Bigquery Observability

by google55b4e13eba6dNo license21K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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).

FeaturedInstructions only

BigQuery Observability

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.
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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:

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

    bash
    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:

    bash
    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).

    bash
    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:

    googlesql
    `{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:

    googlesql
    `{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, and billing model evaluation.

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, 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).

Source and attribution

Source:google/skillsinskills/cloud/bigquery-observabilityat commit55b4e13

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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