Test Infrastructure, Environments, and Parallel Execution Questions

Clarify goal
Avoid test flakiness from shared /tmp on NFS: tests must never collide, must see consistent files, and cleanup must be reliable without harming other processes.

High-level approach

• Per-test isolated temp dirs on local storage (node-local SSD) with deterministic mapping and fallback to namespaced NFS only when necessary.
• Always write atomically and use strong cleanup patterns with lease/ttl and background reclamation.

Patterns

1. Isolated temp storage

• Create per-run directory: /mnt/local-ssd/test-run/{ci-job-id}/{pid}-{uuid}/
• If SSD unavailable, use NFS under a namespace: /nfs/tmp/{ci-job-id}/{node}/{uuid}/
• Set restrictive permissions (0700) and unique names (job, node, timestamp, uuid).

2. Atomic writes

• Write to a temp file in the same directory, then rename:
  bash

  # write then atomic mv
  tmpfile=$(mktemp "$DIR/file.XXXX")
  echo data > "$tmpfile"
  mv "$tmpfile" "$DIR/file"
  

• For multi-writer coordination use file locks (flock) or create lockfiles with mkdir (atomic on POSIX).

3. Cleanup

• Test teardown removes its directory; implement robust guardrails:
  • verify ownership (uid) and job-id before rm -rf
  • use short-lived leases: create a heartbeat file; if stale > threshold, background janitor reclaims.
  • CI runner performs post-job cleanup and cron-based garbage collector with exponential backoff.

4. Performance: local SSD vs NFS

• Prefer local SSD for I/O-heavy tests (latency, fsync). Use NFS only for artifacts that must be shared.
• Hybrid: run tests on SSD, stream artifacts to NFS asynchronously (rsync or upload step) to avoid NFS latency during test run.

Trade-offs & monitoring

• SSD requires enough capacity; implement disk usage quotas and eviction.
• NFS provides persistence but risks contention—restrict to artifacts.
• Monitor temp-dir growth, failed deletions, and file-lock waits; surface metrics to CI dashboard.

This design prevents cross-node collisions, keeps writes atomic, and ensures safe reclamation while balancing performance.

Situation & goal
I’m the SDET on-call. Staging is production-like but CI runs are 3x slower. My goals: restore CI speed quickly (minimize developer impact) while preserving environment fidelity and root-causing the regression to prevent recurrence.

Step-by-step diagnostic plan

1. Verify & scope

• Confirm CI job start/end times and which pipelines/tests are slow.
• Check if problem is cluster-wide or specific services/nodes.

2. Collect immediate metrics (first 5–15 min)

• CI job duration per stage, queue time, and pod/node allocation.
• Host metrics: CPU %, memory %, disk I/O (iops, latency), network throughput, context switches.
• App metrics: request latency P50/P95/P99, error rates, DB query latency, queue lengths, GC pause times, thread pool saturation.
• Infra: container restart count, kube events, autoscaler events.
• Tools: prometheus/grafana, cloud provider metrics, APM (Jaeger/NewRelic), top/htop/iostat, kubectl top, kubectl describe pods.

3. Fast triage (15–30 min)

• Correlate CI slow windows with resource saturation or specific service latency spikes.
• Run smoke jobs against staging to reproduce and isolate (API-only vs full-stack).

4. Lightweight profiling

• Capture flamegraphs or CPU profiles of slow services. Sample DB slow queries (EXPLAIN ANALYZE).
• Check cache hit rates (Redis/memcached) and network latency.

Quick mitigations (reduce CI time now)

• Temporarily scale up critical nodes or increase pod replicas for bottlenecked services.
• Route CI runs to a known-good staging fleet or a lightweight parallel test cluster.
• Reduce CI work: run high-priority tests first, split long suites (test slicing), skip non-blocking e2e tests, use previously validated artifact snapshots.
• Clear caches or restart a misbehaving service if leaking memory/threads.
• Use test parallelism tuning: lower concurrency if contention causing thrashing; or increase if CPU-bound and machines idle.

