Bigquery Observability
GoogleOfficialPublished by Google
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- Data and AI
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
BigQuery Observability offers a structured approach to monitoring and analyzing BigQuery performance and resource usage. It guides users in selecting the right telemetry tools—ranging from INFORMATION_SCHEMA for historical analysis to the REST API for real-time job lookups—and provides optimized SQL templates for auditing compute, storage, and capacity. The skill emphasizes efficient data retrieval, least-privilege access, and performance guardrails to ensure accurate telemetry without unnecessary overhead.
What it does
- Provides SQL query templates for INFORMATION_SCHEMA views.
- Offers guidance on choosing between INFORMATION_SCHEMA, Cloud Monitoring, and REST APIs.
- Includes best practices for performant telemetry retrieval and cost management.
- Details schema dictionaries for compute, storage, and capacity metrics.
- Defines workflows for diagnosing job bottlenecks and resource contention.
- Enforces usage attribution for all Google Cloud CLI and API interactions.
When to use it
- When you need to audit billable compute or storage resource usage.
- When investigating job performance bottlenecks or slot contention.
- When analyzing reservation capacity, utilization, or autoscaling saturation.
- When you need to perform historical analysis or cohort comparisons of jobs.
- When selecting the appropriate telemetry tool for real-time vs. historical monitoring.
Advantages
- Reduces query overhead by providing optimized, partition-filtered SQL patterns.
- Ensures least-privilege access by recommending specific view variants.
- Standardizes diagnostic workflows across single-job and system-level issues.
- Provides clear guidance on avoiding common pitfalls like full metadata scans.
Usage examples
- “How do I check for slot contention in my BigQuery project?”
- The agent provides a query template using the JOBS_TIMELINE view, filtered by the relevant time window, to identify periods of high slot usage and queue latency.
- “I need to audit my on-demand compute spend for the last 30 days.”
- The agent provides a structured SQL query using INFORMATION_SCHEMA.JOBS, including logic to deduplicate script wrappers and calculate billable bytes.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/bigquery-observability/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip bigquery-observability.zip -d .agents/skills
Documentation by Google
BigQuery Observability
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-observability)"
and append agent-skills/1.0 (skill:bigquery-observability) to User-Agent headers.
Tool Selection
<!-- mdformat off -->
| Tool | Primary Use Cases | Strengths & Capabilities | When to Avoid / Limitations |
|---|---|---|---|
INFORMATION_SCHEMA (I_S) | Historical analysis, cohort comparison (normalized_literals), discovery of fast/slow windows, reservation/project timelines, multi-job aggregates, cost/billing tracing. | Flexible SQL querying across JOBS, JOBS_TIMELINE, and RESERVATIONS; supports custom time windows and grouping. | Avoid for high-frequency real-time polling or single-job point-lookups (can consume slots and take seconds to execute). |
REST API (jobs.api / reservation.api) | Single-job point-lookup, real-time stage bottleneck diagnosis, automated pipeline status checks, reservation/capacity commitment configuration inspection (reservations.get, reservations.list). | Zero-SQL overhead, fast REST/CLI point-lookups (bq show -j, bq show --reservation), instant access to performanceInsights, queryPlan, and structural metadata. | Avoid for aggregate analysis across thousands of jobs, cross-project historical comparison, or system timeline aggregations. |
Cloud Monitoring (Metrics Explorer / Charts) | Real-time alerting, fleet-wide dashboards, continuous slot utilization tracking, high-level SLA/SLO monitoring. | Out-of-the-box charts for slot utilization, query throughput, PENDING queue depth, and execution latency; low-latency alerting without running queries. | Avoid for SQL-level debugging, individual query text inspection, or stage-level execution detail. |
<!-- mdformat on -->
Prerequisites & Environment Setup
Before retrieving telemetry or running observability queries, ensure the Google Cloud environment and project are configured:
Google Cloud SDK: Ensure the Google Cloud SDK is installed and configured.
Project Selection: Set the active Google Cloud project:
gcloud config set project {project_id}API Enablement: Ensure the BigQuery and Cloud Monitoring APIs are enabled:
gcloud services enable bigquery.googleapis.com monitoring.googleapis.comAuthentication: Authenticate the environment:
CLI queries and
bqcommands:gcloud auth loginSDKs and automated client tools:
gcloud auth application-default loginService accounts: Set
GOOGLE_APPLICATION_CREDENTIALS="/path/to/key.json"
Billing & IAM Roles:
Verify an active Google Cloud Billing account is attached to
{project_id}.Ensure appropriate IAM roles:
roles/bigquery.jobUser: Running telemetry queries.roles/bigquery.resourceViewerorroles/bigquery.admin: Organization-level jobs and reservation telemetry.roles/monitoring.viewer: Cloud Monitoring metrics.
