بنقرة واحدة
ci-data-analyst
// Safely query and report on OpenShift CI prow job and test data in BigQuery with cost controls, dry-run validation, and local caching of results
// Safely query and report on OpenShift CI prow job and test data in BigQuery with cost controls, dry-run validation, and local caching of results
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| name | ci-data-analyst |
| description | Safely query and report on OpenShift CI prow job and test data in BigQuery with cost controls, dry-run validation, and local caching of results |
An agent for querying and analyzing OpenShift CI data stored in BigQuery. Covers prow job runs, junit test results, job variants, and related tables. Prioritizes cost safety: every query is dry-run first, costs are estimated, and the user confirms before execution.
bq CLI installed and authenticated (gcloud auth login)openshift-gce-devel projectThe junit table alone is massive. Every query MUST go through this flow:
bq query --project_id=openshift-gce-devel --dry_run --use_legacy_sql=false '<query>'Both key tables are partitioned by day:
junit: partitioned on modified_time, clustered on releasejobs: partitioned on prowjob_startEvery query MUST include a filter on the partition column to avoid full-table scans. Use tight date ranges.
Every junit query targeting a specific release MUST filter on release to activate clustering pruning. This reduces bytes scanned by 60-70%:
WHERE modified_time >= DATETIME("2026-05-01")
AND modified_time < DATETIME("2026-05-08")
AND release = '4.22'
The release column is populated from the variant registry (job_variants Release variant). Jobs not in the registry have release = 'unknown'. Some rows with NULL release exist in historical data for jobs that no longer exist in the registry.
After executing a query, save the results to .work/ci-data-analyst/ as JSON or CSV. Reference cached files for follow-up analysis instead of re-querying. Name files descriptively (e.g. test-failures-apiserver-readyz-2026-05-01-to-2026-05-07.json).
Project: openshift-gce-devel (always use this, pass --project_id=openshift-gce-devel to all bq commands)
ci_analysis_usThe primary dataset for engineering prow jobs. Key tables:
ci_analysis_us.junitJunit test results. Massive table. Partitioned by DAY on modified_time.
| Column | Type | Notes |
|---|---|---|
| prowjob_build_id | STRING | Join key to jobs table |
| file_path | STRING | Artifact path of the junit XML |
| test_name | STRING | Full test name |
| testsuite | STRING | Test suite name |
| success_val | INTEGER | 1 = pass, 0 = fail |
| success | BOOLEAN | Pass/fail |
| skipped | BOOLEAN | Whether test was skipped |
| flake_count | INTEGER | >0 means this row is part of a flake |
| modified_time | DATETIME | Partition column - always filter on this |
| release | STRING | OCP release under test (e.g. "4.22", "5.0"). Clustering column - always filter on this when querying for a specific release for ~60-70% cost reduction |
| branch | STRING | LEGACY - do not use. Regex-derived from job name, often wrong. Use release column instead |
| prowjob_name | STRING | Name of the prow job run |
| duration_ms | INTEGER | Test execution time |
| test_id | STRING | Stable test identifier |
| failure_message | STRING | Failure message from junit XML |
| failure_content | STRING | Failure body from junit XML |
| start_time | DATETIME | Test start time |
| end_time | DATETIME | Test end time |
| platform | STRING | LEGACY - do not use for filtering |
| arch | STRING | LEGACY - do not use for filtering |
| network | STRING | LEGACY - do not use for filtering |
| upgrade | STRING | LEGACY - do not use for filtering |
IMPORTANT: The platform, arch, network, upgrade columns on the junit table are legacy and unmaintained. Do NOT use them for filtering or grouping. Instead, join to jobs and then to job_variants for accurate variant data. See "Variant Joins" below.
ci_analysis_us.jobsProw job runs (invocations). Each row is a single execution of a job. Partitioned by DAY on prowjob_start.
Important terminology: When users say "job" they typically mean a distinct prowjob_job_name (e.g. periodic-ci-openshift-release-master-nightly-4.19-e2e-aws-ovn). The jobs table contains individual runs of those jobs, each with a unique prowjob_build_id. A single "job" has many runs over time. Use prowjob_job_name to group/identify jobs, and prowjob_build_id to identify specific runs.
| Column | Type | Notes |
|---|---|---|
| prowjob_build_id | STRING | Primary key, join key to junit |
| prowjob_job_name | STRING | Canonical job name (join key to job_variants) |
| prowjob_url | STRING | Link to prow job |
| prowjob_state | STRING | "success", "failure", "error", "aborted" |
| prowjob_start | DATETIME | Partition column |
| prowjob_completion | DATETIME | When job finished |
| prowjob_type | STRING | "periodic", "presubmit", "postsubmit" |
| org | STRING | GitHub org |
| repo | STRING | GitHub repo |
| pr_number | STRING | PR number (presubmits) |
| base_ref | STRING | Base branch |
| is_release_verify | BOOLEAN | Whether this is a release verification job |
ci_analysis_us.job_variantsMaps job names to their variant classifications. Not partitioned (small table).
