Re: Pylons Optimizing on Multiprocessor Server?
3: fapws has basic example on running as wsgi server.
2: You can compress/gzip your HTML payload. The easiest way is to use
paste.gzipper inside your middleware.py:
from paste.gzipper import middleware
If you use mako, you can take advantage of its function-level caching.
- Didip -
On Sun, Nov 15, 2009 at 2:03 PM, kevbaker kba...@missionvi.com wrote:
I am working on optimizing our Pylons servers for better performance.
Currently I am getting what I would consider pretty poor performance.
I have some data and questions.
= Questions
1. Does Pylons take advantage of multiple processors on my server?
2. Other than shutting off debugging what can I do to improve
performance?
3. Are there any other options outside of Paster and Cherrypy? Which
should I use?
= Configuration
- Pylons 0.9.6.2
- Amazon EC2 c1.medium, 1.7 GB Ram, 5 EC2 Compute Units (2 virtual
cores)
- Ubuntu 8.04.3
= Data
- Running Apache Benchmark to get usable numbers to measure
performance improvements after config changes. All tests were using
ab -n 400 -c 10 {MY URL}
- Pylons direct, no proxy, load static file - response:36.86
- Pylons direct, no proxy, load template file with a few variables -
resp/sec:30.28
- Pylons direct, no proxy, load template with database query - resp/
sec:16.06
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Pylons Optimizing on Multiprocessor Server?
I am working on optimizing our Pylons servers for better performance.
Currently I am getting what I would consider pretty poor performance.
I have some data and questions.
= Questions
1. Does Pylons take advantage of multiple processors on my server?
2. Other than shutting off debugging what can I do to improve
performance?
3. Are there any other options outside of Paster and Cherrypy? Which
should I use?
= Configuration
- Pylons 0.9.6.2
- Amazon EC2 c1.medium, 1.7 GB Ram, 5 EC2 Compute Units (2 virtual
cores)
- Ubuntu 8.04.3
= Data
- Running Apache Benchmark to get usable numbers to measure
performance improvements after config changes. All tests were using
ab -n 400 -c 10 {MY URL}
- Pylons direct, no proxy, load static file - response:36.86
- Pylons direct, no proxy, load template file with a few variables -
resp/sec:30.28
- Pylons direct, no proxy, load template with database query - resp/
sec:16.06
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Re: Pylons Optimizing on Multiprocessor Server?
On Nov 15, 2009, at 2:03 PM, kevbaker wrote:
I am working on optimizing our Pylons servers for better performance.
Currently I am getting what I would consider pretty poor performance.
I have some data and questions.
= Questions
1. Does Pylons take advantage of multiple processors on my server?
Yes, it generally runs via a multi-threaded web server like Paste http server,
CherryPy, or mod_wsgi Apache, or even J2EE containers under Jython. Of course
how much parallelism you can really get is hindered by Python's Global
Interpreter Lock.
2. Other than shutting off debugging what can I do to improve
performance?
The easiest gain is to serve the static files (in the public/ directory) via a
faster frontend server like Apache, nginix, or a CDN. To really reap the
benefits of this, you must switch the static_files flag in the ini file to
false. Then Pylons no longer checks if URLs are for a static files (which takes
precedent over everything else) during every request.
3. Are there any other options outside of Paster and Cherrypy? Which
should I use?
There's also FAPWS, Spawning, and some others. Try mod_wsgi if you're trying to
squeeze out every bit of performance.
= Configuration
- Pylons 0.9.6.2
- Amazon EC2 c1.medium, 1.7 GB Ram, 5 EC2 Compute Units (2 virtual
cores)
- Ubuntu 8.04.3
= Data
- Running Apache Benchmark to get usable numbers to measure
performance improvements after config changes. All tests were using
ab -n 400 -c 10 {MY URL}
- Pylons direct, no proxy, load static file - response:36.86
- Pylons direct, no proxy, load template file with a few variables -
resp/sec:30.28
- Pylons direct, no proxy, load template with database query - resp/
sec:16.06
Regardless of what you do with Pylons your database is the larger bottleneck in
at least some cases. On the Pylons side, assuming you're using SQLAlchemy, you
can speedup some operations by using the SQL expression layer directly instead
of the ORM layer, to avoid its object creation. However the data i/o is
probably a larger hit. Consider caching some of it.
More important than anything I've said is to benchmark your app and go by the
numbers.
--
Philip Jenvey
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