High Performance Python Data driven programming framework for Web Crawler,ETL,Data pipeline work.
- Data-driven programming framework
- Paralleled in coroutines and ThreadPool
- Type- and content-based route function
Install and update using pip:
pip install -U databot
What's data-driven programming?
All functions are connected by pipes (queues) and communicate by data.
When data come in, the function will be called and return the result.
Think about the pipeline operation in unix:
. Decouple data and functionality
. Easy to reuse
Databot provides pipe and route. It makes data-driven programming and powerful data flow processes easier.
Databot is easy to use and maintain, does not need configuration files, and knows about
asyncio and how to parallelize computation.
Here's one of the simple applications you can make:
Load the price of Bitoin every 2 seconds. Advantage price aggregator sample can be found
.. code-block:: python
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 parse as json print, #print out ) BotFrame.render('simple_bitcoin_price') BotFrame.run() main()
below is the flow graph generated by databot.
Nodes will be run in parallel, and they will perform well when processing stream data.
With render function:
databot will render the data flow network into a graphviz image.
With replay mode enabled:
when an exception is raised at step N, you don't need to run from setup 1 to N.
Databot will replay the data from nearest completed node, usually step N-1.
It will save a lot of time in the development phase.
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