N+1 Problem: Typical Bugs Genspark Embeds and Their Complete Solutions

📑 Table of Contents

Introduction: Performance Destroyed Unbeknownst to You

A fortune-telling website developed with Genspark suddenly became **extremely slow** one day. Upon investigating the cause, it was found that **over 1,000 queries were being issued to the database just to display a single page**.

This was the infamous **"N+1 problem"** in the programming world. And the one who embedded this bug was none other than **Genspark**.

This article explains the N+1 problem, which is particularly severe among typical bugs AI embeds, with a real-world example and a complete solution.

What is the N+1 Problem: An Explanation for Beginners

Definition of the N+1 Problem

The **N+1 problem** is an inefficient database query pattern that refers to the following situation:

  1. **First query (1 time)**: Fetches parent data (e.g., a list of users)
  2. **Additional queries (N times)**: Individually fetches related data for each parent data item (e.g., posts for each user)

As a result, **1 + N queries** are issued, hence it's called the "N+1 problem."

Concrete Example: Blog Article List

Scenario

On a blog's article list page, you want to display the "author name" for each article.

❌ Code with N+1 Problem

// 1. Fetch all articles (1st query) const articles = await db.query('SELECT * FROM articles'); // 2. Fetch author information for each article (N queries) for (const article of articles) { const author = await db.query( 'SELECT * FROM users WHERE id = ?', [article.author_id] ); article.authorName = author.name; }

Problem: If there are 100 articles, **101 queries** are issued (1 + 100)

Why is it a Problem?

  • **Increased Database Load**: The number of queries increases exponentially.
  • **Response Delay**: Page display can take several seconds to tens of seconds.
  • **Reduced Scalability**: The server crashes as the number of users grows.
  • **Increased Cost**: Cloud DB pay-as-you-go fees skyrocket.
Important: The N+1 problem is a bug where the code "works" but is "slow." It's hard to notice in the early stages and becomes severe as data increases.

Why AI Embeds the N+1 Problem

Reason 1: Prioritizes "Working Code"

AI prioritizes generating **functionally working code**. Performance tends to be a secondary concern.

Reason 2: Prefers Simple Logic

Code that issues queries within a loop is **easy to understand and write**, which AI prefers to use.

Reason 3: Avoids JOIN Complexity

Efficient SQL (JOINs, subqueries, etc.) can be **complex**, and AI may not generate them correctly. Therefore, it resorts to simple but N-inefficient code.

Reason 4: Misunderstanding ORM Usage

In ORM libraries (Prisma, Sequelize, TypeORM, etc.), the N+1 problem automatically occurs if proper **eager loading** is not used. AI often lacks this knowledge.

Real Case: N+1 Problem Occurred in a Fortune-Telling Website

Project Background

The fortune-telling website I was developing had the following structure:

  • Article (article table): Fortune-telling articles
  • Category (category table): "Love fortune-telling," "Money fortune-telling," etc.
  • Author (author table): Article writers

Code Generated by Genspark (with bug)

// Fetch article list const articles = await prisma.article.findMany(); // Fetch category and author individually for each article for (const article of articles) { article.category = await prisma.category.findUnique({ where: { id: article.categoryId } }); article.author = await prisma.author.findUnique({ where: { id: article.authorId } }); }

Scale of the Problem

  • Number of articles: 500
  • Queries issued: **1,001 times** (1 + 500 × 2)
  • Page load time: **8 seconds** (should ideally be under 0.5 seconds)
⚠️ Actual Damage: Rated "Poor" by Google Page Speed Insights. SEO rankings plummeted, and traffic significantly decreased.

How to Detect the N+1 Problem: 3 Signs

Sign 1: Page Load is Abnormally Slow

If there were no issues when the data volume was small, but it **becomes slow as data increases**, there's a high probability of an N+1 problem.

Sign 2: Database Logs are Numerous

Checking the database query logs in the development environment reveals that **a large number of similar queries are being issued**.

