Validator for the Model key_name property in Google App Engine datastore (Python)

August 4, 2010

The Google App Engine datastore provides convenient data modeling with Python. One important aspect is the validation of the data stored in a Model instance. Each data key-value is stored as a Property which is an attribute of a Model class.

While every Property can be validated automatically by specifying a “validator” function, there is no option for the Model key name to be automatically validated. Note that we can manually specify by our code the value of the key name, and therefore this key name can be considered user-data and must be validated. The key name is by the way the only unique index constraint, similar to the “primary key” in relational databases, which is supported by the Google datastore, and can be specified manually.

Here is my version for a validation function for the Model’s key name:

from google.appengine.ext import db
import re

def ModelKeyNameValidator(self, regexp_string, *args, **kwargs):
	gotKey = None
	className = self.__class__.__name__

	if len(args) >= 2:
		if gotKey: raise Exception('Found key for second time for Model ' + className)
		gotKey = 'args'
		k = args[1] # key_name given as an unnamed argument
	if 'key' in kwargs:
		if gotKey: raise Exception('Found key for second time for Model ' + className)
		gotKey = 'Key'
		k = kwargs['key'].name() # key_name given as Key instance
	if 'key_name' in kwargs:
		if gotKey: raise Exception('Found key for second time for Model ' + className)
		gotKey = 'key_name'
		k = kwargs['key_name'] # key_name given as a keyword argument

	if not gotKey:
		raise Exception('No key found for Model ' + className)

	id = '%s.key_name(%s)' % (self.__class__.__name__, gotKey)
	if (not re.search(regexp_string, k)):
		raise ValueError('(%s) Value "%s" is invalid. It must match the regexp "%s"' % (id, k, regexp_string))

class ClubDB(db.Model):
	# key = url
	def __init__(self, *args, **kwargs):
		ModelKeyNameValidator(self, '^[a-z0-9-]{2,32}$', *args, **kwargs)
		super(self.__class__, self).__init__(*args, **kwargs)

	name = db.StringProperty(required = True)

As you can see, the proposed solution is not versatile enough, and requires you to copy and alter the ModelKeyNameValidator() function again and again for every new validation type. I strictly follow the Don’t Repeat Yourself principle in programming, so after much Googling and struggling with Python, I got to the following solution which I actually use in my projects (click “show source” to see the code):

from google.appengine.ext import db
import re

def re_validator(id, regexp_string):
	def validator(v):
		string_type_validator(v)
		if (not re.search(regexp_string, v)):
			raise ValueError('(%s) Value "%s" is invalid. It must match the regexp "%s"' % (id, v, regexp_string))
	return validator

def length_validator(id, minlen, maxlen):
	def validator(v):
		string_type_validator(v)
		if minlen is not None and len(v) < minlen:
			raise ValueError('(%s) Value "%s" is invalid. It must be more than %s characters' % (id, v, minlen))
		if maxlen is not None and len(v) > maxlen:
			raise ValueError('(%s) Value "%s" is invalid. It must be less than %s characters' % (id, v, maxlen))
	return validator

def ModelKeyValidator(v, self, *args, **kwargs):
	gotKey = None

	if len(args) >= 2:
		if gotKey: raise Exception('Found key for second time for Model ' + self.__class__.__name__)
		gotKey = 'args'
		k = args[1] # key_name given as unnamed argument
	if 'key' in kwargs:
		if gotKey: raise Exception('Found key for second time for Model ' + self.__class__.__name__)
		gotKey = 'Key'
		k = kwargs['key'].name()
	if 'key_name' in kwargs:
		if gotKey: raise Exception('Found key for second time for Model ' + self.__class__.__name__)
		gotKey = 'key_name'
		k = kwargs['key_name']

	if not gotKey:
		raise Exception('No key found for Model ' + self.__class__.__name__)

	v.execute('%s.key_name(%s)' % (self.__class__.__name__, gotKey), k) # validate the key now

class DelayedValidator:
	''' Validator class which allows you to specify the "id" dynamically on validation call '''
	def __init__(self, v, *args): # specify the validation function and its arguments
		self.validatorArgs = args
		self.validatorFunction = v

	def execute(self, id, value):
		if not isinstance(id, basestring):
			raise Exception('No valid ID specified for the Validator object')
		func = self.validatorFunction(id, *(self.validatorArgs)) # get the validator function
		func(value) # do the validation

class ClubDB(db.Model):
	# key = url
	def __init__(self, *args, **kwargs):
		ModelKeyValidator(DelayedValidator(re_validator, '^[a-z0-9-]{2,32}$'), self, *args, **kwargs)
		super(self.__class__, self).__init__(*args, **kwargs)

	name = db.StringProperty(
		required = True,
		validator = length_validator('ClubDB.name', 1, None))

You probably noticed that in the second example I also added a validator for the “name” property too. Note that the re_validator() and length_validator() functions can be re-used. Furthermore, thanks to the DelayedValidator class which accepts a validator function and its arguments as constructor arguments, the ModelKeyValidator class can be re-used without any modifications too.

