Sending and receiving messages in FastAPI - Performance & Optimization

Performance: Sending and receiving messages

MEDIUM IMPACT

This affects how quickly messages are sent and received over the network, impacting server response time and user interaction speed.

Handling message sending and receiving in a FastAPI app

FastAPI

from fastapi import FastAPI
import asyncio
app = FastAPI()

@app.post('/send')
async def send_message(message: str):
    # Simulate async processing
    await asyncio.sleep(0.1)
    return {'status': 'sent'}

Using async def and non-blocking await allows server to handle other requests concurrently, improving responsiveness.

📈 Performance GainNon-blocking, reduces INP, improves server throughput

Handling message sending and receiving in a FastAPI app

FastAPI

from fastapi import FastAPI
app = FastAPI()

@app.post('/send')
def send_message(message: str):
    # Simulate heavy processing
    import time
    time.sleep(5)
    return {'status': 'sent'}

Blocking the request with time.sleep delays response, causing slow user experience and server thread blocking.

📉 Performance CostBlocks server worker for 5 seconds, increasing INP and reducing throughput

Performance Comparison

Pattern	Server Blocking	Concurrency	Response Time	Verdict
Synchronous blocking with time.sleep	High (blocks worker)	Low (serial requests)	Slow (delays response)	[X] Bad
Asynchronous with await asyncio.sleep	Low (non-blocking)	High (handles many requests)	Fast (quick response)	[OK] Good

Rendering Pipeline

Message sending and receiving in FastAPI affects the server's ability to quickly process requests and send responses, which impacts the time until the browser can update UI after user interaction.

→Server Processing

→Network Transfer

→Browser Rendering

⚠️ BottleneckServer Processing when blocking synchronous code is used

Core Web Vital Affected

INP

This affects how quickly messages are sent and received over the network, impacting server response time and user interaction speed.

Optimization Tips

1Avoid blocking calls like time.sleep in FastAPI endpoints.

2Use async def and await for non-blocking message processing.

3Monitor request durations in DevTools Network tab to catch slow responses.

Performance Quiz - 3 Questions

What is the main performance problem with using time.sleep in a FastAPI message handler?

A It causes layout shifts in the browser

B It blocks the server worker, delaying other requests

C It increases network latency

D It reduces CSS selector efficiency

DevTools: Network

How to check: Open DevTools, go to Network tab, send a message request, and observe the time taken for the request to complete.

What to look for: Look for long request durations indicating blocking; shorter times indicate better async handling.

Practice

1. What is the main purpose of using Pydantic models in FastAPI when sending and receiving messages?

easy

A. To create HTML templates for message display

B. To manage user authentication tokens

C. To handle database connections automatically

D. To clearly define the structure and data types of messages

Solution

1. Step 1: Understand Pydantic's role in FastAPI

   Pydantic models define how data should look, including types and required fields.

2. Step 2: Connect Pydantic to message handling

   Using Pydantic ensures messages sent or received follow a clear, expected format.

3. Final Answer:

   To clearly define the structure and data types of messages -> Option D

4. Quick Check:

   Pydantic models = message structure [OK]

Hint: Pydantic models describe message shape and types [OK]

Common Mistakes:

• Confusing Pydantic with HTML templating
• Thinking Pydantic manages databases
• Assuming Pydantic handles authentication

2. Which of the following is the correct way to declare a POST endpoint in FastAPI that receives a message model named Message?

easy

A. @app.post('/send')\ndef send_message():\n return Message

B. @app.post('/send')\nasync def send_message(msg: Message):\n return msg

C. @app.get('/send')\nasync def send_message(msg: Message):\n return msg

D. @app.put('/send')\nasync def send_message(msg: Message):\n return msg

Solution

1. Step 1: Identify the HTTP method for sending messages

   POST is used to send data to the server, so @app.post is correct.

2. Step 2: Check function signature for receiving a Pydantic model

   Function must accept a parameter typed as Message to receive the message body.

