Bigquery Observability

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Tool integration
Data and AI
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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

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google/bigquery-observability/download

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

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.

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.com
```
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"
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.
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_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

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

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

references/schema_storage.md

BigQuery Storage & Catalog INFORMATION_SCHEMA Reference

A comprehensive dictionary, column definition guide, and physical unit specification for all Storage, Table, Column, Partition, Snapshot, and Dataset views in BigQuery INFORMATION_SCHEMA.

View Scoping & Qualification Matrix (Lines 47-62)
3. Storage Footprint, Partitions & Table Metadata Views (Lines 63-434)
- INFORMATION_SCHEMA.TABLE_STORAGE_USAGE_TIMELINE (and _BY_PROJECT, _BY_ORGANIZATION, _BY_FOLDER) (Lines 65-114)
- INFORMATION_SCHEMA.TABLE_STORAGE (and _BY_PROJECT, _BY_ORGANIZATION, _BY_FOLDER) (Lines 115-166)
- INFORMATION_SCHEMA.TABLES (and _BY_PROJECT) (Lines 167-213)
- INFORMATION_SCHEMA.TABLE_OPTIONS (Lines 214-241)
- INFORMATION_SCHEMA.TABLE_SNAPSHOTS (and _BY_PROJECT) (Lines 242-268)
- INFORMATION_SCHEMA.PARTITIONS (Lines 269-300)
- INFORMATION_SCHEMA.COLUMNS (Lines 301-345)
- INFORMATION_SCHEMA.COLUMN_FIELD_PATHS (Lines 346-378)
- INFORMATION_SCHEMA.VIEWS (Lines 379-405)
- INFORMATION_SCHEMA.MATERIALIZED_VIEWS (Lines 406-434)
4. Datasets, Replication & Sharing Views (Lines 435-619)
- INFORMATION_SCHEMA.SCHEMATA (Lines 437-466)
- INFORMATION_SCHEMA.SCHEMATA_OPTIONS (Lines 467-492)
- INFORMATION_SCHEMA.SCHEMATA_REPLICAS (and _BY_FAILOVER_RESERVATION) (Lines 493-527)
- INFORMATION_SCHEMA.SCHEMATA_LINKS & SHARED_DATASET_USAGE (Lines 528-559)
- INFORMATION_SCHEMA.TABLE_CONSTRAINTS (Lines 560-589)
- INFORMATION_SCHEMA.KEY_COLUMN_USAGE (Lines 590-619)

View Scoping & Qualification Matrix

Storage, table, and dataset views fall into Dual-Scoped, Region-Only, and Dataset-Only qualification categories:

View Family	INFORMATION_SCHEMA View(s)	Qualification Scope	Syntax Pattern & Scoping Rules
Storage Footprints	`TABLE_STORAGE` (`_BY_PROJECT`, `_BY_FOLDER`, `_BY_ORGANIZATION`), `TABLE_STORAGE_USAGE_TIMELINE`	Region-Only	`{project_id}`.`region-{region}`.INFORMATION_SCHEMA.TABLE_STORAGE<br>• Storage billing models (Logical vs Physical) and daily usage integrals (`MiB seconds`).
Tables & Metadata	`TABLES`, `TABLE_OPTIONS`, `COLUMNS`, `COLUMN_FIELD_PATHS`, `VIEWS`, `MATERIALIZED_VIEWS`	Dual-Scoped	`{project_id}`.`region-{region}`.INFORMATION_SCHEMA.{view_name}<br>OR<br> `{project_id}`.`{dataset_id}`.INFORMATION_SCHEMA.{view_name}
Table Partitions	`PARTITIONS`	Dataset-Only	`{project_id}`.`{dataset_id}`.INFORMATION_SCHEMA.PARTITIONS<br>• Partition boundaries and storage tiers. Must be qualified with `{dataset_id}`.
Table Snapshots	`TABLE_SNAPSHOTS*` (`_BY_PROJECT`)	Region-Only	`{project_id}`.`region-{region}`.INFORMATION_SCHEMA.TABLE_SNAPSHOTS*<br>• Table snapshot metadata, base table references, and expiration times.