Log Example

SELECT * FROM articles SELECT * FROM categories WHERE id = 1 SELECT * FROM authors WHERE id = 5 SELECT * FROM categories WHERE id = 2 SELECT * FROM authors WHERE id = 3 SELECT * FROM categories WHERE id = 1 ← Same query repeated SELECT * FROM authors WHERE id = 5 ← Same query repeated ... (over 500 times)

Sign 3: Long Network Latency

If the API response's **waiting time is several seconds** in the browser's developer tools (Network tab), an N+1 problem is occurring on the server side.

Complete Solution: Specific Steps for Query Optimization

Solution 1: Use JOINs (Direct SQL)

❌ N+1 Problem Present

const articles = await db.query('SELECT * FROM articles'); for (const article of articles) { const category = await db.query( 'SELECT * FROM categories WHERE id = ?', [article.categoryId] ); article.categoryName = category.name; }

✅ Optimized with JOIN

const articles = await db.query(` SELECT articles.*, categories.name as categoryName FROM articles JOIN categories ON articles.categoryId = categories.id `);

Solution 2: Use Prisma's include

❌ N+1 Problem Present

const articles = await prisma.article.findMany(); for (const article of articles) { article.category = await prisma.category.findUnique({ where: { id: article.categoryId } }); }

✅ Optimized with include

const articles = await prisma.article.findMany({ include: { category: true, author: true } }); // Automatically JOINed, fetched in a single query

Solution 3: DataLoader Pattern (GraphQL)

If using GraphQL, use the DataLoader library for batching + caching:

import DataLoader from 'dataloader'; const categoryLoader = new DataLoader(async (ids) => { const categories = await db.query( 'SELECT * FROM categories WHERE id IN (?)', [ids] ); // Return in the order of ids return ids.map(id => categories.find(c => c.id === id)); }); // When using const category = await categoryLoader.load(article.categoryId);

Performance Improvement Results

Item Before Improvement After Improvement
Number of Queries 1,001 times 1 time
Page Load Time 8 seconds 0.3 seconds
Database Load 100% 5%

Prevention: How to Instruct AI to Avoid the N+1 Problem

Instruction Example 1: Explicitly Request JOIN

「When fetching the article list, include category and author information. To avoid the N+1 problem, use JOIN or include to fetch in a single query.」

Instruction Example 2: Specify ORM Best Practices

「Please fetch the article list using Prisma. Be sure to pre-load related data with the include option to avoid the N+1 problem.」

Instruction Example 3: Explicitly State Performance Requirements

「Implement the article list API. Requirements: Database queries within 10 times, response time under 500ms.」
Important: If you don't explicitly instruct AI to "avoid the N+1 problem," it will generate inefficient code by default.

Useful Tools: N+1 Detection Tools Introduction

1. Prisma Studio

Prisma Studio is the official GUI tool for Prisma. You can check query logs in real-time.

2. Bullet (Ruby on Rails)

For Rails development, Bullet automatically detects N+1 problems.

3. Django Debug Toolbar

In Django (Python), the Debug Toolbar visualizes the number of queries and N+1 problems.

4. New Relic / Datadog

In production environments, APM tools like New Relic and Datadog can detect N+1 problems.

5. Browser Developer Tools

The simplest method is to check the API response time in the browser's "Network" tab.

Summary: Performance Testing is Essential

The N+1 problem is one of the typical pitfalls of AI coding. Keep the following points in mind:

  • Definition of N+1 Problem: Inefficient pattern where 1 + N queries are issued.
  • Reasons AI embeds it: Prioritizes simplicity, avoids JOINs, ORM misuse.
  • Detection Methods: Page slowdown, numerous logs, network latency.
  • Solutions: JOIN, Prisma include, DataLoader.
  • Prevention: Explicit instructions, specifying performance requirements.
  • Tools: Prisma Studio, APM, browser developer tools.
Conclusion: Code generated by AI "works" but is not necessarily "optimal." Especially around the database, always perform performance testing to detect N+1 problems early.

As a next step, please also learn about AI code review processes and AI utilization in the testing phase to achieve high-quality development.

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