PyCon6 Apr2015, Florence, Italy

Art & Music VS AppEngineThomas Alisi, Solution Architect @grudelsud @stinkdigital

photo courtesy of https://www.flickr.com/photos/8064990@N08/

See our 2015 showreel

Stinkdigital is an interactive production company, working with clients and advertising agencies worldwide.

Our services include creative concepting, design and high-end execution. We create everything from live-action films and websites, through to mobile apps and installations.

Does this number look familiar to you?

86,400

Does this number look familiar to you?

86,400= 60’’ x 60’ x 24h[number of seconds in 1 day]

What about this one?

31,536,000

What about this one?

31,536,000= 86,400’’ x 365d[number of seconds in 1 year]

OK, now try and guess the last one

252,000,000

OK, now try and guess the last one

252,000,000= 31.5M’’ x 8y[number of seconds in 8 years]

but also…

252,000,000= 36M x 7’’[7 seconds saved for each of the 36M visits we had during the first 6 months on DevArt]

How did we do it?1. Google App Engine (GAE) and AngularJS 2. data structures 3. assets management 4. load testing 5. GAE benchmarking tool and task inspector 6. GAE asynchronous API

Revolutions in Sound - RedBull, Google+ http://archive.revolutionsinsound.com/

DevArt - Google CL https://devart.withgoogle.com/

Inside Abbey Road - Google CL https://insideabbeyroad.withgoogle.com/

1. Google App Engine (GAE) and AngularJS 2. data structures 3. assets management 4. load testing 5. GAE benchmarking tool and task inspector 6. GAE asynchronous API

- Grunt, Gulp, Browserify, Webpack… AAARGH!

- we like vanilla JS and have been fan of Gulp (at least over the past 10 minutes…)

- we (I) tend to use a super simple Gulp-Browserify-AngularJS boilerplate https://github.com/grudelsud/angularjs-gulp-browserify-boilerplate

- but there are other good examples too e.g. https://github.com/unit9/coffee-bone

divide et impera

- GAE is not opinionated (which is good)

- default webapp2 shipped with GAE is really super simple (a bit too simple…)

- Flask is OK and does not have preferences for a specific ORM (which is great)

- don’t use Django on GAE unless you really want to (e.g. use legacy modules or deploy something really quick)

class Jsonifiable(ndb.Model):def from_dict(cls, dict):

pass

def to_dict(self, async=False):pass

def resolve_future_blobs(cls, async_blobs):pass

class JsonRestHandler(webapp2.RequestHandler):JSON_MIMETYPE = "application/json"

def write(self, data):self.response.out.write(data)

def send_success(self, obj=None, cache_expiry=None):self.response.headers["Content-Type"] = self.JSON_MIMETYPE

self.write(json.dumps(obj, cls=JsonifiableEncoder))

webapp2 bespoke REST micro-framework

asynchronous conversion

bespoke encoder

basic permission check (skipped here)

extend ndb.Model

extend webapp2.RequestHandler

kibble_authenticator = ModelAuthenticatior()admin = kibble.Kibble( 'admin', __name__, kibble_authenticator, label='Admin', static_url_path='/kibble/static', default_gcs_bucket=app_identity.get_default_gcs_bucket_name(),)

security_headers.apply(app)

class CustomJsonEncoder(flask.json.JSONEncoder): def default(self, obj): try: if isinstance(obj, blobstore.BlobKey): return str(obj) if isinstance(obj, fields.Vertex): return [obj.x, obj.y, obj.z] if isinstance(obj, GCSFile): return obj.cloudstore_url if isinstance(obj, ndb.Key): return obj.flat() except TypeError: pass return super(CustomJsonEncoder, self).default(obj)

app = flask.Flask(__name__, template_folder='../templates')app.json_encoder = CustomJsonEncoderadmin.autodiscover(paths=['admin'])app.register_blueprint(admin, url_prefix='/admin')

