The document discusses debugging techniques in Django, focusing on the importance of logging for tracking events during code execution. It explains the differences between print statements and the logger module, emphasizing the structured capabilities of logger through filtering, log levels, and configuration. A step-by-step guide on configuring a logger in Django and visualizing logging data is also provided.

1. Intro to Django debugging
& building Insights
Backend Engineer @Continual Engine
Board member @Django Software Foundation (DSF)
Open Source Contributor | Mentor

2. AGENDA
Debugging Techniques
What is Logging ?
Print vs Logger
Deep dive into Logger
Configuring with Django
settings.py
Build a visualisation using
logger data
Conclusion

3. Let's Begin!

4. DEBUGGING
TECHNIQUES
Using output statements
Using Flags
Comments
Using Debugging Tools
Identify -> Analyze -> Remove

5. LOGGER AND
LOGGING
Logging is basically tracking events once
a code/software runs. It is very essential
for debugging your software
Maintaining a log file helps is fixing
crashes faster
Logger is a built-in python module that
allows debugging and recording status
messages

6. Print statements works for
small scale
With Print statements it's
difficult to control the log
information one want to
capture
Cannot map the severity level
with print statements
Cannot map the timestamp
with the message emitted
PRINT VS LOGGER

7. Deep Dive into Logger (4 Pillars)
FILTER
Filters provide a fine grained facility
for determining which log, records to
output
LOGGER
A logger is the entry point into the
logging system.
A logger is configured to have a log
level
FORMATTER
Specifies layer/entities of record in
the output
HANDLER
Handler defines the destination of
the log record
For example - To a file, Console

8. MORE ABOUT LOGGER
LEVELS
DEBUG - Information used for debugging purposes
INFO - General message
WARNING - Information used for Minor fault occurrence
ERROR - Information used for Major problem occurrence
CRITICAL - Information used for Critical issue occurrence

9. EXAMPLE USAGE
DEBUG - logger.debug()
INFO - logger.info()
WARNING - logger.warning()
ERROR - logger.error()
CRITICAL - logger.critical()

10. USING LOGGER IN DJANGO
S T E P B Y S T E P

11. BASIC CONFIGURATION
(SETTINGS.PY)
integer-> representing the schema version
configures root/parent logger to log all the
messages in console level with level DEBUG
or higher

12. ADVANCE CONFIGURATION
App level -planets is the name of
the app
file path
Defining formatter
formatter call

13. A P P L I C A T I O N
Presentations are
communication tools
that can be used as
demonstrations.
R E T E N T I O N
logging INFO (views.py)
Instance of a logger with
default __name__
creating a log

15. DEVELOPING
INSIGHTS FROM
LOGGER FILE

16. GETTING OUR DATA READY
S T O R E D A T A
I N D E S I R E D
F O R M A T
tail -f
/tmp/debug.log
>out.txt
C O N V E R S I O N
To CSV/TSV etc
C H O O S E
T H E D A T A
P O I N T S
B U I L D ( ^ _ ^ )
I N S I G H T F U L
V I S U L I Z A T I O N

18. BAR GRAPH - LOGGING LEVEL VS FREQUENCY

19. DIVING INTO CODE

20. THAT'S NOT ALL!
T H E R E A R E E N D L E S S P O S S I B I L I T I E S ^ @ , @ ^

21. THANK YOU SO MUCH :)
@SAYABANIK