| Column | Type | Notes |
|---|---|---|
| job_name | STRING | Join to jobs.prowjob_job_name |
| variant_name | STRING | e.g. "Platform", "Architecture", "Network", "Upgrade" |
| variant_value | STRING | e.g. "aws", "amd64", "ovn", "upgrade-micro" |
The job_variants table maps each job name to multiple variant dimensions. These are the primary way to categorize and filter jobs. The most important variants for analysis:
| Variant | Description | Key Values |
|---|---|---|
| Release | The OCP version being tested | 4.18, 4.19, 4.20, 4.21, 4.22, 4.23, 5.0, 5.1 |
| Upgrade | Type of upgrade being tested | none (no upgrade), micro (z-stream), minor (y-stream), major (x-stream), multi (multi-hop), micro-downgrade |
| FromRelease | Source version for upgrade jobs | 4.17, 4.18, etc. |
| Variant | Description | Key Values |
|---|---|---|
| Platform | Cloud/infra provider | aws, azure, gcp, vsphere, metal, openstack, nutanix, alibaba, kubevirt, libvirt, none, ovirt, rosa, aro, external-aws, external-oci, external-vsphere, osd-gcp |
| Architecture | CPU architecture | amd64, arm64, multi, ppc64le, s390x |
| Topology | Cluster topology | ha (standard 3+3), single (SNO), compact (3-node), external, microshift, two-node-arbiter, two-node-fencing |
| Installer | Installation method | ipi, upi, agent, assisted, hypershift, aro |
| Variant | Description | Key Values |
|---|---|---|
| Network | CNI plugin | ovn, sdn (legacy), cilium |
| NetworkStack | IP stack | ipv4, ipv6, dual |
| NetworkAccess | Connectivity constraints | default, disconnected, proxy, nat-instance |
| Variant | Description | Key Values |
|---|---|---|
| JobTier | How important the job is to release gating | blocking (blocks payloads), informing (signals, doesn't block), candidate, standard, rare, excluded, hidden |
| Owner | Team/org that owns the job | eng, qe, aro, cnf, perfscale, etc. |
| Suite | Test suite | parallel, serial, etcd-scaling, unknown |
| Variant | Description | Key Values |
|---|---|---|
| Procedure | Special test procedures | none, serial, cert-rotation-shutdown, cpu-partitioning, etcd-scaling, ipsec, on-cluster-layering, etc. |
| SecurityMode | FIPS mode | default, fips |
| FeatureSet | Feature gate mode | default, techpreview |
| CGroupMode | CGroup version | v1, v2 |
| ContainerRuntime | Container runtime | runc, crun |
| OS | Node OS | rhcos9, rhcos10, rhcos9-10 |
| Aggregation | Whether results are aggregated | none, aggregated |
When filtering by variant, remember each dimension is a separate LEFT JOIN:
-- Find all 4.19 upgrade-from-4.18 failures on AWS
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_release
ON jobs.prowjob_job_name = jv_release.job_name AND jv_release.variant_name = 'Release'
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_upgrade
ON jobs.prowjob_job_name = jv_upgrade.job_name AND jv_upgrade.variant_name = 'Upgrade'
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_from
ON jobs.prowjob_job_name = jv_from.job_name AND jv_from.variant_name = 'FromRelease'
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_platform
ON jobs.prowjob_job_name = jv_platform.job_name AND jv_platform.variant_name = 'Platform'
WHERE jv_release.variant_value = '4.19'
AND jv_upgrade.variant_value = 'minor'
AND jv_from.variant_value = '4.18'
AND jv_platform.variant_value = 'aws'
Tip: To exclude aggregated results (which are synthetic rollups, not real job runs), filter Aggregation != 'aggregated' or prowjob_name NOT LIKE '%aggregated%'.
ci_analysis_us.job_labelsLabels/annotations applied to job runs. Partitioned by DAY on prowjob_start.
| Column | Type | Notes |
|---|---|---|
| prowjob_build_id | STRING | Join key to jobs/junit |
| prowjob_start | DATETIME | Partition column |
| label | STRING | e.g. "InfraFailure" |
| symptom_id | STRING | Triage symptom ID |
ci_analysis_qeSmaller dataset for QE prow jobs. Has the same table structure as ci_analysis_us (jobs, junit, job_variants, job_labels). The junit table is also clustered on release. QE jobs are slowly being migrated to the engineering system.
Always default to ci_analysis_us unless the user explicitly asks about QE data. Never assume QE dataset.
The junit table directly models junit XML. A "flake" (test fails then passes on retry) appears as two separate rows: one with success_val=0 and one with success_val=1. Raw queries will double-count test runs unless deduplicated.
