How can I aggregate my MongoDB results by ObjectId date. Example:
Default cursor results:
cursor = [
{'_id': ObjectId('5220b974a61ad0000746c0d0'),'content': 'Foo'},
{'_id': ObjectId('521f541d4ce02a000752763a'),'content': 'Bar'},
{'_id': ObjectId('521ef350d24a9b00077090a5'),'content': 'Baz'},
]
Projected results:
projected_cursor = [
{'2013-09-08':
{'_id': ObjectId('5220b974a61ad0000746c0d0'),'content': 'Foo'},
{'_id': ObjectId('521f541d4ce02a000752763a'),'content': 'Bar'}
},
{'2013-09-07':
{'_id': ObjectId('521ef350d24a9b00077090a5'),'content': 'Baz'}
}
]
This is what I'm currently using in PyMongo to achieve these results, but it's messy and I'd like to see how I can do it using MongoDB's aggregation framework (or even MapReduce):
cursor = db.find({}, limit=10).sort("_id", pymongo.DESCENDING)
messages = [x for x in cursor]
this_date = lambda x: x['_id'].generation_time.date()
dates = set([this_date(message) for message in messages])
dates_dict = {date: [m for m in messages if this_date(m) == date] for date in dates}
And yes, I know that the easiest way would be to simply add a new date field to each record then aggregate by that, but that's not what I want to do right now.
Thanks!