Caching strategies in FastAPI - Performance & Optimization

Performance: Caching strategies

HIGH IMPACT

Caching strategies improve page load speed by reducing server processing and network delays for repeated requests.

Serving frequently requested API data

FastAPI

from fastapi import FastAPI
from fastapi_cache import FastAPICache
from fastapi_cache.backends.inmemory import InMemoryBackend
from fastapi_cache.decorator import cache

app = FastAPI()

@app.on_event('startup')
async def startup():
    FastAPICache.init(InMemoryBackend())

@app.get('/data')
@cache(expire=60)
async def get_data():
    data = await fetch_data_from_db()
    return data

Caches the response for 60 seconds, serving repeated requests instantly without hitting the database.

📈 Performance GainReduces server processing time to near zero for cached requests, improving LCP and reducing CPU load

Serving frequently requested API data

FastAPI

from fastapi import FastAPI
app = FastAPI()

@app.get('/data')
async def get_data():
    # Fetch fresh data every request
    data = await fetch_data_from_db()
    return data

Fetching data from the database on every request causes slow responses and high server load.

📉 Performance CostBlocks response for database query time on every request, increasing LCP and server CPU usage

Performance Comparison

Pattern	DOM Operations	Reflows	Paint Cost	Verdict
No caching, fetch fresh data every request	Minimal DOM nodes	Multiple reflows due to slow response	High paint cost due to delayed content	[X] Bad
Cache API responses for repeated requests	Minimal DOM nodes	Single reflow with fast response	Low paint cost with quick content display	[OK] Good

Rendering Pipeline

Caching reduces server response time, allowing the browser to receive content faster and start rendering sooner.

→Network

→Server Processing

→First Paint

⚠️ BottleneckServer Processing time waiting for data fetch

Core Web Vital Affected

LCP

Caching strategies improve page load speed by reducing server processing and network delays for repeated requests.

Optimization Tips

1Cache frequently requested data to reduce server processing time.

2Set appropriate cache expiration to balance freshness and performance.

3Use server-side caching to improve Largest Contentful Paint (LCP).

Performance Quiz - 3 Questions

How does caching API responses affect Largest Contentful Paint (LCP)?

A It improves LCP by reducing server response time.

B It worsens LCP by adding extra processing.

C It has no effect on LCP.

D It only affects layout shifts, not LCP.

DevTools: Network

How to check: Open DevTools, go to Network tab, reload the page or API call, and observe response times and status codes.

What to look for: Look for faster response times and 200 status codes with cache headers indicating cached responses.

Practice

1. What is the main purpose of using caching in a FastAPI application?

easy

A. To increase the size of the application code

B. To remove all data from the server

C. To make the app slower for testing

D. To save repeated work and speed up response times

Solution

1. Step 1: Understand caching concept

   Caching stores results of expensive operations to reuse later.

2. Step 2: Identify caching benefit in FastAPI

   This reuse saves repeated work and speeds up response times.

3. Final Answer:

   To save repeated work and speed up response times -> Option D

4. Quick Check:

   Caching purpose = speed up responses [OK]

Hint: Caching means saving work to respond faster [OK]

Common Mistakes:

• Thinking caching increases code size
• Believing caching slows down the app
• Confusing caching with deleting data

2. Which of the following is the correct way to apply caching with expiration in FastAPI using the decorator?

easy

A. @cache(expire=60)

B. @cache_time(60)

C. @cache(expiration=60)

D. @cache(60_seconds)

Solution

1. Step 1: Recall FastAPI cache decorator syntax

   The correct decorator is @cache(expire=seconds) to set expiration time.

2. Step 2: Match options with correct syntax

   Only @cache(expire=60) matches the correct pattern.

