Production API Standards

The Standard for Production APIs

Up to this point, we have focused on building features using smart AI agents. We've learned how to organize tasks and run development tracks in parallel. However, in the professional world, writing code that just works is only half the battle. To make an API "production-ready," we must ensure it can handle mistakes, block hackers, and stay fast when many people use it at once. We do this using a Quality Pipeline . This is a series of checks that every piece of code must pass before it reaches our users. Think of it like a safety inspection for a car. It doesn't matter how fast the car is if the brakes don't work or the doors don't lock. The Quality Pipeline focuses on four main areas: Coverage : Do our tests check every single line of code, including the parts where things go wrong? Security : Can a user access or delete data that belongs to someone else? Performance : Does the API stay fast when 50 people use it at the same time? Documentation : Is the instruction manual (OpenAPI) up to date? In this lesson, we will move through each of these stages to finish our Task Comments feature.

Reaching 95% Test Coverage

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Security Review: Beyond Simple Logins

A Security review is a systematic check to find vulnerabilities. Even if a user is logged in, they shouldn't be allowed to do everything. A common mistake is forgetting to check if a user actually owns the data they are trying to change.

Let's look at a typical API route for deleting a comment:

Python

# src/api/routes/comments.py

@router.delete("/comments/{comment_id}")
def delete_comment(
    comment_id: UUID,
    current_user: User = Depends(get_current_user)
):
    comment = comment_repo.get_by_id(comment_id)
    # The code gets the comment, but doesn't check WHO is deleting it!
    comment_repo.delete(comment_id)
    return {"status": "deleted"}

# src/api/routes/comments.py

@router.delete("/comments/{comment_id}")
def delete_comment(
    comment_id: UUID,
    current_user: User = Depends(get_current_user)
):
    comment = comment_repo.get_by_id(comment_id)
    # The code gets the comment, but doesn't check WHO is deleting it!
    comment_repo.delete(comment_id)
    return {"status": "deleted"}

In the code above, Depends(get_current_user) ensures the person is logged in. However, any logged-in user could delete any comment just by knowing the comment_id. This is a high-priority security flaw.

To fix this, we must add an ownership check:

Python

@router.delete("/comments/{comment_id}")
def delete_comment(
    comment_id: UUID,
    current_user: User = Depends(get_current_user)
):
    comment = comment_repo.get_by_id(comment_id)
    task = task_repo.get_by_id(comment.task_id)
    
    # Check: Does the user own the comment, or do they own the task it belongs to?
    is_comment_author = comment.user_id == current_user.id
    is_task_owner = task.owner_id == current_user.id
    
    if not (is_comment_author or is_task_owner):
        raise HTTPException(status_code=403, detail="Not authorized to delete this comment")
        
    comment_repo.delete(comment_id)
    return {"status": "deleted"}

@router.delete("/comments/{comment_id}")
def delete_comment(
    comment_id: UUID,
    current_user: User = Depends(get_current_user)
):
    comment = comment_repo.get_by_id(comment_id)
    task = task_repo.get_by_id(comment.task_id)
    
    # Check: Does the user own the comment, or do they own the task it belongs to?
    is_comment_author = comment.user_id == current_user.id
    is_task_owner = task.owner_id == current_user.id
    
    if not (is_comment_author or is_task_owner):
        raise HTTPException(status_code=403, detail="Not authorized to delete this comment")
        
    comment_repo.delete(comment_id)
    return {"status": "deleted"}

By adding that if statement, we've protected the data. A professional security review involves going through every endpoint and asking:

Is the user logged in?
Does the user have the right to act on this specific resource — either because they own it directly, or because they own a parent resource (like the task)?
Is the input (like the comment text) safe and within length limits?

Performance: Validating Under Pressure

Performance testing ensures that your API doesn't slow down when many people use it. We often measure p95 latency. This means that 95% of requests are faster than this value — in other words, only the slowest 5% of users experience longer wait times. We want our p95 to be under 500ms (half a second) so that almost everyone has a fast experience.

We can build a simple script to test this. First, we need a way to simulate a single user's actions.

Python

import asyncio
import httpx
import time

async def user_session():
    async with httpx.AsyncClient(base_url="http://localhost:8000") as client:
        start = time.time()
        # Simulate a user viewing their tasks
        await client.get("/api/tasks", headers={"Authorization": "Bearer test-token"})
        # Calculate how long it took in milliseconds
        return (time.time() - start) * 1000

import asyncio
import httpx
import time

async def user_session():
    async with httpx.AsyncClient(base_url="http://localhost:8000") as client:
        start = time.time()
        # Simulate a user viewing their tasks
        await client.get("/api/tasks", headers={"Authorization": "Bearer test-token"})
        # Calculate how long it took in milliseconds
        return (time.time() - start) * 1000

Now, we need to run many of these sessions at the same time and calculate the results.

Python

import statistics

async def main():
    # Simulate 20 users hitting the API at once
    tasks = [user_session() for _ in range(20)]
    # gather runs them all in parallel
    results = await asyncio.gather(*tasks)
    
    # Calculate the p95 (the 95th percentile)
    p95 = statistics.quantiles(results, n=100)[94]
    print(f"p95 Latency: {p95:.0f}ms")

if __name__ == "__main__":
    asyncio.run(main())

import statistics

async def main():
    # Simulate 20 users hitting the API at once
    tasks = [user_session() for _ in range(20)]
    # gather runs them all in parallel
    results = await asyncio.gather(*tasks)
    
    # Calculate the p95 (the 95th percentile)
    p95 = statistics.quantiles(results, n=100)[94]
    print(f"p95 Latency: {p95:.0f}ms")

if __name__ == "__main__":
    asyncio.run(main())

If the result is 420ms, we pass! If it is 2000ms (2 seconds), we know something is wrong. Usually, slowness is caused by the database. If we find a bottleneck, we might add an "index" to the database or fix a loop that is making too many requests.

Production API Standards

The Standard for Production APIs

Reaching 95% Test Coverage

Join the 1M+ learners on CodeSignal

Security Review: Beyond Simple Logins

Performance: Validating Under Pressure

Summary: Completing Your Production Pipeline