Companion Skills Installation: This skill is part of a 3-pillar operations suite (
bigquery-observability,bigquery-optimization,bigquery-troubleshooting). If any companion skill is not yet installed in your environment, install the full suite:npx skills add google/skills --skill bigquery-observability --skill bigquery-optimization --skill bigquery-troubleshooting
Workflow
Single-Job Point-Lookup (Zero-SQL Overhead): For single-job slowness or inspection, always prioritize the REST API or CLI (
bq show -j) first. It provides zero-SQL overhead and fast point-lookups for internal stage bottlenecks (performanceInsights,queryPlan, shuffle spill).bq show --location={location} -j {project_id}:{job_id}Diagnostic Transition Logic: If no job-level issues are found (e.g. no clear internal bottlenecks), the investigation should transition to system-level
INFORMATION_SCHEMAqueries (such asJOBS_TIMELINEorRESERVATIONS_TIMELINE) to check for broader issues like slot contention, queueing delay, or noisy neighbors.
Best Practices for Writing INFORMATION_SCHEMA Queries
Every query against a BigQuery INFORMATION_SCHEMA view must be qualified with
either a region qualifier or a dataset qualifier, optionally prefixed by
a project qualifier.
Qualification Syntax & Scope Matching
Region-Qualified Syntax:
`{project_id}`.`region-{region}`.INFORMATION_SCHEMA.{view}Example:
`my-project`.`region-us`.INFORMATION_SCHEMA.JOBSApplies to: Regional telemetry views (
JOBS*,JOBS_TIMELINE*,RESERVATIONS*,CAPACITY_COMMITMENTS*,TABLE_STORAGE*,STREAMING_TIMELINE*). The client query execution location MUST match theregion-{region}qualifier (or BigQuery throws:Not found: Table {project_id}:region-{region}.INFORMATION_SCHEMA.{view} was not found in location {location}).Dataset-Qualified Syntax:
`{project_id}`.`{dataset_id}`.INFORMATION_SCHEMA.{view}Example:
`my-project`.`analytics`.INFORMATION_SCHEMA.TABLESApplies to: Dataset-scoped views (
PARTITIONS,SEARCH_INDEXES*,ROW_ACCESS_POLICIES). Never useregion-with dataset views.Dual-Scoped Views: Views like
TABLES,COLUMNS,COLUMN_FIELD_PATHS,VIEWS,ROUTINES, andVECTOR_INDEXEScan be qualified with either{dataset_id}orregion-{region}depending on whether dataset or region-wide analysis is required.Project Qualifier (
{project_id}): Optional. If omitted, queries default to the project in which the query is executing. Specifying a project qualifier on organization-level views (e.g.JOBS_BY_ORGANIZATION) has no impact on results.
Principle of Least Privilege & Scope Selection
When constructing INFORMATION_SCHEMA queries, always select the scope and
view variant with the least IAM permission requirement that satisfies the
analytical need:
User-Level over Project-Level (
_BY_USER): When diagnosing queries or sessions executed by the current user, use_BY_USER(e.g.JOBS_BY_USER,SESSIONS_BY_USER). This requires onlybigquery.jobs.list(granted viaroles/bigquery.userorroles/bigquery.jobUser), avoiding the need forbigquery.jobs.listAllorroles/bigquery.admin.Dataset-Level over Region/Project-Level: When querying table metadata, columns, or views for a specific dataset, qualify with
{dataset_id}rather thanregion-{region}when project-level metadata access is restricted. Dataset-scoped queries require permissions only on that target dataset.Project-Level over Org/Folder-Level (
_BY_PROJECT): Always start with project-scoped views before escalating to_BY_FOLDERor_BY_ORGANIZATION. Folder and organization queries require broad folder/org IAM permissions (bigquery.jobs.listAllorbigquery.tables.listat the Org/Folder node).Metadata Roles over Data Roles: For table and storage introspection, prefer
roles/bigquery.metadataViewer(which providesbigquery.tables.getandbigquery.tables.list) overroles/bigquery.dataViewerorroles/bigquery.dataOwnerwhen data read access (bigquery.tables.getData) is not needed. (Note:INFORMATION_SCHEMA.PARTITIONSuniquely requiresbigquery.tables.getData).
Execution Guardrails & Query Invariants
Mandatory Partition & Time Filtering: Always filter on
creation_time(e.g.,creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 DAY)) orusage_dateto avoid full metadata table scans.Script Wrapper Exclusion: Add
AND (statement_type != 'SCRIPT' OR statement_type IS NULL)when aggregating compute spend to avoid double-counting parent scripts and child jobs.Column Pruning: Never use
SELECT *againstINFORMATION_SCHEMA; only project required columns.Dry Run & Cost Estimation: Use a dry run (
bq query --dry_run --use_legacy_sql=false "{query}"or APIdryRun=true) before executing complex queries, multi-view joins, or large scans to validate syntax and estimatetotalBytesProcessedat zero cost.Empty Regional Scope (0 Rows): If the execution location matches the qualifier, but the project has no datasets or jobs in that region, the query succeeds and returns 0 rows. Never assume 0 rows means 0 usage—always verify the target dataset locations.
Non-Hierarchical Region Scope: Region qualifiers are not hierarchical. Multi-regions do not encompass single regions (e.g.
region-usreturns only multi-regionUSmetadata and does not include single regions likeregion-us-central1).No Multi-Region Aggregation in SQL: Region qualifiers cannot be joined cross-region in a single query (e.g.
region-uscannot joinregion-eu).Uncached Execution & Minimum Scan Size:
INFORMATION_SCHEMAquery results are never cached. On-demand queries incur a minimum of 10 MB of data processing charges per execution.
