Serverless platforms handle concurrency by automatically managing the execution of functions in response to incoming requests. When a function is invoked, the serverless platform creates an isolated execution environment for that function. This means that if multiple requests come in at the same time, the platform can spin up multiple instances of that function, allowing each instance to process a request independently. Developers don’t need to worry about the underlying server infrastructure or manually scaling resources, as the platform takes care of these aspects based on the volume of incoming traffic.
For example, in AWS Lambda, if you have a function that processes images and receives five requests simultaneously, AWS Lambda will create separate instances of that function to handle each request. Each instance receives an isolated environment with its own memory and runtime. By doing this, AWS Lambda can process all requests concurrently without delay. However, it’s important to be aware of concurrency limits set by the platform which can vary by account and service. If the incoming requests exceed the account’s concurrent execution limit, subsequent requests may be throttled or delayed until more capacity is available.
In addition to managing execution environments, serverless platforms often provide metrics and monitoring tools to help developers keep track of performance and concurrency levels. For instance, Google Cloud Functions allows you to view logs that show how many instances were created for a given period. These insights enable developers to optimize their functions and understand how well they scale under load. Overall, serverless platforms simplify concurrency management, allowing developers to focus on writing and deploying code without needing to manage the complexities of server infrastructure.