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Inspecting Trino on ice

For those unfamiliar, Stripe is an online payment processor that facilitates online payments for digital-native merchants. They use Trino to facilitate ad hoc analytics, enable dashboarding, and provide an API for internal services and data apps to utilize Trino. In Kevin Liu’s session at Trino Fest 2023, he showcases the Trino Iceberg connector and how it can replace more complex usage to access Iceberg metadata. He also discusses how Trino is a core part of operations at Stripe.

Check out the slides!


Trino is the foundational infrastructure on which other data apps and services are built upon. In Kevin’s words, “I call Trino the Swiss army knife in the data ecosystem.”

At Stripe, they use Iceberg tables extensively, replacing legacy Hive tables. But Iceberg isn’t perfect: one problem with Iceberg is reading its metadata from S3. To work with Iceberg metadata, Stripe developed an internal CLI tool. The tool requires a privileged internal machine, which is only accessible to developers. And outputs the result in JSON format, which is difficult to process, read, and use for further analysis. However, Kevin found that the Trino Iceberg connector can replace most of the functionality of the Iceberg CLI. The connector brings Iceberg metadata information to Trino’s powerful analytical engine and facilitates lightning fast debugging and analysis.

Unfortunately, there was no way to grab all desired table property information from the Trino Iceberg connector, because they were using an older version. Thus, they use the Trino PostgreSQL connector to connect directly to the backend database of the Hive Metastore, allowing them to inspect table metadata directly. With the two connectors, they have all the information about the data warehouse, powering their analysis and meta-analysis of the data and how it’s used.

They also use Trino to inspect Iceberg usage patterns. They log every Trino query using the Trino event listener and store that in another PostgreSQL database. This gives the full information of every query that has ever run through Trino, and allows them to perform analysis using historical queries. Combined with Trino’s built-in query metadata enrichment, this method enables a multitude of auditing, debugging, and optimization use cases.

In the future, they plan to use Trino to improve data quality by leveraging it as a validation framework, to perform Iceberg table maintenance, and to optimize tables based on historical read patterns.

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