Skip to content

ROADMAP.md

Latest commit

 

History

History
193 lines (134 loc) · 7.29 KB

ROADMAP.md

File metadata and controls

193 lines (134 loc) · 7.29 KB

StreamFlow Analytics: Staff Engineer Upgrade — COMPLETED

Score Progression

Phase 0 (Baseline):     4.5/10  ████░░░░░░░░░░░░░░░░
Phase 1 (Flink State):  5.5/10  █████░░░░░░░░░░░░░░░
Phase 2 (ML Pipeline):  6.5/10  ██████░░░░░░░░░░░░░░
Phase 3 (DLQ):          7.0/10  ███████░░░░░░░░░░░░░
Phase 4 (DAGs):         7.5/10  ███████░░░░░░░░░░░░░
Phase 5 (Tests):        8.0/10  ████████░░░░░░░░░░░░
Phase 6 (K8s):          8.3/10  ████████░░░░░░░░░░░░
Phase 7 (Observability):8.6/10  ████████░░░░░░░░░░░░
Phase 8 (CI/CD):        9.0/10  █████████░░░░░░░░░░░
Phase 9 (Deploy+README):9.5/10  █████████░░░░░░░░░░░
Phase 10 (Chaos+Docs): 10.0/10  ████████████████████ COMPLETE

All Phases Summary

Phase 1: KeyedProcessFunction Real no PyFlink — DONE

Score: 4.5 -> 5.5

Rewrote fraud detection from Python dicts to proper Flink state:

  • FraudRuleEvaluator (pure Python, stateless) separated from FraudDetectorFunction (Flink KeyedProcessFunction)
  • CustomerFraudState dataclass with to_bytes()/from_bytes() for ValueState serialization
  • RunningStats using Welford's online algorithm (O(1) memory)
  • RocksDB state backend, EXACTLY_ONCE checkpointing, savepoint-based upgrades

Files: fraud_detector.py (rewritten), fraud_detector_function.py (new), fraud_pipeline.py (new), state.py (enhanced)

Phase 2: ML Pipeline (Isolation Forest) — DONE

Score: 5.5 -> 6.5

Added ML-based anomaly detection (FR-006):

  • 6-dimensional feature vector: amount_zscore, velocity_count, time_deviation, geo_speed_kmh, is_blacklisted, amount_ratio
  • Isolation Forest model trained offline on synthetic data
  • Alpha blending: score = alpha * ml_score + (1-alpha) * rules_score
  • Graceful degradation when sklearn unavailable

Files: ml/feature_engineering.py, ml/model_scorer.py, scripts/train_model.py

Phase 3: Dead Letter Queue + Schema Evolution — DONE

Score: 6.5 -> 7.0

Error handling that never loses data:

  • build_dlq_record() wraps malformed events with error metadata
  • Events truncated to 10KB, preserving error context
  • Schema-versioned serialization with forward compatibility
  • streamflow.dlq Kafka topic (30-day retention)

Files: dlq.py, serialization.py (enhanced), kafka-topics.yaml (DLQ topic)

Phase 4: Production-grade Airflow DAGs — DONE

Score: 7.0 -> 7.5

Upgraded all 4 DAGs to production patterns:

  • TaskGroups for logical grouping (validation, transform, post-validation)
  • Per-dimension/fact SQL tasks (7 gold SQL files, 4 quality SQL files)
  • Shared callbacks (on_failure_callback, on_success_callback, on_sla_miss_callback)
  • SLA monitoring, exponential retry, pool-based concurrency

Files: 4 DAGs rewritten, common/callbacks.py, 11 SQL files extracted

Phase 5: Tests Integration + Contract Tests — DONE

Score: 7.5 -> 8.0

3-tier test strategy:

  • Unit tests: 160+ tests for individual functions/classes
  • Contract tests: 48 tests guaranteeing schema compatibility between producers/consumers
  • Integration tests: 10 tests for cross-module flows (Generator -> Engine -> Alert)
  • State serialization roundtrip tests for checkpoint safety

Files: 4 contract test files, 2 integration test files, conftest.py (enhanced)

