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Bigquery Troubleshooting

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  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
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From the author

Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-observability).
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

BigQuery Troubleshooting provides a structured, step-by-step methodology for diagnosing and resolving issues with BigQuery jobs. It focuses on identifying root causes for performance degradation, unexpected cost increases, and system-level bottlenecks. The skill guides users through a diagnostic funnel, from initial symptom identification and baseline scanning to deep-dive analysis of execution graphs, reservation configurations, and resource contention. It is designed to work alongside companion skills for observability and optimization, ensuring a clear separation between incident diagnosis, raw telemetry retrieval, and proactive planning.

What it does

  • Diagnoses performance bottlenecks in BigQuery jobs and execution stages.
  • Investigates unexpected cost spikes related to on-demand usage, capacity commitments, and storage.
  • Provides workflows for auditing reservation changes and autoscaling behavior.
  • Analyzes system contention, including slot saturation and noisy neighbor scenarios.
  • Guides users through comparative analysis to isolate shifts in performance or cost.
  • Ensures proper attribution for all diagnostic commands executed via CLI or API.

When to use it

  • When a BigQuery job is failing, running slower than expected, or hanging.
  • When you observe an unexplained spike in BigQuery billing or resource consumption.
  • When you need to investigate reservation autoscaling or capacity commitment issues.
  • When you need to identify the root cause of a performance regression compared to a previous baseline.
  • When you need to troubleshoot shuffle spills, join explosions, or data skew in query plans.

Advantages

  • Standardizes the troubleshooting process for complex BigQuery environments.
  • Prevents diagnostic overhead by using bounded baseline scans before deep-diving.
  • Provides clear routing boundaries to avoid mixing incident response with proactive optimization.
  • Ensures consistent and accurate usage attribution for all diagnostic operations.
  • Facilitates data-driven root cause analysis through comparative metrics.

Usage examples

“Why is my BigQuery job running so much slower today than it did last week?”
The agent will initiate a performance diagnostic workflow, comparing the current job's execution statistics against a baseline from the previous week to identify shifts in data volume, slot contention, or configuration.
“I noticed a massive spike in my BigQuery capacity costs. Can you help me find out why?”
The agent will execute a 4-step cost diagnostic funnel to determine if the spike was caused by autoscaler thrashing, uncovered baseline penalties, or specific high-consumption queries.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/bigquery-troubleshooting/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip bigquery-troubleshooting.zip -d .agents/skills

Documentation by Google

BigQuery Troubleshooting

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-troubleshooting)" and append agent-skills/1.0 (skill:bigquery-troubleshooting) to User-Agent headers.

Prerequisites & Environment Setup

Before running diagnostic queries or investigating incident telemetry:

  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 BigQuery and Cloud Monitoring APIs are enabled:

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

    • CLI commands (bq show -j): gcloud auth login

    • SDKs and automated diagnostic scripts: 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: Executing diagnostic queries.

      • roles/bigquery.resourceViewer or roles/bigquery.admin: Inspecting reservation and job execution telemetry.

      • roles/monitoring.viewer: Cloud Monitoring metrics.

      • roles/billing.viewer: Cloud Billing reports and cost attribution.

  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

    (If bigquery-observability is not installed, use the self-contained baseline formulas and query templates provided directly in the reference sections below).

Workflow

  1. Scope & Symptom Identification: Identify the primary symptom, target project_id, region, reservation_id, or job_id, and domain (Performance, Compute Cost, or Storage Cost). If the request falls outside incident diagnosis or asks for a sibling domain, follow Routing Boundaries below.

  2. Telemetry Tool Selection: Follow the tool-selection guidance in bigquery-observability (bigquery_observability) to select the appropriate telemetry interface (REST API bq show --location={location} -j {project_id}:{job_id} for single-job stage bottlenecks vs. INFORMATION_SCHEMA for system-wide factors). Diagnostic workflows, symptom-to-cause mappings, key tables/fields, CLI triage commands, and remediation levers are fully defined in this skill. For pre-composed SQL query templates and full schema dictionaries, consult bigquery-observability.

  3. Open-Ended Triage (Stage 1 Baseline Scan & Conversational Gate): When the user inquiry is open-ended or vague (e.g. "Why is BigQuery slow today?" or "Why did my bill spike?"), execute a bounded high-level baseline scan to isolate the affected domain before drilling into deep-dive diagnostics:

    • Bounded Initial Scan: Follow the baseline scan guidance in the corresponding domain reference under Domain References. Ensure initial queries are strictly bounded (e.g. 7-day Period-over-Period with partition and job-type filters; for unspecified cost spikes, scan the 3 primary vectors: On-Demand TiB, Capacity slot-hours, and Storage GiB) to keep diagnostic telemetry overhead minimal.

    • Conversational Gate: Factually summarize high-level baseline findings first and propose 2–3 focused drill-down options rather than dumping downstream sub-vector queries unsolicited.

