A practical guide to ship fast without wreckage. Ranked by wartime impact (speed + survivability now) vs. effort to adopt.
Use this as a checklist and code reference for your team.
💡 Prefer straightforward designs and code over clever abstractions.
Why: Simplicity ships faster, fails in simpler ways, and is easier to roll back under pressure.
How
- Write straight-line code where possible; avoid deep call chains and indirection.
- Prefer one clear code path; hide experiments behind feature flags.
- Choose boring, well-known libraries; avoid framework acrobatics.
- Keep functions/classes small, single-purpose; name them after the business action.
- Remove premature abstractions; duplicate once or twice if it saves time now.
💡 Prefer changes you can turn off or undo quickly.
Why: Lets you move fast safely; wrong bets don’t become outages.
How
- Ship behind feature flags / kill switches; default off for risky changes.
- Small PRs with clear revert points; avoid mega-commits.
- Branch-by-abstraction: keep old/new impls switchable during refactors.
- Backward-compatible DB migrations (additive first; remove later).
- Automate one-click rollback in CI/CD.
💡 Design so failures are isolated and the system degrades gracefully.
Why: One broken dependency shouldn’t take the whole product down.
How
- Add timeouts + retries with jitter to all network calls.
- Use circuit breakers to fail fast when a dependency is sick.
- Introduce rate limits/quotas per client/tenant to protect shared resources.
- Make handlers idempotent so retries are safe.
- Isolate resources with bulkheads (separate pools/queues).
💡 Build visibility (logs, metrics, traces) into new code paths immediately.
Why: You can’t fix what you can’t see; fast detection enables fast rollback.
How
- Add structured logs with trace/request IDs and key fields.
- Emit golden signals: latency, error rate, throughput, saturation.
- Instrument critical spans with distributed tracing.
- Create per-release dashboards + alerts before rollout.
- Define SLOs and use error budgets to gate releases.
💡 Deliver the smallest end-to-end outcome that moves the metric.
Why: Real feedback fast; less unfinished scaffolding; lower coordination overhead.
How
- Slice by user journey (input → process → output), not layers.
- Stub/memoize slow or unavailable dependencies to keep flow working.
- Define “good-enough” acceptance tests; time-box unknowns.
- Cut scope aggressively; create a “Not Doing Now” list.
- Avoid parallel partial layers — always close the loop.
💡 Novelty is a tax in wartime; choose stable, known tools.
Why: Fewer unknowns, faster onboarding, simpler ops when you’re sleep-deprived.
How
- Standardize on known stacks; avoid introducing new frameworks mid-crunch.
- Use managed services (DB, cache, queues, auth) over self-hosted.
- Reuse existing patterns in the repo; resist greenfield rewrites.
- Document one blessed way to do common tasks (HTTP, DB, logging).
💡 Push behavior to the object with the data (Tell, Don't Ask). Interact only with direct collaborators, not chained internals (LoD).
Why: Reduces coupling and “train-wreck” calls; changes stay local and fast.
How
- Replace
a.b().c().d()with facade methods:a.doThing(). - Move decision logic into the object that owns the state.
- Add adapters around messy dependencies to expose clean commands.
- Test via behavioral contracts (public methods), not internal state.
💡 Derive solutions from goals and constraints, not habit.
Why: Avoids cargo culting; finds leaner routes when precedent doesn’t fit.
How
- Define success metrics (e.g., p95 latency < 300ms, <1% error rate).
- List hard constraints (SLOs, budget, headcount, deadlines).
- Remove nice-to-haves; cut to minimal requirement set.
- Compare options with a simple scorecard (impact × time × risk).
- Decide quickly; make it reversible when possible.
💡 Assemble behavior by combining small parts, not extending deep hierarchies.
Why: Localizes change; parallelizes work; fewer “fragile base class” surprises.
How
- Use interfaces + dependency injection to swap implementations.
- Prefer Strategy/Decorator over base-class branching.
- Keep modules replaceable; avoid global singletons.
- Test at seams with contract tests.
💡 Solve load by adding resources/config first; optimize code later.
Why: Buys time quickly without risky refactors.
How
- Increase replicas/instances; raise CPU/memory limits.
- Add a cache (CDN, in-memory) in front of hot paths.
- Use autoscaling with sane floor/ceiling and health checks.
- Defer algorithmic optimizations until demand stabilizes.
- Debt Ledger: Log every shortcut with an owner and due date.
- Pre-Ship / Post-Ship Checklist: Verify flag toggles, rollback tested, metrics live, runbook linked.