Maestro

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

Get started

Prerequisite

• Git
• Java 21
• Gradle
• Docker

Build it

• ./gradlew build

Run it

• ./gradlew bootRun

Run it with AWS module

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

Create a sample workflow

• 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

Get the sample workflow definition

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

Trigger to run the sample workflow

• 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"}}'

Get the sample workflow instance

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

Delete the sample workflow and its data

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

Register a sample job template and run a workflow that uses it as a template step

• curl --header "user: tester" -X POST 'http://127.0.0.1:8080/api/v3/job-templates' -H "Content-Type: application/json" -d @maestro-common/src/testFixtures/resources/fixtures/api/sample-job-template-positions-create-request.json
• 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-template-wf.json
• curl --header "user: tester" -X POST 'http://127.0.0.1:8080/api/v3/workflows/sample-template-wf/versions/latest/actions/start' -H "Content-Type: application/json" -d '{"initiator": {"type": "manual"}}'
• For an example that runs a generator in a Kubernetes pod to build the list of steps to execute, use sample-job-template-step-preparer-create-request.json together with sample-template-step-preparer-wf.json.

Run it with maestro-extensions (foreach flattening service)

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.

Run it with Kubernetes support

• 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"}}'

Python SDK client

Installation

pip install maestro-sdk

Creating a workflow

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()

Pushing a workflow to Maestro server

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)

Starting a workflow

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.

Get in touch

Join our community Slack workspace for discussions!

License

Copyright 2024 Netflix, Inc.

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

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.