Intro to Django Debugging & Building Insights
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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.
Intro to Django Debugging & Building Insights
- 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
- 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