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architecture-paradigm-space-based

@athola/claude-night-market
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Employ space-based or data-grid architectures to scale high-traffic, stateful workloads across multiple nodes. Use when building high-scale systems with in-memory data grid patterns.

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SKILL.md

name architecture-paradigm-space-based
description Employ space-based or data-grid architectures to scale high-traffic, stateful workloads across multiple nodes. Use when building high-scale systems with in-memory data grid patterns.
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

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 ensure 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 robust 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.