Flask Blueprints and GAEhttps://github.com/xlevus/flask-kibble

1. Google App Engine (GAE) and AngularJS 2. data structures 3. assets management 4. load testing 5. GAE benchmarking tool and task inspector 6. GAE asynchronous API

this always seems a good way to start

class ClubNight(BaseModel):name = ndb.StringProperty()content = ContentProperty(ndb.TextProperty)blob_key_logo = ContentProperty(ndb.BlobKeyProperty)genre = ndb.KeyProperty(kind=’Genre’)website = ContentProperty(ndb.StringProperty)address = ContentProperty(ndb.TextProperty)location = ContentProperty(ndb.GeoPtProperty)

class Connection(ndb.Model):from_key = ndb.KeyProperty()to_key = ndb.KeyProperty()

abstraction

class ClubNight(BaseModel):name = ndb.StringProperty()content = ContentProperty(ndb.TextProperty)blob_key_logo = ContentProperty(ndb.BlobKeyProperty)genre = ndb.KeyProperty(kind=’Genre’)website = ContentProperty(ndb.StringProperty)address = ContentProperty(ndb.TextProperty)location = ContentProperty(ndb.GeoPtProperty)

class Connection(ndb.Model):from_key = ndb.KeyProperty()to_key = ndb.KeyProperty()to_name = ndb.StringProperty()to_kind = ndb.StringProperty()to_slug = ndb.StringProperty()to_genre_colour = ndb.StringProperty()to_image = ndb.BlobKeyProperty()to_popularity = ndb.IntegerProperty()is_published = ndb.BooleanProperty()is_public = ndb.BooleanProperty(default=False)is_featured = ndb.BooleanProperty(default=False)

reality

class Vertex(namedtuple('Vertex', ['x', 'y', 'z'])):

def __json__(self): return json.dumps(self)

def __str__(self): return 'x={0.x:.2f}, y={0.y:.2f}, z={0.z:.2f}'.format(self)

class VertexProperty(ndb.StringProperty): # Dummy XYZ field, used to quickly add validation through # admin.base.ModelConverter

def _validate(self, value): if not isinstance(value, (tuple, Vertex)): raise TypeError("Expected Vertex got %r" % repr(value)) if len(value) != 3: raise TypeError("Expected 3-tuple/Vertex, got %r" % repr(value))

def _to_base_type(self, value): return "%s,%s,%s" % value

def _from_base_type(self, value): if value: return Vertex(*[float(x) for x in value.split(",")]) return None

finding the sweet spot

1. Google App Engine (GAE) and AngularJS 2. data structures 3. assets management 4. load testing 5. GAE benchmarking tool and task inspector 6. GAE asynchronous API

The get_serving_url() method allows you to generate a stable, dedicated URL for serving web-suitable image thumbnails. You simply store a single copy of your original image in Blobstore, and then request a high-performance per-image URL. [https://cloud.google.com/appengine/docs/python/images/]

ex.

// Resize the image to 32 pixels (aspect-ratio preserved) http://your_app_id.appspot.com/randomStringImageId=s32

// Crop the image to 32 pixels http://your_app_id.appspot.com/randomStringImageId=s32-c

Due diligence

=s500 =s100

- success == sleepless_nights (not for me! :P)

- PR scheduled on all major funnels: - Google homepage - Youtube homepage - all major blogs (the verge, wired, etc.) - London silicon roundabout

- 1.2M unique visits, 10M views during the FIRST 48H

si vis pacem, para bellum

from admin.publish_pipeline import Publishrec = urlmap.PublishRecord.new()p = Publish('admin.web.app', version=rec.key.id())p.start()

[…]

class Publish(Pipeline): """ Root Pipeline. Creates a :py:class:`common.models.urlmap.PublishRecord` for the pipeline, and spawns children.

