Struct CanaryAnalysis 

pub struct CanaryAnalysis {
    pub enabled: bool,
    pub window_seconds: u64,
    pub step: u32,
    pub min_requests: u64,
    pub cooldown_seconds: u64,
    pub promote: CanaryRule,
    pub rollback: CanaryRule,
}

Auto-canary analysis (DW-091): metrics-driven promotion/rollback of canary split weights. A background controller compares the canary side against the baseline on error rate or latency; on regression it rolls the canary weight back (toward 0), on success it promotes (toward the total weight). Severe regression (>2x the rollback threshold) triggers an immediate rollback to 0. The total weight stays constant: when the canary weight changes, the baseline absorbs the difference, preserving the hash distribution for existing sessions (the DW-040 invariant). Weight changes are TRANSIENT (a Generation swap) and revert on config reload.

Fields

enabled: bool

Master switch. Default true: an analysis block is active unless explicitly disabled.

window_seconds: u64

Look-back window for metrics, in seconds. The controller evaluates canary health over the recent window of observations.

step: u32

Weight delta per adjustment (in weight units). Promote: increase canary weight by step. Rollback: decrease by step. Severe regression (>2x the rollback threshold) rolls back to 0 immediately, ignoring the step.

min_requests: u64

Minimum total requests (baseline + canary) before the controller acts. Prevents acting on insufficient data.

cooldown_seconds: u64

Cooldown between adjustments, in seconds. Prevents rapid oscillation: after one adjustment, the next is suppressed until the cooldown elapses.

promote: CanaryRule

The promotion rule: when the canary metric is BELOW this threshold (good), the controller promotes (increases canary weight by step).

rollback: CanaryRule

The rollback rule: when the canary metric is ABOVE this threshold (bad), the controller rolls back (decreases canary weight by step). When the canary metric exceeds 2x this threshold (severe regression), the controller rolls back to 0 immediately.

Trait Implementations

impl Clone for CanaryAnalysis

fn clone(&self) -> CanaryAnalysis

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CanaryAnalysis

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for CanaryAnalysis

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl JsonSchema for CanaryAnalysis

fn schema_name() -> Cow<'static, str>

The name of the generated JSON Schema.

fn schema_id() -> Cow<'static, str>

Returns a string that uniquely identifies the schema produced by this type.

fn json_schema(generator: &mut SchemaGenerator) -> Schema

Generates a JSON Schema for this type.

fn inline_schema() -> bool

Whether JSON Schemas generated for this type should be included directly in parent schemas, rather than being re-used where possible using the $ref keyword.

impl PartialEq for CanaryAnalysis

fn eq(&self, other: &CanaryAnalysis) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for CanaryAnalysis

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for CanaryAnalysis