Home
/
Blog
/
Tech Tutorials
/
Scaling database with Django and HAProxy

Scaling database with Django and HAProxy

Author
Guest Author
Calendar Icon
October 7, 2013
Timer Icon
3 min read
Share

Explore this post with:

MySQL – Primary data store

At HackerEarth, we use MySQL database as the primary data store. We have experimented with a few NoSQL databases on the way, but the results have been largely unsatisfactory. The distributed databases like MongoDB or CouchDB aren't very scalable or stable. Right now, our status monitoring services use RethinkDB for storing the data in JSON format. That's all about using NoSQL database for now.

With the growing amount of data and number of requests every second, it turns out that the database becomes a major bottleneck to scale the application dynamically. At this point if you are thinking that there are mythical (cloud) providers who can handle the growing need of your application, you couldn't be more wrong. To make matters worse, you can't spin a new database whenever you want to just like you do with your frontend servers. Achieving horizontal scalability at all levels requires massive re-architecture of the system while being completely transparent to the end user. This is what a part of our team has focused on in the last few months, resulting in high uptime and availability.

It was becoming difficult for the master (and only) MySQL database to handle the heavy load. We thought we will delay any scalability at this level till the single database could handle the load. We would work on other high priority tasks instead. But that wasn't to be, and we experienced some down time. After that we did a rearchitecture of our application, sharded the database, wrote database routers and wrappers on top of django ORM, put HAProxy load balancer infront of the MySQL databases, and refactored our codebase to optimize it significantly.

The image below shows a part of the architecture we have at HackerEarth. Many other components have been omitted for simplicity.

Database slaves and router

The idea was to create read replicas and route the write queries to the master database and read queries to slave (read replica) databases. But that was not simple either. We couldn't and wouldn't want to route all the read queries to slaves. There were some read queries which couldn't afford stale data, which comes as a part of database replication. Though stale data might be the order of just a few seconds, these small number of read queries couldn't even afford that. The first database router was simple:
class MasterSlaveRouter(object):

"""
Represents the router for database lookup.
"""
def __init__(self):
if settings.LOCAL:
self._SLAVES = []
else:
self._SLAVES = SLAVES

def db_for_read(self, model, **hints):
"""
Reads go to default for now.
"""
return 'default'

def db_for_write(self, model, **hints):
"""
Writes always go to default.
"""
return 'default'

def allow_relation(self, obj1, obj2, **hints):
"""
Relations between objects are allowed if both objects are
in the default/slave pool.
"""
db_list = ('default',)
for slave in zip(self._SLAVES):
db_list += slave

if obj1._state.db in db_list and obj2._state.db in db_list:
return True
return None

def allow_migrate(self, db, model):
return True

All the write and read queries go to the master database, which you might think is weird here. Instead, we wrote getfromslave(), filterfromslave(), getobjector404fromslave(), getlistor404fromslave(), etc. as part of django ORM in our custom managers to read from slave. So whenever we know we can read from slaves, we call one of these functions. This was a sacrifice made for those small number of read queries which couldn't afford the stale data. Custom database manager to fetch data from slave:
# proxy_slave_X is the HAProxy endpoint, which does load balancing

# over all the databases.
SLAVES = ['proxy_slave_1', 'proxy_slave_2']

def get_slave():
"""
Returns a slave randomly from the list.
"""
if settings.LOCAL:
db_list = []
else:
db_list = SLAVES

return random.choice(db_list)

class BaseManager(models.Manager):
# Wrappers to read from slave databases.
def get_from_slave(self, *args, **kwargs):
self._db = get_slave()
return super(BaseManager, self).get_query_set().get(*args, **kwargs)

def filter_from_slave(self, *args, **kwargs):
self._db = get_slave()
return super(BaseManager, self).get_query_set().filter(
*args, **kwargs).exclude(Q(hidden=True) | Q(trashed=True))

HAProxy for load balancing

Now there could me many slaves at a time. One option was to update the database configuration in settings whenever we added/removed a slave. But that was very cumbersome and inefficient. A better way was to put a HAProxy load balancer in front of all the databases and let it detect which one is up or down and route the read queries according to that. This would mean never editing the database configuration in our codebase — just what we wanted. A snippet of /etc/haproxy/haproxy.cfg:
listen mysql *:3305

mode tcp
balance roundrobin
option mysql-check user haproxyuser
option log-health-checks
server db00 db00.xxxxx.yyyyyyyyyy:3306 check port 3306 inter 1000
server db01 db00.xxxxx.yyyyyyyyyy:3306 check port 3306 inter 1000
server db02 db00.xxxxx.yyyyyyyyyy:3306 check port 3306 inter 1000

The configuration for the slave in settings now looked like this:
DATABASES = {

'default': {
'ENGINE': 'django.db.backends.mysql',
'NAME': 'db_name',
'USER': 'username',
'PASSWORD': 'password',
'HOST': 'db00.xxxxx.yyyyyyyyyy',
'PORT': '3306',
},
'proxy_slave_1': {
'ENGINE': 'django.db.backends.mysql',
'NAME': 'db_name',
'USER': 'username',
'PASSWORD': 'password',
'HOST': '127.0.0.1',
'PORT': '3305',
},
'analytics': {
'ENGINE': 'django.db.backends.mysql',
'NAME': 'db_name',
'USER': 'username',
'PASSWORD': 'password',
'HOST': 'db-analytics.xxxxx.yyyyyyyyyy',
'PORT': '3306',
},
}

But there is a caveat here too. If you spin off a new server with the HAproxy configuration containing some endpoints which don't exist, HAproxy will throw an error and it won't start, making the slave useless. It turns out there is no easy solution to this, and haproxy.cfg should contain existing server endpoints while initializing. The solution then was to let the webserver update its HAproxy configuration from a central location whenever it starts. We wrote a simple script in fabric to do this. Besides, the webserver already used to update its binary when the spin off is from an old image.

