Sliding window algorithm in Rest API - Mini Project: Build & Apply

Sliding Window Algorithm with REST API

📖 Scenario: You are building a simple REST API that processes a list of numbers sent by users. The API will find the maximum sum of any continuous sub-list of a fixed size using the sliding window algorithm. This technique helps efficiently analyze data streams or time series.

🎯 Goal: Create a REST API endpoint that accepts a list of integers and a window size, then returns the maximum sum of any continuous sub-list of that size using the sliding window algorithm.

📋 What You'll Learn

Use Python with FastAPI to create the REST API

Accept JSON input with a list of integers called numbers and an integer window_size

Implement the sliding window algorithm to find the maximum sum of any continuous sub-list of length window_size

Return the maximum sum as JSON response

💡 Why This Matters

🌍 Real World

Sliding window algorithms are used in real-time data processing, such as analyzing sensor data, financial time series, or network traffic to find patterns or anomalies efficiently.

💼 Career

Understanding how to implement efficient algorithms and expose them via REST APIs is valuable for backend developers, data engineers, and software engineers working with data streams or microservices.

1

Set up the FastAPI app and input data model

Write code to import FastAPI and Pydantic's BaseModel. Create a FastAPI app called app. Define a Pydantic model called NumbersInput with two fields: numbers as a list of integers and window_size as an integer.

Rest API

from fastapi import FastAPI
from pydantic import BaseModel

# Your code here

Need a hint?

Remember to import FastAPI and BaseModel first. Then create the app and the input data model with the exact field names.

2

Create the API endpoint and receive input

Add a POST endpoint /max_sum_window to the app that accepts a JSON body of type NumbersInput. Inside the function, assign the input list to numbers and the window size to k.

Rest API

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class NumbersInput(BaseModel):
    numbers: list[int]
    window_size: int

@app.post("/max_sum_window")
async def max_sum_window(data: NumbersInput):
    # Your code here

Need a hint?

Define the POST route and extract the input values into variables named exactly numbers and k.

3

Implement the sliding window algorithm to find max sum

Inside the max_sum_window function, write code to calculate the maximum sum of any continuous sub-list of length k using the sliding window algorithm. Use variables window_sum for the current window sum and max_sum for the maximum sum found. Iterate over the list with a for loop using i as the index.

Rest API

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class NumbersInput(BaseModel):
    numbers: list[int]
    window_size: int

@app.post("/max_sum_window")
async def max_sum_window(data: NumbersInput):
    numbers = data.numbers
    k = data.window_size
    
    # Your code here

Need a hint?

Start by summing the first k numbers. Then slide the window by adding the new number and subtracting the old one. Update max_sum if the current window sum is bigger.

4

Return the maximum sum as JSON response

At the end of the max_sum_window function, return a dictionary with the key max_sum and the value as the calculated maximum sum.

Rest API

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class NumbersInput(BaseModel):
    numbers: list[int]
    window_size: int

@app.post("/max_sum_window")
async def max_sum_window(data: NumbersInput):
    numbers = data.numbers
    k = data.window_size
    
    window_sum = sum(numbers[:k])
    max_sum = window_sum
    for i in range(k, len(numbers)):
        window_sum += numbers[i] - numbers[i - k]
        if window_sum > max_sum:
            max_sum = window_sum
    
    # Your code here

Need a hint?

Return a dictionary with the key max_sum and the value as the maximum sum found.