Module workload 

Workload Learning and Edge-Aware Query Optimization

This module implements RadixDB’s unique optimization features:

1. Workload Learning: Learns from historical query patterns to predict future behavior

   • Query pattern fingerprinting and frequency tracking
   • Automatic index recommendation based on access patterns
   • Hot column detection for pre-materialization hints
   • Temporal pattern detection (batch vs interactive workloads)

2. Edge-Aware Planning: Special optimizations for edge computing environments

   • Memory-constrained execution strategies
   • Network partition tolerance (graceful degradation)
   • Battery-aware query scheduling (for IoT/mobile)
   • Incremental result computation for slow connections

These features make RadixDB unique in that it learns from your specific workload patterns rather than relying solely on static cost models.

Structs

EdgeAwarePlanner

Edge-aware query planner enhancements

IndexRecommendation

Index recommendation from workload analysis

PatternStats

Learned statistics for a query pattern

WorkloadConfig

Configuration for workload-aware optimization

WorkloadHints

Optimization hints derived from workload learning

WorkloadLearner

Workload learner - learns from query patterns to optimize future queries

Enums

EdgeJoinRecommendation

Join recommendation for edge computing

EdgeMode

Edge computing mode

QueryPattern

Query pattern classification

TemporalPattern

Temporal workload pattern

Functions

global_workload_learner

Get the global workload learner instance