This document discusses various techniques for profiling Django applications, including: 1. Using the Django debug toolbar to profile views and SQL queries. 2. Using cProfile and kernprof to profile Python code like Django commands and functions. This helps identify slow parts of code. 3. Benchmarking queries, bulk create operations, and the ORM to find performance bottlenecks in database interactions. Unittests on SQLite can also help profile code.

1. Profiling Django

2. 1. models = MyModel.object.all()
2. filtered = models.filter( size__lte=20 )
3. fronts = filtered[:9]
4. print(fronts[0].attribute)

3. • QuerySet lazy evaluation
• https://docs.djangoproject.com/es/1.9/ref/models/
querysets/

4. •
• profile

5. Profile
•
• Profile

6. view
• django-debug-toolbar
• django-debug-toolbar-line-profiler

9. Performance

10. silk
• view benchmark
• Query

13. Demo

14. Django Command

15. cProfile
python -m cProfile -o my-profile-data.out manage.py
cprofilev -f my-profile-data.out -a 0.0.0.0 -p 4000

17. •
• function.

18. line profiler
•

19. kernprof -l manage.py <Command>
python -m line_profiler manage.py.lprof

21. • db
• django orm orz…

22. code snippet
def store_conversion(post_back, conversion_type):
time_stamp = dateutil.parser.parse(post_back['at'])
nested_data = post_back['data']
if not nested_data:
return
try:
ConversionRecord.objects.create( time_stamp=time_stamp )
except IntegrityError:
pass

23. Profile
• Python Parse

25. 60

26. •

27. for index in range(1, piece+1):
r = update_metric_task(index,
total_count,
piece,
metrics_qs,
records)
jobs.append(r)
Query task

28. benchmark
• query set…

29. Reason
Pickling QuerySets¶
If you pickle a QuerySet,
this will force all the results to be loaded into memory prior to pickling.

30. • Query Set Task
• Task QuerySet

31. • bulk create
• call save method
•

32. • Unittest Sqlite
Process
• TEST_NAME, Sqlite lock
• postgres sql

33. • Profile
• Django ORM