Skill: athola/nm-archetypes-architecture-paradigm-space-based (version 1.8.5)

--- name: architecture-paradigm-space-based description: | Apply data-grid architecture for high-traffic stateful workloads with in-memory processing and linear scalability version: 1.9.5 triggers: - architecture - space-based - data-grid - scalability - in-memory - stateful metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/archetypes", "emoji": "\ud83c\udfd7\ufe0f"}} source: claude-night-market source_plugin: archetypes --- > **Night Market Skill** — ported from [claude-night-market/archetypes](https://github.com/athola/claude-night-market/tree/master/plugins/archetypes). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # The Space-Based Architecture Paradigm ## When To Use - High-traffic applications needing elastic scalability - Systems requiring in-memory data grids ## When NOT To Use - Low-traffic applications where distributed caching is overkill - Systems with strong consistency requirements over availability ## When to Employ This Paradigm - When traffic or state volume overwhelms a single database node. - When latency requirements demand in-memory data grids located close to processing units. - When linear scalability is required, achieved by partitioning workloads across many identical, self-sufficient units. ## Adoption Steps 1. **Partition Workloads**: Divide traffic and data into processing units, each backed by a replicated data cache. 2. **Design the Data Grid**: Select the appropriate caching technology, replication strategy (synchronous vs. asynchronous), and data eviction policies. 3. **Coordinate Persistence**: Implement a write-through or write-behind strategy to a durable data store, including reconciliation processes. 4. **Implement Failover Handling**: Design a mechanism for leader election or heartbeats to validate recovery from node loss without data loss. 5. **Validate Scalability**: Conduct load and chaos testing to confirm the system's elasticity and self-healing capabilities. ## Key Deliverables - An Architecture Decision Record (ADR) detailing the chosen grid technology, partitioning scheme, and durability strategy. - Runbooks for scaling processing units and for recovering from "split-brain" scenarios. - A monitoring suite to track cache hit rates, replication lag, and failover events. ## Risks & Mitigations - **Eventual Consistency Issues**: - **Mitigation**: Formally document data-freshness Service Level Agreements (SLAs) and implement compensation logic for data that is not immediately consistent. - **Operational Complexity**: - **Mitigation**: The orchestration of a data grid requires mature automation. Invest in production-grade tooling and automation early in the process. - **Cost**: - **Mitigation**: In-memory grids can be resource-intensive. Implement aggressive monitoring of utilization and auto-scaling policies to manage costs effectively. ## Troubleshooting ### Common Issues **Command not found** Ensure all dependencies are installed and in PATH **Permission errors** Check file permissions and run with appropriate privileges **Unexpected behavior** Enable verbose logging with `--verbose` flag


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