Datasets & Replication	`SCHEMATA`, `SCHEMATA_OPTIONS`, `SCHEMATA_REPLICAS`, `SCHEMATA_LINKS`, `SHARED_DATASET_USAGE`	Region-Only	`{project_id}`.`region-{region}`.INFORMATION_SCHEMA.SCHEMATA*<br>• Dataset catalog, cross-region replication, and failover status.
Table Constraints	`TABLE_CONSTRAINTS`, `KEY_COLUMN_USAGE`	Dual-Scoped	`{project_id}`.`region-{region}`.INFORMATION_SCHEMA.{view_name}<br>OR<br> `{project_id}`.`{dataset_id}`.INFORMATION_SCHEMA.{view_name}

3. Storage Footprint, Partitions & Table Metadata Views

`INFORMATION_SCHEMA.TABLE_STORAGE_USAGE_TIMELINE` (and `_BY_PROJECT`, `_BY_ORGANIZATION`, `_BY_FOLDER`)

High-Level Description

Daily historical storage usage time integral. This view is the definitive source of truth for Cloud Billing invoice storage charges across Active vs. Long-term and Logical vs. Physical billing tiers.

Required IAM Permissions & Predefined Roles

Project-Level (Least Privilege): Requires bigquery.tables.get and bigquery.tables.list on the project. Granted by BigQuery Metadata Viewer (roles/bigquery.metadataViewer), BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).
_BY_FOLDER: Requires bigquery.tables.get and bigquery.tables.list on the parent folder.
_BY_ORGANIZATION: Requires bigquery.tables.get and bigquery.tables.list on the organization.

Column Name	Data Type	Physical Unit	Description
`billable_active_logical_usage`	`INT64`	MiB-seconds	Active logical usage time integral (<90 days unmutated).
`billable_active_physical_usage`	`INT64`	MiB-seconds	Active physical usage time integral.
`billable_long_term_logical_usage`	`INT64`	MiB-seconds	Long-term logical usage time integral (>=90 days unmutated).
`billable_long_term_physical_usage`	`INT64`	MiB-seconds	Long-term physical usage time integral.
`billable_total_logical_usage`	`INT64`	MiB-seconds	Total logical usage time integral.
`billable_total_physical_usage`	`INT64`	MiB-seconds	Total physical compressed usage time integral.
`folder_numbers`	`ARRAY<INT64>`	Array	Folder hierarchy IDs containing the project (only in `_BY_FOLDER`).
`project_id`	`STRING`	Project ID	Project containing the table.
`project_number`	`INT64`	Numeric ID	Numeric project ID.
`table_catalog`	`STRING`	Project ID	Table catalog / project name.
`table_name`	`STRING`	Table ID	Table identifier.
`table_schema`	`STRING`	Dataset ID	Dataset containing the table.
`usage_date`	`DATE`	PST Date	Daily snapshot partition aligned strictly to US Pacific Time (`PST8PDT`).

[!CAUTION] Dimensional Unit Invariant: TABLE_STORAGE_USAGE_TIMELINE stores storage continuously integrated over time in MiB-seconds. To compute daily average billable GiB, always divide by 86,400 (seconds in a day) and then by 1,024: (usage / 86400) / 1024.

[!NOTE] Access Requirements: Accessing TABLE_STORAGE_USAGE_TIMELINE requires bigquery.admin or bigquery.resourceAdmin IAM permissions (or granular bigquery.tables.get and bigquery.datasets.get) to view cross-dataset storage usage. Does not require enable_info_schema_storage opt-in.

`INFORMATION_SCHEMA.TABLE_STORAGE` (and `_BY_PROJECT`, `_BY_ORGANIZATION`, `_BY_FOLDER`)

High-Level Description

Real-time point-in-time snapshot of table storage sizes, active vs. long-term uncompressed bytes, compressed physical bytes, Time Travel retention overhead, and Fail-Safe bytes. Requires storage metadata opt-in or Resource Admin IAM roles for new projects.

Required IAM Permissions & Predefined Roles

Project-Level (Least Privilege): Requires bigquery.tables.get and bigquery.tables.list on the project. Granted by BigQuery Metadata Viewer (roles/bigquery.metadataViewer), BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).
_BY_FOLDER: Requires bigquery.tables.get and bigquery.tables.list on the parent folder.
