English version of the presentation on Google's ndb API for the App Engine datastore given at PythonBrasil 2011, on September 29th

1. Boredom comes to those
who wait
Asynchronous calls to Datastore Plus

2. What you should know
•toplevels/tasklets
•futures
•yield (you are going to build lots of generators)
•you'll use yield instead of return (most of the time)
•don't think about threads
•..._async
•the query API changes
•an insanely great use for comparator overrides

3. How it used to be done
class Fact(model.Model):
text = model.TextProperty()
rating = model.FloatProperty(default = 400.)
random_index = model.ComputedProperty(
lambda self : random.randint(0,
sys.maxint))
(...)
for i in range(10):
Fact(text = 'Fact %d' % i).put()

4. Digression: _ah/stats
•A good way to understand the performance of your apps
•If you are doing something wrong, it'll become obvious
Easy to enable on app.yaml:
builtins:
(...)
- appstats: on

5. How the first example
behaves locally

6. … and on the server

7. The asynchronous way
futures = []
for i in range(10):
futures.append(Fact(text = 'Fact %d' %
i).put_async())
[ f.get_result() for f in futures ]
Gives the opportunity to aggregate puts into one large datastore
operation (and we don't have to worry about it)

8. _ah/stats

9. On the server

10. Better: toplevel/tasklet
@context.toplevel
(decorating something – usually a request handler – that will call the following tasklet)
@tasklets.tasklet
def init_facts()
futures = []
for i in range(10):
futures.append(Fact(text = 'Fact %d' %
i).put_async())
yield futures
Yield allows the toplevel event loop to manage other generators making asynchronous calls

11. … even better
@context.toplevel
(decorating your handler)
@tasklets.tasklet
def init_facts()
Futures = []
for i in range(10):
futures.append(Fact(text = 'Fact %d'
% i).put_async())
raise tasklets.Return(futures)
Because it's considered polite to raise things when a generator has nothing else to generate

12. ab -n 10000 -c 50 (synchronous)
Connection Times (ms)
min mean[+/-sd] median max
Connect: 140 159 69.1 145 976
Processing: 338 7408 5452.2 6231 46247
Waiting: 338 7407 5452.2 6230 46247
Total: 482 7567 5442.4 6377 46401
Percentage of the requests served within a certain time (ms)
50% 6377
66% 8540
75% 10131
80% 11068
90% 13419
95% 16077
98% 23883
99% 30173
100% 46401 (longest request)

13. ab -n 10000 -c 50 (asynchronous)
Connection Times (ms)
min mean[+/-sd] median max
Connect: 140 669 1375.6 151 21193
Processing: 189 338 300.0 256 15320
Waiting: 189 335 243.7 255 4143
Total: 332 1007 1407.6 438 21450
Percentage of the requests served within a certain time (ms)
50% 438
66% 565
75% 732
80% 1272
90% 3372
95% 3456
98% 3762
99% 9366
100% 21450 (longest request)

14. What's the difference?

15. Wrong
for f in Fact.query():
f.rating = random.normalvariate(400, 20)
f.put()

16. Why?

17. Right: map_async
@tasklets.tasklet
def randomize_rating(f):
f.rating = random.normalvariate(400, 20)
raise tasklets.Return(f.put_async())
@context.toplevel
def randomize_all():
Fact.query().map_async(randomize_rating)

18. Right: map_async

19. What else should I know?
•context and its event loop
•caches
•new datatypes
•new names for old types
•repeated = True
•StructuredProperty, LocalStructuredProperty
•compress
•shorter response times and more efficient instance usage

20. Where do I find it?
•official builds
•http://code.google.com/p/appengine-ndb-experiment/downloads/list
•"Bleeding" edge
•hg clone https://code.google.com/p/appengine-ndb-experiment/
•version 0.7 released yesterday

21. To know more
•documentation:
http://code.google.com/p/appengine-ndb-experiment/
•Google group:
http://groups.google.com/group/appengine-ndb-discuss/

22. Thanks
Thanks to the fine people who hang out on the appengine-ndb-discuss
group, in special to Guido and Vladimir, whose suggestions pointed me on
the right direction.