamlessly transition to the most recent model.
Answer overview
Amazon MWAA supplies two major improve options:
- In-place improve – This technique works finest when you may accommodate deliberate downtime. You deploy the brand new model instantly in your present infrastructure. In-place model upgrades on Amazon MWAA are supported for environments working Apache Airflow model 2.x and later. Nonetheless, when you’re working model 1.10.z or older variations, you should create a brand new surroundings 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 You create a brand new Amazon MWAA surroundings with the goal model after which transition out of your outdated surroundings to the brand new one.
Every resolution provides a distinct strategy that can assist 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 may schedule a upkeep window for the improve course of. Throughout this window, Amazon MWAA preserves your workflow historical past. This technique works finest when you may accommodate deliberate downtime. It helps keep historic knowledge, supplies an easy improve course of, and consists of rollback capabilities if points happen throughout provisioning. You additionally use fewer assets since you don’t must create a brand new surroundings.
You possibly can carry out in-place upgrades via the AWS Administration Console with a single operation. This course of helps cut back operational overhead by managing many improve steps for you.
Throughout the improve course of, your surroundings can’t schedule or run new duties. Amazon MWAA helps handle the improve course of and implements security measures—if points happen through the provisioning section, the service makes an attempt to revert to the earlier secure 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 should utilize 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 exhibits the Amazon MWAA in-place improve workflow and states.
Seek advice from Introducing in-place model upgrades with Amazon MWAA for extra particulars.
Cutover improve
A cutover improve supplies another resolution when that you must reduce downtime, although it requires extra handbook steps and operational planning. With this strategy, you create a brand new Amazon MWAA surroundings, migrate your metadata, and handle the transition between environments. Though this technique provides 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 advanced workflows, significantly whenever you plan to make vital adjustments alongside the model improve. The strategy provides a number of advantages: you may reduce manufacturing downtime, carry out complete testing earlier than switching environments, and keep the flexibility to return to your unique surroundings if wanted. You can even evaluate and replace your configurations through the transition.
Take into account the next elements of the cutover strategy. Once you run two environments concurrently, you pay for each environments. The pricing for every Amazon MWAA surroundings is dependent upon:
- Period of surroundings uptime (billed hourly with per-second decision)
- Atmosphere dimension configuration
- Computerized scaling capability for employees
- Scheduler capability
AWS calculates the price of further automated scaled employees individually. You possibly can estimate prices to your particular configuration utilizing the AWS Pricing Calculator.
To assist forestall knowledge duplication or corruption throughout parallel operation, we advocate implementing idempotent DAGs. The Airflow scheduler robotically populates some metadata tables (dag, dag_tag, and dag_code) in your new surroundings. Nonetheless, that you must plan the migration of the next further metadata parts:
- DAG historical past
- Variables
- Slot pool configurations
- SLA miss data
- XCom knowledge
- Job data
- Log tables
You possibly can select this strategy when your necessities prioritize minimal downtime and you’ll handle the extra operational complexity.
The cutover improve course of entails three important steps: creating a brand new surroundings, restoring it with the prevailing knowledge, and performing the improve. The next diagram illustrates the complete workflow.
Within the following sections, we stroll via the important thing steps to carry out a cutover improve.
Stipulations
Earlier than you start the improve course of, full the next steps:
Create a brand new surroundings
Full the next steps to create a brand new surroundings:
- Generate a template to your new surroundings configuration utilizing the AWS CLI:
- Modify the generated JSON file:
- Copy configurations out of your backup file
.json to .json. - Replace the surroundings identify.
- Maintain the AirflowVersion parameter worth out of your present surroundings.
- Overview and replace different configuration parameters as wanted.
- Copy configurations out of your backup file
- Create your new surroundings:
Restore the brand new surroundings
Full the next steps to revive the brand new surroundings:
- 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 surroundings.
- Confirm that your new surroundings incorporates the anticipated metadata out of your unique surroundings.
Carry out the model improve
Full the next steps to carry out the model improve:
- Improve your surroundings:
- Monitor the improve:
- Monitor the surroundings standing on the console.
- Look ahead to error messages or warnings.
- Confirm the surroundings reaches the AVAILABLE
Plan your transition timing rigorously. When your unique surroundings continues to course of workflows throughout this improve, the metadata between environments can change.
Clear up
After you confirm the steadiness of your upgraded surroundings via monitoring, you may start the cleanup course of:
- Take away your unique Amazon MWAA surroundings utilizing the AWS CLI command:
- Clear up your related assets by eradicating unused backup knowledge from S3 buckets, deleting short-term AWS Id and Entry Administration (IAM) roles and insurance policies created for the improve, and updating your DNS or routing configurations.
Earlier than eradicating any assets, ensure you observe your group’s backup retention insurance policies, keep obligatory backup knowledge to your compliance necessities, and doc configuration adjustments made through the improve.
This strategy helps you carry out a managed improve with alternatives for testing and the flexibility to return to your unique surroundings 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 surroundings transitions via a number of states through the improve course of, together with UPDATING and CREATING. When your surroundings exhibits the AVAILABLE state, you may 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 keep constant surroundings administration and assist repeatable deployments. Schedule metadata backups utilizing mwaa-dr in periods of low exercise to assist shield your knowledge. When designing your workflows, implement idempotent pipelines to assist handle potential interruptions, and keep documentation of your configurations and dependencies.
Conclusion
A profitable Amazon MWAA improve begins with deciding on an strategy 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 obtainable 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 added particulars and code examples on Amazon MWAA, confer with the Amazon MWAA Person Information and Amazon MWAA examples GitHub repo.
Apache, Apache Airflow, and Airflow are both registered logos or logos of the Apache Software program Basis in america and/or different nations.
In regards to the Authors
Anurag Srivastava works as a Senior Massive Knowledge Cloud Engineer at Amazon Internet Providers (AWS), specializing in Amazon MWAA. He’s enthusiastic about serving to prospects 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 prospects work backwards from enterprise outcomes to develop modern options on AWS. Over time, he has helped a number of prospects on knowledge platform transformations throughout trade verticals. His core space of experience embrace Expertise Technique, Knowledge Analytics, and Knowledge Science. In his spare time, he enjoys taking part in sports activities, binge-watching TV exhibits, and taking part in Tabla.
Venu Thangalapally is a Senior Options Architect at AWS, based mostly in Chicago, with deep experience in cloud structure, knowledge and analytics, containers, and software modernization. He companions with Monetary Providers trade prospects to translate enterprise objectives into safe, scalable, and compliant cloud options that ship measurable worth. Venu is enthusiastic about leveraging expertise to drive innovation and operational excellence. Exterior of labor, he enjoys spending time together with his household, studying, and taking lengthy walks.
Chandan Rupakheti is a Senior Options Architect at AWS. His important 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 modern options within the cloud. Exterior of his skilled life, he loves spending time together with his household and mates, and listening to and taking part in music.