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name: architecture-paradigm-space-based description: |

Triggers: data-grid, space, architecture, based, in-memory Data-grid architecture for high-traffic stateful workloads with linear scalability.

Triggers: space-based, data grid, in-memory, linear scaling, high traffic Use when: traffic overwhelms database nodes or linear scalability needed DO NOT use when: data doesn't fit in memory or simpler caching would work. version: 1.0.0 category: architectural-pattern tags: [architecture, space-based, data-grid, scalability, in-memory, stateful] dependencies: [] tools: [data-grid-platform, replication-manager, load-tester] usage_patterns:

• paradigm-implementation
• high-traffic-workloads
• linear-scalability complexity: high estimated_tokens: 800

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The Space-Based Architecture Paradigm

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