sfini 0.1.0

Description:

sfini 0.1.0

sfini




Create, run and manage AWS Step Functions easily. Pronounced "SFIN-ee".
This package aims to provide a user-friendly interface into defining and
running Step Functions. Things you can do in sfini to interact with AWS Step
Functions:

Implement and register activities
Define and register state machines
Start, track and stop executions
Run workers for activities
Get information for registered activities and state machines
De-register state machines and activities

Note: this is not a tool to convert Python code into a Step Functions state
machine. For that, see pyawssfn.
Getting started
Prerequisites

AWS (Amazon Web Services) account, with
access to Step Functions
AWS IAM (Identity and Access Management)
credentials

Installation
pip install sfini

Usage
Documentation
Check the documentation or use
the built-in help:
pydoc sfini

import sfini
help(sfini)

AWS Step Functions
AWS Step Functions (SFN) is a
workflow-management service, providing the ability to coordinate tasks in a
straight-forward fashion. Further documentation can be found in the
AWS documentation.
Usage of Step Functions consists of two types: state-machines and activities.
A state-machine is a graph of operations which defines a workflow of an
application, comprised of multiple types of "states", or stages of the
workflow. An activity processes input to an output, and is used to process a
task "state" in the state-machine (multiple task states can have the same
activity assigned it.
Once a state-machine is defined and registered (along with the used
activities), you run executions of that state-machine on different inputs to
run the workflow. sfini allows you to start, stop and get the history of
these executions.
State-machines support conditional branching (and therefore loops), retries
(conditional and unconditional), exception-catching, external AWS service
support for running tasks, parallel execution and input/output processing.
External services including AWS Lambda, so you don't have to deploy your own
activity runners.
Once state-machines and activities are defined and registered, you can view and
update their details in the SFN web console.
Role ARN
Every state-machine needs a role ARN (Amazon Resource Name). This is an AWS IAM
role ARN which allows the state-machine to process state executions. See AWS
Step Functions documentation for more information.
Example
More examples found in the documentation.
import sfini

# Define activities
activities = sfini.ActivityRegistration(prefix="test")


@activities.activity("addActivity")
def add_activity(data):
return data["a"] + data["b"]


# Define state-machine
add = sfini.Task("add", add_activity)
sm = sfini.construct_state_machine("testAdding", add)

# Register state-machine and activities
activities.register()
sm.register()

# Start activity worker
worker = sfini.Worker(add_activity)
worker.start()

# Start execution
execution = sm.start_execution(execution_input={"a": 3, "b": 42})
print(execution.name)
# testAdding_2019-05-13T19-07_0354d790

# Wait for execution and print output
execution.wait()
print(execution.output)
# 45
print(execution.format_history())
# ExecutionStarted [1] @ 2019-06-23 20:03:52.817000+10:00
# TaskStateEntered [2] @ 2019-06-23 20:03:52.840000+10:00:
# name: add
# ActivityScheduled [3] @ 2019-06-23 20:03:52.840000+10:00:
# resource: arn:aws:states:us-west-2:ACCID:activity:testaddActivity
# ActivityStarted [4] @ 2019-06-23 20:03:53.954000+10:00:
# worker: hostname-3a4fb480
# ActivitySucceeded [5] @ 2019-06-23 20:03:55.028000+10:00
# TaskStateExited [6] @ 2019-06-23 20:03:55.028000+10:00:
# name: add
# ExecutionSucceeded [7] @ 2019-06-23 20:03:55.028000+10:00
# Output: 45

# Stop activity workers
worker.end()
worker.join()

# Deregister state-machine and activities
activities.deregister()
sm.deregister()