Managing concurrency in distributed systems can be challenging. In a monolithic application, you use familiar concepts such as in-memory locks to avoid overloading a database or prevent overwriting a customer record by two users at the same time. With a distributed system, where your application is dispersed across computing environments without shared memory, these methods are no longer available to you. By James Beswick.
In this blog, you use AWS Step Functions to control concurrency in your distributed system. This helps you avoid overloading limited resources in your serverless data processing pipeline or reduce availability risk by controlling velocity in your IT automation workflows. With this sample application, you implement a distributed semaphore using AWS Step Functions and Amazon DynamoDB to control concurrent invocations of a function in AWS Lambda.
The article the explains:
- Deploying the concurrency control application
- Running the concurrency control application
- How it works
- Handling failure
To apply this concurrency control in your own application, replace the branch defined in the Do Work parallel state with a state machine definition that contains the business logic of your application. You also update the ConcurrentAccessLimit value template.yaml to specify the level of concurrency that your application requires. Nice one!
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