Repository pattern for data access in Flask - Performance & Optimization

Performance: Repository pattern for data access

MEDIUM IMPACT

This pattern affects how data is fetched and managed, impacting server response time and client perceived loading speed.

Fetching user data multiple times in different parts of the app

Flask

class UserRepository:
    def __init__(self):
        self.cache = {}

    def get_user(self, user_id):
        if user_id not in self.cache:
            self.cache[user_id] = User.query.filter_by(id=user_id).first()
        return self.cache[user_id]

class UserService:
    def __init__(self, repo):
        self.repo = repo

    def get_user_name(self, user_id):
        return self.repo.get_user(user_id).name

    def get_user_email(self, user_id):
        return self.repo.get_user(user_id).email

Caches user data on first fetch, avoiding repeated database queries and reducing server load.

📈 Performance GainSingle database query per user, reducing server response time and improving LCP.

Fetching user data multiple times in different parts of the app

Flask

class UserService:
    def get_user_name(self, user_id):
        return User.query.filter_by(id=user_id).first().name

    def get_user_email(self, user_id):
        return User.query.filter_by(id=user_id).first().email

Multiple queries to the database for the same user cause repeated database hits and slow response.

📉 Performance CostTriggers multiple database queries, increasing server response time and delaying LCP.

Performance Comparison

Pattern	Database Queries	Server Response Time	Client Load Impact	Verdict
Direct multiple queries	Multiple per user	High	Slower LCP	[X] Bad
Repository with caching	Single per user	Low	Faster LCP	[OK] Good

Rendering Pipeline

The repository pattern reduces redundant data fetching, which lowers server processing time and speeds up sending data to the client, improving the critical rendering path.

→Server Processing

→Network Transfer

→Client Rendering

⚠️ BottleneckServer Processing due to multiple database queries

Core Web Vital Affected

LCP

This pattern affects how data is fetched and managed, impacting server response time and client perceived loading speed.

Optimization Tips

1Cache data in the repository to avoid repeated database queries.

2Minimize server processing time to improve LCP.

3Use repository pattern to centralize and optimize data access.

Performance Quiz - 3 Questions

How does using a repository pattern with caching affect database queries?

A Reduces repeated queries by caching data

B Increases the number of queries

C Has no effect on queries

D Makes queries slower

DevTools: Network

How to check: Open DevTools, go to Network tab, reload the page, and observe the number and timing of API calls fetching data.

What to look for: Look for repeated API calls fetching the same data; fewer calls indicate better repository caching.