3. Final Answer:

   @app.post('/send')\nasync def send_message(msg: Message):\n return msg -> Option B

4. Quick Check:

   POST + typed param = correct endpoint [OK]

Hint: Use @app.post and typed parameter for message body [OK]

Common Mistakes:

• Using GET instead of POST for sending data
• Missing parameter type annotation
• Using wrong HTTP method like PUT

3. Given this FastAPI endpoint code, what will be the response when sending a POST request with JSON {"text": "Hello"}?

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Message(BaseModel):
    text: str

@app.post('/echo')
async def echo_message(msg: Message):
    return {"message": msg.text.upper()}

medium

A. {"message": "HELLO"}

B. {"message": "Hello"}

C. {"text": "HELLO"}

D. 500 Internal Server Error

Solution

1. Step 1: Understand the input and model

   The input JSON has key "text" with value "Hello", matching Message model.

2. Step 2: Analyze the endpoint return

   The function returns a dict with key "message" and value as msg.text.upper(), which converts "Hello" to "HELLO".

3. Final Answer:

   {"message": "HELLO"} -> Option A

4. Quick Check:

   Uppercase text returned in 'message' key [OK]

Hint: Look for .upper() call on input text [OK]

Common Mistakes:

• Returning original text without uppercasing
• Returning wrong key name in response
• Expecting an error instead of valid response

4. What is wrong with this FastAPI endpoint code for receiving a message?

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Message(BaseModel):
    content: str

@app.post('/receive')
def receive_message(msg: Message):
    return {"received": msg.content}

medium

A. The endpoint function is missing async keyword

B. The parameter msg should be annotated with Request

C. The endpoint must use @app.get instead of @app.post

D. The function must return a Pydantic model instance

Solution

1. Step 1: Check function declaration for FastAPI async support

   FastAPI supports async endpoints; missing async can cause blocking or warnings.

2. Step 2: Verify parameter and decorator correctness

   Parameter is correctly typed as Message; @app.post is correct for receiving data.

3. Final Answer:

   The endpoint function is missing async keyword -> Option A

4. Quick Check:

   Async keyword needed for async endpoints [OK]

Hint: Use async def for FastAPI POST endpoints [OK]

Common Mistakes:

• Using sync def without async in async context
• Wrong HTTP method decorator
• Incorrect parameter type annotation

5. You want to create a FastAPI endpoint that receives a list of messages, each with a text field, and returns a list of all texts in uppercase. Which code snippet correctly implements this?

hard

A. from fastapi import FastAPI
from pydantic import BaseModel
from typing import List

app = FastAPI()

class Message(BaseModel):
text: str

@app.get('/batch')
def batch_messages(msgs: List[Message]):
return [msg.text.upper() for msg in msgs]

B. from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Message(BaseModel):
text: str

@app.post('/batch')
async def batch_messages(msgs: Message):
return [msg.text.upper() for msg in msgs]

C. from fastapi import FastAPI
from pydantic import BaseModel
from typing import List

app = FastAPI()

class Message(BaseModel):
text: str

@app.post('/batch')
async def batch_messages(msgs: List[Message]):
return [msg.text.upper() for msg in msgs]

D. from fastapi import FastAPI
from pydantic import BaseModel
from typing import List

app = FastAPI()

class Message(BaseModel):
text: str

@app.post('/batch')
async def batch_messages(msgs: List[str]):
return [text.upper() for text in msgs]

Solution

1. Step 1: Use List[Message] to receive multiple messages

   Typing msgs as List[Message] allows FastAPI to parse a list of message objects.

2. Step 2: Return uppercase texts from each message

   List comprehension extracts each msg.text and converts it to uppercase correctly.

3. Final Answer:

   Code snippet with List[Message] parameter and async POST endpoint -> Option C

4. Quick Check:

   List of Message + async POST + uppercase list [OK]

Hint: Use List[Message] and async POST for batch message input [OK]

Common Mistakes:

• Using single Message instead of list
• Using GET instead of POST for sending data
• Typing list as List[str] instead of List[Message]