Root cause & longer-term fixes

• Root-cause analysis: combine traces, logs, and profiles to identify whether cause is DB, external dependency, resource exhaustion, or a traffic change.
• Permanent fixes:
  • Autoscaling policies and capacity buffers for staging that match CI load patterns.
  • Improve observability: add synthetic CI-specific dashboards (CI queue time, per-test latency), distributed tracing for test paths.
  • Performance tests in PR gate: lightweight perf smoke to catch regressions.
  • Test suite improvements: better parallelization, flaky test detection, test prioritization, and smaller e2e scopes.
  • Infrastructure as code + runbooks for rapid reproducible scaling and rollback.
  • Database indexing or query optimization, caching strategy fixes, connection pool tuning as required.

Validation & prevention

• After mitigation, run representative CI jobs and monitor SLOs (e.g., median CI time, 95th percentile).
• Create postmortem documenting root cause, timeline, actions, and owners.
• Add automated alerts for key metrics (CI duration increase, P95 latency, queue length) and a periodic capacity test simulating peak CI runs.

I would communicate status to stakeholders during incident, apply quick mitigations first, then implement the targeted long-term fixes and monitoring to prevent recurrence.

Per-test (setup/teardown per test)

• Isolation: Max — each test gets fresh fixtures, no shared state.
• Runtime: Slowest — repeated setup/teardown increases cost, especially for heavy resources (DB, services).
• Flakiness risk: Lowest — state leakage minimized.
• When to choose: Unit-like tests, flaky-prone tests, or when correctness > speed.

Per-class/module

• Isolation: Moderate — tests in same class share fixture; reset state between tests as needed.
• Runtime: Balanced — fewer setups than per-test, good for related tests.
• Flakiness risk: Medium — sharing can introduce hidden dependencies.
• When to choose: Groups of tests interacting with same resource where full isolation is unnecessary.

Session-level shared

• Isolation: Low — single instance for whole suite.
• Runtime: Fastest — heavy setup done once.
• Flakiness risk: High — accumulated state causes order-dependent failures.
• When to choose: Read-only fixtures (immutable test data), expensive global resources, with strong reset/cleanup strategies.

Cached snapshots (memoized fixtures / recorded state)

• Isolation: High-ish if snapshot is restored per test; runtime: fast after first creation.
• Flakiness risk: Low-to-medium — depends on restore fidelity (race conditions or stale caches cause issues).
• When to choose: Complex state that’s expensive to build but reproducible (DB snapshot, VM image). Combine with validation and occasional full rebuilds.

Guidance for mixed suites:

• Default to per-test for new or flaky tests.
• Use per-class for cohesive test groups.
• Use session or cached snapshots only for expensive, deterministic resources and pair with strict cleanup, health checks, and occasional rebuilds to mitigate flakiness.

Situation & Goals (brief)
As an SDET I’d design test infra to give fast PR feedback, high prod parity, and cost limits across hundreds of microservices.

High-level architecture

• Central CI coordinator (GitHub Actions/Drone/Jenkins X) triggers per-PR pipelines.
• Orchestrator (Kubernetes) runs ephemeral namespaces per PR with tenant limits.
• Shared service mesh (Istio/Linkerd) and same config templating (Helm/Kustomize) for parity.

Ephemeral PR environments

• Lightweight app deployment: deploy only impacted services (see Test Selection).
• Use Blue/Green base images from prod artifact registry; feature builds layered on top.
• Auto-delete on merge/timeout.

Dependency virtualization

• Contract mocks via Pact Broker; stateful services virtualized with WireMock/MockServer and local Postgres replicas with synthetic data.
• Use traffic replay for e2e where full prod services needed.

Test selection & fast feedback

• Change-impact analysis (git diff + static/service graph) to select unit, integration, and smoke suites.
• Run fast unit/integration in parallel; schedule long e2e on dedicated windows or on-demand.

Resource pooling & cost control

• Shared cluster autoscaler with node pools sized by priority (PR fast lane vs nightly).
• Pod eviction policies, spot instances for noncritical runs, and job preemption.
• Cache images/artifacts with distributed registry and warm pools for common services.