_BY_ORGANIZATION: Requires bigquery.tables.get and bigquery.tables.list on the organization.

Column Name	Data Type	Physical Unit	Description & Conversion Formula
`active_logical_bytes`	`INT64`	Bytes	Uncompressed active logical bytes (<90 days unmutated). GiB: `active_logical_bytes / POWER(1024, 3)`.
`active_physical_bytes`	`INT64`	Bytes	Compressed active physical bytes (includes active baseline + time travel). GiB: `active_physical_bytes / POWER(1024, 3)`.
`creation_time`	`TIMESTAMP`	UTC Timestamp	Table creation timestamp.
`current_physical_bytes`	`INT64`	Bytes	Baseline physical bytes before Time Travel / Fail-Safe additions.
`deleted`	`BOOLEAN`	Boolean	`true` if the table has been deleted and is currently retained in Time Travel or Fail-Safe.
`fail_safe_physical_bytes`	`INT64`	Bytes	Physical storage consumed by 7-day non-configurable Fail-Safe disaster recovery. Billed at active physical rates under Physical model; free under Logical model.
`folder_numbers`	`ARRAY<INT64>`	Array	Folder hierarchy IDs containing the project (only populated in `_BY_FOLDER`).
`last_metadata_index_refresh_time`	`TIMESTAMP`	UTC Timestamp	Last metadata indexing refresh timestamp.
`long_term_logical_bytes`	`INT64`	Bytes	Uncompressed long-term logical bytes (>=90 days unmutated). GiB: `long_term_logical_bytes / POWER(1024, 3)`.
`long_term_physical_bytes`	`INT64`	Bytes	Compressed long-term physical bytes (>=90 days unmutated). GiB: `long_term_physical_bytes / POWER(1024, 3)`.
`managed_table_type`	`STRING`	Enum	`'NATIVE'` or `'ICEBERG'`.
`project_id`	`STRING`	Project ID	Project containing table.
`project_number`	`INT64`	Numeric ID	Numeric project ID.
`storage_last_modified_time`	`TIMESTAMP`	UTC Timestamp	Last storage mutation timestamp.
`table_catalog`	`STRING`	Project ID	Table catalog / project name.
`table_deletion_reason`	`STRING`	Enum / Reason	Reason for deletion: `'TABLE EXPIRATION'`, `'DATASET DELETION'`, `'USER_DELETED'`.
`table_deletion_time`	`TIMESTAMP`	UTC Timestamp	Exact timestamp when the table was deleted.
`table_name`	`STRING`	Table ID	Table name.
`table_schema`	`STRING`	Dataset ID	Dataset name.
`time_travel_physical_bytes`	`INT64`	Bytes	Physical storage consumed by Time Travel retention (1 to 7 days). Billed at active physical rates under Physical model; free under Logical model.
`total_logical_bytes`	`INT64`	Bytes	Sum of `active_logical_bytes` + `long_term_logical_bytes`.
`total_partitions`	`INT64`	Count	Number of partitions.
`total_physical_bytes`	`INT64`	Bytes	Sum of `active_physical_bytes` + `long_term_physical_bytes`.
`total_rows`	`INT64`	Count	Current row count.
`table_type`	`STRING`	Enum	`'BASE TABLE'`, `'CLONE'`, `'SNAPSHOT'`, `'VIEW'`, `'MATERIALIZED VIEW'`.

`INFORMATION_SCHEMA.TABLES` (and `_BY_PROJECT`)

High-Level Description

Lists all tables, views, clones, snapshots, and BigLake tables in the dataset or project, along with creation times, DDL definitions, and replication statuses.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get and bigquery.tables.list (and bigquery.routines.get/list for routines) on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Editor (roles/bigquery.dataEditor), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`base_table_catalog`	`STRING`	Project ID	For table snapshots and clones, the base table project ID.
`base_table_name`	`STRING`	Table ID	For table snapshots and clones, the base table name.
`base_table_schema`	`STRING`	Dataset ID	For table snapshots and clones, the base table dataset ID.
`creation_time`	`TIMESTAMP`	UTC Timestamp	Timestamp when the table was created.
`ddl`	`STRING`	SQL String	Complete GoogleSQL DDL statement to reproduce the table.
