Is Mongodb Aggregation framework faster than map/reduce?

Every test I have personally run (including using your own data) shows aggregation framework being a multiple faster than map reduce, and usually being an order of magnitude faster.

Just taking 1/10th of the data you posted (but rather than clearing OS cache, warming the cache first - because I want to measure performance of the aggregation, and not how long it takes to page in the data) I got this:

MapReduce: 1,058ms
Aggregation Framework: 133ms

Removing the $match from aggregation framework and {query:} from mapReduce (because both would just use an index and that's not what we want to measure) and grouping the entire dataset by key2 I got:

MapReduce: 18,803ms
Aggregation Framework: 1,535ms

Those are very much in line with my previous experiments.

My benchmark:

== Data Generation ==

Generate 4million rows (with python) easy with approximately 350 bytes.Each document has these keys:

• key1, key2 (two random columns to test indexing, one with cardinality of 2000, and one with cardinality of 20)
• longdata: a long string to increase size of each document
• value: a simple number (const 10) to test aggregation

db = Connection('127.0.0.1').test # mongo connectionrandom.seed(1)for _ in range(2):    key1s = [hexlify(os.urandom(10)).decode('ascii') for _ in range(10)]    key2s = [hexlify(os.urandom(10)).decode('ascii') for _ in range(1000)]    baddata = 'some long date ' + '*' * 300    for i in range(2000):        data_list = [{                'key1': random.choice(key1s),                'key2': random.choice(key2s),                'baddata': baddata,                'value': 10,                } for _ in range(1000)]        for data in data_list:            db.testtable.save(data)