The slide consists some tips about good programming practices in python/django as well as some tools and techniques to make application faster.

1. Faster Programming in
Python/Django
Subit Raj Pokharel
@ rajsubit,
rajsubit

2. First Code then optimize

3. Response Time Limits
● 0.1 seconds
○ limit for having the user feel that the system is reacting
instantaneously
● 1 seconds
○ limit for the user's flow of thought to stay uninterrupted
● 10 seconds
○ limit for keeping the user's attention focused on the dialogue
http://www.nngroup.com/articles/response-times-3-important-limits/

4. Time consuming function
New Relic
http://newrelic.com/

5. def find_recent_blog_post():
...
url = requests.get("http://blog.flipkarma.com/feed")
soup = BeautifulSoup.BeautifulSoup(url.text)
recent_post = soup.findAll('item')[:2]
...
A simple example

6. Analysis with Line Profiler
Line # Hits Time Per Hit % Time Line Contents
==============================================================
70 1 1112952 1112952.0 73.4 url = requests.get("http://blog.flipkarma.com/feed")
71 1 103695 103695.0 6.8 soup = BeautifulSoup.BeautifulSoup(url.text)
72 1 2186 2186.0 0.1 recent_post = soup.findAll('item')[:2]
https://github.com/rkern/line_profiler
$ pip install line_profiler

7. Optimization with Memcached
# Original code
url = requests.get("http://blog.flipkarma.com/feed")
soup = BeautifulSoup.BeautifulSoup(url.text)
recent_post = soup.findAll('item')[:2]
# Using Memcached
key = "flipkarma_blog_post"
cache_time = 10000
result = cache.get(key)
if result:
recent_post = result
else:
url = requests.get("http://blog.flipkarma.com/feed")
soup = BeautifulSoup.BeautifulSoup(url.text)
recent_post = soup.findAll('item')[:2]
cache.set(key, recent_post, cache_time)
http://memcached.org/

8. Line # Hits Time Per Hit % Time Line Contents
==============================================================
65 1 3 3.0 0.0 cache_time = 10000 # time to live in seconds
66 1 17973 17973.0 1.3 result = cache.get(cache_key)
67 1 4 4.0 0.0 if result:
68 1 31 31.0 0.0 parameters["result"] = result
69 1 3 3.0 0.0 if not result:
74 url = requests.get("http://blog.flipkarma.com/feed")
soup = BeautifulSoup.BeautifulSoup(url.text)
recent_post = soup.findAll('item')[:2]
cache.set(cache_key, recent_post, cache_time)
Result

9. New Relic
● analyze the application
● help understand the stories of application
data
● real-time business insights
http://newrelic.com/

11. Profiling
Python Tools:
● cProfile
● profile
● hotshot
Django Debug Toolbar
Line Profiler

12. Django Debug Toolbar

13. SQL Query

14. Query Time

15. Reducing Query

16. Query Time

17. Some Tips

18. If it's fast enough, don't optimise it.

19. Find the slowest step first.

20. Make the slowest operation faster
● Python functions have an overhead. Inline if possible
● List comprehensions are faster than for or if

21. Reduce the number of hits.

22. Perform each operation as rarely as
possible.
● Cache the result if speed is more important than memory
● Move slower operations inside if conditions
● assign operation to variable only if necessary

23. Reducing Queries
● Use of Class Based Views
● proper use of query sets
● select_related() and prefetch_related()

24. Change the algorithm.
This has the second-biggest impact on speed

25. The largest impact comes from
eliminating code.

26. Functionality is an asset. Code is a
liability.

27. Thank You