ERP Caching Strategies for Application Performance
Caching is the most effective technique for reducing ERP database load and improving user-perceived response times. ERP systems repeatedly query the same reference data--item master, customer master, pricing tables, configuration settings--across hundreds of concurrent user sessions. Without caching, every screen load and transaction triggers redundant database queries for data that changes infrequently. Well-implemented caching reduces ERP database load by 40-70% and improves screen load times by 50-80% for data-intensive operations. The challenge is implementing cache invalidation that keeps cached data consistent with ERP transaction updates.
Cache Layer Architecture for ERP Systems
ERP caching operates at multiple layers, each serving different data patterns. Application-level caching (in-memory within the ERP application server process) provides the fastest access for session-specific and frequently accessed reference data. Distributed caching (Redis, Memcached) shares cached data across multiple ERP application server instances and survives individual server restarts. Database-level caching (SQL Server buffer pool, result set caching) handles query result reuse transparently. The optimal ERP caching architecture uses all three layers with clear policies for which data lives at each level.
- Use application-level in-memory caching (ConcurrentDictionary, Caffeine, Guava) for session context and user preference data
- Deploy Redis or Memcached as a distributed cache for reference data shared across ERP application server farm nodes
- Cache ERP configuration and system parameter tables with long TTL (1-4 hours) since they change only during admin operations
- Store frequently accessed master data (item descriptions, UOM conversions, tax rates) in distributed cache with 15-30 minute TTL
- Implement a cache-aside pattern: check cache first, query database on miss, populate cache on return for ERP read operations
Cache Invalidation Strategies for ERP Data
Cache invalidation is the hardest problem in ERP caching because ERP data changes through multiple pathways: user transactions, batch processing, API integrations, and direct database updates. Time-based expiration (TTL) is the simplest approach but creates windows of stale data. Event-driven invalidation uses SQL Server Service Broker, Change Data Capture (CDC), or ERP application events to invalidate cache entries immediately when underlying data changes. For ERP systems, a hybrid approach works best: short TTL for transaction data (30-60 seconds) and event-driven invalidation for master data changes.
- Implement TTL-based expiration with data-type-appropriate durations: 30s for inventory, 15 min for master data, 4h for config
- Use SQL Server Change Tracking or CDC to trigger cache invalidation when ERP tables are modified by any source
- Subscribe to ERP application events (item master change, price list update) to invalidate specific cache entries immediately
- Implement cache stampede protection with probabilistic early expiration or lock-based single-caller refresh patterns
- Monitor cache hit ratios per data type: target 90%+ hit ratio for master data and 70%+ for transaction data caches
ERP-Specific Caching Patterns and Anti-Patterns
ERP caching has unique patterns that differ from web application caching. ERP dropdown lists, LOV (List of Values) lookups, and form initialization queries are prime cache candidates because they execute thousands of times per hour with identical results. Conversely, caching inventory on-hand quantities or open order balances is dangerous because stale data causes incorrect business decisions. The anti-pattern of caching everything and relying on invalidation leads to subtle data consistency bugs that are extremely difficult to diagnose in ERP environments.
- Cache ERP dropdown/LOV query results aggressively: item lists, customer lists, warehouse lists, status codes with 15-minute TTL
- Cache BOM and routing structures with event-driven invalidation: these change rarely but are queried heavily by planning modules
- Never cache inventory on-hand quantities or available-to-promise values: stale data causes overselling and scheduling errors
- Implement read-through caching for ERP report parameters and filter metadata to eliminate redundant database round-trips
- Monitor cache memory usage and implement eviction policies (LRU) to prevent cache growth from consuming application server memory
Want to dramatically reduce ERP database load with intelligent caching? Netray implements production-grade caching strategies for ERP systems--request a performance assessment.
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