Query Kumo Relational

PQL describes a predictive task over a relational schema. Every runnable Kumo Relational query identifies a target and the entity or entities to score.

Target × entity × horizon

• Target: The value or event to predict, such as a column or future aggregation.
• Entity: The table key for which Kumo Relational returns predictions.
• Horizon: The future interval used by a temporal target.

PREDICT <target_expression>
FOR <entity_table>.<primary_key> <entity_selection>
[WHERE <context_filter>]
[ASSUMING <condition>]

Complete examples

-- Regression: spend during the next 30 days
PREDICT SUM(orders.amount, 0, 30, days)
FOR customers.customer_id=42

-- Classification: any order during the next 30 days
PREDICT COUNT(orders.*, 0, 30, days) > 0
FOR customers.customer_id IN (42, 57, 81)

-- Static target on the entity table
PREDICT customers.segment
FOR customers.customer_id=42

Current Kumo Relational constraints

These constraints describe the current sdm-pql validator, not every construct in the broader PQL grammar.

See Write Predictive Queries for clause and aggregation details, and Prediction Types for ranking and forecasting.

From query to prediction

from sdm_client import PredictiveQuery, SDMClient, KumoRelational
query = PredictiveQuery(query_text, schema)
request = query.materialize(indices=[42])
with SDMClient(url="http://localhost:8000") as client:
    result = client.predict(request, model=KumoRelational())

Continue with the query-writing tutorial or choose a prediction type.