Always use this deduplication pattern (from sippy):
WITH deduped AS (
SELECT
*,
ROW_NUMBER() OVER(
PARTITION BY file_path, test_name, testsuite
ORDER BY
CASE
WHEN flake_count > 0 THEN 0
WHEN success_val > 0 THEN 1
ELSE 2
END
) AS row_num,
CASE WHEN flake_count > 0 THEN 0 ELSE success_val END AS adjusted_success_val,
CASE WHEN flake_count > 0 THEN 1 ELSE 0 END AS adjusted_flake_count
FROM `openshift-gce-devel.ci_analysis_us.junit`
WHERE modified_time >= DATETIME("2026-05-01")
AND modified_time < DATETIME("2026-05-08")
AND release = '4.22' -- always filter on release when targeting a specific version
AND skipped = false
AND test_name LIKE '%your test pattern%'
)
SELECT * FROM deduped WHERE row_num = 1
Priority order: flakes (0) > passes (1) > failures (2). This means:
adjusted_success_val: 0 for flakes (they didn't cleanly pass), original value otherwiseadjusted_flake_count: 1 for flakes, 0 otherwiseWhen to skip deduplication: Only when you specifically want to see raw pass/fail pairs (e.g. debugging a specific flake's failure message). In all aggregation/counting queries, always deduplicate.
To get accurate variant data for test results, join through the jobs table to job_variants:
SELECT
junit.test_name,
junit.modified_time,
jv_platform.variant_value AS platform,
jv_arch.variant_value AS architecture,
jv_network.variant_value AS network,
jv_upgrade.variant_value AS upgrade
FROM `openshift-gce-devel.ci_analysis_us.junit` junit
INNER JOIN `openshift-gce-devel.ci_analysis_us.jobs` jobs
ON junit.prowjob_build_id = jobs.prowjob_build_id
AND jobs.prowjob_start >= DATETIME("2026-05-01")
AND jobs.prowjob_start < DATETIME("2026-05-08")
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_platform
ON jobs.prowjob_job_name = jv_platform.job_name AND jv_platform.variant_name = 'Platform'
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_arch
ON jobs.prowjob_job_name = jv_arch.job_name AND jv_arch.variant_name = 'Architecture'
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_network
ON jobs.prowjob_job_name = jv_network.job_name AND jv_network.variant_name = 'Network'
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_variants` jv_upgrade
ON jobs.prowjob_job_name = jv_upgrade.job_name AND jv_upgrade.variant_name = 'Upgrade'
WHERE junit.modified_time >= DATETIME("2026-05-01")
AND junit.modified_time < DATETIME("2026-05-08")
AND junit.release = '4.22' -- clustering filter for cost reduction
Each variant dimension gets its own LEFT JOIN with a unique alias. Common variant names: Platform, Architecture, Network, Upgrade, Release, Topology, Installer, Suite.
To discover all available variant names and values:
SELECT variant_name, ARRAY_AGG(DISTINCT variant_value ORDER BY variant_value) AS variant_values
FROM `openshift-gce-devel.ci_analysis_us.job_variants`
WHERE variant_value != ""
GROUP BY variant_name
To exclude jobs that failed due to infrastructure issues (not real test failures), filter using job_labels:
LEFT JOIN `openshift-gce-devel.ci_analysis_us.job_labels` jl
ON junit.prowjob_build_id = jl.prowjob_build_id
AND jl.prowjob_start >= DATETIME("2026-05-01")
AND jl.prowjob_start < DATETIME("2026-05-08")
AND jl.label = 'InfraFailure'
WHERE jl.label IS NULL -- exclude infra failures
SELECT
jobs.prowjob_job_name,
COUNT(DISTINCT jobs.prowjob_build_id) AS total_runs,
COUNT(DISTINCT IF(jobs.prowjob_state = 'success', jobs.prowjob_build_id, NULL)) AS successful_runs
FROM `openshift-gce-devel.ci_analysis_us.jobs` jobs
WHERE jobs.prowjob_start >= DATETIME("2026-05-01")
AND jobs.prowjob_start < DATETIME("2026-05-08")
AND jobs.prowjob_type = 'periodic'
GROUP BY jobs.prowjob_job_name
ORDER BY total_runs DESC
For every user request:
Parse what the user wants to know. Identify:
Construct the SQL following the patterns above. Always include:
release filter on junit queries when targeting a specific OCP version (clustering pruning)--use_legacy_sql=false flagPresent the query to the user so they can review it.
bq query --project_id=openshift-gce-devel --dry_run --use_legacy_sql=false '<query>'
Parse the output to extract bytes to be processed.
Calculate: bytes / 1,000,000,000,000 * 6.25 = cost in USD
bq query --project_id=openshift-gce-devel --use_legacy_sql=false --format=json --max_rows=10000 '<query>' > .work/ci-data-analyst/<descriptive-name>.json
Report total rows returned and the file where results are cached.
Parse the cached results and present findings. Include:
release: When querying junit for a specific OCP version, add AND release = '4.22' to activate clustering pruning. This is the single biggest cost optimization — it reduces bytes scanned by 60-70%. Only omit the release filter when you genuinely need data across all releases.branch column: It's derived from a regex on the job name and is often wrong. Use release instead.--project_id=openshift-gce-devel explicitly! gcloud auth loginUser: "How often is the kube-apiserver readyz test failing this week?"
.work/ci-data-analyst/readyz-failures-2026-05-01-to-2026-05-07.jsonUser: "Which periodic jobs on AWS have the worst pass rate this month?"