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Cardinality

docs/documentation/aggregates/metrics/cardinality.mdx

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The cardinality aggregation estimates the number of distinct values in a field.

<CodeGroup> ```sql SQL SELECT pdb.agg('{"cardinality": {"field": "rating"}}') FROM mock_items WHERE id @@@ pdb.all(); ```
python
from paradedb import Agg, All, ParadeDB

MockItem.objects.filter(
    id=ParadeDB(All())
).aggregate(agg=Agg('{"cardinality": {"field": "rating"}}'))
python
from sqlalchemy import select
from sqlalchemy.orm import Session
from paradedb.sqlalchemy import pdb, search

stmt = (
    select(pdb.agg({"cardinality": {"field": "rating"}}))
    .select_from(MockItem)
    .where(search.all(MockItem.id))
)

with Session(engine) as session:
    session.execute(stmt).all()
ruby
MockItem.search(:id)
        .match_all
        .facets_agg(agg: { cardinality: { field: "rating" } })
</CodeGroup>
ini
      agg
----------------
 {"value": 5.0}
(1 row)

Unlike SQL's DISTINCT clause, which returns an exact value but is very computationally expensive, the cardinality aggregation uses the HyperLogLog++ algorithm to closely approximate the number of distinct values.

See the Tantivy documentation for all available options.