# Architecture Paradigm Space Based

> Applies data-grid architecture for high-traffic stateful workloads. Use when a single database cannot scale and in-memory partitioning is needed.

- **Type:** Skill
- **Install:** `agentstack add skill-athola-claude-night-market-architecture-paradigm-space-based`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [athola](https://agentstack.voostack.com/s/athola)
- **Installs:** 0
- **Category:** [Databases](https://agentstack.voostack.com/c/databases)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [athola](https://github.com/athola)
- **Source:** https://github.com/athola/claude-night-market/tree/master/plugins/archetypes/skills/architecture-paradigm-space-based
- **Website:** https://athola.github.io/claude-night-market

## Install

```sh
agentstack add skill-athola-claude-night-market-architecture-paradigm-space-based
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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.

## Concrete Components

These vocabulary items name the concrete tools and abstractions
that show up when the paradigm is implemented. They are not
required dependencies and they are not part of the skill's
``tools:`` frontmatter (which is reserved for Claude Code tool
restrictions). Use this list to disambiguate during architecture
discussions.

- ``data-grid-platform``: Hazelcast, Apache Ignite, or similar; in-memory partitioned data store
- ``replication-manager``: moves writes asynchronously to durable storage and across regions
- ``load-tester``: drives the grid past its single-region ceiling to validate scale-out

## Exit Criteria

- [ ] An ADR documents the chosen grid technology, partitioning scheme, replication strategy
  (sync vs. async), data eviction policies, and durability SLA before any processing unit
  is deployed.
- [ ] Runbooks for scaling processing units and recovering from split-brain scenarios exist and
  have been exercised in a non-production environment.
- [ ] Load and chaos testing confirms the system handles >= 2x expected peak traffic without
  data loss, measured before production promotion.
- [ ] A monitoring suite tracks cache hit rates, replication lag, and failover events with
  alerting thresholds set before the system accepts live traffic.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [athola](https://github.com/athola)
- **Source:** [athola/claude-night-market](https://github.com/athola/claude-night-market)
- **License:** MIT
- **Homepage:** https://athola.github.io/claude-night-market

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-athola-claude-night-market-architecture-paradigm-space-based
- Seller: https://agentstack.voostack.com/s/athola
- Browse the marketplace: https://agentstack.voostack.com/browse

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