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One Slick Way Yieldbot Uses MongoDB

As a key/value store. Wait, what?  Yeah, MongoDB as a key/value store.

Why? Because we were already using Mongo and planned to move some of our data to a key/value store. But we also didn’t want to wait until we made a decision on a specific solution to make the transitions in our code.

First, as if you wouldn’t have guessed, here’s how easy a “Python library for MongoDB as a key/value store” is:

 

def kv_put(collection, key, value):
    """key/value put interface into mongo collection"""
    collection.save({'_id': key, 'v': value}, safe=True)

 

def kv_get(collection, key):
    """key/value get interface into mongo collection"""
    v = collection.find_one({'_id':key})
    if v:
        return v['v']

 

Note: kv_get() returns None if nothing found, so technically this doesn’t gracefully handle the case where you want None to be a possible value.

What was the pain point?

We basically found that we had collections with nightly analytics results that were really big, and whose indexes were really, really big.  And the index requirements were going way beyond our 70GB RAM server.  We didn’t want to shard our Mongo server because of the cost involved, so instead decided to take a different appraoch.  Since this data was read-only results of analytics, where we once had collections that had entries that were multiply indexed, we now have collections that are pages of the old entries in a defined sorted order and are accessed as key/value.

How did the change work out? Great. We still haven’t switched from MongoDB for this data. Still plan to, but in a startup once you address a pain point you move on to the next one.

You definitely can’t argue that MongoDB isn’t flexible.