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Data · Version 1.6.0 · Reviewed 2026-08-02

BigQuery Query Advisor

Make data systems more correct and operable for scan-cost reduction and partitioning review with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Analyzes bytes scanned, partition pruning, clustering, joins, nested fields, slot use, materialized views, and reservation strategy.

₹99 one-time

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What this skill helps you do

  • Scan-cost reduction
  • Partitioning review
  • Slot-utilization analysis

How BigQuery Query Advisor works

You provide

Schema, query plans, and the real access pattern

It inspects

Plan accuracy and lock behavior for scan-cost reduction

It decides

A partitioning review change weighed against write cost

You verify

Re-measured plan with buffer reads and timing compared

What it checks first

BigQuery Query Advisor analyzes bytes scanned, partition pruning, clustering, joins, nested fields, slot use, materialized views, and reservation strategy. Use it when the work involves Scan-cost reduction, Partitioning review, Slot-utilization analysis.

  1. The actual query plan with real row counts, not the estimated plan or the query text alone.
  2. Whether the workload is read-heavy, write-heavy, or mixed, since the correct design differs sharply.
  3. Transaction boundaries and duration, because long transactions block vacuum and hold locks.
  4. Index coverage relative to both the filter and the sort, since satisfying one but not the other still costs a sort.
  5. Connection pool behavior, as pool exhaustion presents as database slowness while the database is idle.

Failure modes it recognizes

  • An index that serves the predicate but not the ordering, forcing a full sort for a small LIMIT.
  • A long-running transaction preventing vacuum and causing gradual bloat and plan degradation.
  • Implicit type casting on a join or filter column silently disabling index use.
  • Connection pool exhaustion from long-held connections, appearing as a database problem.
  • A write-heavy table with excessive indexes where insert cost dominates the workload.
  • Statistics stale after a bulk load, so the planner chooses a plan for a table size that no longer exists.

Answers it will reject

  • Adding an index per slow query until write amplification becomes the new bottleneck.
  • Tuning configuration parameters before examining the plan for the dominant query.
  • Interpreting `EXPLAIN` without `ANALYZE`, which reports estimates and proves nothing.
  • Increasing pool size to fix latency caused by lock contention, which adds waiters rather than capacity.

Decision rules it applies

  • Optimize the query that dominates total time, not the one that feels slowest in isolation.
  • Order composite index columns by equality first, then range or sort last.
  • Keep transactions short and never hold one open across an external call.
  • Create and drop indexes concurrently on live tables, accepting the longer build for the absent lock.

Evidence it asks for

  • `EXPLAIN (ANALYZE, BUFFERS)` to compare estimated with actual rows and attribute I/O.
  • Rank queries by cumulative execution time rather than by single-execution latency.
  • Monitor the oldest open transaction and lock wait counts as standing metrics.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to scan-cost reduction.
  2. Trace concrete failure or abuse paths for partitioning review; do not report checklist items without a mechanism.
  3. Prioritize slot-utilization analysis findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Scan-cost reduction assessment
  • Partitioning review decision and action plan
  • Slot-utilization analysis verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

This dashboard query scans 12 TB each refresh even though users select a single day and one customer.

Expected output

The timestamp is wrapped in DATE() inside the predicate, preventing partition pruning, and customer_id lacks clustering. Filter directly on the partition column range before considering more slots...

Boundaries and compatibility

Ideal for

  • Scan-cost reduction: produce a decision or artifact grounded in supplied evidence.
  • Partitioning review: produce a decision or artifact grounded in supplied evidence.
  • Slot-utilization analysis: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.