# maestro

**Repository Path**: daoos_admin/maestro

## Basic Information

- **Project Name**: maestro
- **Description**: 工作流
- **Primary Language**: Java
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-30
- **Last Updated**: 2025-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Maestro
===================================
[Maestro](https://netflixtechblog.com/orchestrating-data-ml-workflows-at-scale-with-netflix-maestro-aaa2b41b800c)
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](https://netflixtechblog.com/maestro-netflixs-workflow-orchestrator-ee13a06f9c78)
- [Orchestrating Data/ML Workflows at Scale With Netflix Maestro](https://netflixtechblog.com/orchestrating-data-ml-workflows-at-scale-with-netflix-maestro-aaa2b41b800c)
- [100X Faster: How We Supercharged Netflix Maestro's Workflow Engine](https://netflixtechblog.com/100x-faster-how-we-supercharged-netflix-maestros-workflow-engine-028e9637f041)
- [Incremental Processing using Netflix Maestro and Apache Iceberg](https://netflixtechblog.com/incremental-processing-using-netflix-maestro-and-apache-iceberg-b8ba072ddeeb)

# 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'`

## 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

```bash
pip install maestro-sdk
```

### Creating a workflow

```python
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

```python
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

```python
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](https://github.com/jun-he/maestro-python) project for more details.


## Get in touch
Join our community [Slack workspace](https://join.slack.com/t/maestro-oss/shared_invite/zt-3is5iwz9e-Px3UqLfzG8lEoWhTk5D4yA) 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.