Serverless Shipping Functions: When They Work (and When They Don't)

The rise of serverless computing, exemplified by AWS Lambda, Vercel Functions, and Cloudflare Workers, has revolutionized how we approach shipping integrations. These functions thrive in environments where operations are brief and sporadic, such as fetching rate quotes, generating shipping labels, or checking tracking statuses. However, despite their flexibility and cost-effectiveness, serverless solutions come with their own set of challenges that can impact both reliability and expenses.

When Serverless Shines

Serverless functions are particularly well-suited for tasks that are stateless, quick, and able to handle variable loads. Take rate quotes, for instance. A serverless function can effectively receive input regarding the origin, destination, and package dimensions, and then query multiple carriers simultaneously. The result is a list of sorted rates returned within mere seconds. The stateless nature of this interaction, combined with the typical 1-3 second execution time, means serverless functions can adapt to traffic spikes without the need for additional provisioning.

Similarly, webhook handlers benefit greatly from serverless architecture. Carrier webhooks can be unpredictable, with traffic ranging from a trickle to a flood in moments. Serverless functions can scale effortlessly to meet this demand. The process involves acknowledging the webhook promptly by returning a 200 status code, pushing the payload to a queue, and delegating the actual processing to another function. This way, you only pay for the computing power you use, no matter how erratic the incoming requests.

Tracking lookups also align well with serverless capabilities. These operations usually consist of straightforward GET requests to a carrier's API, followed by returning the result. They're low on computational demands and effortlessly scalable, making serverless functions an ideal choice.

When Serverless Falls Short

However, serverless isn't a panacea. Batch label generation is a case in point. When generating hundreds of labels, the process can extend beyond a few minutes. Although AWS Lambda's 15-minute timeout might seem adequate, factors like cold starts and carrier API latency can introduce significant unreliability. For such long-running tasks, a dedicated container or EC2 instance proves more dependable.

Another problematic area is PDF processing, particularly when merging label PDFs for batch printing. This task is both CPU-intensive and memory-hungry, often brushing up against Lambda's 10GB memory limit when dealing with large batches. A dedicated service, with more generous resource allocations, is more suitable here.

Lastly, maintaining long-polling connections for real-time tracking updates is beyond serverless's capabilities. Functions terminate between requests, making persistent connections via WebSocket or Server-Sent Events (SSE) impractical. In these situations, utilizing a persistent service such as ECS, EC2, or a managed WebSocket service like Pusher is more effective.

The Impact of Cold Starts

In the shipping domain, cold starts can have a more pronounced impact than in many other applications. Consider a rate quote function: a 2-second cold start might not seem like much, but it can extend the customer's wait time to 4-5 seconds at checkout, as opposed to a more acceptable 2-3 seconds. To mitigate these delays, deploying strategies such as provisioned concurrency can be beneficial. Keeping a small number of instances warm during peak hours ensures quicker response times. Additionally, minimizing the size of bundles by excluding unused carrier SDKs can reduce cold start times. Reusing connections by initializing carrier API clients outside the handler function is another effective tactic.

Weighing the Costs

Cost is a crucial factor when deciding between serverless and traditional server environments. For example, rate quotes using a 1-second, 256MB Lambda function might cost around $5.50 per million calls, whereas maintaining an EC2 instance capable of handling over a million calls could run about $50 per month. Similarly, label generation on Lambda could cost $33 for the same number of operations, whereas an EC2 would still be around $50. Webhook processing, being less resource-intensive, is cheaper on Lambda at about $1.10 for a million calls. The tipping point for serverless cost benefits generally lies below 500,000 operations per month; beyond that, containers offer a more economical solution.

Embracing a Hybrid Architecture

For optimal efficiency, many businesses are turning to a hybrid architecture model. This approach leverages the strengths of both serverless functions and traditional containers. Serverless functions handle the request/response layer, where scalability and cost per use are paramount. API gateways and rate quotes fit well here, as do webhook intakes, which benefit from serverless's ability to manage spikes without incurring idle costs. Meanwhile, containers take over the heavy lifting of background processing. Label generation queues and batch processing tasks, which require predictable execution and can endure longer runtimes without timeouts, are ideal candidates for container environments. Moreover, tracking pages benefit from Cloudflare Workers, achieving low latency on a global scale thanks to edge caching.

Managing Environment Variables Securely

One often-overlooked aspect of serverless functions is the management of environment variables, particularly when it comes to sensitive data such as carrier API keys. It's critical to use secure management systems like AWS Secrets Manager or Doppler instead of plain environment variables. This approach allows for safer key storage and rotation without the need to redeploy functions. Additionally, never logging full API keys, even in error messages, is a best practice to prevent accidental exposure. If possible, utilizing separate keys for each function can also help minimize the impact of a compromised key.

For those interested in how serverless can be effectively integrated into shipping solutions, Atoship provides a compelling example of serverless deployment at the edge, offering both flexibility and efficiency in modern shipping operations.

By understanding the strengths and limitations of serverless functions, businesses can create more efficient, cost-effective shipping solutions that leverage the best of both worlds.