Connector Caching System

A Connector Caching System is a connector-optimized multi-layer integration caching system that can support connector cache management tasks for integration connectors to reduce API latency and backend load.

• AKA: Connector Cache Layer, Integration Response Cache, Connector Data Cache.
• Context:
  • It can typically store Connector Response Datas in connector cache storages using connector in-memory caches, connector distributed caches, and connector persistent caches.
  • It can typically implement Connector Cache Strategys through connector caching policys including connector cache-aside pattern, connector write-through cache, and connector write-behind cache.
  • It can typically manage Connector Cache Invalidations through connector cache evictions based on connector TTL expiry, connector LRU policy, and connector event-based invalidation.
  • It can typically perform Connector Cache Key Generations through connector key algorithms using connector request parameters, connector header values, and connector user contexts.
  • It can typically handle Connector Cache Warmings through connector preload mechanisms implementing connector startup cache, connector scheduled refresh, and connector predictive loading.
  • ...
  • It can often provide Connector Cache Partitionings through connector cache segmentations organizing connector user-specific caches, connector tenant-based caches, and connector resource-type caches.
  • It can often enable Connector Cache Compressions through connector data optimizations applying connector response compression, connector binary serialization, and connector delta encoding.
  • It can often support Connector Cache Synchronizations through connector cache replications maintaining connector cache consistency, connector cache coherence, and connector cache convergence.
  • It can often maintain Connector Cache Analyticss through connector cache monitorings tracking connector hit ratios, connector miss rates, and connector cache efficiency.
  • ...
  • It can range from being a Single-Level Connector Caching System to being a Multi-Level Connector Caching System, depending on its connector caching hierarchy.
  • It can range from being a Local Connector Caching System to being a Distributed Connector Caching System, depending on its connector caching architecture.
  • It can range from being a Static Connector Caching System to being an Adaptive Connector Caching System, depending on its connector caching flexibility.
  • It can range from being a Read-Only Connector Caching System to being a Read-Write Connector Caching System, depending on its connector caching operation support.
  • It can range from being a Time-Based Connector Caching System to being an Event-Based Connector Caching System, depending on its connector caching invalidation trigger.
  • ...
  • It can integrate with CDN Service for connector edge caching.
  • It can connect to Redis Cluster for connector distributed caching.
  • It can interface with Database System for connector persistent caching.
  • It can communicate with Message Queue for connector cache invalidation events.
  • It can synchronize with Load Balancer for connector cache-aware routing.
  • ...
• Example(s):
  • API Gateway Connector Caching Systems, such as:
    • AWS API Gateway Connector Caching Systems, such as:
      • API Gateway Cache Cluster for REST API response caching.
      • CloudFront Distribution Cache for global edge caching.
    • Azure API Management Connector Caching Systems, such as:
      • Azure APIM Internal Cache for API response caching.
      • Azure Redis Cache Integration for external cache storage.
  • Database Connector Caching Systems, such as:
    • Query Result Connector Caching Systems, such as:
      • MySQL Query Cache for SQL result caching.
      • PostgreSQL Shared Buffer Cache for database page caching.
    • ORM Connector Caching Systems, such as:
      • Hibernate Second-Level Cache for entity caching.
      • Django ORM Cache for queryset caching.
  • Application-Level Connector Caching Systems, such as:
    • Spring Framework Connector Caching Systems, such as:
      • Spring Cache Abstraction for method-level caching.
      • Spring Boot Cache Manager for application caching.
    • Node.js Connector Caching Systems, such as:
      • Node-Cache Module for in-process caching.
      • Redis Client Cache for distributed Node.js caching.
  • ...
• Counter-Example(s):
  • Direct API Call, which bypasses cache layer for real-time data.
  • Stateless Connector, which maintains no cached state between requests.
  • Write-Only System, which performs data modification without read caching.
• See: Caching System, Content Delivery Network, Redis Cache, Integration Connector, API Gateway, Performance Optimization System, Data Cache.
• Reference(s):
  • https://www.gabormelli.com/RKB/Application_Programming_Interface_(API)
  • https://www.gabormelli.com/RKB/Backend_for_Frontend_(BFF)-based_System
  • https://www.gabormelli.com/RKB/MediaWiki_Software_Engine

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