  4. Domain Deep Dive & Comparative Analysis: Execute the step-by-step diagnostic workflow defined in the corresponding domain reference file listed under Domain References below, then run the corresponding query from bigquery-observability following its INFORMATION_SCHEMA best practices to isolate the root cause via comparative analysis against a normal baseline.

Performance Context: The Relativity of "Slow"

Performance is relative. Always approach performance troubleshooting as a comparative exercise: identify a comparable past execution, compare the statistics, and isolate which dimension shifted between a fast baseline and the slow execution:

  • Data Processed: Data volume increase, partition/cluster pruning changes, data skew, input record amplification.

  • Underlying Definitions: View changes, schema modifications.

  • System Contention: Noisy neighbors, saturated capacity (>95% slot utilization), idle slot availability, concurrent query spikes.

  • Configuration Changes: Slot capacity/autoscale max slots changes, expired capacity commitments, idle slot setting changes, or reservation reassignments.

Cost Context: The 4-Step Diagnostic Funnel

Cost troubleshooting requires tracing physical resource consumption (Slot-Hours, TiB Billed, GiB Stored) rather than fluctuating contract rates:

  1. Gather: Determine scope and pull 7-day PoP (or explicit MoM / 180-day) baseline metrics.

  2. Isolate: Pinpoint whether spend surged from query volume, a single "Bully Query", BQML 50x multipliers, uncovered PAYG baselines, autoscaling bursts, 90-day storage timer resets, or physical Fail-Safe retention drain.

  3. Explain: Correlate with administrative events (INFORMATION_SCHEMA.RESERVATION_CHANGES, INFORMATION_SCHEMA.CAPACITY_COMMITMENT_CHANGES_BY_PROJECT, INFORMATION_SCHEMA.SCHEMATA_OPTIONS, or actor user_email / query_hash).

  4. Remediate: Deliver actionable levers (partition filter enforcement, query caps, commitment purchases, or Time Travel reduction).

Domain References

Performance Troubleshooting
  • Resource Contention & Performance Slowness (references/performance_resource_contention.md): Diagnostic workflows for isolating single-job stage bottlenecks (slot_contention, spill_to_disk), cohort baseline comparisons (normalized_literals), incident window discovery, 1-second reservation slot saturation, timeframe contention comparisons, fleet performance variance, and table-level concurrency.

  • Capacity & Configuration Changes (references/performance_config_changed.md): Diagnostic workflows for auditing reservation slot_capacity and autoscale.max_slots edits, tracking active capacity commitment timelines, diagnosing reservation assignment modifications, and evaluating autoscaling headroom saturation.

  • Execution Graph & Query Plan Troubleshooting (references/query_plan_execution_graph.md): Diagnostic workflows for investigating single-job stage bottlenecks (bq show point-lookups), isolating slowest stages (end_ms - start_ms), diagnosing join explosions (records_written >> records_read, high_cardinality_joins), shuffle spills (shuffle_output_bytes_spilled), and partition skew, substep intermediate variable disambiguation ($1, $2), mandatory bytes scanned vs records read corrections, and UI execution graph grounding concepts.

Cost Troubleshooting
  • On-Demand Compute Costs (references/cost_compute_ondemand.md): Diagnostic workflows for unpartitioned runaway scans (the "Bully Query"), hidden Row-Level Security (RLS) redaction gaps, BigQuery ML (BQML) 50x model training rate multipliers, and user/service account query quotas.

  • Capacity (Editions) Compute Costs (references/cost_compute_capacity.md): Diagnostic workflows for uncovered baseline slot penalties (baseline > commitments), reservation baseline reductions triggering autoscale surges (RESERVATION_BASELINE_CHANGED), autoscaler thrashing from batch cron spikes, and serverless Apache Spark stored procedure slot-hours.

  • Storage Footprint & Retention Costs (references/cost_storage.md): Diagnostic workflows for historical partition 90-day timer resets (the DML trap), unpartitioned table active data traps, physical Time Travel and Fail-Safe churn on daily overwrites, and dropped table Fail-Safe drain periods.

Routing Boundaries

If a user request shifts outside incident diagnosis during troubleshooting, execute the corresponding handoff:

  • SQL Query Optimizations: When the user asks to optimize the SQL query (e.g., rewriting joins or eliminating SELECT *), hand off to bigquery-optimization.

  • Raw Telemetry & Schema Retrieval: When the user asks for standalone INFORMATION_SCHEMA queries without an active performance regression or incident (e.g. general telemetry queries), hand off to bigquery-observability.

  • Proactive Capacity & Storage Planning: When the user requests future reservation sizing, commitment purchasing, or storage billing model evaluations, hand off to bigquery-optimization.

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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Published by
Google
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Source
google/skills · skills/cloud/bigquery-troubleshooting · commit 2194d3b
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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