Operations Laravel Package

Product Decisions This Supports

• Release safety as a product bet. Every manual "run this SQL after deploy" step is an unversioned, unreviewed mutation against production. This package converts those steps into operation classes that execute exactly once per environment and are tracked in a table, so the PM decision is really: do we keep paying the incident tax of manual post-deploy steps, or do we spend a day to remove them?
• Scoping data work that has no home today. Migrations own schema, seeders own repeatable data — nothing owns the one-time stateful change. That gap is exactly where backfills, column normalization after a key rotation, reference-data population, third-party ID remapping, and post-feature-flag cleanup live. If your roadmap has any of these, this is the tool category to standardize on.
• Long-running backfills just became a first-class use case. v1.2.0 adds opt-in checkpoints: an operation can persist progress and resume after a failure instead of restarting from zero. That materially changes the calculus for the one data task teams most often hand-roll into a throwaway script — a multi-hour backfill over a large table, where a mid-run crash previously meant either a restart or a manual "where did we get to?" investigation.
• Build vs. buy, framed honestly. The underlying mechanism is small — a tracking table, a runner command, a base class. A ~50-line in-house command with a unique-constraint guard reproduces most of the value. Adopt the package when you want the convention, the scaffolding generator, and the status command now; build in-house when you need guarantees (locking, dry-run, rollback, dependency ordering, audit) the package may not offer, or when a dependency you can't reason about during an incident is itself the risk. Checkpoints narrow the build-vs-buy gap in the package's favor for resumable batch work specifically — that is real engineering you don't have to write or test.
• Roadmap input for a release-automation initiative. If your team maintains a wiki page, Notion doc, or Slack ritual of post-deploy manual steps, treat that as evidence that deployment orchestration is underinvested. This package is a cheap probe: pilot it, and the pilot's friction tells you how much bigger the platform investment should be.
• Use cases worth sizing: automated backfills wired into CI/CD, resumable large-table data migrations, multi-environment promotion (staging → production) with per-environment run-once semantics, and shrinking the on-call engineer's release checklist.
• A forcing function for process, not just code. Adopting it implies conventions: one operation per PR, mandatory second-engineer review, a written "what happens when an operation fails mid-deploy" procedure, and an owner for the operations directory. Checkpoints let that procedure become "resume" rather than "remediate," but only if operation authors design idempotent, checkpoint-safe units of work — which is a review-standard decision, not a library decision. Those process outputs are arguably the more valuable result.

When to Consider This Package

Adopt when:

• You're on Laravel and your deploys are already automated (Forge, Envoyer, Vapor, Deployer, GitHub Actions, GitLab CI) with a reliable post-deploy hook after migrate.
• You have recurring one-time data tasks currently living in tickets, runbooks, or engineer memory — especially ones you'd otherwise comment out after running once.
• You have long-running or failure-prone data work (large backfills, third-party API backfills with rate limits, multi-batch remapping) where restarting from scratch is expensive, and you're willing to write operations as resumable units.
• You deploy across multiple environments and need to know whether an operation ran, or deliberately did not run, in each one.
• You want these mutations code-reviewed and versioned alongside the application rather than executed by hand in a database console.
• Your deploy topology is single-instance, or you're prepared to add pipeline-level locking yourself for rolling/blue-green deploys.

Look elsewhere or build in-house when:

• You need first-class rollback (down counterparts), dry-run previews, dependency ordering between operations, or scheduling. Checkpoints cover resume, not reverse or preview — v1.2.0 does not give you an undo. Verify each against your requirements before committing; the documentation is thin and the source is the source of truth.
• You cannot tolerate partial completion semantics. Checkpoints mean an operation can be left half-applied by design; if your business logic requires all-or-nothing, do not opt in, and consider whether a single tracking row plus your own transaction boundaries is sufficient.
• You have compliance or audit obligations requiring immutable execution logs, approvals, or four-eyes controls. A single tracking row plus checkpoint rows is operational state, not an audit trail.
• You're not on Laravel, or your pipeline has no dependable post-deploy execution point (serverless, read-replica-only read paths).
• Vendor risk is material. Low star count, zero dependents, a single-maintainer profile, and a weak aggregate score mean essentially no community validation. v1.2.0 is a meaningful capability addition and a sign of life, but one PR from one maintainer is not a track record. If you adopt: pin the version explicitly, read the source (it's small enough to audit in an afternoon — do it now, since the checkpoint code path is new), cover your specific operations with your own tests, and be prepared to fork and vendor. Do not make it load-bearing for anything irreversible.
• Specific unknowns to resolve before commitment: whether concurrent deploy pods can double-execute (lock vs. atomic insert vs. nothing); whether operation bodies and checkpoint writes share a transaction, and what happens if a batch commits but the checkpoint write fails (double-processing on resume) or the checkpoint commits but a batch rolls back (skipped work); whether checkpoints are scoped per environment in the same way the run-once record is; what triggers a resume (re-running the deploy command, an explicit flag, or nothing at all); how checkpoints are cleaned up; whether operations are wrapped in transactions by default and can opt out for DDL; how it behaves across your actual DB driver; and whether it keeps pace with Laravel majors — a lagging package becomes a blocker on your framework upgrade path.

How to Pitch It (Stakeholders)

To executives: "We currently run post-deployment data steps by hand — each one is a chance to forget a step, run it twice, or run it against the wrong environment. This is a small, free, MIT-licensed Laravel library that lets us define those steps in code and guarantees each runs exactly once per environment, with a status command so we know what ran where. The newest version adds resumable progress tracking, so a long backfill that dies halfway can pick up where it left off instead of starting over or being finished by hand at midnight. It reduces release risk and toil for roughly a day of engineering. The trade-off is that it's a low-profile open-source project with one maintainer and no visible production users, so we'd pin the version, keep our own tests around each operation, and treat it as a utility we're willing to maintain or fork ourselves. We'd pilot it on non-critical data work and keep it off anything irreversible until it's been through a couple of release cycles."

To engineering: "It's a thin Laravel package for tracked one-time deployment operations — effectively data migrations with a run-once guarantee, sitting in the gap between migrate and seed. Install, publish the tracking migration, run migrate, then add the operations command to the deploy pipeline right after migrations. Operations are classes in app/Operations resolved through the container, so they're testable and reviewable like any other code. v1.2.0 adds opt-in checkpoints: an operation can save progress and resume after a failure, which is the difference between a six-hour backfill being restartable and being a manual recovery exercise. It solves the 'did anyone run the backfill on prod?' problem and gets the runbook out of a wiki and into version control. Before we go wide, nail down four things: whether execution is serialized under concurrent deploys (if not, we add a pipeline lock); transaction semantics per operation and for checkpoint writes, specifically the failure modes where a batch and its checkpoint disagree; whether resumability holds across our DB driver and how a resume is actually triggered; and that it supports our current and next Laravel major. Surface area is small enough to audit and fork if the project stalls — audit the new checkpoint path before we rely on it. Recommendation: adopt behind a version pin, on the non-critical path, with a written operations policy — one operation per PR, second-engineer review, operations written as idempotent checkpointable batches, logging, and a documented failure-and-resume procedure — and run it in parallel with the manual process for at least one release."