`default_collation_name`	`STRING`	String	Default string collation specification.
`is_change_history_enabled`	`STRING`	`'YES'` / `'NO'`	Indicates whether BigQuery change data capture (CDC) history is retained.
`is_fine_grained_mutations_enabled`	`STRING`	`'YES'` / `'NO'`	Indicates whether fine-grained mutations are enabled on the table.
`is_insertable_into`	`STRING`	`'YES'` / `'NO'`	Indicates whether the table supports DML `INSERT` operations.
`is_typed`	`STRING`	`'NO'`	Always `'NO'` for BigQuery tables.
`managed_table_type`	`STRING`	Enum	`'NATIVE'` or `'ICEBERG'` (for BigLake Managed Tables).
`replica_source_catalog`	`STRING`	Project ID	For read-only table replicas, the source table project ID.
`replica_source_name`	`STRING`	Table ID	For read-only table replicas, the source table name.
`replica_source_schema`	`STRING`	Dataset ID	For read-only table replicas, the source dataset ID.
`replication_error`	`RECORD`	Error Struct	Replication error details if table replication fails (`reason`, `location`, `message`).
`replication_status`	`STRING`	Enum	Table replication status (`'ACTIVE'`, `'FAILED'`, `'OUT_OF_SYNC'`).
`snapshot_time_ms`	`TIMESTAMP`	UTC Timestamp	For snapshots/clones, the exact base table snapshot point in time.
`sync_status`	`RECORD`	Sync Struct	Metadata sync status for Apache Iceberg and BigLake tables (`status`, `last_sync_time`, `message`).
`table_catalog`	`STRING`	Project ID	Project name.
`table_name`	`STRING`	Table ID	Table name.
`table_schema`	`STRING`	Dataset ID	Dataset name.
`table_type`	`STRING`	Enum	`'BASE TABLE'`, `'VIEW'`, `'MATERIALIZED VIEW'`, `'EXTERNAL'`, `'SNAPSHOT'`, `'CLONE'`.
`upsert_stream_apply_watermark`	`TIMESTAMP`	UTC Timestamp	CDC merge watermark timestamp for BigQuery Storage Write API UPSERT streams.

`INFORMATION_SCHEMA.TABLE_OPTIONS`

High-Level Description

Lists all table-level configuration options specified in the DDL OPTIONS(...) clause, such as partition expiration, KMS encryption keys, and mandatory partition filter requirements.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get and bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Key Values	Description
`option_name`	`STRING`	`'partition_expiration_days'`, `'require_partition_filter'`, `'kms_key_name'`, `'friendly_name'`, `'description'`	Table configuration parameter.
`option_type`	`STRING`	SQL Type	GoogleSQL type of the option.
`option_value`	`STRING`	String	Active option value.
`table_catalog`	`STRING`	Project ID	Project name.
`table_name`	`STRING`	Table ID	Table name.
`table_schema`	`STRING`	Dataset ID	Dataset name.

`INFORMATION_SCHEMA.TABLE_SNAPSHOTS` (and `_BY_PROJECT`)

High-Level Description

Detailed metadata for BigQuery Table Snapshots, tracking base table lineages, snapshot creation timestamps, and snapshot retention.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`base_table_catalog`	`STRING`	Project ID	Project ID of the base table.
`base_table_name`	`STRING`	Table ID	Base table name.
`base_table_schema`	`STRING`	Dataset ID	Dataset ID of the base table.
`snapshot_time`	`TIMESTAMP`	UTC Timestamp	Base table snapshot point-in-time timestamp.
`table_catalog`	`STRING`	Project ID	Project containing the snapshot.
`table_name`	`STRING`	Table ID	Snapshot table name.
`table_schema`	`STRING`	Dataset ID	Dataset containing the snapshot.

`INFORMATION_SCHEMA.PARTITIONS`

High-Level Description

Provides partition-level metadata for partitioned tables, including row counts, uncompressed logical bytes, billable bytes, mutation timestamps, and storage tiers.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.getData on the table or dataset. (Note: Unlike metadata views, PARTITIONS requires table data access).
Predefined Roles: BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Editor (roles/bigquery.dataEditor), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`last_modified_time`	`TIMESTAMP`	UTC Timestamp	Last write/mutation timestamp. Partitions unmutated for >= 90 days drop to discounted Long-Term rates.
