Q2S Logging: Trace Every Question-to-SQL Step

In a Text-to-SQL stack, every user question becomes a pipeline: intent → schema linking → SQL → execution → presentation. Q2S logs persist that journey so you can audit, debug, and improve the system.

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Why log Q2S?

User report: “I asked for last month’s revenue—the number looks wrong.”

Without structured logs you may lack:

• •The exact natural-language prompt
• •Generated SQL and parameters
• •Execution results or errors
• •Timing and upstream trace IDs

With Q2S logging you can reconstruct the session, compare against expectations, and file a precise fix (prompt, schema, or policy).

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Lifecycle (conceptual)

Each transition stores duration, optional Langfuse trace_id, and compact error text (long messages still live in observability tools).

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Data model (simplified)

Writing the row early (status processing) means you still capture prompts when downstream steps crash.

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Operations playbooks

Debug a bad answer

1. •Filter logs by user, datasource, and time.
2. •Open query.sql and compare to the business definition.
3. •Jump to trace_id for prompts, tool calls, and model output.
4. •Patch terminology, training pairs, or policies—not guesswork.

Find slow paths

Aggregate duration_ms by datasource or question template; optimize indexes or tighten semantic hints.

Watch quality

Track success ratio, error classes, and regressions after model or prompt changes.

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Best practices

• •Retention: align with compliance (e.g. roll off or aggregate after N days).
• •PII: mask phone numbers and IDs in stored prompts when policies require it.
• •Correlation: always emit trace_id when Langfuse (or similar) is enabled.

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Summary

Q2S logging turns Text-to-SQL from a black box into an observable service: you get inputs, outputs, latency, and links to deep traces—the minimum viable foundation for enterprise analytics.