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blogs about appengine
Instrumentation of google appengine's datastore.#5298 - latest update on 2008/08/04, created on 2008/06/23 by Sylvain Thenault Here is a piece of code I've written which I thought may be useful to some other people...
You'll find here a simple python module to use with the Google AppEngine SDK to monkey patch the datastore API in order to get an idea of the calls performed by your application.
To instrument of the datastore, put at the top level of your handler file
import instrdatastore
Note that it's important to have this before any other import in your application or in the google package to avoid that some modules will use the unpatched version of datastore functions (and hence calls to those functions wouldn't be considered).
Then add at the end of your handler function
instrdatastore.print_info()
The handler file should look like this
"""my handler file with datastore instrumenting activated"""
import instrdatastore
# ... other initialization code
# main function so this handler module is cached
def main():
from wsgiref.handlers import CGIHandler
from ginco.wsgi.handler import ErudiWSGIApplication
application = ErudiWSGIApplication(config, vreg=vreg)
CGIHandler().run(application)
instrdatastore.print_info()
if __name__ == "__main__":
main()
Now you should see in your logs the number of Get/Put/Delete/Query which has been done during request processing
2008-06-23 06:59:12 - (root) WARNING: datastore access information
2008-06-23 06:59:12 - (root) WARNING: nb Get: 2
2008-06-23 06:59:12 - (root) WARNING: arguments (args, kwargs):
((datastore_types.Key.from_path('EGroup', u'key_users', _app=u'winecellar'),), {})
((datastore_types.Key.from_path('EUser', u'key_test@example.com', _app=u'winecellar'),), {})
2008-06-23 06:59:12 - (root) WARNING: nb Query: 1
2008-06-23 06:59:12 - (root) WARNING: arguments (args, kwargs):
(({'for_user =': None}, 'EProperty'), {})
2008-06-23 06:59:58 - (root) WARNING: nb Put: 1
2008-06-23 06:59:58 - (root) WARNING: arguments (args, kwargs):
(({u'login': None, u'last_usage_time': 1214204398.2022741, u'data': ""},), {})
I'll probably extend this as the time goes. Also notice you may encounter some problems with the automatic reloading feature of the dev app server when instrumentation is activated, in which case you should simply restart the web server.
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First version of LAX Book#5283 - latest update on 2008/06/24, created on 2008/06/16 by Arthur Lutz Previous documentation was merged into a LAX Book now featuring step-by-step screenshots to get up and running faster.
http://lax.logilab.org/lax-book
Don't we all like screenshots...
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Implementing scalable applications with AppEngine#5223 - latest update on 2008/07/01, created on 2008/06/11 by Nicolas Chauvat 
At Google IO, a large part of the Tools track was dedicated to
AppEngine. Brett Slatkin gave a talk titled Building scalable Web
Applications with Google AppEngine which focused on optimizing the
server part of web apps. As other presenters demonstrated it, like
Steve Souders in his talk Even Faster Websites, optimizing the
browser part of webapps is not to be neglected either.
First of all, I must confess I am used to repeat that "early
optimisation is the root of all evil" and "delay commitment until the
last responsible time". But reading about AppEngine and listening to
the Google IO talks, it appears that the tools we have today ask for
human intervention to reach web-scale performance, even when "we"
stands for "Google".
In order for web-scale applications to handle the kind of load they
are facing, they must be designed and implemented carefully. As
carefully as any application was designed before the exponential
growth of PC computation power let us move away from low-level
implementation details and made some inefficiencies acceptable as long
as the time spent developing was short enough.
It all depends on the parameters of your cost function, but for
web-scale applications, it seems like we have not enough computer-time
and can not trade it for human-time.
To get a better idea of the work constraints, one should know that a
disk seek is about 10ms, which means there will be a maximum of 100
accesses per second. On the other hand, if we need consistent data as
opposed to transactional data (the latter implying that data is
fetched each time it is asked for), data can be read from disk once
then cached. Following reads are done from memory at a rate of about
4GB/sec, which means 4000 accesses per second if entities are around
1MB in size. Result of this back of the envelope approximation is 40
reads equals one write.
It follows that, although the actual time depends on the size and shape
of data, writes are very expensive compared to reads and both are
better done in batches to optimise disk access.
The AppEngine Datastore was designed with this constraints in
mind. Entities are sets of property name/value pairs. Each entity may
have a parent. An entity without a parent is the root of a hierarchy
called an entity group.
Entities of the same group are stored on disk close to each other, but
two distinct entity groups may be stored on different computers. Read
access to entities of the same group is thus faster than read access
to entities of different groups.
Write access is serialized per entity group. As opposed to a
traditionnal RDBMS that provides row locking, the datastore only
provides entity group locking. Writes to the a single entity group
will always happen in sequence, even though changes concern different
entities.
There is no limit to the number of entity groups or to the number of
entities per group, but because of the locking strategy, large entity
groups will cause high contention and a lot of failed
transactions. Since writes are expensive, not thinking about write
throughput is a very bad idea when designing an AppEngine application
if one want it to scale.
On the other hand, the parallel nature of the datastore make it scale
wide and there is no limit to the number of entity groups that can be
written to in parallel, nor to the number of reads that can be done in
parallel.
To understand this design in details, you will have to read about GFS,
BigTable and other technologies developed by Google to implement
large-scale clustering.
