Crate alice_analytics 

ALICE-Analytics — High-Performance Telemetry & Statistical Estimation

Probabilistic data structures for streaming analytics with mathematical error guarantees and minimal memory footprint.

Modules

• sketch: HyperLogLog / DDSketch / Count-Min / Heavy Hitters
• stats: Percentile rank, IQR, covariance, Welford streaming stats
• window: Tumbling / sliding / hierarchical window aggregates
• anomaly: MAD / Z-Score / EWMA / composite anomaly detectors
• privacy: deprecated, not private (see its Security section); use alice_crypto::dp
• pipeline: Lock-free metric aggregation pipeline
• export: JSON / Prometheus export of metric snapshots
• streaming_ops: Streaming aggregation operators
• law (feature law): residual distribution of a fitted alice_zip::law::SignalLaw, summarised with a DDSketch

Example

use alice_analytics::sketch::{DDSketch, HyperLogLog};

// quantiles with a relative-error guarantee of 1 %
let mut latency = DDSketch::new(0.01);
for ms in 1..=1000 {
    latency.insert(f64::from(ms));
}
let p99 = latency.quantile(0.99);
assert!((p99 - 990.0).abs() <= 0.01 * 990.0);

// distinct count in 16 KiB of registers
let mut users = HyperLogLog::new();
for id in 0..10_000_u64 {
    users.insert(&id);
}
let n = users.cardinality();
assert!((n - 10_000.0).abs() < 0.05 * 10_000.0);

Modules

anomaly

Streaming Anomaly Detection

export

Data Export — JSON / Prometheus テキスト形式

pipeline

Lock-Free Metric Aggregation Pipeline

privacy

Local Differential Privacy (LDP) Mechanisms — deprecated, not private

sketch

Probabilistic Sketch Data Structures

stats

Advanced Statistical Functions

streaming_ops

Streaming Operators — 変化率・移動平均・線形回帰

window

Time-Window Aggregation

Constants

SEMANTICS_ID

Identifier of the arithmetic every estimate in this crate is computed with