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The pdb.agg function accepts an Elasticsearch-compatible JSON aggregate query string. It executes the aggregate using the columnar portion of the ParadeDB index, which can significantly accelerate performance compared to vanilla Postgres. For example, the following query counts the total number of results for a search query. 
SELECT pdb.agg('{"value_count": {"field": "id"}}')
FROM mock_items
WHERE category === 'electronics';
Expected Response
      agg
----------------
 {"value": 5.0}
(1 row)
This query counts the number of results for every distinct group: 
SELECT rating, pdb.agg('{"value_count": {"field": "id"}}')
FROM mock_items
WHERE category === 'electronics'
GROUP BY rating
ORDER BY rating
LIMIT 5;
Expected Response
 rating |      agg
--------+----------------
      3 | {"value": 1.0}
      4 | {"value": 3.0}
      5 | {"value": 1.0}
(3 rows)

​
Multiple Aggregations

To compute multiple aggregations at once, simply include multiple pdb.agg functions in the target list: 
SELECT
  pdb.agg('{"avg": {"field": "rating"}}') AS avg_rating,
  pdb.agg('{"value_count": {"field": "id"}}') AS count
FROM mock_items
WHERE category === 'electronics';
Expected Response
   avg_rating   |     count
----------------+----------------
 {"value": 4.0} | {"value": 5.0}
(1 row)

​
JSON Fields

If metadata is a JSON field with key color, use metadata.color as the field name: 
SELECT pdb.agg('{"terms": {"field": "metadata.color"}}')
FROM mock_items
WHERE id @@@ pdb.all();
If a text or JSON field is used inside pdb.agg, it must use the literal tokenizer.