`partition_id`	`STRING`	Partition Key	Partition key string (e.g. `'20260815'`, `'__NULL__'`).
`storage_tier`	`STRING`	Enum	`'ACTIVE'` (<90 days unmutated) or `'LONG_TERM'` (>= 90 days unmutated).
`table_catalog`	`STRING`	Project ID	Project name.
`table_name`	`STRING`	Table ID	Table identifier.
`table_schema`	`STRING`	Dataset ID	Dataset identifier.
`total_billable_bytes`	`INT64`	Bytes	Billable bytes for partition.
`total_logical_bytes`	`INT64`	Bytes	Uncompressed logical size of partition.
`total_rows`	`INT64`	Count	Number of rows in partition.

`INFORMATION_SCHEMA.COLUMNS`

High-Level Description

Defines schema column specifications, GoogleSQL data types, ordinal ordering, nullability constraints, partitioning columns, clustering positions, policy tags, and data masking policies.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get and bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`async_generation_status`	`RECORD`	Status Struct	For stored generated columns, the status of background asynchronous population (`status`, `message`, `end_time`).
`clustering_ordinal_position`	`INT64`	Position	1-indexed position in the table's clustering key specification.
`collation_name`	`STRING`	Collation	Active string collation rule.
`column_default`	`STRING`	SQL Literal	Default value expression.
`column_name`	`STRING`	Field Name	Top-level column name.
`data_governance_tags`	`RECORD`	Tags Struct	IAM and Data Governance tags attached to column.
`data_policies`	`RECORD`	Policy Struct	Data policy masking definitions applied to column.
`data_type`	`STRING`	SQL Type	GoogleSQL type (`'INT64'`, `'ARRAY<STRING>'`, `'STRUCT<...>'`).
`generation_expression`	`STRING`	SQL Expression	GoogleSQL expression used to calculate generated column values.
`is_generated`	`STRING`	`'ALWAYS'` / `'NEVER'`	Indicates whether the column is a generated column.
`is_hidden`	`STRING`	`'YES'` / `'NO'`	`'YES'` if column is hidden from `SELECT *` expansion.
`is_identity`	`STRING`	`'YES'` / `'NO'`	Indicates whether the column is an auto-generated sequence identity.
`is_nullable`	`STRING`	`'YES'` / `'NO'`	Nullability constraint.
`is_partitioning_column`	`STRING`	`'YES'` / `'NO'`	`'YES'` if this column partitions the table.
`is_stored`	`STRING`	`'YES'` / `'NO'`	Indicates whether a generated column is stored on disk or evaluated at query time.
`is_system_defined`	`STRING`	`'YES'` / `'NO'`	`'YES'` if the column is created and managed automatically by BigQuery.
`is_updatable`	`STRING`	`'YES'` / `'NO'`	Always `'YES'` for BigQuery tables.
`ordinal_position`	`INT64`	Position	1-indexed column ordinal position.
`policy_tags`	`STRING`	Resource Path	Cloud Data Catalog policy tag resource paths for Column-Level Security (CLS).
`rounding_mode`	`STRING`	Enum	Rounding mode for `NUMERIC` / `BIGNUMERIC` columns.
`table_catalog`	`STRING`	Project ID	Project name.
`table_name`	`STRING`	Table ID	Table name.
`table_schema`	`STRING`	Dataset ID	Dataset name.

`INFORMATION_SCHEMA.COLUMN_FIELD_PATHS`

High-Level Description

Defines nested field path specifications for RECORD and STRUCT columns in BigQuery tables.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get and bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`table_catalog`	`STRING`	Project ID	Project name.
`table_schema`	`STRING`	Dataset ID	Dataset name.
`table_name`	`STRING`	Table ID	Table name.
`column_name`	`STRING`	Field Name	Top-level column name.
`field_path`	`STRING`	Dot Path	Dot-delimited nested leaf path.
`data_type`	`STRING`	SQL Type	GoogleSQL type of the nested field.
`description`	`STRING`	Text	Field description.
`collation_name`	`STRING`	Collation	Collation rule.
`rounding_mode`	`STRING`	Enum	Rounding mode.
`policy_tags`	`STRING`	Resource Path	Policy tags attached to nested field.
