Async batch processing in Rest API - Mini Project: Build & Apply

Async Batch Processing with REST API

📖 Scenario: You are building a REST API that processes a batch of tasks asynchronously. This is useful when you have many tasks to run but don't want to block the client while waiting for all tasks to finish.Imagine a system that receives a list of job IDs and processes each job in the background, returning a batch ID immediately. Later, the client can check the batch status or results.

🎯 Goal: Create a simple REST API with endpoints to submit a batch of jobs for asynchronous processing, track their progress, and retrieve results.

📋 What You'll Learn

Create an endpoint /submit_batch that accepts a JSON list of job IDs and returns a batch ID immediately.

Store the batch jobs and their statuses in a dictionary.

Process each job asynchronously with a simulated delay.

Create an endpoint /batch_status that returns the status of all jobs in a batch.

Create an endpoint /batch_results that returns the results of all completed jobs in a batch.

💡 Why This Matters

🌍 Real World

Async batch processing is common in web services that handle many tasks without blocking clients, such as image processing, data import, or sending emails.

💼 Career

Understanding async batch processing is valuable for backend developers, API designers, and anyone working with scalable web applications or microservices.

1

Setup initial data structures

Create a dictionary called batches to store batch information. Each batch will have a unique ID as key and a dictionary with job statuses as value. Also, create a variable batch_counter initialized to 0 to generate batch IDs.

Rest API

# Your code here

Need a hint?

Use a dictionary for batches and an integer batch_counter starting at 0.

2

Create batch submission endpoint

Write a function submit_batch(job_ids) that increments batch_counter, creates a new entry in batches with the new batch ID, and initializes each job ID with status 'pending'. Return the new batch ID.

Rest API

batches = {}
batch_counter = 0

# Write submit_batch(job_ids) function here
# Your code here

Need a hint?

Use a dictionary comprehension to set each job's status to 'pending'. Remember to declare batch_counter as global to modify it.

3

Process jobs asynchronously

Write an async function process_batch(batch_id) that loops over each job in batches[batch_id], simulates processing with await asyncio.sleep(1), and updates the job status to 'done'.

Rest API

batches = {}
batch_counter = 0

def submit_batch(job_ids):
    global batch_counter
    batch_counter += 1
    batch_id = batch_counter
    batches[batch_id] = {job_id: 'pending' for job_id in job_ids}
    return batch_id

import asyncio

# Write async process_batch(batch_id) function here
# Your code here

Need a hint?

Use async def and await asyncio.sleep(1) to simulate asynchronous processing.

4

Display batch status and results

Write two functions: batch_status(batch_id) that returns the dictionary of job statuses for the batch, and batch_results(batch_id) that returns a list of job IDs with status 'done'. Then, print the status and results for batch ID 1 after processing it asynchronously.

Rest API

batches = {}
batch_counter = 0

def submit_batch(job_ids):
    global batch_counter
    batch_counter += 1
    batch_id = batch_counter
    batches[batch_id] = {job_id: 'pending' for job_id in job_ids}
    return batch_id

import asyncio

async def process_batch(batch_id):
    for job_id in batches[batch_id]:
        await asyncio.sleep(1)  # simulate job processing
        batches[batch_id][job_id] = 'done'

# Write batch_status(batch_id) and batch_results(batch_id) functions
# Then submit a batch with job IDs [101, 102, 103], process it, and print status and results
# Your code here

Need a hint?

Use dictionary get method to safely access batches. Use list comprehension to filter jobs with status 'done'. Use asyncio.run(main()) to run the async main function.