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Data Processing Framework that allow you to processing data like building blocks

Project description

a framework let you build data processing procedure for production both easy and robust, and help you to deal with parallels, performancing, error handling, debugging and data reprocessing

What for

When building a procedure to process data, we are facing some challenges:

  • Collect data from different data source and process it in real time.
  • Different procedure for different type of data.
  • The processing code should structured in a clean and elegant form, to make it easy to write, easy to read, and easy to extend.
  • Reprocess the data and clean up the out result easily
  • Easy to debug by collecting the unhandled exception, and reprocess the data when bugs fixed.
  • Do not missing one data.
  • Run in parallels, and easily to scale up.
  • Monitoring ongoing process status.

This framework is build to solve all these problems. It has been up and running at my company for 2 years.


  1. datapipe is made as a Django reusable module
  2. Postgres 9.4 or above is required as we need the jsonb field type.
  3. `Celery <>`__ is required for concurrent processing.


pip install datapipe

Then add datapipe and django-errorlog to INSTALLED_APPS of Django’s settings.

Now you can write pipes at under each apps.

To start celery worker to run pipeline in parallels, check out the docs of Celery.


  • Pipe is a single data processing step
  • Pipeline is the assemble of multiple pipes.
  • Session is in charge of managing the running Pipelines.

How it works

In the project of my company, there is a MQ(message queue), and the datapipe running inside Celery workers which listen to the MQ. Then when any data source put data in the MQ, datapipe will start to processing. Say here is a piece of code:

source_list=[...]  # a list of numbers
# prepare
for source in source_list:  # process
    total+=source  # GradulSum
    results.append(total) # Save
    total*=0.99  # DiscountSum

And following is the equavilent pipeline: source

# -*- coding: utf-8 -*-
from ..models import SourceItem, ResultItem
from datapipe.pipe import Pipe, pipeline

class GradualSum(Pipe):
    """initialize total and add number for each item to it"""

    def prepare(self, items):
        assert isinstance(items[0], SourceItem) = 0
        return super(GradualSum, self).prepare(items)

    def process(self):
        item = super(GradualSum, self).process() += item.number =
        return item

class DiscountSum(GradualSum):
    """discount the total"""
    discount = 0.99

    def process(self):
        item = super(DiscountSum, self).process() *=
        return item

@pipeline("discount_sum")  # register the last pipe with a name, that is the pipeline
class Save(DiscountSum):
    """save current total to result"""

    result_model = ResultItem

    def process(self):
        item = super(Save, self).process()

        return item

SourceItem is the input items, and the result will be put into ResultItem. Both of them are django Model with only an integer field named number.

In above code piece, we build a pipeline named discount_sum, contains three pipes, which is GradualSum, DiscountSum and Save.

To run the pipeline:

from datapipe import get_session
sess=get_session()  # create the session instance"discount_sum",SourceItem.objects.all())  # run discount_sum pipeline to all the SourceItems

Run it in parallels with celery worker:

from datapipe import get_session


Think about a Pipeline as the real assembly line.



Execute flow

  • At runtime, a list of input items will be feed into pipeline.
  • Method pipe.prepare(items) will be called with all the input items, it returns a list of items to be processed. It’s like there’s person stand before the conveyor belt to do some check of the items and filter the bad input.
  • Method pipe.process(item) will run with the items prepare(items) returned one by one.
  • Non-None return of the pipe.process(item) will be collected, and be sent to pipe.finish(results) after all input item process() finished.

Following pseudo code shows how this processing go:

def run(pipeline, items):
    prepared_item = pipeline().prepare(items)
    for item in prepared_items:
        if result is not None:
  • In process(),when processing one item, you can control the flow by throwing the following exceptions:
    • PipeContinue: stop the processing of current item, continue processing next item;
    • PipeIgnore: stop the processing of current item, rollback to the beginning of this item, continue processing next item;
    • PipeBreak: stop the processing of current item, rollback to the beginning of this item, stop processing the rest items;
    • PipeError: stop processing, rollback all the items.

