Summary
Allow generators to produce values following a statistical distribution (normal/Gaussian, log-normal, Pareto, Poisson, etc.) rather than uniform random within a range.
Motivation
Production data is rarely uniformly distributed. A realistic load test or data pipeline test should reflect the actual distribution (e.g. order amounts follow a log-normal distribution, event inter-arrival times follow Poisson).
Rough design
Extend the type syntax, e.g.:
amount: decimal[normal(mean=50.0, stddev=15.0)]
event_gap_ms: int[poisson(lambda=100)]
Priority
Activate when a solid concrete use case emerges (e.g. a user needing realistic financial data distribution for a benchmark).
Summary
Allow generators to produce values following a statistical distribution (normal/Gaussian, log-normal, Pareto, Poisson, etc.) rather than uniform random within a range.
Motivation
Production data is rarely uniformly distributed. A realistic load test or data pipeline test should reflect the actual distribution (e.g. order amounts follow a log-normal distribution, event inter-arrival times follow Poisson).
Rough design
Extend the type syntax, e.g.:
Priority
Activate when a solid concrete use case emerges (e.g. a user needing realistic financial data distribution for a benchmark).