architecture-paradigm-space-based
CommunityScale stateful workloads with in-memory data grids.
Software Engineering#architecture#scalability#distributed systems#high-traffic#in-memory#stateful#space-based#data grid
Authorathola
Version1.0.0
Installs0
System Documentation
What problem does it solve?
High-traffic, stateful workloads can overwhelm traditional database nodes, leading to performance bottlenecks. This skill guides you in using space-based/data-grid architectures to achieve linear scalability and low-latency processing.
Core Features & Use Cases
- Workload Partitioning: Split traffic into processing units backed by replicated data caches.
- Data Grid Design: Choose caching technology, replication strategy, and eviction policies.
- Persistence Coordination: Implement write-through or write-behind to durable stores.
- Failover Handling: Build leader election and heartbeat mechanisms for resilience.
- Use Case: Design a high-frequency trading platform where real-time market data needs to be processed with extremely low latency and scaled linearly across many nodes, using an in-memory data grid.
Quick Start
Partition your application's workloads into processing units, then design your data grid by choosing a caching technology and replication strategy.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: architecture-paradigm-space-based Download link: https://github.com/athola/claudenomicon/archive/main.zip#architecture-paradigm-space-based Please download this .zip file, extract it, and install it in the .claude/skills/ directory.