Maestro is a general-purpose workflow orchestrator that provides a fully managed workflow-as-a-service (WAAS) to the data platform users at Netflix.

It serves thousands of users, including data scientists, data engineers, machine learning engineers, software engineers, content producers, and business analysts, for various use cases. It schedules hundreds of thousands of workflows, millions of jobs every day and operates with a strict SLO even when there are spikes in the traffic. Maestro is highly scalable and extensible to support existing and new use cases and offers enhanced usability to end users.

You can read more details about it in our series of blog posts

• Maestro: Data/ML Workflow Orchestrator at Netflix
• Orchestrating Data/ML Workflows at Scale With Netflix Maestro
• 100X Faster: How We Supercharged Netflix Maestro's Workflow Engine
• Incremental Processing using Netflix Maestro and Apache Iceberg

• Git
• Java 21
• Gradle
• Docker

• ./gradlew build

• ./gradlew bootRun

• docker compose -f maestro-aws/docker-compose.yml up
• ./gradlew bootRun --args='--spring.profiles.active=aws'

• curl --header "user: tester" -X POST 'http://127.0.0.1:8080/api/v3/workflows' -H "Content-Type: application/json" -d @maestro-server/src/test/resources/samples/sample-dag-test-1.json

• curl -X GET 'http://127.0.0.1:8080/api/v3/workflows/sample-dag-test-1/versions/latest'

• curl --header "user: tester" -X POST 'http://127.0.0.1:8080/api/v3/workflows/sample-dag-test-1/versions/latest/actions/start' -H "Content-Type: application/json" -d '{"initiator": {"type": "manual"}}'

• curl -X GET 'http://127.0.0.1:8080/api/v3/workflows/sample-dag-test-1/instances/1/runs/1'

• curl --header "user: tester" -X DELETE 'http://127.0.0.1:8080/api/v3/workflows/sample-dag-test-1'

The maestro-extensions module runs as a separate Spring Boot service that listens to maestro events via SQS (subscribed to the SNS topic maestro-server publishes to) and provides additional functionality such as foreach step flattening views.

To run maestro-server and maestro-extensions together locally:

1. Start LocalStack (provides local SQS/SNS):
   • docker compose -f maestro-aws/docker-compose.yml up -d
2. Start maestro-server (port 8080):
   • ./gradlew :maestro-server:bootRun --args='--spring.profiles.active=aws'
3. Start maestro-extensions (port 8081):
   • ./gradlew :maestro-extensions:bootRun

Once both services are running, maestro-extensions will consume step instance status change events from the maestro-event SQS queue and process foreach flattening. Query the flattened views via the extensions REST API on port 8081.

• setup kubernetes configs so the kubectl command works
• ./gradlew bootRun
• curl --header "user: tester" -X POST 'http://127.0.0.1:8080/api/v3/workflows' -H "Content-Type: application/json" -d @maestro-server/src/test/resources/samples/sample-kubernetes-wf.json
• curl --header "user: tester" -X POST 'http://127.0.0.1:8080/api/v3/workflows/sample-kubernetes-wf/versions/latest/actions/start' -H "Content-Type: application/json" -d '{"initiator": {"type": "manual"}}'

pip install maestro-sdk

from maestro import Workflow, Job

wf = Workflow(id="test-wf")
wf.owner("tester").tags("test")
wf.job(Job(id="job1", type='NoOp'))
wf_yaml = wf.to_yaml()

from maestro import Workflow, Job, MaestroClient

wf = Workflow(id="test-wf")
wf.owner("tester").tags("test")
wf.job(Job(id="job1", type='NoOp'))
wf_yaml = wf.to_yaml()

client = MaestroClient(base_url="http://127.0.0.1:8080", user="tester")
response = client.push_yaml(wf_yaml)
print(response)

from maestro import MaestroClient

client = MaestroClient(base_url="http://127.0.0.1:8080", user="tester")
response = client.start(workflow_id="test-wf", run_params={"foo": {"value": "bar", "type": "STRING"}})
print(response)

Please check Maestro python project for more details.

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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

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