Hi Matija,
Code:
from time import time
from google.appengine.ext import db
class Test(db.Model):
pass
keys = [db.Key.from_path('Test', i) for i in xrange(1, 21)]
tests = [Test(key=key) for key in keys]
db.put(tests)
t = time()
Test.all().fetch(20)
print 'fetch 20:', time() - t
t = time()
db.get(keys)
print 'get 20:', time() - t
t = time()
config = db.create_config(deadline=5, read_policy=db.EVENTUAL_CONSISTENCY)
db.get(keys, config=config)
print 'get 20 (EVENTUAL_CONSISTENCY):', time() - t
key = db.Key.from_path('Test', 21)
test = Test(key=key)
t = time()
test.put()
print 'put 1:', time() - t
db.delete(keys + [key])
Result:
Master/Slave:
> fetch 20: 0.0107522010803
get 20: 0.0108997821808
get 20 (EVENTUAL_CONSISTENCY): 0.0107271671295
put 1: 0.0120298862457
> ms=193 cpu_ms=2085 api_cpu_ms=2015 cpm_usd=0.058186
High Replication:
> fetch 20: 0.0176739692688
get 20: 0.101014137268
get 20 (EVENTUAL_CONSISTENCY): 0.0169019699097
put 1: 0.0453717708588
> ms=690 cpu_ms=5078 api_cpu_ms=4987 cpm_usd=0.141334
I think you can ignore the differences because the dataset is too small, but
the time would be longer if you have many properties.
----------
keakon
My blog(Chinese): www.keakon.net
Blog source code: https://bitbucket.org/keakon/doodle/
On Fri, Jan 7, 2011 at 6:14 PM, Matija <[email protected]> wrote:
> Hi Keakon,
>
> could you put benchmark results for master/slave and high replication
> datastore with 20 entities (for get and fetch) and 1 entity for put and
> delete instead of 500. We present data in 20 entity size batch and save only
> one entity (or entity group) per request so to us this would be more
> realistic benchmark. Please use original code without single entity group.
>
> Tnx, MATijA.
>
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