Django

If your Django queries feel slow, the problem might not be your database — it might be your ORM. Recently, I was working with a query that took 25 seconds to run through the Django ORM, but the underlying SQL completed in just 2 seconds. With a single change, I got the ORM query down to 2 seconds as well — and reduced the memory footprint by 70%. Let’s walk through what happened, and why using .values() instead of .only() can dramatically improve performance. ...

A simple insert query turned into a silent performance killer. Our frontend pings our server every few minutes to track device activity. Each ping attempts to insert a row into a DevicePingDaily table, which has a unique constraint on (device_id, date) to ensure only one record per device per day. In Django, the logic looked like this: try: DevicePingDaily.objects.create(device=device, date=today) except IntegrityError: pass It seemed harmless. But as traffic grew, latency spiked and API timeouts increased. Observability tools quickly pointed to the culprit: ...

In a Django application, fetching the latest record for each group is a common yet challenging task, especially when working with large datasets. Whether you’re building an analytics dashboard or managing grouped data, finding an efficient solution is key. In this blog post, we’ll explore five different approaches to tackle this problem, ranked from least to most effective based on performance and readability, using the following Book model: class Book(models.Model): title = models.CharField(max_length=255) author = models.ForeignKey( Author, on_delete=models.CASCADE, related_name="books" ) genre = models.CharField(max_length=255) published_at = models.DateTimeField() is_featured = models.BooleanField(default=False) Solution 1: Python Max with Prefetch latest_books = [ max(author.books.all(), key=lambda x: x.published_at, default=None) for author in Author.objects.prefetch_related("books") ] Performs heavy computation in Python rather than leveraging the database, which is bad for performance. Also makes two database queries (better solutions do it in one). ...

Preventing downtime during deployments is crucial for maintaining service availability and ensuring a positive user experience. Blue-green deployments have emerged as a popular strategy to achieve this goal. However, they introduce challenges, especially when dealing with database changes. This article delves into what blue-green deployments are, why database changes can be tricky in this context, and how to navigate common change scenarios effectively in Django. Blue-Green Deployments A blue-green deployment is a release management strategy that utilizes two separate production environments called “blue” and “green”. At any given time, only one environment is live, serving all production traffic. Changes are deployed to green, and after thorough testing, traffic is switched over from the blue to green. This approach minimizes downtime and provides a quick rollback option by reverting traffic to blue if issues occur. ...

Background As software engineers, one of the most crucial skills we develop is the ability to search through code efficiently. Whether it’s finding a specific function, understanding how a certain feature is implemented, or tracing a bug, being able to quickly navigate a codebase is essential for productivity. However, many codebases can be complex and sprawling, leading to noisy search results that hinder rather than aid our progress. JetBrains provides a few tools to help you refine your code search and focus on what’s important. ...