Docs.rs
avl-console-0.3.0
avl-console 0.3.0
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Docs.rs crate page
MIT
OR
Apache-2.0
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crates.io
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avilaops
Dependencies
anyhow ^1.0
normal
askama ^0.12
normal
axum ^0.7
normal
futures ^0.3
normal
serde ^1.0
normal
serde_json ^1.0
normal
thiserror ^2.0
normal
tokio ^1.40
normal
tower ^0.5
normal
tower-http ^0.6
normal
tracing ^0.1
normal
tracing-subscriber ^0.3
normal
tokio-test ^0.4
dev
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44.33%
of the crate is documented
Platform
x86_64-unknown-linux-gnu
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docs.rs
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Module monitoring
avl_
console
0.3.0
Module monitoring
Module Items
Structs
Functions
In crate avl_
console
avl_console
Module
monitoring
Copy item path
Source
Structs
§
Alert
Alert data structure
MLInsight
ML Insight data structure
Metric
Metric data structure
Functions
§
detect_
anomaly
Anomaly detection using simple statistical methods In production, this would use proper ML models (e.g., Isolation Forest, LSTM)
router
Create router for monitoring