Cache-aside (Lazy loading) Pattern

Lazy loading, also known as cache-aside, is the primary pattern used and involves attempting to read from the cache first. If the information is not in the cache, it is considered a cache miss and the system reads from the source data store.


  1. Attempts to read from cache: When the application needs data, it first checks the cache to see if it's already available.
  2. If not found, cache miss: If the data is not found in the cache, this is called a cache miss.
  3. Read from data source and save in memory: When a cache miss occurs, the application fetches the data from the main data source (e.g., a database), saves it in the cache, and returns it to the user.


Let's create a Flask server that connects to the PostgreSQL database and uses Redis for caching with lazy loading pattern:

Imagine we have users and comments data stored in PostgreSQL like this:

CREATE TABLE users ( id SERIAL PRIMARY KEY, name VARCHAR(255) NOT NULL, email VARCHAR(255) NOT NULL UNIQUE ); INSERT INTO users (name, email) VALUES ('Alice', ''), ('Bob', ''), ('Charlie', ''); CREATE TABLE comments ( id SERIAL PRIMARY KEY, content TEXT NOT NULL, user_id INTEGER REFERENCES users(id) ); INSERT INTO comments (content, user_id) VALUES ('Great post!', 1), ('Interesting read.', 1), ('Thanks for sharing!', 2), ('I learned something new today.', 2), ('Very insightful!', 3);

And our app uses this query to get user details

SELECT,,,, comments.content FROM users JOIN comments ON = comments.user_id WHERE = %s AND = %s;

Now let’s use the query as cache key and implement easy loading.

from flask import Flask, jsonify, request import redis import psycopg2 import json app = Flask(__name__) # Connect to Redis cache = redis.Redis(host='localhost', port=6379, db=0) # Database configuration db_config = { "dbname": "your_database_name", "user": "your_user", "password": "your_password", "host": "your_host", "port": "your_port" } def fetch_data_from_database(name, email): conn = psycopg2.connect(**db_config) cursor = conn.cursor() query = ''' SELECT,,,, comments.content FROM users JOIN comments ON = comments.user_id WHERE = %s AND = %s; ''' cursor.execute(query, (name, email)) data = cursor.fetchall() conn.close() return data def get_data(name, email): # Generate a cache key from the query parameters cache_key = f"comments:name={name}:email={email}" data = cache.get(cache_key) # Cache miss if data is None: print(f"Fetching data for name={name} and email={email} from database...") data = fetch_data_from_database(name, email) if data: cache.set(cache_key, json.dumps(data), ex=600) # Set a TTL of 600 seconds else: print(f"No comments found for name={name} and email={email}") return None # Cache hit else: print(f"Fetching data for name={name} and email={email} from cache...") data = json.loads(data.decode('utf-8')) return data @app.route('/comments') def get_comments(): name = request.args.get('name', '') email = request.args.get('email', '') data = get_data(name, email) if data: return jsonify([ { "user_id": row[0], "user_name": row[1], "user_email": row[2], "comment_id": row[3], "comment_content": row[4] } for row in data ]) else: return jsonify({"error": f"No comments found for name={name} and email={email}"}), 404 if __name__ == '__main__':,,,, comments.content FROM users JOIN comments ON = comments.user_id WHERE = %s AND = %s

Notice that there are several key concepts related to caching in the code:

Grasping the building blocks ("the lego pieces")

This part of the guide will focus on the various components that are often used to construct a system (the building blocks), and the design templates that provide a framework for structuring these blocks.

Core Building blocks

At the bare minimum you should know the core building blocks of system design

  • Scaling stateless services with load balancing
  • Scaling database reads with replication and caching
  • Scaling database writes with partition (aka sharding)
  • Scaling data flow with message queues

System Design Template

With these building blocks, you will be able to apply our template to solve many system design problems. We will dive into the details in the Design Template section. Here’s a sneak peak:

System Design Template

Additional Building Blocks

Additionally, you will want to understand these concepts

  • Processing large amount of data (aka “big data”) with batch and stream processing
    • Particularly useful for solving data-intensive problems such as designing an analytics app
  • Achieving consistency across services using distribution transaction or event sourcing
    • Particularly useful for solving problems that require strict transactions such as designing financial apps
  • Full text search: full-text index
  • Storing data for the long term: data warehousing

On top of these, there are ad hoc knowledge you would want to know tailored to certain problems. For example, geohashing for designing location-based services like Yelp or Uber, operational transform to solve problems like designing Google Doc. You can learn these these on a case-by-case basis. System design interviews are supposed to test your general design skills and not specific knowledge.

Working through problems and building solutions using the building blocks

Finally, we have a series of practical problems for you to work through. You can find the problem in /problems. This hands-on practice will not only help you apply the principles learned but will also enhance your understanding of how to use the building blocks to construct effective solutions. The list of questions grow. We are actively adding more questions to the list.

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