`data_governance_tags`	`RECORD`	Struct	Data governance tags.
`data_policies`	`RECORD`	Struct	Masking policies applied to field.

`INFORMATION_SCHEMA.VIEWS`

High-Level Description

Tracks definitions for logical SQL views, including underlying queries, check options, and SQL dialect flags.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get and bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`table_catalog`	`STRING`	Project ID	Project name.
`table_schema`	`STRING`	Dataset ID	Dataset name.
`table_name`	`STRING`	Table ID	View name.
`view_definition`	`STRING`	SQL Query	Query expression defining the logical view.
`check_option`	`STRING`	String	View check option.
`use_standard_sql`	`STRING`	`'YES'` / `'NO'`	`'YES'` for GoogleSQL views.

`INFORMATION_SCHEMA.MATERIALIZED_VIEWS`

High-Level Description

Monitors Materialized Views, automatic background refresh statuses, and refresh watermarks.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get and bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`table_catalog`	`STRING`	Project ID	Project name.
`table_schema`	`STRING`	Dataset ID	Dataset name.
`table_name`	`STRING`	Table ID	Materialized View name.
`last_refresh_time`	`TIMESTAMP`	UTC Timestamp	Last completed Materialized View refresh timestamp.
`refresh_watermark`	`TIMESTAMP`	UTC Timestamp	Upstream base table data watermark incorporated into the MV cache.
`last_refresh_status`	`RECORD`	Struct	Error details if the last automatic MV refresh failed (`reason`, `message`).

`INFORMATION_SCHEMA.SCHEMATA`

High-Level Description

Defines dataset-level metadata, physical location regions, creation timestamps, DDL statements, and default collation rules.

Required IAM Permissions & Predefined Roles

Requires bigquery.datasets.get on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Key Values	Description
`catalog_name`	`STRING`	Project ID	Project name.
`schema_name`	`STRING`	Dataset ID	Dataset identifier.
`schema_owner`	`STRING`	String	Owning identity.
`creation_time`	`TIMESTAMP`	UTC Timestamp	Dataset creation timestamp.
`last_modified_time`	`TIMESTAMP`	UTC Timestamp	Last metadata modification timestamp.
`location`	`STRING`	Region	Physical geographic storage region (e.g. `'US'`, `'EU'`, `'us-central1'`).
`default_collation_name`	`STRING`	Collation	Default collation rule inherited by new tables.
`ddl`	`STRING`	SQL String	DDL statement to reproduce the dataset.
`sync_status`	`JSON`	JSON	Cross-region replication sync health details.

`INFORMATION_SCHEMA.SCHEMATA_OPTIONS`

High-Level Description

Lists configured dataset options including storage billing models, Time Travel durations, and default partition expiration days.

Required IAM Permissions & Predefined Roles

Requires bigquery.datasets.get on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Key Values	Description
`catalog_name`	`STRING`	Project ID	Project name.
`schema_name`	`STRING`	Dataset ID	Dataset identifier.
`option_name`	`STRING`	`'storage_billing_model'`, `'max_time_travel_hours'`, `'default_table_expiration_days'`, `'default_partition_expiration_days'`	Dataset configuration option.
`option_type`	`STRING`	SQL Type	Type of option.
`option_value`	`STRING`	`'LOGICAL'`, `'PHYSICAL'`, `'48'`, `'14'`	Configured option value.

`INFORMATION_SCHEMA.SCHEMATA_REPLICAS` (and `_BY_FAILOVER_RESERVATION`)

High-Level Description

Monitors cross-region dataset replication configurations, secondary replica health, lag staleness, failover reservations, and backfill progress.

Required IAM Permissions & Predefined Roles

Requires bigquery.datasets.get on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`catalog_name`	`STRING`	Project ID	Project name.
`creation_complete`	`BOOLEAN`	Boolean	`TRUE` if initial backfill is complete and replica is fully synchronized.
`creation_time`	`TIMESTAMP`	UTC Timestamp	Timestamp when replica creation started.
`failover_reservation_name`	`STRING`	Reservation ID	Failover reservation name (only in `_BY_FAILOVER_RESERVATION`).
