EpicFEL Oct2014, Sadler’s Wells, London

Performance + OptimizationThomas Alisi, Solution Architect @grudelsud @stinkdigital

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

See our 2013 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.

VIEW SITE

Project Title

Body text goes here.

BrandDevart

VIEW SITE

A new platform from Google and the Barbican that aims to reward and inspire creative coders everywhere.

Google + The Barbican

VIEW SITE

Revolutions in Sound

We teamed up with Google+ & Red Bull Music Academy to create a living archive of UK club culture.

Red Bull

ABOUT THE TECHNOLOGY

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. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. GAE asynchronous API

1. Google App Engine (GAE) and AngularJS 2. data structures 3. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. 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) !

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

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

- webapp2 is really super simple (a bit too simple…)

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))

bespoke REST micro-framework

asynchronous conversion

bespoke encoder

basic permission check (skipped here)

extend ndb.Model

extend webapp2.RequestHandler

1. Google App Engine (GAE) and AngularJS 2. data structures 3. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. 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

1. Google App Engine (GAE) and AngularJS 2. data structures 3. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. GAE asynchronous API

- Frontend caching: most of the API requests are fired when initialising the view !

- Only request data that will be used/visible !

- Remove the tap delay on mobile !

- Request images in the size they will be actually displayed !

- Rendering optimizations, be aware of DOM structure and calls to RenderObject https://speakerdeck.com/jaffathecake/rendering-without-lumps !

- Create a custom font with all icons used (glyphs) https://icomoon.io/

1. Google App Engine (GAE) and AngularJS 2. data structures 3. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. 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

=s500

=s100

1. Google App Engine (GAE) and AngularJS 2. data structures 3. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. 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 = ApiNav min_wait = 5000 max_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. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. GAE asynchronous API

1. Google App Engine (GAE) and AngularJS 2. data structures 3. views optimisations 4. image management 5. load testing 6. GAE benchmarking tool and task inspector 7. GAE asynchronous API

@classmethoddef fix_dict(cls, dict, async=False):! async_blobs = []! def _fix_dict(k, v):! # blob if isinstance(v, blobstore.BlobKey): try: # create futures and put them apart, we'll resolve these later output = {'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 futures for 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 two notes: !1. we are hiring! send us your CV - careers@stinkdigital.com 2. we organise a Meetup with UNIT9 and B-Reel, get in touch!

tomalisi@stinkdigital.com