P.S. It seems that all “validator” functions are executed every time a Model class is being instantiated. This means that no matter if you are updating/creating the data object, or you are simply reading it from the datastore, the assigned values are always validated. This surely wastes some CPU cycles, but for now I have no idea how to easily circumvent this.

Disclaimer: I’m new to Python and Google App Engine. But they seem fun! :) Sorry for the long lines…


Resources:


C++ vs. Python vs. Perl vs. PHP performance benchmark (part #2)

August 2, 2010

This time we will focus on the startup time. The process start time is important if your processes are not persistent. If you are using FastCGI, mod_perl, mod_php, or mod_python, then these statistics are not so important to you. However, if you are spawning many processes which do something small and live for a very short time, then you should consider the CPU resources which get wasted while the script interpreter is being initialized.

The benchmarked scripts do only one thing – say “Hello, world” on the standard output. They do not include any additional modules in their source code – this may, or may not be your use-case. Though, very often the scripting languages have pretty many built-in functions, and for simple tasks you never need to include other modules.

Here are the benchmark results:

Language CPU time Slower than
User System Total C++ previous
C++ (with or w/o optimization) 2.568 3.536 6.051 - -
Perl 12.561 6.096 18.723 209% 209%
PHP (w/o php.ini) 20.473 13.877 34.918 477% 86%
Python 27.014 11.881 39.318 550% 13%
Python + Psyco 32.986 14.845 48.132 695% 22%

The clear winner among the script languages this time is… Perl. :)

All scripts were invoked 3000 times using the following Bash loop:

time ( i=3000 ; while [ "$i" -gt 0 ]; do $CMD >/dev/null ; i=$(($i-1)); done )

All tests were done on a Kubuntu Lucid box. The versions of the used software packages follow:

  • g++ (GNU project C and C++ compiler) 4.4.3
  • Python 2.6.5
  • Python Psyco 1.6 (1ubuntu2)
  • Perl 5.10.1
  • PHP 5.3.2 (1ubuntu4.2 with Suhosin-Patch), Zend Engine 2.3.0

The C++ implementation follows, click “show source” below to see the full source:

#include <iostream>
using namespace std;

int main() {
	cout << "Hello, world!\n";
	return 0;
}

The Perl implementation follows, click “show source” below to see the full source:

use strict;
use warnings;

print "Hello, world!\n";

The PHP implementation follows, click “show source” below to see the full source:

<?php
echo "Hello, world!\n";

The Python implementation follows, click “show source” below to see the full source:

#import psyco
#psyco.full()

print 'Hello, world!'


Update (Jan/14/2012): Copied the used test environment info here.


C++ vs. Python vs. Perl vs. PHP performance benchmark

July 1, 2010

Update: There is a part #2 of the benchmark results.


This all began as a colleague of mine stated that Python was so damn slow for maths. Which really astonished me and made me check it out, as my father told me once that he was very satisfied with Python, as it was very maths oriented.

The benchmarks here do not try to be complete, as they are showing the performance of the languages in one aspect, and mainly: loops, arrays with numbers, basic math operations.

Note: Give your ideas and use-cases on what to benchmark, and I’ll try to implement it for you. I.e. “benchmark the languages for reading a file, then splitting it to tokens by white-space and finally outputting all unique elements and their count”.

Out of curiosity, Python was also benchmarked with and without the Psyco Python extension, which people say could greatly speed up the execution of any Python code without any modifications.

Here are the benchmark results:

Language CPU time Slower than Language
version
Source
code
User System Total C++ previous
C++ (optimized with -O2) 1,520 0,188 1,708 - - g++ 4.5.2 link
C++ (not optimized) 3,208 0,184 3,392 99% 99% g++ 4.5.2 link
Javascript (nodejs) 3,096 0,384 3,480 104% 3% 0.2.6 link
Java 8,521 0,192 8,713 410% 150% 1.6.0_26 link
Python + Psyco 13,305 0,152 13,457 688% 54% 2.6.6 link
Python 27,886 0,168 28,054 1543% 108% 2.7.1 link
Perl 41,671 0,100 41,771 2346% 49% 5.10.1 link
PHP 94,622 0,364 94,986 5461% 127% 5.3.5 link

The clear winner among the script languages is… Python. :)

NodeJS JavaScript is pretty fast too, but internally it works more like a compiled language. See the comments below.

The times include the interpretation/parsing phase for each language, but it’s so small that its significance is negligible. The math function is called 10 times, in order to have more reliable results. All scripts are using the very same algorithm to calculate the prime numbers in a given range. The correctness of the implementation is not so important, as we just want to check how fast the languages perform. The original Python algorithm was taken from http://www.daniweb.com/code/snippet216871.html.

The tests were run on an Ubuntu Linux machine.

You can download the source codes, an Excel results sheet, and the benchmark batch script at:
http://www.famzah.net/download/langs-performance/


Update (Jul/24/2010): Added the C++ optimized values.
Update (Aug/02/2010): Added a link to the benchmarks, part #2.
Update (Mar/31/2011): Using range() in PHP improves performance with 5%.
Update (Jan/14/2012): Re-organized the results summary table and the page. Added Java.


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