`failover_reservation_project_id`	`STRING`	Project ID	Admin project hosting the failover compute reservation (only in `_BY_FAILOVER_RESERVATION`).
`location`	`STRING`	Region	Geographic region where replica is hosted.
`replica_name`	`STRING`	Replica ID	Unique replica identifier.
`replica_primary_assigned`	`BOOLEAN`	Boolean	`TRUE` if this replica has the primary assignment.
`replica_primary_assignment_complete`	`BOOLEAN`	Boolean	`TRUE` if primary role assignment has completed.
`replica_primary_assignment_completion_time`	`TIMESTAMP`	UTC Timestamp	Timestamp when primary switch to replica was completed.
`replica_primary_assignment_time`	`TIMESTAMP`	UTC Timestamp	Timestamp when primary switch to replica was initiated.
`replication_time`	`TIMESTAMP`	UTC Timestamp	Replication watermark for the most stale table within the dataset.
`schema_name`	`STRING`	Dataset ID	Dataset name.
`sync_status`	`JSON`	JSON	Sync health details between primary and secondary replicas.

`INFORMATION_SCHEMA.SCHEMATA_LINKS` & `SHARED_DATASET_USAGE`

High-Level Description

Tracks linked datasets created via Analytics Hub data exchanges, Data Clean Rooms (DCR), and Cross-Project Shared Dataset usage.

Required IAM Permissions & Predefined Roles

Requires bigquery.datasets.get on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), BigQuery Data Owner (roles/bigquery.dataOwner), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`catalog_name`	`STRING`	Project ID	Source dataset project name.
`schema_name`	`STRING`	Dataset ID	Source dataset name.
`linked_schema_catalog_name`	`STRING`	Project ID	Subscriber project hosting the linked dataset.
`linked_schema_catalog_number`	`INT64`	Numeric ID	Subscriber project number.
`linked_schema_name`	`STRING`	Dataset ID	Subscriber linked dataset identifier.
`linked_schema_org_display_name`	`STRING`	String	Organization name of the subscriber.
`linked_schema_creation_time`	`TIMESTAMP`	UTC Timestamp	Creation timestamp of linked dataset.
`shared_asset_id`	`STRING`	Asset ID	Analytics Hub listing or DCR asset identifier.
`link_type`	`STRING`	Enum	`'ANALYTICS_HUB'`, `'DATA_CLEAN_ROOM'`.

`INFORMATION_SCHEMA.TABLE_CONSTRAINTS`

High-Level Description

Defines primary key and foreign key relational constraints on base tables.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get (for primary/foreign key definitions) and bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`constraint_catalog`	`STRING`	Project ID	Project name.
`constraint_schema`	`STRING`	Dataset ID	Dataset name.
`constraint_name`	`STRING`	Constraint ID	Unique constraint identifier.
`table_catalog`	`STRING`	Project ID	Table project name.
`table_schema`	`STRING`	Dataset ID	Table dataset name.
`table_name`	`STRING`	Table ID	Table name.
`constraint_type`	`STRING`	Enum	`'PRIMARY KEY'` or `'FOREIGN KEY'`.
`is_deferrable`	`STRING`	`'NO'`	Indicates deferrability.
`initially_deferred`	`STRING`	`'NO'`	Indicates initial deferral status.
`enforced`	`STRING`	`'NO'`	`'NO'` for informational GoogleSQL constraints.

`INFORMATION_SCHEMA.KEY_COLUMN_USAGE`

High-Level Description

Defines column-level participation in primary and foreign key constraints.

Required IAM Permissions & Predefined Roles

Requires bigquery.tables.get and bigquery.tables.list on the target dataset or project.
Predefined Roles: BigQuery Metadata Viewer (roles/bigquery.metadataViewer - Least Privilege), BigQuery Data Viewer (roles/bigquery.dataViewer), or BigQuery Admin (roles/bigquery.admin).

Column Name	Data Type	Physical Unit	Description
`constraint_catalog`	`STRING`	Project ID	Project name.
`constraint_schema`	`STRING`	Dataset ID	Dataset name.
`constraint_name`	`STRING`	Constraint ID	Constraint identifier.
`table_catalog`	`STRING`	Project ID	Table project ID.
`table_schema`	`STRING`	Dataset ID	Table dataset ID.
