dev.to

When 15 Minutes Isn't Enough: Overcoming Lambda Timeout Limits

“My Lambda Timeout Is Set to 15 Minutes—Why Isn’t My Process Finishing?” You’ve maxed out your AWS Lambda timeout to 15 minutes, yet your process still doesn’t complete because the external API takes too long to respond. Sound familiar? If you’re wondering, “I already increased the timeout to the max—what else can I do?”, you’re not alone. I’ve been there myself. On one project, I had a setup where AWS Lambda was calling a heavy external API for data processing. The average response time was around 10 minutes—but sometimes it stretched to 13 or even 14. With Lambda’s 15-minute limit, I thought I was safe. But in reality, network latency and service instability pushed us over the edge again and again. All I saw in the logs was: Task timed out after 900.00 seconds That was the moment I hit the limits of Lambda. In this post, I’ll explain how to break free from the constraints of time-bound functions like Lambda by adopting asynchronous processing patterns—and how to design for them effectively. We’ll walk through the basics of async processing, using Lambda and EC2 as practical examples. By the end, you’ll see that async design isn’t that hard—and you’ll be equipped to confidently handle workloads longer than 15 minutes. You’ll gain the ability to build reliable systems without fearing timeouts. What Is Asynchronous Processing? Asynchronous processing means "continuing without waiting for other tasks to finish." By delegating or decoupling tasks, you can avoid issues like timeouts and blocking delays. Synchronous vs. Asynchronous Processing Synchronous processing: The program waits until a task is complete before moving on. The caller blocks until a response is returned. Delays in APIs or I/O can cause serious bottlenecks. # Synchronous example in Python response = requests.get("https://api.example.com/data") print(response.json()) # Waits for the response before continuing Asynchronous processing: The program continues while tasks are still running Results are handled once they're ready Heavy tasks can run on separate threads or processes # Asynchronous example using Python asyncio async def fetch_data(): response = await aiohttp.get("https://api.example.com/data") return await response.json() Quick Summary: Perspective Synchronous Asynchronous Behavior Waits until done Moves on while waiting Best Use Lightweight tasks Heavy or external tasks Downsides Easily blocked by slow ops Slightly more complex design Example Direct API call & wait Offload to queue and process separately How to Handle Workloads That Exceed Lambda’s 15-Minute Limit → TL;DR: Offload to EC2 using an asynchronous architecture! ✅ Step 1: Offload Long-Running Tasks to EC2 via a Queue** Don’t run the process now—queue it up to run later. ▶ Architecture Overview: The frontend or Lambda function sends a processing request to SQS. A worker application on EC2 polls SQS and picks up the request. The EC2 worker handles the long-running external API call (10, 20 minutes—no problem). Once done, it stores results in a database or sends a notification.

May 12, 2025 - 12:57
 0
When 15 Minutes Isn't Enough: Overcoming Lambda Timeout Limits

“My Lambda Timeout Is Set to 15 Minutes—Why Isn’t My Process Finishing?”

You’ve maxed out your AWS Lambda timeout to 15 minutes, yet your process still doesn’t complete because the external API takes too long to respond.

Sound familiar?

If you’re wondering, “I already increased the timeout to the max—what else can I do?”, you’re not alone.

I’ve been there myself.

On one project, I had a setup where AWS Lambda was calling a heavy external API for data processing. The average response time was around 10 minutes—but sometimes it stretched to 13 or even 14.

With Lambda’s 15-minute limit, I thought I was safe. But in reality, network latency and service instability pushed us over the edge again and again. All I saw in the logs was:

Task timed out after 900.00 seconds

That was the moment I hit the limits of Lambda.

In this post, I’ll explain how to break free from the constraints of time-bound functions like Lambda by adopting asynchronous processing patterns—and how to design for them effectively.

We’ll walk through the basics of async processing, using Lambda and EC2 as practical examples.

By the end, you’ll see that async design isn’t that hard—and you’ll be equipped to confidently handle workloads longer than 15 minutes.

You’ll gain the ability to build reliable systems without fearing timeouts.

What Is Asynchronous Processing?

Asynchronous processing means "continuing without waiting for other tasks to finish."

By delegating or decoupling tasks, you can avoid issues like timeouts and blocking delays.

Synchronous vs. Asynchronous Processing

Synchronous processing:

  • The program waits until a task is complete before moving on.
  • The caller blocks until a response is returned.
  • Delays in APIs or I/O can cause serious bottlenecks. 
# Synchronous example in Python
response = requests.get("https://api.example.com/data")
print(response.json())  # Waits for the response before continuing

Asynchronous processing:

  • The program continues while tasks are still running
  • Results are handled once they're ready
  • Heavy tasks can run on separate threads or processes 
# Asynchronous example using Python asyncio
async def fetch_data():
    response = await aiohttp.get("https://api.example.com/data")
    return await response.json()

Quick Summary:

Perspective Synchronous Asynchronous
Behavior Waits until done Moves on while waiting
Best Use Lightweight tasks Heavy or external tasks
Downsides Easily blocked by slow ops Slightly more complex design
Example Direct API call & wait Offload to queue and process separately

How to Handle Workloads That Exceed Lambda’s 15-Minute Limit

→ TL;DR: Offload to EC2 using an asynchronous architecture!

✅ Step 1: Offload Long-Running Tasks to EC2 via a Queue**

Don’t run the process now—queue it up to run later.

▶ Architecture Overview:

  1. The frontend or Lambda function sends a processing request to SQS.
  2. A worker application on EC2 polls SQS and picks up the request.
  3. The EC2 worker handles the long-running external API call (10, 20 minutes—no problem).
  4. Once done, it stores results in a database or sends a notification.

Read More

Tags:

Previous Article

The Stress-Anxiety-Junk Food Loop: How Comfort Eating Can Worsen Mental Health

Next Article

C# Inheritance: Virtual, Override, New, and Hidden Dangers

Related Posts

How the Childfree Demographic Is Impacting the Real Estate Market

How the Childfree Demographic Is Impacting the Real Est...

May 10, 2025  0

Virtualization | Hypervisor | Containerization

Virtualization | Hypervisor | Containerization

May 11, 2025  0

The Stress-Anxiety-Junk Food Loop: How Comfort Eating Can Worsen Mental Health

The Stress-Anxiety-Junk Food Loop: How Comfort Eating C...

May 12, 2025  0