Counters are a good example to address when discussing write
throughput, because the datastore locking strategy makes writing to
global data very expensive.
Let us assume that we want to display on the main page of a wiki
application the total number of comments posted.
A global counter would serialize all its updates. If 100 users were to
add comments at the same time, some of them would have to wait several
seconds for their action to complete: one write for the comment, one
write for the counter, at most 100 writes per second for the counter
and a lot of time lost due to failed transaction that need to be
restarted.
The solution to make the counter scale is to partition it among all
entity groups then sum these partial counters when the global value is
needed.
Since chances are low that a given user will write more than one
comment at a time, comment entities for a user can be grouped together
and a partial counter can be added to the same entity group. Creating
a new comment and increasing the partial counter will be done in the
same batch.
When a new request for the main page is received, the counter total is
looked up in the cache. If it is not found, all partial counters are
fetched and summed up, then the cache is refreshed with a short
timeout, for example one minute.
During the next minute, the counter will be "consistent", read no too
far-off, and served extremely fast from the cache.
To sum things up, when implementing applications on top of AppEngine
with web-scale usage as a goal, everything that can be done to save
time should be considered. Including the following:
- importing python modules as late as possible will minimize the
python runtime overhead
- retrieving data that is not going to be used is a waste
- repeated queries and queries returning large result sets must be
avoided
- when Get() if sufficient, do not spend time on Query()
- landing pages are traffic intensive and would better use the same
query for everyone
- entity groups have to be designed to match the load and aim at low
write contention
- caching must be used aggressively (it is no surprise that
memcache was the first improvement that followed within a month of
the AppEngine public release)
As a conclusion, the interface AppEngine is exhibiting today requires
to optimize early, but I would bet that in the years to come, new
languages and domain-specific compilers or database engines will take
part of that burden off the hands of the developers.
Did not Yahoo and Google start developping PigLatin and Sawzall to
make it easier to write parallel data-processing programs ? The same
could happen with describe a data-model in a high-level language and
get a tool to optimize it for write contention and web-scale
application.
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Google App Engine future directions#5216 - latest update on 2008/06/10, created on 2008/06/09 by Nicolas Chauvat 
Several of us went to San Francisco last week to attend Google IO. As usual with conferences, meeting people was more interesting than listening to most talks. The AppEngine Fireside Chat was a Q&A session that lasted about an hour. Here is what I learned from this session and various chats with AppEngineers.
- Google has decided to provide its scalable datastore architecture as a service. At this point, the datastore is the product and the goal it to make it as widely accessible as possible.
- The google.appengine.api.datastore API alone would not have made for a very sexy launch. In order to attract more people and lower the bar the beginners would have to jump over, they looked for a higher level programming interface.
- Since some people working at Google have been using Django and know it, they reimplemented part of its interface for defining data models. Late in the project, they added GQL because Django-like queries were a bit too difficult. In both case, the goal was to make it easier for external developers to get started.
- But Google is not in the business of providing web application frameworks and AppEngineers made explicit that they would not be officially supporting a specific framework or a specific version of a given framework (not even Django 0.96, although there is a django-appengine-helper project on code.google.com). They expect frameworks to be provided by communities of developers.
My conclusion is twofold:
- They will be focusing on supporting other languages in AppEngine (I would bet on Java being the next one available) rather than extending Python frameworks support.
- Anyone is free to join with his own framework and provide support for it, the One True Interface being the one defined by google.appengine.api.datastore, not the one defined by db.model and GQL.
This is why Logilab published its own framework running on App Engine as free software and is providing support for it: Logilab Appengine eXtension.
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Nouvelle version de LAX - Logilab Appengine eXtension#5207 - latest update on 2008/06/09, created on 2008/06/09 by Arthur Lutz
La version 0.3.0 de LAX est sortie aujourd'hui voir : http://lax.logilab.org/
Il suffit de 10 petites minutes pour avoir une application qui tourne, suivez le guide :
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LAX - Logilab Appengine eXtension is a full-featured web application framework running on AppEngine#5205 - latest update on 2008/06/09, created on 2008/06/09 by Arthur Lutz
LAX version 0.3.0 was released today, see http://lax.logilab.org/
Get a new application running in ten minutes with the install guide
and the tutorial:
Enjoy!
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Flying to Google I/O#5095 - latest update on 2008/05/27, created on 2008/05/27 by Arthur Lutz  Three of us from Logilab are going to San Francisco to listen, share and discuss at Google I/O.
It's a two day developer gathering in San Francisco, with various talks about google technologies : http://code.google.com/events/io/
We're hoping to show and talk about LAX (http://lax.logilab.org) which uses Google AppEngine |
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LAX - Logilab Google AppEngin Sprint at Pycon-FR#5034 - latest update on 2008/06/11, created on 2008/05/20 by Arthur Lutz Here are a few pictures from the sprint we organized at Pycon-FR
We got a few people to install Google AppEngine and LAX on their machines, and explained the concepts at hand to a bunch of other people.
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HOWTO quickly get lax running on linux#5026 - latest update on 2008/05/19, created on 2008/05/19 by Arthur Lutz This is how easy it is to get lax to run on your linux machine
hg clone http://www.logilab.org/hg/lax/
wget http://googleappengine.googlecode.com/files/google_appengine_1.0.2.zip
unzip google_appengine_1.0.2.zip
./google_appengine/dev_appserver.py lax/skel/
Point your favorite browser to http://localhost:8080/
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