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Bigquery Observability

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
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From the author

Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).
— the description by Google

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

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/bigquery-observability/download
  2. Unzip 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 -->

ToolPrimary Use CasesStrengths & CapabilitiesWhen 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:

  1. Google Cloud SDK: Ensure the Google Cloud SDK is installed and configured.

  2. Project Selection: Set the active Google Cloud project:

    gcloud config set project {project_id}
  3. API Enablement: Ensure the BigQuery and Cloud Monitoring APIs are enabled:

    gcloud services enable bigquery.googleapis.com monitoring.googleapis.com
  4. Authentication: Authenticate the environment:

    • CLI queries and bq commands: gcloud auth login

    • SDKs and automated client tools: gcloud auth application-default login

    • Service accounts: Set GOOGLE_APPLICATION_CREDENTIALS="/path/to/key.json"

  5. 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.resourceViewer or roles/bigquery.admin: Organization-level jobs and reservation telemetry.

      • roles/monitoring.viewer: Cloud Monitoring metrics.

  6. 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

  1. 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}
  2. Diagnostic Transition Logic: If no job-level issues are found (e.g. no clear internal bottlenecks), the investigation should transition to system-level INFORMATION_SCHEMA queries (such as JOBS_TIMELINE or RESERVATIONS_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
  1. Region-Qualified Syntax:

    `{project_id}`.`region-{region}`.INFORMATION_SCHEMA.{view}

    Example: `my-project`.`region-us`.INFORMATION_SCHEMA.JOBS

    Applies to: Regional telemetry views (JOBS*, JOBS_TIMELINE*, RESERVATIONS*, CAPACITY_COMMITMENTS*, TABLE_STORAGE*, STREAMING_TIMELINE*). The client query execution location MUST match the region-{region} qualifier (or BigQuery throws: Not found: Table {project_id}:region-{region}.INFORMATION_SCHEMA.{view} was not found in location {location}).

  2. Dataset-Qualified Syntax:

    `{project_id}`.`{dataset_id}`.INFORMATION_SCHEMA.{view}

    Example: `my-project`.`analytics`.INFORMATION_SCHEMA.TABLES

    Applies to: Dataset-scoped views (PARTITIONS, SEARCH_INDEXES*, ROW_ACCESS_POLICIES). Never use region- with dataset views.

  3. Dual-Scoped Views: Views like TABLES, COLUMNS, COLUMN_FIELD_PATHS, VIEWS, ROUTINES, and VECTOR_INDEXES can be qualified with either {dataset_id} or region-{region} depending on whether dataset or region-wide analysis is required.

  4. 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:

  1. 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 only bigquery.jobs.list (granted via roles/bigquery.user or roles/bigquery.jobUser), avoiding the need for bigquery.jobs.listAll or roles/bigquery.admin.

  2. Dataset-Level over Region/Project-Level: When querying table metadata, columns, or views for a specific dataset, qualify with {dataset_id} rather than region-{region} when project-level metadata access is restricted. Dataset-scoped queries require permissions only on that target dataset.

  3. Project-Level over Org/Folder-Level (_BY_PROJECT): Always start with project-scoped views before escalating to _BY_FOLDER or _BY_ORGANIZATION. Folder and organization queries require broad folder/org IAM permissions (bigquery.jobs.listAll or bigquery.tables.list at the Org/Folder node).

  4. Metadata Roles over Data Roles: For table and storage introspection, prefer roles/bigquery.metadataViewer (which provides bigquery.tables.get and bigquery.tables.list) over roles/bigquery.dataViewer or roles/bigquery.dataOwner when data read access (bigquery.tables.getData) is not needed. (Note: INFORMATION_SCHEMA.PARTITIONS uniquely requires bigquery.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)) or usage_date to 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 * against INFORMATION_SCHEMA; only project required columns.

  • Dry Run & Cost Estimation: Use a dry run (bq query --dry_run --use_legacy_sql=false "{query}" or API dryRun=true) before executing complex queries, multi-view joins, or large scans to validate syntax and estimate totalBytesProcessed at 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-us returns only multi-region US metadata and does not include single regions like region-us-central1).

  • No Multi-Region Aggregation in SQL: Region qualifiers cannot be joined cross-region in a single query (e.g. region-us cannot join region-eu).

  • Uncached Execution & Minimum Scan Size: INFORMATION_SCHEMA query 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

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Requirements and compatibility

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

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Node.js

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google/skills · skills/cloud/bigquery-observability · commit 2194d3b
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  • LICENSE (added by AutoGineer from LICENSE)
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