agent timed out when agent tried to profile a billion row table for null checks

TL;DR

Profile by aggregation, not by reading rows: have the agent compute null counts, distinct counts, and percentiles with warehouse-side aggregate queries per partition, and forbid full scans or row samples pulled into the agent. Billion-row profiling is a query-design problem, and the warehouse, not the agent, should do the counting.

agent timed out when agent tried to profile a billion row table for null checks

Use this when

• The check or run fails with this symptom: the agent's profiling run times out against the billion-row table.
• You are working on an agent profiling a billion-row table for null checks that times out and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• Profiling a small table where direct reads are fine and aggregation design is overkill instead.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Inspect the queries the agent issued and identify any that scan the full table or return row-level data. Verify: the offending query shapes are listed
2. Rewrite the profiling plan as aggregate queries: counts, null counts, min, max, and approximate distinct counts grouped by partition. Verify: the plan uses only aggregate queries with partition grouping
3. Run the aggregates partition by partition, storing each partition's profile before moving on. Verify: per-partition profiles are stored incrementally
4. Set a per-query timeout and a row budget in the agent's tool configuration so an unbounded query fails fast instead of hanging the run. Verify: query limits are configured in the tool layer
5. Assemble the table profile from the partition results and confirm it matches a warehouse-side summary query. Verify: the assembled profile matches the summary check

Variant phrasings

agent timed out profiling billion row table null checks

The verbatim billion-row phrasing.

warehouse profiling aggregate queries null count partitions

The aggregate-query phrasing for partition profiling.

agent profiling timeout full scan query design

The query-design phrasing for warehouse-side statistics.

Compatibility: Agent profiling jobs on Snowflake, BigQuery, and Spark tables at billion-row scale

Why it happens

Agents default to reading data to reason about it, which collapses at billion-row scale. Profiling only needs statistics, and warehouses compute those statistics efficiently in place; the timeout is the symptom of moving computation to the wrong side of the connection.

Edge cases / pitfalls

• Approximate distinct counts are fine for profiling. Exact counts are the expensive habit to break.
• Cache partition profiles and only re-profile changed partitions on later runs.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_oXYip9ko9SLKSR-rB8FOvQ