On completion, sets publish record to completed. """ def run(self, app, locales=None, version=None): rec = urlmap.PublishRecord.get_by_id(version) rec.pipeline_id = self.root_pipeline_id rec.put()

urlmaps = yield URLMaps(version)

with After(urlmaps): static = yield WriteStaticFiles.pipeline(app, version) sitemap = yield PublishSitemap(app, version) short_urls = yield CreateShortUrls.pipeline(app, version)

with After(static, sitemap, short_urls): json = yield PublishJSON(app, version)

with After(json): yield CleanShortUrls(app, version) yield Cleanup.pipeline()

static publishing pipelinehttps://github.com/GoogleCloudPlatform/appengine-mapreduce/

create the pipeline and run process

extend mapreduce pipeline

spawn separate generators

1. Google App Engine (GAE) and AngularJS 2. data structures 3. assets management 4. load testing 5. GAE benchmarking tool and task inspector 6. GAE asynchronous API

GAE docs are rubbish! :) i.e. read it, then forget it: https://cloud.google.com/appengine/articles/load_test

3rd party services are OK, but run your own if you can

create a meaningful simulation of users’ behaviour

hit it as hard as you can, but don’t forget your wallet!

class ApiNav(TaskSet):@task(1)def api_global(self):

self.client.get('/api/global?locale=%s' % langs[random.randint(0, len(langs)-1)], **kwargs)

@task(1)def api_user(self):

self.client.get('/api/user', **kwargs)

@task(4)def api_gallery(self):

self.client.get('/api/gallery?i=0&l=15', **kwargs)

@task(8)def api_search(self):

self.client.get('/api/gallery?i=0&l=15&q=%s' % terms[random.randint(0, len(terms)-1)], **kwargs)

@task(6)def api_feeling_lucky(self):

self.client.get('/api/page/feeling_lucky', **kwargs)

@task(2)def api_big_gallery(self):

self.client.get('/api/gallery?i=0&l=30', **kwargs)

@task(2)def api_featured(self):

self.client.get('/api/gallery?i=0&l=15&t=featured', **kwargs)

class MyLocust(HttpLocust):host = 'https://sd-goog-devart.appspot.com'task_set = ApiNavmin_wait = 5000max_wait = 15000

locust

init data

gallery

random search

random project page

categorized views

- approximately 5000 concurrent user hitting the backend API with a "casual navigation" simulation from different location (London, New York, AWS data centre in Ireland)

- 85 running instances (class F2) at peak

- no errors reported other than random https sockets timeout

- average response times - < 2s for gallery content navigation - < 1s for singe project page navitation - < 3s for static contend (loaded just once)

1. Google App Engine (GAE) and AngularJS 2. data structures 3. assets management 4. load testing 5. GAE benchmarking tool and task inspector 6. GAE asynchronous API

1. Google App Engine (GAE) and AngularJS 2. data structures 3. assets management 4. load testing 5. GAE benchmarking tool and task inspector 6. GAE asynchronous API

@classmethoddef fix_dict(cls, dict, async=False):

async_blobs = []

def _fix_dict(k, v):

# blobif isinstance(v, blobstore.BlobKey):

try:# create futures and put them apart, we'll resolve these lateroutput = {'key': str(v), 'url': '', 'rpc': images.get_serving_url_async(v, secure_url=True)}async_blobs.append(output)return output

except BaseException as e:# logging.warn('error while fetching serving url for [%s] maybe using corrupted image?' % (v,))return None

for k, v in dict.iteritems():dict[k] = _fix_dict(k, v)

if async is True:return dict, async_blobs

else:cls.resolve_future_blobs(async_blobs)return dict

@classmethoddef resolve_future_blobs(cls, async_blobs):

# resolve futuresfor blob in async_blobs:

try:blob['url'] = blob['rpc'].get_result()

except BaseException:blob['url'] = ''

del(blob['rpc'])

get_serving_url_async

1. get async url

3. get real url

2. resolve future blobs

Thanks!uh, just few notes:

1. we are hiring! check www.stinkdigital.com/careers and send us your CV - careers@stinkdigital.com

2. we organise a Meetup in London with UNIT9 and B-Reel, get in touch! tomalisi@stinkdigital.com

3. www.slideshare.net/grudelsud