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Orkestra

The elegance of Airflow + the power of AWS

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examples/hello_orkestra.py

import random
from typing import *
from uuid import uuid4

from aws_lambda_powertools import Logger, Tracer
from pydantic import BaseModel

from orkestra import compose
from orkestra.interfaces import Duration


def dag():
    (
        generate_item
        >> add_price
        >> copy_item
        >> double_price
        >> (do_nothing, assert_false)
        >> say_hello
        >> [random_int, random_float]
        >> say_goodbye
    )


class Item(BaseModel):
    id: str
    name: str
    price: Optional[float] = None

    @classmethod
    def random(cls):
        return cls(
            id=str(uuid4()),
            name=random.choice(
                [
                    "potato",
                    "moon rock",
                    "hat",
                ]
            ),
        )


logger = Logger()

tracer = Tracer()


default_args = dict(
    enable_powertools=True,
    timeout=Duration.seconds(6),
)


@compose(**default_args)
def generate_item(event, context):
    logger.info("generating random item")
    item = Item.random().dict()
    logger.info(item)
    tracer.put_metadata("GenerateItem", "SUCCESS")
    return item


@compose(model=Item, **default_args)
def add_price(item: Item, context):
    price = 3.14
    logger.info(
        "adding price to item",
        extra={
            "item": item.dict(),
            "price": price,
        },
    )
    item.price = price
    return item.dict()


@compose(model=Item, **default_args)
def copy_item(item: Item, context) -> list:
    logger.info(item.dict())
    return [item.dict()] * 10


@compose(model=Item, is_map_job=True, **default_args)
def double_price(item: Item, context):
    item.price = item.price * 2
    return item.dict()


@compose(**default_args)
def assert_false(event, context):
    assert False


@compose(**default_args)
def do_nothing(event, context):
    logger.info({"doing": "nothing"})


@compose(**default_args)
def say_hello(event, context):
    return "hello, world"


@compose(**default_args)
def say_goodbye(event, context):
    return "goodbye"


@compose(**default_args)
def random_int(event, context):
    return random.randrange(100)


@compose(**default_args)
def random_float(event, context):
    return float(random_int(event, context))


dag()

app.py

#!/usr/bin/env python3
from aws_cdk import core as cdk

from examples.hello_orkestra import generate_item


class HelloOrkestra(cdk.Stack):
    def __init__(self, scope, id, **kwargs):

        super().__init__(scope, id, **kwargs)

        generate_item.schedule(
            self,
            expression="rate(5 minutes)",
            state_machine_name="hello_orkestra",
        )


app = cdk.App()


app.synth()

state machine

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orkestra's Issues

Is it possible to run AWS Batch Jobs?

For longer running tasks, or which may require GPU or more resources, AWS Lambda functions may not be sufficient.

Is orkestra's current architecture capable of generating and dispatching AWS Batch job definitions? How hard would it be to implement such capability?

I suppose the Batch environment setup (job queue etc.) could be left to the user. orkestra could act more as generator for Step Functions definitions.

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