• Data driven programming framework .
• Paralleled in coroutines .
• Type and content based route function.

Install and update using `pip`_:

pip install -U databot

All Functions are connected by pipe(queue) and communidate by data.

When data come in ,the function will be called and return the result.

Think about pipeline operation in unix. ls|grep|sed.

Benefits:

1. decouple data and functionality
2. easy to reuse

Databot provides pipe and route. It makes data-drivend programming more easy,and powerfull data flow process.

• Simple

Databot is easy to use and maintain, and does not need configuration files and know about asyncio and how to paralle .

Here's one of the simple applications you can make.

load the bitoin prices every 2 sencond.advantage price aggreagator sample can be found here. https://github.com/kkyon/databot/tree/master/examples

from databot.flow import Pipe,Timer
from databot.botframe import BotFrame
from databot.http.http import HttpLoader


def main():
    Pipe(

        Timer(delay=2),#send timer data to pipe every 2 sen
        "http://api.coindesk.com/v1/bpi/currentprice.json", #send url to pipe when timer trigger
        HttpLoader(),#read url and load http response
        lambda r:r.json['bpi']['USD']['rate_float'], #read http response and parese as json
        print, #print out
    )

    BotFrame.render('simple_bitcoin_price')
    BotFrame.run()

main()

• flow graph

below is the flow graph generated by databot .

• Fast

Node will be run in parallel ,and it will get high performance when processing stream data.

• Visualliztion

with render function BotFrame.render('bitcoin_arbitrage') databot will render the data flow network into a graphiz image. https://github.com/kkyon/databot/blob/master/examples/bitcoin_arbitrage.png

• Replay-able

with replay mode enable config.replay_mode=True

when excpeiton is raised at step N ,you no need to run from setup 1 to N .databot will replay the data from nearest completed node ,usally step N-1 . it will save a lot time in development phase .

Data-driven programming is a programming paradigm which describe the data to be matched and the processing required rather than defining a sequence of steps to be taken. Standard examples of data-driven languages are the text-processing languages sed and AWK,where the data is a sequence of lines in an input stream. Data-driven programming is typically applied to streams of structured data, for filtering, transforming, aggregating (such as computing statistics), or calling other programs

Databot have few basic concept to impelement DDP.

• Pipe

  it is the main stream process of the programe . all unit will work inside.

• Node

  it is the process logic node . it will driven by data. custom function work as Node . There are some built-in node :

  • Loop:work as for
  • Timer: it will send message in the pipe by timer param . delay, max_time
  • HttpLoader: get a url and return Httpresponse
  • Mysql query or insert: for mysql querying and insert
  • File read write: for file write.

• Route

  It will be used to create complex data flow network,not just only one main process. Databot can nest Route in side a Route. it would very powerfull. There are some pre built-in Route:

  • Branch : will duplicte data from parent pipe to a branch .
  • Return : will duplicate data from parent pipe, and return finally result to parent pipe.
  • Filter : drop out data from pipe by some condition
  • Fork : will duplicate data to many branch.
  • Join : duplicate data to many branches ,and return result to pipe.

All unit(Pipe,Node,Route) communicates via queue and paralle in coroutine . but User of the databot not care too much the detail of asyncio .

Below some graphes will get you some basic concept for the Route:

branch:https://github.com/kkyon/databot/blob/master/docs/databot_branch.jpg fork:https://github.com/kkyon/databot/blob/master/docs/databot_fork.jpg join:https://github.com/kkyon/databot/blob/master/docs/databot_join.jpg return:https://github.com/kkyon/databot/blob/master/docs/databot_return.jpg