Practical Google App Engine Applications In Py

Practical Google App Engine
Applications in Python

上官林傑 (ericsk)
COSCUP 2009

http://tinyurl.com/coscup-appengine

Outline

Effective Datastore API
Data Manipulation Efficiency
Effective Memcache
Zip Import & Zip Serve
Conclusion

Quota Limit on App Engine

from: http://www.flickr.com/photos/kevin814/3587610731/

What's Datastore

Datastore is a kind of key-value database built on GFS.
scalable
Kind-based data entities. (not table-based)
add/remove properties dynamically

Relational DB Table

Datastore

Avoid Heavily-Indexing

Datastore will create index on each property.
If there're many properties in your data, indexing will
downgrade performance.
If a property is not used for filtering nor ordering, add
indexed=False to the data model declaration.

class Foo(db.Model):
name = db.StringProperty(required=True)
bar = db.StringProperty(indexed=False)

Minimize Datastore API Calls

CRUD data entities by keys:
Ineffective Way:
keys = [key1, key2, key3, ..., keyN]
products = []
for key in keys:
products.append(db.get(key))
...
Effective Way:
keys = [key1, key2, key3, ..., keyN]
products = db.get(keys)
Same as db.put(), db.delete().

Re-bind GqlQuery Object

Use prepared GqlQuery data:
Ineffective way:
conds = [['abc', 'def'], ['123', '456'], ...]
for cond in conds:
query = db.GqlQuery('SELECT * FROM Foo WHERE first = :
first, second = :second', first=cond[0], second=cond[1])
....
Effective way:
conds = [['abc', 'def'], ['123', '456'], ...]
prepared_query = db.GqlQuery('SELECT * FROM Foo WHERE first
= :first, second = :second')
for cond in conds:
query = prepared_query.bind(first=cond[0], second=cond
[1])
....

Avoid Disjunctions

IN or != operator generates more queries.
SELECT * FROM Foo WHERE a IN ('x', 'y') and b != 3
splits into 4 queries
SELECT * FROM Foo WHERE a == 'x'
SELECT * FROM Foo WHERE a == 'y'
SELECT * FROM Foo WHERE b < 3
SELECT * FROM Foo WHERE b > 3
Fetches all data and filters them manually.

How to Fetch More Than 1000 Results

Datastore API fetches no more than 1000 results once a call
Fetches more than 1000 results (SLOW, may cause TLE)

data = Foo.gql('ORDER BY __key__').fetch(1000)
last_key = data[-1].key()
results = data

while len(data) == 1000:
data = Foo.gql('WHERE __key__ > :1 ORDER BY __key__',
last_key).fetch(1000)
last_key = data[-1].key()
results.extend(data)

Put Data into Entity Group (cont.)

Put data into an entity group:
forum = Forum.get_by_key_name('HotForum')
topic = Topic(key_name='Topic1',......, parent=forum).put()
Load data from an entity group:
topic = Topic.get_by_key_name('Topic1',
parent=db.Key.from_path('Forum', 'HotForum'))

Sharding Data

Write data in parallel
avoiding write contention
Sharding data with key_name:
class Counter(db.Model):
name = db.StringProperty()
count = db.IntegerProperty()
...
def incr_counter(counter_name):
shard = random.randint(0, NUM_SHARDS - 1)
counter = Counter.get_or_insert(shard, name=counter_name)
counter.count += 1
counter.put()

Effective Caching

Caching page content
Without caching
....
self.response.out.write(
template.render('index.html', {})
)
...
With Caching
page_content = memcache.get('index_page_content')
if page_content is None:
page_content = template.render('index.html',{})
self.response.out.write(page_content)

Effective Caching (cont.)

Caching frequently fetched entities
Without caching
....
products = Product.gql('WHERE price < 100').fetch(1000)
from django.utils import simplejson
self.response.out.write(simplejson.dumps(products))
With caching
...
products = memcache.get('products_lt_100')
if products is None:
products = Product.gql('WHERE price < 100').fetch(1000)
from django.utils import simplejson
self.response.out.write(simplejson.dumps(products))

Zipimport & ZipServe

ZipImport:
Zip your library and then import modules within it.
ZipServe:
Zip your static/asset files, then serve them with zipserve.
WHY?
You can ONLY put 1000 files in your application.

Zipimport

For example, you want to use Google Data client library in
your application.
You have to put gdata/ and atom/ packages into your
application directory.
With zipimport, you can zip them:
application/
app.yaml
....
atom.zip
gdata.zip
....

Zipimport (cont.)

import gdata modules in your script:
...
import sys

sys.path.append('atom.zip')
sys.path.append('gdata.zip')
....
from gdata.doc import service

Zipserve

For example, you want to use TinyMCE library
You have to put TinyMCE library into your directory. However, it
contains lots of files.
With zipserve, you can zip the library, and configure the app.
yaml:
...
- url: /tinymce/.*
script: $PYTHON_LIB/google/appengine/ext/zipserve

The filename MUST be the same as the directory name. In this
sample, the TinyMCE library should be zipped into tinymce.
zip.

Conclusion - How to Write Better
GAE Apps?
Read the Articles on Google App Engine site.
Trace the source from SDK
Maybe you will find the undocumented API.
Read http://practicalappengine.blogspot.com/ (in Traditional
Chinese)
Develop apps!

台北, Taipei

http://taipei-gtug.org/

Practical Google App Engine Applications In Py

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