Database sharding

Next, we sharded the database. We created another database — analytics. It stores all the computed data, and it forms a major part of read queries. All the queries to the analytics database are routed using the following router:
class AnalyticsRouter(object):

"""
Represents the router for analytics database lookup.
"""
def __init__(self):
if settings.LOCAL:
self._SLAVES = []
self._db = 'default'
else:
self._SLAVES = []
self._db = 'analytics'

def db_for_read(self, model, **hints):
"""
All reads go to analytics for now.
"""
if model._meta.app_label == 'analytics':
return self._db
else:
return None

def db_for_write(self, model, **hints):
"""
Writes always go to analytics.
"""
if model._meta.app_label == 'analytics':
return self._db
else:
return None

def allow_relation(self, obj1, obj2, **hints):
"""
Relations between objects are allowed if both objects are
in the default/slave pool.
"""

if obj1._meta.app_label == 'analytics' or \
obj2._meta.app_label == 'analytics':
return True
else:
return None

def allow_migrate(self, db, model):
if db == self._db:
return model._meta.app_label == 'analytics'
elif model._meta.app_label == 'analytics':
return False
else:
return None

To enable the two routers, we need to add them in our global settings:
DATABASE_ROUTERS = ['core.routers.AnalyticsRouter', 'core.routers.MasterSlaveRouter']


Here the order of routers is important. All the queries for analytics are routed to the analytics database and all the other queries are routed to the master database or their slaves according the nature of queries. For now, we have not put slaves for analytics database but as the usage grows that will be fairly straightforward to do. At the end, we had an architecture where we could spin off new read replicas and route the queries fairly simply and had a high performance load-balancer in front of the databases. All this has resulted in a much higher uptime and stability in our application, and we could focus more on what we love to do — building products for programmers. We already had an automated deployment system in place, which made the experimentation easier and enabled us to test everything thoroughly. The refactoring and optimization that we did in codebase and architecture also reduced the server count by more than two times. This has been a huge win for us, and we are now focusing on rolling out exciting products in the next few weeks. Stay tuned!

I would love to know how others have solved similar problems. Do give suggestions and point out potential quirks.

P.S. You might be interested in The HackerEarth Data Challenge that we are running.

Follow me @vivekprakash. Write to me at vivek@hackerearth.com.

This post was originally written for the HackerEarth Engineering blog by Vivek Prakash.

Subscribe to The HackerEarth Blog

Get expert tips, hacks, and how-tos from the world of tech recruiting to stay on top of your hiring!

Author
Guest Author
Calendar Icon
October 7, 2013
Timer Icon
3 min read
Share

Hire top tech talent with our recruitment platform

Access Free Demo
Related reads

Discover more articles

Gain insights to optimize your developer recruitment process.

How I used VibeCode Arena platform to build code using AI and leant how to improve it

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

  • Clean, semantic HTML
  • Smooth CSS transitions
  • Keyboard navigation support
  • ARIA labels for accessibility
  • Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

  • No DNS-prefetch hints for external image domains
  • Missing width/height attributes causing layout shift
  • No preload directives for critical resources
  • Missing CSS containment properties
  • No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

  • No 'use strict' directive in JavaScript
  • Missing package.json for dependency management
  • No test files
  • Missing README documentation
  • No .gitignore or version control setup
  • Could use functional array methods for cleaner code
  • Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

  • Security vulnerabilities
  • Performance/Efficiency issues
  • Performance optimization opportunities
  • Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

  • What actually improved (and by how much)
  • What new issues I might have introduced
  • Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

  • How to implement Content Security Policy correctly
  • Why DOM depth matters for rendering performance
  • What CSS containment does and when to use it
  • How to structure code for better maintainability
  • Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

The Mobile Dev Hiring Landscape Just Changed

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Article content

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Article content

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin
This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way
Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

For Recruiters & Hiring Managers:

  • Accurately assess real-world development skills.
  • Gain deeper insights into candidate proficiency.
  • Hire with greater confidence and speed.
  • Reduce candidate drop-off from technical friction.

For Candidates:

  • Enjoy a seamless, efficient assessment experience.
  • No need to switch between different tools or manage complex setups.
  • Focus purely on showcasing skills, not environment configurations.
  • Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

Vibe Coding: Shaping the Future of Software

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

Vibe Coding Difference

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable or Hostinger Horizons enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Top Products

Explore HackerEarth’s top products for Hiring & Innovation

Discover powerful tools designed to streamline hiring, assess talent efficiently, and run seamless hackathons. Explore HackerEarth’s top products that help businesses innovate and grow.
Frame
Hackathons
Engage global developers through innovation
Arrow
Frame 2
Assessments
AI-driven advanced coding assessments
Arrow
Frame 3
FaceCode
Real-time code editor for effective coding interviews
Arrow
Frame 4
L & D
Tailored learning paths for continuous assessments
Arrow
Get A Free Demo