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Query Hopsworks feature store tables via Trino SQL using the hops CLI.
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Basierend auf der SOC-Berufsklassifikation
| name | hops-trino-sql |
| description | Query Hopsworks feature store tables via Trino SQL using the hops CLI. |
The offline store is lakehouse tables (Hudi, Delta, or Iceberg), and these are queryable directly as Trino tables. Trino SQL is a direct-table read path for interactive exploration and EDA; training and inference pipelines should read through a feature view instead, so that model-dependent transformations and point-in-time joins stay consistent. The hops CLI is the preferred path for interactive Trino queries over writing Python.
Offline feature groups are queried through Trino with this reference pattern:
delta.<project_name>_featurestore.<fg_name>_<fg_version>
hudi.<project_name>_featurestore.<fg_name>_<fg_version>
delta for Delta feature groups, hudi for Hudi ones.<project_name>_featurestore (project name lowercase).<fg_name>_<version> — the version suffix is required.iceberg, so always fully-qualify with
delta./hudi. (or pass --catalog delta) when reading feature groups.--limit caps rows (default soft cap; --limit 0 = unlimited). -f file.sql
runs a query from a file; -i reads from stdin.YYYY-MM-DD), so range filters like
date >= '2025-01-31' AND date <= '2025-02-28' get partition-pruned.hops trino query "SELECT * FROM delta.<project>_featurestore.<fg>_1 LIMIT 5"
hops trino catalogs # delta, hive, hudi, iceberg, ...
hops trino schemas delta # schemas in a catalog
hops trino tables delta.<project>_featurestore
hops trino describe delta.<project>_featurestore.<fg>_1
Use when writing and deploying an interactive agent (e.g. a LlamaIndex program) as a served Hopsworks deployment.
Use when writing Streamlit or custom Python apps for Hopsworks, deploying from HopsFS or Git, migrating legacy apps off `APP_BASE_URL_PATH`, or managing app routing/readiness, monitoring, and public sharing. Auto-invoke when user wants to create a dashboard, deploy a Python app to Hopsworks, configure app routing/readiness, or access the feature store from an app. Input an app source + env + memory; output a running app and its URL.
Mount or ingest a table from a supported datasource. Mount tables from a datasource as an external feature group or ingest data into a new feature group using DLTHub. Auto-invoke when user works with external data (Snowflake, BigQuery, Redshift, S3, ADLS, GCS, JDBC, SQL, Databricks Unity Catalog, Postgres, MySQL, Oracle, SAP, MongoDB, CRM, REST APIs).
Use when writing Python code that creates, inserts into, or manages tables or feature groups. Auto-invoke when user writes feature pipelines, feature engineering code, or asks about feature group best practices (online vs offline, batching, OOM, materialization, embeddings, statistics).
Use when a Hopsworks job, app, or deployment needs Python libraries that are not in a base environment. Clone a base environment and install requirements or a wheel into the clone. Auto-invoke when the user hits a missing-package error in a job/app/deployment, asks to install custom dependencies, add a pip requirement, install a wheel, or pick which Python environment a workload should run in.
Use when the user asks where something lives in the Hopsworks UI or how to reach a page (Data Sources, Feature Groups, Feature Views, Model Registry, Deployments, Apps, Agents, Jobs, Jupyter, Project Settings). Knowledge skill: the project-scoped sidebar layout and what sits under each section, so you can point users to the right place.