Make a GROUP BY with MapReduce in App Engine

Question

I'm looking for a way to make a GROUP BY operation in a query in datastore using MapReduce. AFAIK App Engine doesn't support GROUP BY itself in GQL and a good approach suggested by other developers is use MapReduce.

I downloaded the source code and I'm studying the demo code, and I tryied to implement in my case. But I hadn't success. Here is how I tryied to do it. Maybe everything I did is wrong. So if anyone could help me to do that, I would thank.

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What I want to do is: I have a bunch of contacts in the datastore, and each contact have a date. There are a bunch of repeated contacts with the same date. What I want to do is simple the group by, gather the same contacts with the same date.

E.g:

Let's say I have this contacts:

1. CONTACT_NAME: Foo1 | DATE: 01-10-2012
2. CONTACT_NAME: Foo2 | DATE: 02-05-2012
3. CONTACT_NAME: Foo1 | DATE: 01-10-2012

So after the MapReduce operation It would be something like this:

1. CONTACT_NAME: Foo1 | DATE: 01-10-2012
2. CONTACT_NAME: Foo2 | DATE: 02-05-2012

For a GROUP BY functionality I think word count does the work.

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EDIT

The only thing that is shown in the log is:

/mapreduce/pipeline/run 200

Running GetContactData.WordCountPipeline((u'2012-02-02',), *{})#da26a9b555e311e19b1e6d324d450c1a

END EDIT

If I'm doing something wrong, and if I'm using a wrong approach to do a GROUP BY with MapReduce, help me in how to do that with MapReduce.

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Here is my code:

from Contacts import Contacts
from google.appengine.ext import webapp
from google.appengine.ext.webapp import template
from google.appengine.ext.webapp.util import run_wsgi_app
from google.appengine.api import mail
from google.appengine.ext.db import GqlQuery
from google.appengine.ext import db


from google.appengine.api import taskqueue
from google.appengine.api import users

from mapreduce.lib import files
from mapreduce import base_handler
from mapreduce import mapreduce_pipeline
from mapreduce import operation as op
from mapreduce import shuffler

import simplejson, logging, re


class GetContactData(webapp.RequestHandler):

    # Get the calls based on the user id
    def get(self):
        contactId = self.request.get('contactId')
        query_contacts = Contact.all()
        query_contacts.filter('contact_id =', int(contactId))
        query_contacts.order('-timestamp_')
        contact_data = []
        if query_contacts != None:
            for contact in query_contacts:
                    pipeline = WordCountPipeline(contact.date)
                    pipeline.start()
                    record = { "contact_id":contact.contact_id,
                               "contact_name":contact.contact_name,
                               "contact_number":contact.contact_number,
                               "timestamp":contact.timestamp_,
                               "current_time":contact.current_time_,
                               "type":contact.type_,
                               "current_date":contact.date }
                    contact_data.append(record)

        self.response.headers['Content-Type'] = 'application/json'
        self.response.out.write(simplejson.dumps(contact_data)) 

class WordCountPipeline(base_handler.PipelineBase):
  """A pipeline to run Word count demo.

  Args:
    blobkey: blobkey to process as string. Should be a zip archive with
      text files inside.
  """

  def run(self, date):
    output = yield mapreduce_pipeline.MapreducePipeline(
        "word_count",
        "main.word_count_map",
        "main.word_count_reduce",
        "mapreduce.input_readers.DatastoreInputReader",
        "mapreduce.output_writers.BlobstoreOutputWriter",
        mapper_params={
            "date": date,
        },
        reducer_params={
            "mime_type": "text/plain",
        },
        shards=16)
    yield StoreOutput("WordCount", output)

class StoreOutput(base_handler.PipelineBase):
  """A pipeline to store the result of the MapReduce job in the database.

  Args:
    mr_type: the type of mapreduce job run (e.g., WordCount, Index)
    encoded_key: the DB key corresponding to the metadata of this job
    output: the blobstore location where the output of the job is stored
  """

  def run(self, mr_type, output):
      logging.info(output) # here I should append the grouped duration in JSON

Answer 1

I based on the code @autumngard provided in this question and modified to fit my purpose and it worked.