In the context of Reactive Systems, being elastic means that the system can dynamically adjust its resource allocation to handle varying workloads efficiently. This characteristic is one of the four key tenets of the Reactive Manifesto, alongside responsiveness, resilience, and being message-driven[4][8][17].
Key Aspects of Elasticity in Reactive Systems
Dynamic Scaling:
Scale Up/Down: The system can increase or decrease the capacity of existing resources (e.g., adding more CPU cores) based on the current demand[4][8].
Scale Out/In: The system can add or remove instances of services or nodes to distribute the load more effectively. This involves adding new servers or nodes when the load increases and removing them when the load decreases[4][8][17].
Resource Management:
Elastic systems manage resources automatically, ensuring that they are provisioned and de-provisioned in real-time to match the workload. This helps in maintaining performance and cost efficiency by avoiding over-provisioning or under-provisioning of resources[4][8][15][16].
Avoiding Bottlenecks:
Elastic systems are designed to avoid central bottlenecks and contention points. This is achieved through techniques like sharding, replication, and distributing inputs among multiple components[4][8][19].
Predictive and Reactive Scaling:
Elastic systems support both predictive scaling, which anticipates future demand based on historical data, and reactive scaling, which responds to real-time changes in workload[4][8][17].
Cost-Effective:
Elasticity allows systems to achieve cost-effective scaling by using commodity hardware and software platforms. This ensures that resources are only used when needed, reducing unnecessary costs[8][19].
Asynchronous Message-Passing:
Elastic systems rely on asynchronous message-passing to manage communication between components. This decouples the components in time and space, allowing for more flexible and scalable interactions[4][8][17][19].
Practical Examples
Cloud Services: Cloud-based systems often use elasticity to handle varyi...
