BigQuery Deep Skill

Manage and analyze Google BigQuery using bq and gcloud commands.

Discovery-First Rule

ALWAYS discover before acting. Never assume dataset names, table names, job IDs, or reservation names.

# List datasets
bq ls --format=json | jq '[.[] | {datasetId: .datasetReference.datasetId, location: .location}]'

# List tables in a dataset
bq ls --format=json "$DATASET" | jq '[.[] | {tableId: .tableReference.tableId, type: .type, numRows: .numRows, numBytes: .numBytes}]'

Parallel Execution Requirement

ALL independent operations MUST run in parallel using background jobs (&) and wait.

for dataset in $(bq ls --format=json | jq -r '.[].datasetReference.datasetId'); do
  {
    bq ls --format=json "$dataset"
  } &
done
wait

Helper Functions

# Get table details
get_table_info() {
  local table="$1"
  bq show --format=json "$table" | jq '{tableId: .tableReference.tableId, type: .type, numRows: .numRows, numBytes: .numBytes, schema: .schema.fields | length, partitioning: .timePartitioning, clustering: .clustering, lastModified: .lastModifiedTime, expirationTime: .expirationTime}'
}

# List recent jobs
list_jobs() {
  local limit="${1:-25}"
  bq ls --jobs --format=json --max_results="$limit" | jq '[.[] | {jobId: .jobReference.jobId, state: .status.state, type: .configuration | keys[0], bytesProcessed: .statistics.totalBytesProcessed, slotMs: .statistics.totalSlotMs, creationTime: .statistics.creationTime}]'
}

# Get job details
get_job_details() {
  local job_id="$1"
  bq show --format=json --job "$job_id" | jq '{jobId: .jobReference.jobId, state: .status.state, errors: .status.errors, bytesProcessed: .statistics.totalBytesProcessed, slotMs: .statistics.totalSlotMs, billingTier: .statistics.query.billingTier, cacheHit: .statistics.query.cacheHit, totalBytesBilled: .statistics.query.totalBytesBilled}'
}

# Estimate query cost
estimate_query() {
  local query="$1"
  bq query --dry_run --use_legacy_sql=false "$query" 2>&1 | grep "processed"
}

Common Operations

1. Dataset and Storage Overview

datasets=$(bq ls --format=json | jq -r '.[].datasetReference.datasetId')
for ds in $datasets; do
  {
    echo "=== Dataset: $ds ==="
    bq ls --format=json "$ds" | jq '[.[] | {table: .tableReference.tableId, type: .type, rows: .numRows, sizeGB: (.numBytes | tonumber / 1073741824 * 100 | round / 100)}]'
  } &
done
wait

2. Job Analysis and Slot Utilization

# Recent jobs with performance stats
bq ls --jobs --format=json --max_results=50 | jq '[.[] | select(.status.state=="DONE") | {job: .jobReference.jobId, bytesProcessed: .statistics.totalBytesProcessed, slotMs: .statistics.totalSlotMs, durationMs: (.statistics.endTime | tonumber) - (.statistics.startTime | tonumber), cacheHit: .statistics.query.cacheHit}] | sort_by(-.slotMs) | .[:10]'

# Slot utilization via monitoring
gcloud monitoring time-series list \
  --filter="metric.type=\"bigquery.googleapis.com/slots/total_available\" OR metric.type=\"bigquery.googleapis.com/slots/allocated\"" \
  --interval-start-time="$(date -u -v-1H +%Y-%m-%dT%H:%M:%SZ 2>/dev/null || date -u -d '1 hour ago' +%Y-%m-%dT%H:%M:%SZ)" \
  --format=json

3. Storage Optimization

# Tables without partitioning or clustering (potential optimization)
for ds in $(bq ls --format=json | jq -r '.[].datasetReference.datasetId'); do
  {
    bq ls --format=json "$ds" | jq --arg ds "$ds" '[.[] | select(.type=="TABLE") | {dataset: $ds, table: .tableReference.tableId, sizeGB: (.numBytes | tonumber / 1073741824 * 100 | round / 100), rows: .numRows}] | sort_by(-.sizeGB) | .[:5]'
  } &
done
wait

# Check partitioning and clustering on large tables
bq show --format=json "$DATASET.$TABLE" | jq '{partitioning: .timePartitioning, clustering: .clustering, rangePartitioning: .rangePartitioning, numBytes: .numBytes, numRows: .numRows}'

4. Materialized Views

# List materialized views
for ds in $(bq ls --format=json | jq -r '.[].datasetReference.datasetId'); do
  {
    bq ls --format=json "$ds" | jq --arg ds "$ds" '[.[] | select(.type=="MATERIALIZED_VIEW") | {dataset: $ds, view: .tableReference.tableId, lastRefresh: .materializedView.lastRefreshTime}]'
  } &
done
wait

5. BI Engine and Reservations

# Check reservations
bq ls --reservation --format=json 2>/dev/null | jq '[.[] | {name: .name, slotCapacity: .slotCapacity, edition: .edition}]'

# Check capacity commitments
bq ls --capacity_commitment --format=json 2>/dev/null | jq '[.[] | {name: .name, slotCount: .slotCount, plan: .plan, state: .state, edition: .edition}]'

# BI Engine reservations
gcloud bq reservations list --format=json 2>/dev/null

Output Format

Present results as a structured report:

Gcp Bigquery Deep Report
════════════════════════
Resources discovered: [count]

Resource       Status    Key Metric    Issues
──────────────────────────────────────────────
[name]         [ok/warn] [value]       [findings]

Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]

Target ≤50 lines of output. Use tables for multi-resource comparisons.

Anti-Hallucination Rules

1. NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
2. NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
3. NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
4. ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
5. ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Shortcut	Counter	Why
"I'll skip discovery and check known resources"	Always run Phase 1 discovery first	Resource names change, new resources appear — assumed names cause errors
"The user only asked for a quick check"	Follow the full discovery → analysis flow	Quick checks miss critical issues; structured analysis catches silent failures
"Default configuration is probably fine"	Audit configuration explicitly	Defaults often leave logging, security, and optimization features disabled
"Metrics aren't needed for this"	Always check relevant metrics when available	API/CLI responses show current state; metrics reveal trends and intermittent issues
"I don't have access to that"	Try the command and report the actual error	Assumed permission failures prevent useful investigation; actual errors are informative

Common Pitfalls

1. On-demand vs flat-rate: On-demand pricing charges per TB scanned. Flat-rate uses slot reservations. Check pricing model before analyzing costs.
2. Cache hits: Cached queries are free but cache invalidates on table changes. cacheHit=true in job stats means zero bytes billed.
3. Partitioning required: Tables over 1TB without partitioning cause full table scans. Always check timePartitioning on large tables.
4. Dry run for estimation: Always use --dry_run to estimate query cost before running expensive queries.
5. Streaming buffer: Recently streamed data sits in a buffer that is not immediately available for DML operations. Check streamingBuffer in table metadata.