Struct ColumnProfile 

pub struct ColumnProfile {
    pub name: String,
    pub data_type: DataType,
    pub null_count: usize,
    pub total_count: usize,
    pub unique_count: Option<usize>,
    pub unique_count_is_approximate: Option<bool>,
    pub invalid_count: Option<usize>,
    pub type_homogeneity: Option<TypeHomogeneity>,
    pub stats: ColumnStats,
    pub patterns: Option<Vec<Pattern>>,
}

Profiling statistics for a single column.

Fields

name: String

data_type: DataType

null_count: usize

total_count: usize

unique_count: Option<usize>

unique_count_is_approximate: Option<bool>

Whether unique_count is an approximate (HyperLogLog) estimate rather than an exact distinct count.

None when unique_count is None (never computed); Some(false) for an exact count; Some(true) once the cardinality estimator has spilled to its HLL sketch (~1% relative error). Consumers running key or high-cardinality/uniqueness gates must treat Some(true) as “do not rely on this as an exact integer” – an exact-looking count with no provenance is unsafe for those checks.

invalid_count: Option<usize>

Non-null values that failed the column type’s raw validity predicate: non-finite or malformed numbers on numeric columns, and values that do not parse directly as calendar dates on date columns. The date predicate intentionally does not trim surrounding whitespace; descriptive date statistics may normalize whitespace independently.

For numeric columns, mean/std_dev cover total_count - null_count - invalid_count values. For date columns, this count audits the strict raw-value quality predicate. None means the check did not run (another column type, or statistics skipped) — never “no invalid values”, which is Some(0).

type_homogeneity: Option<TypeHomogeneity>

How the column’s non-null values distribute across lexical classes.

data_type cannot answer “did this column have a dominant form?”: a column of names and a column that is 60% numbers are both String, and invalid_count is absent on string columns by contract. This carries the evidence, so a consumer can tell a textual column from one that defeated type inference.

Counted over the values the profiler retained — the engine’s bounded reservoir sample on a large source, the whole column on a small or in-memory one. classified_count() against total_count - null_count is what says which happened; treat the shares as sampled whenever it is short.

None means the classification did not run, never “one uniform class”: a column that was classified and had nothing to classify (all-null, or zero rows) is Some with every count zero.

stats: ColumnStats

patterns: Option<Vec<Pattern>>

Detected patterns, or None when pattern detection did not run.

None and Some(vec![]) are not interchangeable: the former means the column was never scanned, the latter that it was scanned and nothing matched. Consumers that gate on sensitivity – redaction, agent-facing output – must treat None as “unknown”, never as “no sensitive data”.

Trait Implementations

impl Clone for ColumnProfile

fn clone(&self) -> ColumnProfile

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ColumnProfile

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for ColumnProfile

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl JsonSchema for ColumnProfile

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 Serialize for ColumnProfile

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

where __S: Serializer,

Serialize this value into the given Serde serializer.