{
  "assumption_density": 0.2727272727272727,
  "assumptions": [
    "Notifications in this Django monolith follow the typical pattern of being event consumers (triggered after payments/profile changes complete), not transactional co-participants sharing ACID boundaries with other domains",
    "The team has or can quickly acquire operational familiarity with AWS SQS, ECS Fargate, and ALB routing — these are standard AWS services but new operational surface area",
    "Zero-downtime extraction is achievable via the strangler fig pattern with ALB routing and feature flags, meaning the monolith's notification path can remain active as a fallback during the entire migration",
    "The 6-engineer team can sustain feature development while 2-3 engineers focus on extraction — if the team is already at capacity, extraction velocity drops significantly",
    "RDS Postgres at 15K RPM has sufficient connection headroom for a new service's separate connection pool without requiring pgBouncer or connection pool infrastructure changes"
  ],
  "confidence": 0.73,
  "id": "655fdf62-f165-4c7e-a8ca-b210253d24eb",
  "next_action": "Create a service extraction RFC documenting: (1) the SQS topic structure for notification events, (2) the specific notification type to extract first, (3) the ALB path-based routing rules for the strangler fig pattern, (4) the idempotency key schema, and (5) the rollback procedure if the new service fails — circulate to all 6 engineers for review within 5 business days.",
  "question": "We have a 200K-line Django monolith serving 15K RPM. Which service should we extract first? The app has payments, notifications, user profiles,\n  search, and an admin dashboard. Team is 6 backend engineers, we're on AWS with RDS Postgres, and downtime during extraction is not acceptable.",
  "question_fit_score": 0,
  "rejected_alternatives": [
    {
      "path": "Extract user profiles first with read/write splitting and logical replication",
      "rationale": "Killed in round 1. User profiles are the most coupled entity — virtually every request touches user data. Extracting the hub node first forces solving the hardest distributed data problem across ALL modules simultaneously. At 15K RPM, adding a network hop to nearly every request path cascades latency. 6 engineers cannot refactor all dependent modules within 8 weeks."
    },
    {
      "path": "Conduct 2-week system analysis before any extraction to identify true bottlenecks",
      "rationale": "b004 at 0.65 confidence. Valid concern but delays action without guaranteeing different conclusions. The architectural argument for notifications-first is structural (leaf node, contained blast radius), not performance-based — it holds regardless of what bottleneck analysis reveals. However, the core insight about database contention is worth integrating: run query profiling in parallel with extraction planning, not as a blocking prerequisite."
    },
    {
      "path": "Optimize the monolith instead of extracting (caching, query optimization, connection pooling, modularization)",
      "rationale": "b006 at 0.75 confidence — a strong reframe. Correctly challenges whether extraction is necessary at 15K RPM. However, the question presupposes extraction is the goal, and this branch provides no actionable extraction guidance. Its insights (optimize first) should be treated as a parallel workstream, not a replacement. If the team's actual problem is deployment velocity or team autonomy rather than performance, monolith optimization doesn't solve it."
    },
    {
      "path": "Extract search first as the probable IO bottleneck",
      "rationale": "Killed in round 2. Search has MORE coupling to the shared schema (reads across multiple domain tables), requires real-time data sync pipelines (CDC via Debezium + Kafka or DMS), and demands operational expertise a 6-person team likely lacks. This is a 3-6 month project versus the operationally trivial SQS approach for notifications."
