Hello Everyone!
Hue Editor, the open-source SQL Assistant for querying databases and data warehouses, is now extending its support to Trino. This integration marks a significant step in enhancing data mesh and SQL federation capabilities.
What is Trino?
Trino is a powerful tool for querying massive datasets, particularly useful for those dealing with terabytes or petabytes of data. Unlike traditional tools that rely on MapReduce jobs, Trino efficiently handles distributed queries across various data sources, including Hadoop's HDFS, relational databases, and NoSQL systems like Cassandra. It's designed for data warehousing and analytics, making it ideal for tasks such as data analysis and report generation. Trino continues to be developed independently by its community and offers robust support for querying large datasets.
Integrating Trino in Hue
To support Trino in Hue, we're leveraging the official Trino Python client. This client provides a robust and efficient way to connect to Trino databases using the DBAPI. By integrating this client, we ensure better compatibility, ongoing support, and access to extensive documentation, facilitating seamless querying and data analysis capabilities within the Hue environment.
Configuration
To configure the Trino editor in Hue, update the hue.ini file as follows:
[notebook]
[[interpreters]]
[[[trino]]]
name=Trino
interface=trino
options='{"url": "http://localhost:8080", "auth_username": "", "auth_password":""}'
Note: Currently, only basic LDAP authentication using username and password or password script is supported. Alternatively, you can establish unsecured Trino connections.
Getting Started
- Launch a Trino container using Docker:
docker run --name trino -d -p 8080:8080 trinodb/trino - Configure Hue: Add the Trino configuration to the hue.ini file as shown above.
- Test and Validate: Test the integration by querying the Trino database through Hue. Ensure all functionalities are working as expected.
By integrating Trino into Hue, you can leverage enhanced SQL federation and data mesh capabilities, providing a more robust and versatile data querying experience.
You can try this feature in the latest Hue version.
For feedback, questions, or suggestions, feel free to comment on the GitHub Discussions and quick start SQL querying!
Upwards and Onwards!
Ayush from the Hue Team