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[Bug][dora] calculateChangeLeadTime loads the whole commits_diffs deployment join into memory

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@mdelacruz-avantio já está trabalhando nisso.

Desde 29/10/2026.

  • #9281 de @mdelacruz-avantio — aberto

Avaliação

Dificuldade
3/5
Tempo estimado
1-2 dias
Facilidade para iniciantes
25/100
Tipo de issue
Bug
Clareza
Claramente especificada
Status de atividade
Estagnada
Stack de tecnologia
go
Domínio
backend, data-engineering

Direção de pesquisa

Start with batchFetchDeployments in backend/plugins/dora/tasks/change_lead_time_calculator.go and inspect how the second phase joins cicd_deployment_commits with commits_diffs. The issue proposes streaming rows into a three-column struct while preserving the joins, filters, ordering, and first-wins tie-break; an open pull request already tracks this work. Done means memory scales with distinct merge commits and the tie-break has an end-to-end test.

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Descrição

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What happened

batchFetchDeployments in backend/plugins/dora/tasks/change_lead_time_calculator.go resolves merge commits to deployments in two phases. The second phase joins cicd_deployment_commits with commits_diffs, loads every joined row into a slice of full CicdDeploymentCommit structs with db.All, and only then keeps the first row per merge commit in Go.

Memory therefore grows with the size of the join, not with the number of merge commits. The join has one row per commit between a production deployment and its previous successful one, for every merge commit in that range. On a project where deployments are far apart in commit history, or where a deployment job records a commit from a second repository with a long history, the join becomes very large.

On our instance the query returns about 936,000 rows for 14,088 distinct merge commits (1,693 production deployment commits). The lake container peaks at about 1.7 GiB in this subtask, is OOMKilled at a 2 GiB limit on every nightly run, and then crash loops because each restart resumes the same pipeline at the same subtask. The row count grows every week.

What do you expect to happen

The memory used by this subtask stays proportional to the number of distinct merge commits, as the first phase already does, whatever the size of the commits_diffs join.

How to reproduce
  1. A project with a few hundred production deployment commits where consecutive deployments are thousands of commits apart, so commits_diffs holds many rows per deployment pair.
  2. Run the DORA pipeline and watch the lake container memory during calculateChangeLeadTime, or count the rows the second phase loads:
SELECT COUNT(*), COUNT(DISTINCT cd.commit_sha)
FROM cicd_deployment_commits dc
LEFT JOIN cicd_deployment_commits p ON dc.prev_success_deployment_commit_id = p.id
INNER JOIN commits_diffs cd ON cd.new_commit_sha = dc.commit_sha AND cd.old_commit_sha = COALESCE(p.commit_sha, '')
LEFT JOIN project_mapping pm ON pm.table = 'cicd_scopes' AND pm.row_id = dc.cicd_scope_id
WHERE dc.prev_success_deployment_commit_id <> '' AND dc.environment = 'PRODUCTION' AND dc.result = 'SUCCESS' AND pm.project_name = '<project>';

The first number is what the subtask materialises, the second is what it keeps.

Anything else

Streaming the second phase with db.Cursor and db.Fetch into a three-column struct and keeping the first row per merge commit, with the same joins, filters and ordering, makes the memory independent of the join size. I will open a pull request with that change and an e2e case for the first-wins tie-break.

Raising the container memory limit and setting GOMEMLIMIT is a stopgap only, the peak keeps growing with the join.

Version

v1.0.3-beta18. The function is unchanged on main at 625e512a4.

Linguagem predominante
Go
Estrelas
3.2k
Forks
830
Merge médio
2d 12h
PRs com merge (30d)
61

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