Intro: Fault-Oblivious Stateful Python Code

cadence-python allows you to create Python functions that have their state (local variables etc..) implicitly saved such that if the process/machine fails the state of the function is not lost and can resume from where it left off.

This programming model is useful whenever you need to ensure that a function runs to completion. For example:

Business logic involving multiple micro services
CI/CD pipelines
Data pipelines
RPA
ETL
Marketing automation / Customer journeys / Customer engagement
Zapier/IFTTT like end user automation.
Chat bots
Multi-step forms
Scheduler/Cron jobs

Behind the scenes, cadence-python uses Cadence as its backend.

For more information about the fault-oblivious programming model refer to the Cadence documentation here

Install Cadencce

wget https://raw.githubusercontent.com/uber/cadence/master/docker/docker-compose.yml
docker-compose up

Register `sample` domain

docker run --network=host --rm ubercadence/cli:master --do sample domain register -rd 1

Installation cadence-python

pip install cadence-client==1.0.1

Hello World Sample

import sys
import logging
from cadence.activity_method import activity_method
from cadence.workerfactory import WorkerFactory
from cadence.workflow import workflow_method, Workflow, WorkflowClient

logging.basicConfig(level=logging.DEBUG)

TASK_LIST = "HelloActivity-python-tasklist"
DOMAIN = "sample"


# Activities Interface
class GreetingActivities:
    @activity_method(task_list=TASK_LIST, schedule_to_close_timeout_seconds=2)
    def compose_greeting(self, greeting: str, name: str) -> str:
        raise NotImplementedError


# Activities Implementation
class GreetingActivitiesImpl:
    def compose_greeting(self, greeting: str, name: str):
        return f"{greeting} {name}!"


# Workflow Interface
class GreetingWorkflow:
    @workflow_method(execution_start_to_close_timeout_seconds=10, task_list=TASK_LIST)
    async def get_greeting(self, name: str) -> str:
        raise NotImplementedError


# Workflow Implementation
class GreetingWorkflowImpl(GreetingWorkflow):

    def __init__(self):
        self.greeting_activities: GreetingActivities = Workflow.new_activity_stub(GreetingActivities)

    async def get_greeting(self, name):
        # Place any Python code here that you want to ensure is executed to completion.
        # Note: code in workflow functions must be deterministic so that the same code paths
        # are ran during replay.
        return await self.greeting_activities.compose_greeting("Hello", name)


if __name__ == '__main__':
    factory = WorkerFactory("localhost", 7933, DOMAIN)
    worker = factory.new_worker(TASK_LIST)
    worker.register_activities_implementation(GreetingActivitiesImpl(), "GreetingActivities")
    worker.register_workflow_implementation_type(GreetingWorkflowImpl)
    factory.start()

    client = WorkflowClient.new_client(domain=DOMAIN)
    greeting_workflow: GreetingWorkflow = client.new_workflow_stub(GreetingWorkflow)
    result = greeting_workflow.get_greeting("Python")
    print(result)

    print("Stopping workers....")
    worker.stop()
    print("Workers stopped...")
    sys.exit(0)

Status / TODO

cadence-python is still under going heavy development. It should be considered EXPERIMENTAL at the moment. A production version is targeted to be released in ~~September of 2019~~ ~~January 2020~~ ~~March 2020~~ April 2020.

1.0