Covers conformance fixtures, adversarial cases, quality metrics, budgets, monitoring, fallback, and incident review for query resource governance, so adopters can bound analytical query cost, latency, concurrency, cardinality, and result size before an agen...
Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.
Decision
For Query resource governance, verify the contract under success, denial, failure, recovery, load, and drift so adopters can bound analytical query cost, latency, concurrency, cardinality, and result size before an agent can overload a warehouse or create runaway spend. Resource policy limits analytical work and shapes safe fallbacks; priority or user seniority never removes hard platform caps. This is a reference contract: database products, numeric budgets, jurisdictions, retention, organizational defaults, and accountable owners remain explicit adoption choices.
Scope
- The Query resource governance actors, inputs, outputs, states, versions, and externally visible outcomes needed to bound analytical query cost, latency, concurrency, cardinality, and result size before an agent can overload a warehouse or create runaway spend.
- The role-specific focus of this block: verify the contract under success, denial, failure, recovery, load, and drift, including primary, cached, asynchronous, export, support, and recovery paths where applicable.
- Adoption-specific configuration, ownership, rollout, evidence retention, and review responsibilities needed to use the contract safely.
Outside this block
- Resource policy limits analytical work and shapes safe fallbacks; priority or user seniority never removes hard platform caps.
- Choosing a universal database, model, renderer, vendor, numeric threshold, retention period, timezone, jurisdiction, or service-level objective.
- Claiming that packaged scenarios ran against a downstream implementation or that this reference grants security, privacy, accessibility, analytical, or legal approval.
Contract
- Verification evidence for Query resource governance must demonstrate this rule: Every metric plan is assigned a workload class with explicit hard limits for execution time, bytes or partitions scanned, rows, memory, concurrency, series, points, and result size.
- Verification evidence for Query resource governance must demonstrate this rule: Preflight estimation or conservative static costing runs before expensive work and rejects plans that cannot demonstrate compliance with their class.
- Verification evidence for Query resource governance must demonstrate this rule: High-cardinality dimensions, broad date ranges, multi-metric fan-out, repeated drill-down, and retries consume one shared request budget.
- Verification evidence for Query resource governance must demonstrate this rule: Priority may choose queue order or reserved capacity but cannot bypass tenant quotas, global safety caps, or database-enforced resource limits.
- Verification evidence for Query resource governance must demonstrate this rule: The gateway prefers approved aggregates, caches, coarser grains, top-N plus other, or asynchronous execution only when semantic consequences are visible.
- Verification evidence for Query resource governance must demonstrate this rule: Cancellation propagates from client through gateway and connector; uncertain cancellation remains observable until the backend query is terminal.
- Verification evidence for Query resource governance must demonstrate this rule: Costs are attributed by caller, tenant, metric, connector, plan digest, cache status, and request without logging sensitive result values.
- Verification evidence for Query resource governance must demonstrate this rule: Retries are bounded, distinguish transient from deterministic failures, and cannot multiply warehouse work after a result or job handle already exists.
Implementation guidance
- Create paired positive and negative fixtures for each public operation, semantic rule, authorization boundary, and terminal state.
- Measure correctness, unsupported output, denial consistency, latency, resource use, and recovery by contract and implementation version.
- Inject dependency, policy, schema, freshness, cancellation, and audit failures before promoting the implementation beyond review.
- Keep human domain review for consequential interpretations and feed disagreements into versioned fixtures and rules.
Failure handling and safeguards
- For Query resource governance, An over-budget plan is denied or narrowed with an explicit explanation of changed period, grain, dimensions, precision, or delivery mode.
- For Query resource governance, Queue saturation, quota exhaustion, estimator failure, or database limit breach returns a retryable or terminal state with bounded retry advice.
- For Query resource governance, If accounting or limit enforcement is unavailable, new agent-originated analytical work fails closed for the affected boundary.
Verification and operations
- For the testing evidence of Query resource governance, load-test concurrent dashboards, drill-downs, retries, cancellations, and multiple tenants while observing both gateway and database limits.
- For the testing evidence of Query resource governance, compare preflight estimates with actual time, scan, memory, rows, points, and cost and recalibrate policy when error exceeds the approved tolerance.
- For the testing evidence of Query resource governance, inject lost responses and worker restarts and prove that durable job identity prevents duplicate expensive execution.
Adoption assumptions
- The adopting product has authenticated identity, a versioned authorization policy, owned metric definitions, bounded telemetry, and a controlled path for change.
- Names and values in the example are fictional adoption fixtures, not universal defaults, production credentials, performance promises, or business targets.
- Referenced specifications constrain protocol, security, accessibility, or vendor behavior; the adopting team must confirm current applicability before promotion.
The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.
References
- ClickHouse documentation, Restrictions on query complexity (applies as of 2026-09-12)
- Google Cloud BigQuery documentation, Run a query (applies as of 2026-09-12)
- OWASP API Security Top 10 (2023)