Query Execution (Trino)

Ask a data question in plain English and watch Studio translate it into optimized Trino SQL, execute it against your account, and render the results — all within the chat stream. Full SQL visibility means you can verify every query before acting on the results.

Execute Trino queries against your Treasure AI account through natural-language requests and receive structured, readable results inline in the conversation.

• Signed in to Treasure AI Studio (Getting Started)
• At least one database accessible in your Treasure AI account

The simplest way to query data is to describe what you want in plain language:

• "Show me the top 10 customers by revenue in the last 30 days"
• "How many rows are in the pageviews table?"
• "What columns does mydb.users have?"

The AI translates your request into a Trino SQL query, executes it using tdx query, and presents the results in a formatted table — all within the chat stream.

1. You describe your query in natural language
2. The AI writes SQL and executes it via the Bash tool: tdx query "SELECT ..."
3. The tool call appears in the chat showing the command being executed
4. Query results render automatically as a structured table (see Query Result Tables)
5. The AI interprets the results and continues the conversation

You can watch each step happen in real time. The tool call indicator shows the running command, and the result table auto-expands when results arrive.

For precise control over query syntax and execution, ask the AI to run the exact command you want:

You: Run tdx query "SELECT count(*) AS total_users FROM mydb.users"

Or ask it to use tdx describe and tdx show for quick schema exploration:

You: Describe the mydb.users table
You: Show me a preview of mydb.users

See TDX Commands for the full command reference.

When a query executes, you see a tool call indicator in the chat stream:

• A spinning loader icon indicates the query is in progress
• The command description appears with the full command preview below in monospace
• You can see exactly what SQL is being executed

• A green checkmark replaces the spinner
• The tool call row becomes expandable — click to see the full command and raw output
• A query result table renders automatically below the tool call, showing structured results with column headers, types, and formatted data
• Execution stats appear below the table (see Query Result Tables for details)
• Duration badge shows execution time if 1 second or longer (e.g., "3.2s")

• A red X icon indicates the query failed
• A "Query failed" error card displays with:
  • The SQL that was executed (in monospace)
  • The error message from the query engine
  • The Job ID for troubleshooting
• If the tool exited with a non-zero status, an Exit code badge (e.g., "Exit 1") appears

If a query is running as part of the AI's response, click the Stop button (square icon) in the chat input area to interrupt the AI's execution stream.

Studio automatically detects query results from Bash tool calls. The UI recognizes commands containing:

• tdx query, tdx describe, tdx show
• trino, presto
• psql, mysql, sqlite3

When detected, the raw output is parsed into a structured table rather than displayed as plain text. Two output formats are recognized:

For complex queries, activate the built-in SQL skills in your project settings to reduce latency and improve efficiency:

Example: With the sql-skills:trino-optimizer skill active, ask the AI to analyze your query for latency bottlenecks. It suggests partition pruning, predicate pushdown, and other optimizations.

See Skills & Marketplace for the full skills catalog.

1. Start with natural language. Let the AI write the first draft of your query. Review the SQL in the tool call before interpreting results.

2. Use tdx describe before querying. Understanding the schema (column names, types) helps both you and the AI write accurate queries.

3. Set a database context. Ask the AI to run tdx use database mydb at the start of a session to avoid typing fully qualified table names in every query.

4. Review generated SQL. The AI writes SQL based on your description and the schema it discovers. Always review the query in the tool call expansion before acting on the results.

5. Use output format flags for large results. When you need to process large result sets, ask the AI to use --json for structured output or --output results.csv to save results to a file.

• Ask "How many rows are in [your_database].[your_table]?" and see a tool call execute tdx query
• See the result rendered as a structured table with column headers and types
• Expand the tool call to see the full SQL and raw output

• Query Result Tables — Formatting, column types, and stats
• TDX Commands — Full CLI command reference
• Skills & Marketplace — SQL skills for optimization and best practices