3. Final Answer:

   @cache(expire=60) -> Option A

4. Quick Check:

   Decorator with expire=60 is correct [OK]

Hint: Use @cache with expire=seconds for expiration [OK]

Common Mistakes:

• Using wrong decorator names like @cache_time
• Using incorrect parameter names like expiration
• Passing expiration time without parameter name

3. Given this FastAPI code snippet, what will be the output on the second call to /data within 10 seconds?

from fastapi import FastAPI
from fastapi_cache.decorator import cache

app = FastAPI()

@cache(expire=10)
async def get_data():
    print('Fetching data')
    return {'value': 42}

@app.get('/data')
async def data():
    return await get_data()

medium

A. {'value': 42} and prints 'Fetching data' once only

B. {'value': 42} and prints 'Fetching data' every call

C. Error because cache decorator is missing setup

D. Empty response because cache expired immediately

Solution

1. Step 1: Check for cache initialization

   The code uses @cache but lacks FastAPICache.init with a backend on startup.

2. Step 2: Analyze execution

   Without init, FastAPICache.get raises AttributeError on first call, before executing get_data or printing.

3. Final Answer:

   Error because cache decorator is missing setup -> Option C

4. Quick Check:

   Missing FastAPICache.init causes runtime error [OK]

Hint: Cache decorator requires FastAPICache.init on startup [OK]

Common Mistakes:

• Assuming caching works without FastAPICache.init, print once only
• Thinking no caching, print every call
• Believing cache expires immediately

4. Identify the error in this FastAPI caching setup:

from fastapi import FastAPI
from fastapi_cache import FastAPICache
from fastapi_cache.backends.inmemory import InMemoryBackend

app = FastAPI()

@app.on_event('startup')
async def startup():
    FastAPICache.init(InMemoryBackend())

@cache(expire=30)
async def get_info():
    return {'info': 'cached data'}

medium

A. InMemoryBackend requires a Redis URL parameter

B. Cache decorator is used without importing it

C. Missing await keyword when calling FastAPICache.init

D. Startup event should be named 'start' instead of 'startup'

Solution

1. Step 1: Check imports for cache decorator

   The code uses @cache but does not import it from fastapi_cache.decorator.

2. Step 2: Verify other parts for correctness

   FastAPICache.init is correctly called; InMemoryBackend needs no parameters; event name 'startup' is correct.

3. Final Answer:

   Cache decorator is used without importing it -> Option B

4. Quick Check:

   Missing import of cache decorator causes error [OK]

Hint: Always import @cache decorator before using it [OK]

Common Mistakes:

• Forgetting to import cache decorator
• Passing Redis URL to InMemoryBackend
• Renaming startup event incorrectly

5. You want to cache user profile data in FastAPI using Redis backend with a 5-minute expiration. Which setup correctly initializes caching and applies it to the get_user_profile function?

from fastapi import FastAPI
from fastapi_cache import FastAPICache
from fastapi_cache.backends.redis import RedisBackend
from fastapi_cache.decorator import cache
import aioredis

app = FastAPI()

@app.on_event('startup')
async def startup():
    redis = aioredis.from_url('redis://localhost')
    FastAPICache.init(RedisBackend(redis), prefix='fastapi-cache')

@cache(expire=300)
async def get_user_profile(user_id: int):
    # Simulate fetching user profile
    return {'user_id': user_id, 'name': 'Alice'}

hard

A. Correct setup: Redis backend initialized with aioredis, cache decorator with expire=300

B. Incorrect: RedisBackend needs a string URL, not aioredis instance

C. Incorrect: Cache decorator expire should be in milliseconds, not seconds

D. Incorrect: FastAPICache.init must be called inside the cached function

Solution

1. Step 1: Verify Redis backend initialization

   Using aioredis.from_url and passing RedisBackend(redis) is correct for Redis caching.

2. Step 2: Check cache decorator usage

   @cache(expire=300) sets 5 minutes expiration correctly in seconds.

3. Final Answer:

   Correct setup: Redis backend initialized with aioredis, cache decorator with expire=300 -> Option A

4. Quick Check:

   Redis backend + expire=300 seconds = correct caching [OK]

Hint: Use aioredis instance with RedisBackend and expire in seconds [OK]

Common Mistakes:

• Passing Redis URL string directly instead of aioredis instance
• Using expire in milliseconds instead of seconds
• Calling FastAPICache.init inside cached function