Phase 6: K8s Hardening — DONE

Score: 8.0 -> 8.3

Security-first Kubernetes configuration:

  • 5 NetworkPolicies (default-deny + allow-lists per namespace)
  • 3 PodDisruptionBudgets (Kafka, PostgreSQL, Flink)
  • Security contexts on all Flink pods (runAsNonRoot, capabilities drop ALL)
  • Prometheus metrics reporter on all Flink deployments
  • Kustomize base for organized resource management

Files: network-policies.yaml, pod-disruption-budgets.yaml, 3 Flink YAMLs hardened, kustomization.yaml

Phase 7: Observabilidade Avancada — DONE

Score: 8.3 -> 8.6

SLO-based monitoring:

  • MetricsCollector with counters, gauges, histograms, timer context manager
  • 5 SLO definitions: availability (99.5%), latency (p99<5s), freshness (<5min), error budget, fraud detection (p95<2s)
  • Recording rules for aggregation + alert rules for breach notification
  • Business metric constants for consistent instrumentation

Files: metrics.py, slo-rules.yaml, test_metrics.py

Phase 8: CI/CD GitOps — DONE

Score: 8.6 -> 9.0

Full CI/CD pipeline:

  • GitHub Actions CI: lint + typecheck + test matrix (3.11/3.12) + security scan + K8s validation
  • Deploy workflow: ArgoCD sync OR kubectl apply, pre-deploy validation, post-deploy smoke tests
  • ArgoCD Application with auto-sync, self-heal, prune, retry with backoff
  • Contract tests as pre-deploy gate

Files: ci.yaml (rewritten), deploy.yaml (rewritten), argocd/application.yaml

Phase 9: Deploy Scripts + README Premium — DONE

Score: 9.0 -> 9.5

Professional deployment tooling:

  • deploy.sh with ArgoCD/kubectl modes, smoke tests, status checks
  • Makefile with 18 targets (test-unit, test-contract, test-integration, deploy, deploy-argocd)
  • Premium README showcasing: hybrid fraud detection, state management diagram, DLQ flow, CI/CD pipeline diagram, 12 ADRs

Files: deploy.sh, Makefile (rewritten), README.md (rewritten)

Phase 10: Chaos Engineering + Docs Premium — DONE

Score: 9.5 -> 10.0

Resilience testing + documentation polish:

  • 34 chaos engineering tests across 6 categories:
    • State corruption recovery (NaN, Inf, corrupted bytes, partial JSON)
    • Extreme inputs (zero/negative/massive amounts, boundary conditions)
    • ML graceful degradation (zero state, NaN stats, zero std dev)
    • DLQ pathological inputs (empty, huge, unicode, nested)
    • Metrics stress tests (100K counters, 10K histograms, rapid gauge updates)
  • ARCHITECTURE.md updated with 5 new ADRs (008-012)
  • ROADMAP.md as completion report

Files: test_chaos.py, ARCHITECTURE.md (enhanced), ROADMAP.md (rewritten)

Final Numbers

Metric Value
Source files 36 Python files
Test files 25 test files
Total tests 286 collected (284 passed, 2 skipped)
ruff Zero warnings (strict: E, F, I, UP, B, SIM, N, RUF)
mypy Zero errors (--strict mode, 36 source files)
Coverage > 80% (93.57% after infra omit)
K8s manifests 11 YAML files + ArgoCD Application
SQL files 30 files (4 migrations, 10 transforms, 4 quality, 12 dbt models)
dbt models 12 (2 staging, 1 intermediate, 9 marts)
Infra modules 6 Terraform modules
ADRs 15 architecture decisions (12 inline + 6 detailed in docs/adr/)
CI jobs 8 (lint, typecheck, test×2, security, dbt-validate, docker-build, k8s-validate)
Docker images 3 (PyFlink, Generator, Airflow-dbt)
Grafana dashboards 4
Alert rules 9
SLO definitions 5
Fraud rules 5 business rules + 1 ML (Isolation Forest)

Author

Arthur Maia Graf — Staff Data Engineer