Question 1

Can TextQL query across multiple Trino catalogs in a single question?

Accepted Answer

Yes. TextQL discovers all catalogs, schemas, and tables available on your Trino cluster. When you ask a question that spans multiple data sources — like joining Hive event data with Postgres user profiles — Ana generates the correct cross-catalog Trino SQL with fully qualified table references.

Question 2

Does TextQL work with Trino connectors like Iceberg, Delta Lake, and Hive?

Accepted Answer

Yes. TextQL works with any Trino connector that supports SELECT queries. This includes Hive, Iceberg, Delta Lake, MySQL, PostgreSQL, Kafka, Elasticsearch, MongoDB, and dozens more. Ana treats each connector as a queryable catalog and generates appropriate SQL for the data it contains.

Question 3

How does TextQL handle Trino's distributed query execution?

Accepted Answer

TextQL submits queries to your Trino coordinator, which handles distribution across workers. Ana generates well-optimized SQL with appropriate predicates to enable pushdown optimization. TextQL monitors query progress and handles retries gracefully if a worker fails.

Question 4

Can I use TextQL with Trino on Kubernetes or Starburst?

Accepted Answer

Yes. TextQL connects to the Trino coordinator endpoint regardless of how your cluster is deployed — bare metal, Kubernetes (via Helm charts), Docker, or managed platforms like Starburst Galaxy. Just provide the coordinator URL and authentication credentials.

Question 5

Does TextQL add another layer of latency on top of Trino?

Accepted Answer

TextQL adds minimal overhead — typically under 500ms for query planning and natural language interpretation. Once Ana generates the SQL, it executes directly on your Trino cluster at native speed. For most analytical queries, the TextQL overhead is imperceptible compared to Trino's own execution time.