Well, as for the time it takes to add a million entries, once you use app
engine for a while you'll get use to using the map/reduce library to perform
these types of tasks. As for the cost assuming your 12 entries per second
you end up with:
Cost ($) / Hour (CPU) = .1
Seconds / Hour = 3600
So, cost per second is $ .00002777
Entries / Second = 12
So, cost per entry is $ .00000231
Total Entries = 1,000,000
Thus, total cost is $ 2.314
Of course, this doesn't include CPU costs associated with overhead for
running the map reduce jobs but basically the cost is less than the Venti
Americano I'm drinking right now. Also, costs for HR datastore would be
greater as I assume this is Master/Slave. You'll have to decide if that cost
is too expensive for you and you'd rather purchase and host your own server,
software (database, os, etc.), load balancers, networking, etc. etc.
On Wed, Feb 2, 2011 at 8:52 AM, rmflow <war...@gmail.com> wrote:
> All right, here is a test app (written in python, imports stripped):
>
> class TheTest(db.Model):
> id = db.IntegerProperty()
> name = db.StringProperty();
>
> class TestHandler(webapp.RequestHandler):
> def get(self):
> start_api = quota.get_request_api_cpu_usage()
> start = quota.get_request_cpu_usage()
> bulk = []
> for i in range(100):
> test = TheTest()
> test.id = random.randint(100,1000000)
> test.name = "abcdefgh"
> bulk.append(test)
> db.put(bulk)
> end_api = quota.get_request_api_cpu_usage()
> end = quota.get_request_cpu_usage()
> self.response.out.write("cost: %d : %d megacycles." % (start - end,
> start_api - end_api))
>
> The result of the request is: cost: -204 : -9800 megacycles.
> And the logs show: /test 200 383ms 8353cpu_ms 8166api_cpu_ms
>
> It is around 12 entries per CPU second. That means, one can forget about
> putting million entries into database (an XML of 20 megs can have such
> amount of data, but it will take ages to parse it)
>
>
>
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