    }
  ],
  "reversal_conditions": [
    {
      "condition": "pg_stat_statements analysis reveals that notification-related queries are among the top 5 contention sources and are transactionally coupled to payment or profile writes",
      "flips_to": "Extract notifications but prioritize database decoupling (separate schema/RDS instance) as phase 1 rather than SQS-first, or reconsider extraction order"
    },
    {
      "condition": "The actual driver for extraction is deployment velocity (teams blocking each other on releases) rather than scalability or modularity",
      "flips_to": "Invest in monolith modularization (Django app boundaries, separate test suites, trunk-based development with feature flags) which delivers deployment independence faster than service extraction"
    },
    {
      "condition": "Team analysis reveals search is consuming \u003e40% of database resources and growing, while notifications are negligible",
      "flips_to": "Extract search first using Elasticsearch with CDC pipeline, accepting the longer timeline but addressing the actual bottleneck"
    }
  ],
  "unresolved_uncertainty": [
    "Whether the monolith's actual bottleneck is database contention, deployment coupling, or team coordination — this affects whether extraction delivers meaningful value or is premature optimization of architecture",
    "The actual notification volume as a percentage of 15K RPM is unknown — if notifications are only 1-2% of traffic, the extraction exercises the pattern but provides negligible operational relief",
    "b006's reframe (optimize monolith first) was not fully defeated — at 15K RPM, a well-tuned Django monolith on RDS may have years of headroom before extraction becomes necessary",
    "Whether the team has prior experience operating distributed systems and message queues — if not, the operational overhead of SQS + ECS Fargate + monitoring + alerting could exceed the 6-engineer team's capacity",
    "The killed b003 branch contained more detailed implementation specifics (ECS Fargate sizing, DynamoDB for idempotency, weighted target groups) than the surviving b002 — the strongest tactical guidance was lost when b003 was killed"
  ],
  "url": "https://vectorcourt.com/v/655fdf62-f165-4c7e-a8ca-b210253d24eb",
  "verdict": "Extract notifications first using AWS SNS/SQS with the strangler fig pattern. Notifications are structurally the safest first extraction because they are a leaf node — they depend on other services but nothing depends on them. If the notification service fails, payments still process and search still works. Deploy a new service (FastAPI or standalone Django) on ECS Fargate behind an internal ALB. The monolith publishes notification events to SQS; the new service consumes and dispatches via SES/SNS/Twilio. Start with ONE notification type (e.g., email confirmations) to validate the pattern. Keep existing notification tables in RDS Postgres initially with read-only access via separate connection pool; migrate to dedicated RDS in phase 2. Route via ALB path-based rules with feature flag fallback. Critical: implement idempotency using notification_id + timestamp hash to handle SQS at-least-once delivery. Add dead-letter queues for failed publishes. Timeline: weeks 1-4 design + SQS integration, weeks 5-12 incremental traffic shift. This extraction's primary value is not performance improvement — it's establishing a proven extraction pattern and team muscle memory for the harder extractions (payments, search) that follow.",
  "verdict_core": {
    "recommendation": "Extract the notifications service first using AWS SNS/SQS as the decoupling layer, starting with a single notification type via the strangler fig pattern, while the monolith continues handling all notifications during the dual-run testing period.",
    "mechanism": "Because notifications are a leaf node in the dependency graph — they consume events from other domains but no other domain depends on them — extraction failures are contained (a downed notification service means missed emails, not failed payments or broken search), and the SQS-based async architecture eliminates dual-write database contention by replacing synchronous DB operations with message publishing.",
    "tradeoffs": [
      "Notifications are not the performance bottleneck — extracting them won't directly reduce database load or improve P99 latency on the critical request path",
      "Team must maintain two deployment targets (monolith + new service) with 6 engineers, reducing velocity on feature work during the 8-12 week extraction window",
      "The strangler fig pattern with dual-run introduces operational complexity in monitoring message delivery guarantees and reconciling notification state across two systems"
    ],
    "failure_modes": [
      "SQS at-least-once delivery causing duplicate notifications without idempotency keys — roughly 0.1-0.3% duplication rate per AWS documentation",
      "Monolith-to-SQS publish timeout (default 5s) causing silent notification loss if not caught with dead-letter queues and monitoring",
      "Database connection pool exhaustion if the new service shares the monolith's RDS instance without a separate connection pool with strict limits",
      "Strangler fig routing misconfiguration during incremental traffic shift causing notification gaps during the 5% → 25% → 50% → 100% rollout"
    ],
    "thresholds": [
      "15K RPM total traffic",
      "SQS cost under $50/month at notification volume",
      "ECS Fargate ~$800-1,500/month for notification-scale traffic",
      "Incremental traffic shift: 5% → 25% → 50% → 100% over weeks 5-12"
    ]
  },
  "verdict_type": ""
}