AWS Lambda is Amazon's serverless compute service. You upload a function, connect it to a trigger such as an HTTP request, a file upload or a schedule, and AWS runs it on demand without you managing servers.
Why it matters
Lambda removes server patching and capacity planning, and it scales by running more copies of the function in parallel. You are billed for the number of invocations and the memory and time each one uses, so spiky or low-traffic workloads can be cheap. It is a core building block for event-driven systems, webhooks and small APIs.
The limits matter too: a maximum run time per invocation, restricted local storage, and cold starts, the extra delay when a new execution environment must be created before your code runs.
Example
def handler(event, context):
name = event.get("name", "world")
return {"statusCode": 200, "body": f"Hello, {name}"}Put this behind an API gateway and each request invokes handler. If no request arrives for a while, no environment is kept running for you.
Common mistakes
- Opening a new database connection on every invocation and exhausting the database.
- Assuming local files or memory persist between invocations.
- Ignoring the concurrency limit, which can throttle requests during a spike.
Related
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