Struct ProfilingSpec 

pub struct ProfilingSpec {
    pub columns: Vec<String>,
    pub exclude: Vec<String>,
    pub max_columns: usize,
    pub top_values: usize,
    pub categorical_max_distinct: u64,
    pub window: u32,
    pub min_history: u32,
    pub method: AnomalyMethod,
    pub sensitivity: Option<f64>,
    pub new_value_min_share: f64,
    pub psi_threshold: f64,
    pub on_drift: OnProfileDrift,
}

Learned column profiles with drift detection. Every run profiles the records it wrote (null rate, distinct count, min / max / mean, string length, type mix, top values) into a bounded-memory sketch, compares the result against a rolling baseline of earlier successful runs, and reports statistically significant changes per column — no thresholds to write.

Fields

columns: Vec<String>

Top-level columns to profile. Empty (the default) profiles every column the run writes, up to max_columns.

exclude: Vec<String>

Columns to leave out, exact names or prefix* globs. Defaults to ["_faucet_*"] so the run-metadata columns are never profiled.

max_columns: usize

Maximum columns profiled per run (default 200). Columns beyond the cap are skipped and the profile is marked truncated.

top_values: usize

How many most-frequent values to keep per categorical column (default 10). 0 disables top values entirely.

categorical_max_distinct: u64

A column whose estimated distinct count exceeds this is treated as high-cardinality: only null / type / length metrics are kept for it and no values are published (default 100).

window: u32

Rolling-window size: how many recent successful-run profiles form the baseline (default 20; must be ≥ min_history).

min_history: u32

Minimum baseline runs before drift detection starts (default 5; at least 2).

method: AnomalyMethod

How a numeric metric (null rate, distinct count, mean, …) is compared against its baseline series. Default zscore.

sensitivity: Option<f64>

Detection threshold for numeric metrics. zscore: max |x − mean| / std (default 3.0). iqr: Tukey fence multiplier (default 1.5).

new_value_min_share: f64

A categorical value never seen in the baseline is reported once its share of the run’s values reaches this fraction (default 0.05).

psi_threshold: f64

Population stability index above which a categorical column’s value distribution counts as drifted (default 0.2; the conventional “significant shift” threshold).

on_drift: OnProfileDrift

What a detected drift does to the run. Default warn.

Implementations

impl ProfilingSpec

pub fn effective_sensitivity(&self) -> f64

The configured sensitivity, or the method’s conventional default.

pub fn validate(&self) -> Result<(), String>

Fail-fast validation, surfaced at config-load time.

pub fn selects(&self, name: &str) -> bool

Whether name is selected for profiling by columns / exclude.

Trait Implementations

impl Clone for ProfilingSpec

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ProfilingSpec

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

Formats the value using the given formatter.

impl Default for ProfilingSpec

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for ProfilingSpec

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl JsonSchema for ProfilingSpec

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 ProfilingSpec

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

Equality operator ==.

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

Inequality operator !=.

impl Serialize for ProfilingSpec

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 ProfilingSpec