`table_name`	`STRING`	Table ID	Table name.
`column_name`	`STRING`	Column Name	Column participating in constraint.
`ordinal_position`	`INT64`	Position	1-indexed position in composite constraint.
`position_in_unique_constraint`	`INT64`	Position	Referenced position in target primary key.

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

Security

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Attribution and licence

Published by

Google

Publisher

Google (Official)

Source

google/skills · skills/cloud/bigquery-observability · commit 2194d3b

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download bigquery-observability.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Google

BigQuery Observability

Attribution

Tool Selection

Prerequisites & Environment Setup

Workflow

Best Practices for Writing INFORMATION_SCHEMA Queries

Qualification Syntax & Scope Matching

Principle of Least Privilege & Scope Selection

Execution Guardrails & Query Invariants

Domain References & SQL Queries

Telemetry Query Guides

Performance & Troubleshooting Guides

Schema Dictionaries (Column Definitions & Units)

Files

references/schema_storage.md

BigQuery Storage & Catalog INFORMATION_SCHEMA Reference

Table of Contents

View Scoping & Qualification Matrix

3. Storage Footprint, Partitions & Table Metadata Views

INFORMATION_SCHEMA.TABLE_STORAGE_USAGE_TIMELINE (and _BY_PROJECT, _BY_ORGANIZATION, _BY_FOLDER)

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.TABLE_STORAGE (and _BY_PROJECT, _BY_ORGANIZATION, _BY_FOLDER)

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.TABLES (and _BY_PROJECT)

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.TABLE_OPTIONS

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.TABLE_SNAPSHOTS (and _BY_PROJECT)

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.PARTITIONS

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.COLUMNS

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.COLUMN_FIELD_PATHS

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.VIEWS

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.MATERIALIZED_VIEWS

High-Level Description

Required IAM Permissions & Predefined Roles

4. Datasets, Replication & Sharing Views

INFORMATION_SCHEMA.SCHEMATA

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.SCHEMATA_OPTIONS

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.SCHEMATA_REPLICAS (and _BY_FAILOVER_RESERVATION)

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.SCHEMATA_LINKS & SHARED_DATASET_USAGE

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.TABLE_CONSTRAINTS

High-Level Description

Required IAM Permissions & Predefined Roles

INFORMATION_SCHEMA.KEY_COLUMN_USAGE

High-Level Description

Required IAM Permissions & Predefined Roles

Requirements and compatibility

Security

Attribution and licence

Related skills

Best Practices for Writing `INFORMATION_SCHEMA` Queries

`INFORMATION_SCHEMA.TABLE_STORAGE_USAGE_TIMELINE` (and `_BY_PROJECT`, `_BY_ORGANIZATION`, `_BY_FOLDER`)

`INFORMATION_SCHEMA.TABLE_STORAGE` (and `_BY_PROJECT`, `_BY_ORGANIZATION`, `_BY_FOLDER`)

`INFORMATION_SCHEMA.TABLES` (and `_BY_PROJECT`)

`INFORMATION_SCHEMA.TABLE_OPTIONS`

`INFORMATION_SCHEMA.TABLE_SNAPSHOTS` (and `_BY_PROJECT`)

`INFORMATION_SCHEMA.PARTITIONS`

`INFORMATION_SCHEMA.COLUMNS`

`INFORMATION_SCHEMA.COLUMN_FIELD_PATHS`

`INFORMATION_SCHEMA.VIEWS`

`INFORMATION_SCHEMA.MATERIALIZED_VIEWS`

`INFORMATION_SCHEMA.SCHEMATA`

`INFORMATION_SCHEMA.SCHEMATA_OPTIONS`

`INFORMATION_SCHEMA.SCHEMATA_REPLICAS` (and `_BY_FAILOVER_RESERVATION`)

`INFORMATION_SCHEMA.SCHEMATA_LINKS` & `SHARED_DATASET_USAGE`

`INFORMATION_SCHEMA.TABLE_CONSTRAINTS`

`INFORMATION_SCHEMA.KEY_COLUMN_USAGE`