惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

月光博客
月光博客
雷峰网
雷峰网
S
SegmentFault 最新的问题
博客园 - 【当耐特】
博客园_首页
量子位
爱范儿
爱范儿
博客园 - 叶小钗
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
V
V2EX
美团技术团队
V
Visual Studio Blog
博客园 - 三生石上(FineUI控件)
IT之家
IT之家
Hugging Face - Blog
Hugging Face - Blog
Apple Machine Learning Research
Apple Machine Learning Research
小众软件
小众软件
博客园 - 聂微东
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
The Cloudflare Blog
宝玉的分享
宝玉的分享
WordPress大学
WordPress大学
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻

Anže's Blog

The 15-Year-Old iptables Rule That Broke My DNS Fedidevs 9h Outage Postmortem Letting Claude Upgrade My Raspberry Pi Agents Day Lisbon DjangoCon Europe 2026 How to Safely Update Your Dependencies Speeding Up Django Startup Times with Lazy Imports Typing Your Django Project in 2026 Claude Fixes User Bug Jekyll to Hugo Migration Advent of Code 2025 🎄 Django bulk_update Memory Issue Migrating Gunicorn to Granian Disable Network Requests When Running Pytest Disable Runserver Warning in Django 5.2 Autogenerating og:images with Jekyll Power Outages and Gunicorn PID Files UV with Django Go-like Error Handling Makes No Sense in JavaScript or Python Packages Do Not Match the Hashes Pip Error Gotchas with SQLite in Production Fedidevs Dev Update #2 Django SQLite Production Config Django Streaming HTTP Responses Deploying a Django Project to My Raspberry Pi (Video) Thoughts on Code Reviews Django SQLite Benchmark Django, SQLite, and the Database Is Locked Error No Downtime Deployments with Gunicorn SQLite Write-Ahead Logging
Your Code Doesn't Have to Be Perfect
Anže Pečar · 2022-11-21 · via Anže's Blog

Let me tell you a little story about the following code snippet:

template_id = request.GET["template_id"]
load_hub = False
try:
    templates = Template.objects.filter(
        company__hubs__tree_id=tree_id,
        is_public=1,
        deleted=0,
    ).exclude(company_id=company_id)
    for template in templates:
        if int(template_id) == template.id:
            load_hub = True
except:
    pass

This code was committed six years ago and was used until very recently by my client’s largest customers.

Code Smells

The first code smell is the bare except. Pylint has a bare except warning that warns you against these. They are dangerous because they might catch things the developer doesn’t expect. If we had a typo, the bare except would swallow the error. This wasn’t the case for us, but the bare except did make it much harder to detect the problem with this code.

The Django ORM query fetches the whole Template row data, even though we only accesses the ids. Even worse, the Python code is doing the filtering by id instead of letting the database do that. Super wasteful!

The problem

Last week this code finally broke. A customer had too many Template objects and the query started to time out. Because of the bare try-except, we haven’t received a Sentry error and we haven’t noticed that the code was broken until the customer reached out 😢

When the query timed out the for loop didn’t execute. The bare except block caught the time out exception, but didn’t do anything with it. The code continued with load_hub = False even though it should have been True based on the data in the database. Later on a permission check failed and the users got an unexpected 404.

Luckily, it didn’t take us long to debug and fix the issue. We rewrote the code into the following:

template_id = request.GET["template_id"]
load_hub = (
    Template.objects.filter(
        id=template_id,
        company__hubs__tree_id=tree_id,
        is_public=True,
        deleted=False,
    )
    .exclude(company_id=company_id)
    .exists()
)

The new code removes the bare except. If the query times out again, we will be notified immediately. It is also much less likely to time out now because we filter the results by the id and only send back a single row.

The lesson

Don’t worry about code perfection. Bugs and poorly performing code are unavoidable and you can do much more damage with premature optimization.

The example code was far from ideal, but it didn’t cause any problems for years. It was good enough and allowed the team to focus on adding value to the customers in other areas of the codebase.

Over the years the company grew to more than 100 employees. That would probably not be possible if the engineers were spending all their time fixing issues like this before they became actual problems.

Do make sure that you can deploy your fixes fast. If the customers have to wait six months for the fix, they will not be happy.

Also make sure to apply this advice only on code that won’t cause harm if it breaks. 🙏