Governance & Observability

• Policy engine (OPA) enforces resource quotas, security, and test coverage gates.
• Central dashboards (Prometheus/Grafana) for test flakiness, cost, and environment drift.
• SLAs: PR feedback < 15m for fast lane; failing gates block merge.

Why this works

• Selective deployment + virtualization gives speed and parity. Resource pooling, spot use, and cleanups control cost. Governance ensures reliability and maintainability.

Clarify requirements & constraints

• Fast, ephemeral environments on demand (containers/VMs/K8s namespaces)
• Per-environment TTL, health monitoring, cost tracking, prioritized reclamation under resource pressure
• Integrates with CI (GitHub Actions/Jenkins/GitLab) and team access controls

High-level architecture

• Environment Provisioner (Kubernetes operator + Terraform for infra)
• Lifecycle Controller (scheduler + TTL enforcer + reclamation logic)
• Monitoring & Cost Collector (Prometheus + cloud billing API)
• State & Audit Store (fast state + durable audit)
• API Gateway & Web UI + CI SDK/hooks
• AuthZ/AuthN layer (OAuth2 + RBAC/SCIM)

Core components

• Provisioner: creates namespaces, network, test data; applies test images; uses templated manifests.
• Lifecycle Controller: stores metadata, enforces TTL, triggers graceful teardown, runs prioritized reclamation when cluster resources low.
• Health Monitor: liveness/readiness checks, integrates with Prometheus Alertmanager.
• Cost Collector: samples resource usage (CPU/memory/pv) per env and maps to cloud unit costs; reports per-env cost.
• Scheduler: admits requests, enforces quota, can prewarm images.
• API Gateway: REST + Webhooks for CI; SDK to request/env status/extend TTL.
• Access Control: maps CI identities and team members to envs via RBAC groups; SCIM sync with org.

State & datastore choices

• Redis (or etcd) for ephemeral state, leader election, fast TTL checks.
• Postgres for durable metadata, audit logs, cost history, reclamation decisions.
• Object store (S3) for artifacts and test results.

APIs (examples)

• POST /environments {template, ttl, team, priority} -> {env_id, endpoints}
• GET /environments/{id} -> status, cost, expires_at
• PATCH /environments/{id} {extend_ttl, priority}
• DELETE /environments/{id}
• Webhook /ci/callback (on job complete to optionally teardown)

TTL & reclamation policy

• Each env has priority (prod-sim > feature-tests > dev). Controller enforces TTL; sends pre-teardown warning webhooks.
• Reclamation: when node utilization > threshold, controller computes reclaim score:
  • score = weight_priority * priority + age_factor * age + cost_factor * cost_per_hour + owner_activity
  • lowest score reclaimed first (graceful drain + snapshot artifacts to S3)

Monitoring & cost reporting

• Prometheus metrics per namespace (kube-state-metrics), aggregated to per-env usage.
• Cost Collector maps usage to cloud prices hourly; stores in Postgres; API returns cost breakdown.
• Alerts for unhealthy envs; dashboard in Grafana showing active envs, cost, TTLs.

CI Integration & workflows

• CI uses REST/SDK to request env before test stage; CI receives endpoints and injects variables.
• On CI job finish, webhook notifies manager to mark env idle; optional auto-teardown or extend via human approval.
• Pre-merge jobs: short TTL; nightly stress tests: longer TTL with higher priority.

Access controls & audit

• Use OAuth2 + OIDC for CI runners and users; enforce RBAC per team/role.
• SCIM sync groups to map who can extend/retain environments.
• All actions logged to Postgres and write-ahead logs for compliance.

Operational considerations

• Rate-limit provisioning, use image pre-warms, garbage-collection backoff.
• Chaos-tested teardown, dry-run reclamations, operator running with least privilege.
• Unit/integration tests for provisioner + e2e in CI (self-test suite).

This design gives SDETs an automated, observable, and policy-driven lifecycle manager that integrates tightly with CI, enforces TTLs, reports cost, and reclaims resources predictably.