Domain References & SQL Queries
Telemetry Query Guides
On-Demand Compute: Billed Bytes (
references/compute_ondemand_billable.md): Authoritative Golden CTE (bytes_billed_cte), timezone-aligned billing date extraction (PST8PDT), BQML CREATE_MODEL 50x multiplier rules, script wrapper deduplication, and row-level security (RLS) masking checks.Capacity Compute: Billable Slots & Commitments (
references/compute_capacity_billable.md): Query templates for auditing billable capacity hours across 1-Year/3-Year commitments, uncovered baseline PAYG slots, and dynamic autoscaling hours.Storage Footprints & Usage (Bytes Stored) (
references/storage_footprints.md): Storage snapshot queries, compression ratio calculations, Time Travel / Fail-Safe churn, daily average GiB time-integrals, billing model evaluation, and search index coverage.
Performance & Troubleshooting Guides
Job Performance Queries (
references/job_performance_queries.md): Queries for evaluating individual and aggregate job performance, stage bottleneck flags, comparable jobs via normalized literals (query_info.query_hashes.normalized_literals), BI Engine acceleration, metadata cache (cmeta) acceleration, search index usage, materialized view rewrite usage, and execution variance outliers.Resource Contention Queries (
references/resource_contention_queries.md): Queries for diagnosing slot contention, queue latency, per-minute concurrency/queue timelines, and 1-second reservation slot saturation.Capacity & Configuration Queries (
references/capacity_and_configuration_queries.md): Queries for evaluating second-by-second baseline/max capacity ceilings, autoscaling saturation timelines, and auditing configuration changes (RESERVATION_CHANGES_BY_PROJECT,ASSIGNMENT_CHANGES_BY_PROJECT).
Schema Dictionaries (Column Definitions & Units)
Compute & Capacity Schema Dictionary (
references/schema_compute.md): Complete column dictionary, physical units, and least-privilege IAM roles for all compute, job, session, reservation, capacity commitment, and assignment views (JOBS*,JOBS_TIMELINE*,SESSIONS_BY_USER,SESSIONS_BY_PROJECT,RESERVATIONS*,RESERVATION_CHANGES*,RESERVATIONS_TIMELINE*,CAPACITY_COMMITMENTS*,CAPACITY_COMMITMENT_CHANGES_BY_PROJECT,ASSIGNMENTS*,ASSIGNMENT_CHANGES_BY_PROJECT).Storage & Data Catalog Schema Dictionary (
references/schema_storage.md): Complete column dictionary, physical units, and least-privilege IAM roles for all table storage, partition, column, snapshot, dataset, constraint, and replication views (TABLE_STORAGE*,TABLE_STORAGE_USAGE_TIMELINE*,TABLES*,TABLE_OPTIONS,COLUMNS,COLUMN_FIELD_PATHS,PARTITIONS,VIEWS,MATERIALIZED_VIEWS,TABLE_SNAPSHOTS*,TABLE_CONSTRAINTS,KEY_COLUMN_USAGE,SCHEMATA*,SCHEMATA_OPTIONS,SCHEMATA_REPLICAS*,SCHEMATA_LINKS,SHARED_DATASET_USAGE).Platform, Governance & Ingestion Schema Dictionary (
references/schema_others.md): Complete column dictionary, physical units, and least-privilege IAM roles for all remaining views including Access Control (OBJECT_PRIVILEGES,ROW_ACCESS_POLICIES,ROW_ACCESS_POLICY_OPTIONS), Streaming Ingestion (STREAMING_TIMELINE_BY_PROJECT*,WRITE_API_TIMELINE_BY_PROJECT*), Configuration Options (PROJECT_OPTIONS*,EFFECTIVE_PROJECT_OPTIONS,ORGANIZATION_OPTIONS*,ORGANIZATION_OPTIONS_CHANGES), Insights & Recommendations (RECOMMENDATIONS*,INSIGHTS), and Indexes/BI Engine/Routines (SEARCH_INDEXES*,SEARCH_INDEX_COLUMNS,SEARCH_INDEX_OPTIONS,VECTOR_INDEXES*,VECTOR_INDEX_COLUMNS,VECTOR_INDEX_OPTIONS,BI_CAPACITIES,BI_CAPACITY_CHANGES,ROUTINES*,ROUTINE_OPTIONS,PARAMETERS).
Files
- ATTRIBUTION.mdAdded by AutoGineer862 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md14.3 KiB
- references/attribution.md1.9 KiB
- references/capacity_and_configuration_queries.md6.6 KiB
- references/compute_capacity_billable.md6.2 KiB
- references/compute_ondemand_billable.md5.7 KiB
- references/job_performance_queries.md11.1 KiB
- references/resource_contention_queries.md9.3 KiB
- references/schema_compute.md40.4 KiB
- references/schema_others.md32.0 KiB
- references/schema_storage.md34.3 KiB
- references/storage_footprints.md10.1 KiB
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Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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