Django Application Performance Optimization — A Checklist

Improve the performance of your Django application by understanding, testing, and implementing some common optimization techniques.

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Django is a popular framework for writing web applications in Python. Top SaaS companies like Pinterest, Instagram, Robinhood, Doordash, and Rippling use Django to develop highly scalable, performant, and secure backend services. Understanding the nuances of the framework, as well as becoming familiar with the available tools and techniques for optimizing your application code is key to unlocking the peak performance offered by Django. In this article, we will learn about some common techniques and best practices to optimize the performance of a Django application.

1. Monitor application performance in production. Gather metrics, logs, errors, and traces from your production app into a centralized monitoring solution to understand how your application performs in a production environment and identify performance bottlenecks. APM tools like New Relic, Datadog, Sentry, and SigNoz provide easy-to-integrate SDKs and ready-to-use dashboards for monitoring several aspects of your Django application.
2. Utilize profiling during development. While understanding the behavior and performance in production is important, analyzing performance profiles during development and fixing the performance issues right away is equally important. django-silk is an easy-to-use tool that helps you profile and inspect HTTP requests and SQL queries, thereby aiding efficient, secure, and responsible application development.
3. Understand how QuerySets work. The QuerySet API is a core part of the Django framework that lets you efficiently perform CRUD operations on the database. QuerySets are lazy, meaning actual database queries are performed only when a QuerySet is evaluated. There are a fixed number of ways to evaluate a QuerySet, and Django caches the results of each evaluation to minimize database access. Understanding how QuerySets work, and using them in an optimal manner can significantly improve the performance of database queries.
4. Optimize database queries. Often slow database queries are a bottleneck for application performance, but there are a number of standard techniques you can deploy to optimize them. Add indexes for fields that you frequently query using the filter(), exclude(), order_by(), etc., methods. Use the SQL profiles to determine what indexes should be added, but beware of the overhead of maintaining indexes. Only retrieve what you need using methods like defer(), only(), values(), and values_list(). Retrieve everything at once if you know you will need it later, and avoid the N+1 queries problem. To this end, you can use select_related() and prefetch_related(). Use bulk methods like bulk_create() and bulk_update(), along with thebatch_size parameter, to create and update a number of objects in batches and reduce the number of queries.
5. Optimize database settings. The default database settings are not always optimal for production deployments. You can instruct Django to use persistent database connections by setting an appropriate value of the CONN_MAX_AGE parameter. If you have a PostgreSQL database, you can consider using third-party tools like PgBouncer, Pgpool, Patroni, and Citus, to build mission-critical, highly scalable, and highly available applications.
6. Cache results. Caching can save you a lot of processing and round trips for repeated queries. You can choose from local-memory caching (default), an in-memory cache like Redis or Memcached, database caching, or filesystem caching as the caching backend. You can cache the results of database queries and other costly operations, user session data, and authentication tokens. But care should be taken to invalidate the cache when data becomes stale. Additionally, you can use the Conditional GET middleware to support in-browser caching of resources using the ETag header. If you have a globally distributed user base, use CDN caching to serve static files.
7. Utilize async processing. Async processing can boost the application performance significantly if it does a lot of I/O bound operations. Rather than blocking on long-running tasks, schedule them using a task queue like Celery. For efficient concurrency control, use the asyncio or gevent libraries which are based on the concept of cooperative multitasking.
8. Understand the nuances of Django and Python. Having sound knowledge of the various tools at disposal and knowing what to use when is a superpower, but they come from experience and practice. The data processing should be offloaded as much to the database since it is built to do that optimally. Filtering and aggregations are much faster in the database than in Python. Use iterators for processing large datasets, rather than converting them to high memory-consuming lists. Always profile and compare the performance of alternatives before adopting one, for example, e.g., QuerySet.count() can be more efficient than len(queryset), QuerySet.exists() than if queryset, Python map() function than loops, and Numpy array than Python lists for larger datasets.
9. Use up-to-date dependencies. It is often, but not always, true that newer versions of packages include performance improvements over the older ones. Newer versions also include security patches and bug fixes. It is a good practice to always update to the latest stable release of a dependency package. Also, ensure that you are not bundling any unnecessary third-party package with your application and that you are not using any obsolete or unsecured packages. You can use a SAST tool for the latter. Finally, use the latest version of Python and Django. Python 3.11 is known to be between 10–60% faster than Python 3.10.
10. Avoid pre-mature optimization. First profile and monitor the application in development and production environments to understand the scope and need of optimization, before optimizing code. It is highly possible that the code that works optimally in your development environment where you have a minimal amount of data doesn’t work as well in production where you have a huge amount of data. Also, optimization involves tradeoffs, which means improvement in one area might cause deterioration in others. An example of premature optimization is using defer() oronly() to load specific fields in a QuerySet, but later accessing other unspecified fields in code which will result in a separate database query. So while you were trying to minimize the payload of a database query, you ended up using an additional query.

Additional Resources

• https://docs.djangoproject.com/en/4.2/topics/performance/
• https://docs.djangoproject.com/en/4.2/topics/db/optimization/
• https://blog.sentry.io/django-performance-improvements-part-1-database-optimizations/
• https://blog.sentry.io/django-performance-improvements-part-2-code-optimization/
• https://blog.sentry.io/django-improvements-part-3-frontend-optimizations/
• https://blog.sentry.io/django-performance-improvements-part-4-caching-in-django-applications/
• https://blog.gitguardian.com/10-tips-to-optimize-postgresql-queries-in-your-django-project/
• https://doordash.engineering/2021/01/19/scaling-efficienc-of-a-python-service-with-gevent/