Pipeline assemble

  • The whole assembly line sequence is a Pipeline, numbers of worker(Pipe) which do specific work are included.
  • The work of each worker has dependency. In datapipe, dependency relationship represented by inheritance of the pipes.
  • The side effect of pipe inheritance is that methods and properties written under each Pipe can be accessed by all other connected pipes. It’s suggested to name the methods and properties of the pipe with 2 beginning underscores, to avoid unintention name collision.
  • Python allow multiple inheritance, which perfectly emulate the branch nature of the data processing. So one Pipe can be inherited from multiple pipes, You have to make sure you have call the same method of the super class in the prepare/process/finish methods, and return the result properly. The executing order is compling to the C3 algorithm of python.
  • For convinience of development, each pipe can be running seperately, output=Pipe.eval(item).

At the first version of datapipe, I was using a list of Pipe classes to stand for different steps, and session instace pass the output of one pipe to the next one as the input. But soon I find it’s not flexable enough, and hard to share variables.

Pipeline Chaining

  • depends: if you need to do some pre-processing of items in batch, you can define a depends in the Pipe, for example
class Main(Pipe):
   depends=["being_depend"]  # **to specify the depends**

   def process(self):
       print "main", item, item*1./self.context.depends_total

class Depends(Pipe):
    def prepare(items):
        return items

    def process(self):
        print "depend", item
        return item

# depend 1
# depend 2
# main 1, 0.33333333
# main 2, 0.66666667

The BeingDepends get the same items as input and puts the result in self.context then Main can access it.

  • trigger: one pipeline needs to run right after some other pipeline finished and also has to run standalone in some time.
class NewsImport(Pipe):
    def process(raw_item_store):
        return result_news

@pipeline("", trigger="news_import")
class NewsTag(Pipe):
    def process(news):
        self.add_to_save(*[NewsTag(news=news,tag=tag_text) for tag_text in tags_text])
        return news

sess=get_session()[news1,news2],"")  # run standalone
# or[raw_item_store1, raw_item_store2],"news.import") # will run after news.import finished

Varible Sharing

In assembly line there are containers for people to store some half products, which will be used later.

In datapipe, in the Pipe, there are three containers with different life span you can put varible by set attribute.

  • self lives for only this one specific item;
  • self.local lives for all the items, and be cleaned at finish();
  • self.context shared with all the chained pipeline.

You can make an analogy with the assembly line, the variable put in self is like there is a bucket together with the item on the conveyor belt, workers can put some stuff in it and these stuff can be used by other person, but after the process of the item has finished, this bucket will gone. self.local is like a big table, every people can put thing on it and be shared, but it will be clean up when all the batch of items finished. self.context is another table next self.local, what different is the stuff on it will be pack together with the output and send together to the next Pipeline be chained.

Result and Error Handling

  • Pipe provide a method pipe.add_to_save(result_item) to let you put a to be saved Django Model object in the buffer, Session will periodically do the save in batch for the sake of performance. Also the object put in the add_to_save will be tracked for reparse.
  • Reparse: When pipeline see one input item that has been processed before, it will remove the result item tracked by add_to_save from database before do the processing again. This feature is usedful when you changed the code of pipes and the output is also changed, then you just need to run this pipeline with old items, the old results will be removed and the new results will be saved.
  • Exception Handler: In assembly line, if some person gets an error, which he has no idea how to deal with in current situation(uncatched exception), he will take the item out of the conveyor belt, clean the related intermediate outputs(rollback the database to the savepoint before starting this item), and put it in some other bucket with a note of the situation(PipelineError Collector). Then he moves on to the next item. Periodically the engeneers will come to check the error colletor bucket and figure out what has happened and teach the worker how to deal with it(You fix the code). Then all the collected items will be put in the pipeline again.
  • Concurrent: sess.run_in_celery(items,pipeline_name,[celery_chunksize=10, queue=None]) will split the input items into chunks, the size of chunk is control by celery_chunksize. Then it will send each chunk to celery worker asynchronously, and celery will run the pipeline in parallels.


get the reparse entity in the pipe

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