5.2.55. Trino¶

Trino is an optional, disabled-by-default SQL query engine for the Data Engineering track. Atlas wires Trino to the existing Iceberg REST catalog and MinIO-backed lakehouse buckets so notebooks, Zeppelin, Airflow tasks, and local tools can query the same tables through a SQL surface.

1. Overview¶

Image: trinodb/trino:482.

The first Atlas slice is intentionally narrow: one single-node coordinator, one lakehouse catalog, and no extra workers. The catalog uses Iceberg REST for table metadata and MinIO for object storage. This keeps Trino aligned with the Spark/Iceberg path already used by the data-eng stack.

2. Access¶

Surface	URL	Notes
Kong	`http://trino.localhost:${KONG_HTTP_PORT}`	Routed only when `TRINO_SOURCE=container`.
Direct	`http://localhost:${TRINO_PORT}`	Coordinator UI and HTTP API.
In-network	`http://trino:8080`	Use from notebooks, Zeppelin JDBC, Airflow tasks, and other containers.

3. Configuration¶

TRINO_SOURCE=disabled
TRINO_IMAGE=trinodb/trino:482
TRINO_PORT=
TRINO_SCALE=
ICEBERG_REST_SOURCE=disabled
MINIO_SOURCE=container

TRINO_SOURCE=container requires both MINIO_SOURCE=container and ICEBERG_REST_SOURCE=container. The bootstrapper fails early if either dependency is disabled.

4. Architecture & Wiring¶

The mounted catalog file at services/trino/catalog/lakehouse.properties defines:

connector.name=iceberg
iceberg.catalog.type=rest
iceberg.rest-catalog.uri=http://iceberg-rest:8181
iceberg.rest-catalog.warehouse=s3://lakehouse/
fs.native-s3.enabled=true for Trino 482 native S3 access to MinIO at http://minio:9000
scoped Iceberg MinIO credentials through ${ENV:MINIO_ICEBERG_ACCESS_KEY} and ${ENV:MINIO_ICEBERG_SECRET_KEY}

This first local-development slice has no Trino authenticator configured. The example user atlas is a convention shared by Atlas notebooks and clients, not an authentication boundary; the local coordinator accepts any user string until a future auth issue adds a real authenticator. Do not treat this no-auth shape as production access control.

Minimal SQL smoke once Spark or another writer has created tables:

SHOW CATALOGS;
SHOW SCHEMAS FROM lakehouse;
SHOW TABLES FROM lakehouse.bronze;
SELECT * FROM lakehouse.bronze.<table_name> LIMIT 10;

Atlas seeds a Zeppelin JDBC interpreter when both ZEPPELIN_SOURCE=container and TRINO_SOURCE=container. Use the named Zeppelin 0.12.1 prefix %trino:

%trino
SHOW TABLES FROM lakehouse.bronze;

The seeded profile uses JDBC URL jdbc:trino://trino:8080/lakehouse, driver class io.trino.jdbc.TrinoDriver, and Maven dependency io.trino:trino-jdbc:482. Zeppelin's generic JDBC docs still describe %jdbc(prefix) for multiple connections; Atlas uses the named interpreter prefix because Zeppelin 0.12.1 created JDBC profiles run as %trino in this stack.

Python clients inside the Docker network can use the official trino DB-API package:

import trino

conn = trino.dbapi.connect(
    host="trino",
    port=8080,
    user="atlas",
    catalog="lakehouse",
    schema="gold",
)
cur = conn.cursor()
cur.execute("SHOW SCHEMAS FROM lakehouse")
print(cur.fetchall())

Host-side Python clients use the assigned host port:

import os
import trino

conn = trino.dbapi.connect(
    host="localhost",
    port=int(os.environ["TRINO_PORT"]),
    user="atlas",
    catalog="lakehouse",
    schema="gold",
)

Live CTAS smoke after the lakehouse path is up:

CREATE SCHEMA IF NOT EXISTS lakehouse.gold;
CREATE TABLE lakehouse.gold.atlas_trino_ctas_smoke AS
SELECT 1 AS id, 'atlas' AS note;
SELECT * FROM lakehouse.gold.atlas_trino_ctas_smoke;
DROP TABLE lakehouse.gold.atlas_trino_ctas_smoke;

Atlas does not create bronze/silver/gold namespaces at stack startup; data-eng-lab or the operator owns those namespaces. The CREATE SCHEMA line is part of the live smoke only.

5. Dependencies & Integrations¶

5.1. Current — Upstream (this service calls)¶

Service	Category
iceberg-rest	data
minio	data

5.2. Current — Downstream (services that call this)¶

Service	Category
kong	infra
zeppelin	apps

5.3. Architecture diagram¶

trino architecture

Open the full-size diagram for a full-screen view.

5.4. Future — Missing pair integrations¶

Add richer notebook examples that create a Spark-written Iceberg table and query it through Trino.

5.5. Future — Candidate new services¶

Superset or another BI UI can sit downstream of Trino once the lakehouse query path is stable.

5.6. Future — Unused features in this service¶

Worker scaling, query resource groups, access-control files, and additional catalogs are intentionally out of scope for this first integration.

6. Troubleshooting¶

Catalog missing: confirm ICEBERG_REST_SOURCE=container and that iceberg-rest is healthy before Trino starts.
S3 access denied: confirm minio-init completed and populated MINIO_ICEBERG_ACCESS_KEY / MINIO_ICEBERG_SECRET_KEY.
Kong alias does not load: run ./start.sh --setup-hosts so trino.localhost resolves locally.