Enum MetricCategory 

pub enum MetricCategory {
    Rows,
    Bytes,
    Timing,
    Uncategorized,
}

Classifies a metric by what it measures.

This is orthogonal to MetricType (Summary / Dev), which controls verbosity. MetricCategory controls what kind of value is shown, so that EXPLAIN ANALYZE output can be narrowed to only the categories that are useful in a given context.

In particular this is useful for testing since metrics differ in their stability across runs:

• Rows and Bytes depend only on the plan and the data, so they are mostly deterministic across runs (given the same input). Variations can existing e.g. because of non-deterministic ordering of evaluation between threads. Running with a single target partition often makes these metrics stable enough to assert on in tests.
• Timing depends on hardware, system load, scheduling, etc., so it varies from run to run even on the same machine.

MetricCategory is especially useful in sqllogictest (.slt) files: setting datafusion.explain.analyze_categories = 'rows' lets a test assert on row-count metrics without sprinkling <slt:ignore> over every timing value.

Metrics that do not declare a category (the default for custom Count / Gauge metrics) are treated as Uncategorized for filtering purposes.

Variants

Rows

Row counts and related dimensionless counters: output_rows, spilled_rows, output_batches, pruning metrics, ratios, etc.

Mostly deterministic given the same plan and data.

Bytes

Byte measurements: output_bytes, spilled_bytes, current_memory_usage, bytes_scanned, etc.

Mostly deterministic given the same plan and data.

Timing

Wall-clock durations and timestamps: elapsed_compute, operator-defined Time metrics, start_timestamp / end_timestamp, etc.

Non-deterministic — varies across runs even on the same hardware.

Uncategorized

Catch-all for metrics that do not fit into Rows, Bytes, or Timing.

Custom Count / Gauge metrics that are not explicitly assigned a category are treated as Uncategorized for filtering purposes.

This variant lets users explicitly include or exclude these metrics, e.g.:

SET datafusion.explain.analyze_categories = 'rows, bytes, uncategorized';

Trait Implementations

impl Clone for MetricCategory

fn clone(&self) -> MetricCategory

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Copy for MetricCategory

impl Debug for MetricCategory

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

Formats the value using the given formatter.

impl Display for MetricCategory

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

Formats the value using the given formatter.

impl Eq for MetricCategory

impl FromStr for MetricCategory

type Err = DataFusionError

The associated error which can be returned from parsing.

fn from_str(s: &str) -> Result<Self, Self::Err>

Parses a string s to return a value of this type.

impl Hash for MetricCategory

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for MetricCategory

fn eq(&self, other: &MetricCategory) -> 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 StructuralPartialEq for MetricCategory