PyFlow

PyFlow is a python based flow programming engine/platform

According to wikipedia, here is the definition of FBP

In computer programming, flow-based programming (FBP) is a programming paradigm that defines applications as networks of "black box" processes, which exchange data across predefined connections by message passing, where the connections are specified externally to the processes. These black box processes can be reconnected endlessly to form different applications without having to be changed internally. FBP is thus naturally component-oriented.

There are lots of software or tools or product that enbrace the flow concept for example:

Apache NiFi
DAG in spark
AWS Step Functions
Azure ML Studio
TensorBoard from Tensorflow
Scratch programing language
argo an open source container-native workflow engine

I am building this tool to help you visualize your function building and execution procedures.

Concept

Core concept of the PyFlow includs Node, Port, Flow and Repository

Node and Port

Node is the basic building block for flow. Each node contains:

a python function that represent the logic of this node
zero or more input port which defines the input of the function
1 or more output port which represent the running result of the function

A nodespec is the scheme or meta data of a node instance. you can create multiple node instance based on specific nodespec, that works like a nodespec is a class and a node instance is an object of that class.

A nodespec contains following information:

title : the name of this node
ports : defines input and output port of this node
func : the python function of this node
id : unique identifier of the node specifiction

here is a sample of nodespec for a function of add operation:

{
	"title": "add",
	"port": {
		"input": [{
			"name": "a",
			"order": 0
		}, {
			"name": "b",
			"order": 1
		}]
	},
	"func": "def func(a, b):\n    return a + b\n",
	"id": "pyflow.transform.add"
}

note, each port has a name and for input port, it has an order. so here a is the first input for add operation node and b is the second input.

Flow

A flow is a DAG that composed by nodes and links between nodes. A link can only link the output port of a node to an input of another node.

A flow description contains:

id : unique id of this flow
name : display name of this flow
nodes : the node instance that compose this flow
links : the links that connect the ports of the nodes

Here is a sample definition of a flow:

{
	"id": "pyflow.builder.gen",
	"name": "SampleFlow",
	"nodes": [{
		"id": "node1515719064230",
		"spec_id": "pyflow.transform.add",
		"name": "add",
		"ports": [{
			"name": "a",
			"value": "1"
		}, {
			"name": "b",
			"value": "2"
		}]
	}, {
		"id": "node1515719065769",
		"spec_id": "pyflow.transform.add",
		"name": "add",
		"ports": [{
			"name": "b",
			"value": "3"
		}]
		"is_end": 1
	}],
	"links": [{
		"source": "node1515719064230:out",
		"target": "node1515719065769:a"
	}]
}

note, each input port can have input values in case there is no output port connected to it. and if the is_end is true which means this node is the last node to run for a flow.

Flow Engine

A simple flow engine is implemented that will scan the the flow, create a stack orderred by the link of the node to make sure running node sequetially according to the dependency of the node. So the node will no depenency will run first and then the linked nodes to those node that run complete. This model is a very simple one and has some limitations.

call exec(spec.get("func")) to execute the python function is not secure, considering to support run the function in container
does not support parallel processing

Flow Rest API

PyFlow server is a flask based REST server, refer to tests/rest/test.yaml for all the support Rest API

UI and Work Flow

PyFlow Web UI leverages jsplumb to provide flow building and visulaizing functions. Through this Web UI, the user can:

create, edit, test, delete nodes
create, view, run flows
import/export the repository that contains all the definition of the flows and nodes

Build and Test

Run locally

install dependecny for front end code

cd /src/static
yarn install

and start the server

cd /src
python server.py

then open http://localhost:5000 for the pyflow web UI

Run with docker

docker run -P naughtytao/pyflow:latest

Docker Build and Run

cd /docker
make docker

then run docker run -P pyflow:latest to run the pyflow in docker

Unit Test

cd /tests/unit
python -m unittest discover

Rest Test

start the pyflow server and then run following command to test the rest API

cd /tests/rest
resttest.py http://localhost:5000 test.yaml --verbose

Open Issues and Todo list

Now a static execution engine (run once per a node in a seperated process) is supported, no streaming support
Port type validations is not implemented
Support running each function as a docker instance instead of python eval which is much more secure and flexible
UI improvements, lots of things to do

andrewssobral / pyflow Goto Github PK

pyflow's Introduction