---

name: buildbuddy-usage-analysis description: Analyze BuildBuddy billing usage and trends data, fetch GetUsage/GetTrend/GetStatHeatmap/GetStatDrilldown/SearchInvocation/SearchExecution data, compare periods, spot anomalies, and recommend cost-saving actions while protecting API keys. metadata: short-description: Analyze BuildBuddy usage trends and cost anomalies.

BuildBuddy Usage Analysis

Overview

Fetch usage + trends data from BuildBuddyService, store responses in a temp dir, analyze for spikes/drops, and recommend cost savings. Prioritize API key safety and small time windows.

Quick start

1. Use references/requests.md to set BASE_URL, API_KEY, GROUP_ID, and OUT_DIR.
2. Call GetUsage for current and prior months.
3. Call GetTrend for a time window aligned with the usage comparison.
4. Use the analyzer script or ad‑hoc math to compare and project.

Preconditions and access

• Verify API key presence without printing it: git config --local buildbuddy.api-key | wc -c.
• If GetUser returns NotFound (common with org API keys), use GetGroup with urlIdentifier to resolve GROUP_ID.
• GetUsage is admin-only. If you get PermissionDenied, switch to an org admin key or admin user for the group.

Fetch data (usage + trends)

Use the request templates in references/requests.md:

• GetUsage for the current month and the prior month (billing comparisons).
• GetTrend for the same window (time-series context and cache hit rates).
• If drilling down: GetStatHeatmap, GetStatDrilldown, then SearchInvocation or SearchExecution for sample events.

Notes:

• Keep windows small (days/weeks) to reduce payload size.
• JSON fields are lowerCamelCase (e.g., dailyUsage, actionCacheHits).

Visual URLs (trends + drilldown)

Use the org base URL from GetGroup (field url).

Trends charts:

Copy<ORG_URL>/trends/?start=YYYY-MM-DD&end=YYYY-MM-DD#builds
<ORG_URL>/trends/?start=YYYY-MM-DD&end=YYYY-MM-DD#cache
<ORG_URL>/trends/?start=YYYY-MM-DD&end=YYYY-MM-DD#cas
<ORG_URL>/trends/?start=YYYY-MM-DD&end=YYYY-MM-DD#savings

Drilldown view:

Copy<ORG_URL>/trends/?start=YYYY-MM-DD&end=YYYY-MM-DD#drilldown&ddMetric=i<INVOCATION_METRIC_ENUM>

Common query params: start, end, days, user, repo, branch, commit, host, command, pattern, tag, status, role.

ddMetric format is i<invocationEnum> or e<executionEnum> (use the stat_filter metric enums from your API docs). Drilldown selections can be pinned via ddSelection or ddZoom (heatmap selection encoding: start|end|bucketStart|bucketEnd|eventsSelected).

Example URLs (agent can adapt as-is):

Copy<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#builds
<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#cache
<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#cas
<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#savings
<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#drilldown&ddMetric=i1
<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#drilldown&ddMetric=i4
<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#drilldown&ddMetric=e7
<ORG_URL>/trends/?start=2026-01-01&end=2026-01-31#drilldown&ddMetric=e9

Example ddMetric labels:

• i1 = invocation duration (total build duration)
• i4 = invocation cache download size (CAS download bytes)
• e7 = execution input download size (RBE cache download bytes)
• e9 = execution wall time (total execution time)

All ddMetric values (agent can use directly):

Invocation metrics (i*)

• i1 duration (total build duration)
• i2 CAS cache misses
• i3 updated at (timestamp)
• i4 CAS cache download size (bytes)
• i5 CAS cache download speed (bytes/sec)
• i6 CAS cache upload size (bytes)
• i7 CAS cache upload speed (bytes/sec)
• i8 action cache misses
• i9 time saved (cached execution time)

Execution metrics (e*)

• e1 queue time
• e2 updated at (timestamp)
• e3 input download time
• e4 real execution time
• e5 output upload time
• e6 peak memory
• e7 input download size
• e8 output upload size
• e9 execution wall time
• e10 execution CPU nanos
• e11 execution average millicores

Analyze for anomalies

Use quick ad-hoc analysis (jq / python) or the script:

• scripts/analyze_usage_trends.py --usage usage.json --trend trend.json

Look for:

• Spikes in totalDownloadSizeBytes or totalUploadSizeBytes.
• Drops in actionCacheHits or casCacheHits.
• Drops in action cache hit rate: actionCacheHits / (actionCacheHits + actionCacheMisses) from trend stats.
• Large changes in execution time or CPU nanos (cloudCpuNanos, cloudRbeCpuNanos).

Project month-to-date usage

• Use dailyUsage to find the latest reported day (the array may not start on day 1).
• Project full-month totals by scaling usage totals by full_days / last_reported_day.
• Call out the projection method and the last reported date explicitly.

Recommendations

When anomalies are found, prioritize cost-saving advice:

• Action Cache: reduce non-determinism, align toolchains, enable remote cache for CI.
• Download egress: avoid downloading large outputs, prefer minimal downloads, limit artifact retention.
• Drill down: use GetStatDrilldown to identify top users/repos/targets, then inspect sample invocations.
• For a specific invocation investigation, use buildbuddy-invocation-troubleshoot.

Resources

• references/requests.md: request templates with safe API key handling and UI URLs.
• scripts/analyze_usage_trends.py: anomaly detector for usage/trends JSON.