Muse Wonder 1.0·续1

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          "text": "# Production-Safe Split: `tenants` → `tenant_metadata` + `tenant_billing`\n\nUse the **expand → migrate → contract** pattern. The old `tenants` table remains the source of truth until cutover is proven, every step is feature-flagged, and nothing destructive happens until after a bake period.\n\n**Target shape (example):**\n- `tenant_metadata`: `tenant_id (PK, same IDs as today)`, name, slug, status, region, settings, `created_at`, `updated_at`\n- `tenant_billing`: `tenant_id (PK, FK → tenant_metadata)`, billing_email, payment_provider_customer_id, subscription_id, plan, currency, tax_id, billing_address, `created_at`, `updated_at`\n- Keep `tenant_id` identical to the existing `tenants.id` so existing foreign keys don't need to be rewritten.\n\n**Cross-cutting controls for all phases:** flags for `dual_write_enabled`, `read_new_pct`, `write_primary_new`; dashboards for write error rate, p99 latency, replica lag, dual-write failures, and row-diff counts; a named rollback owner; no phase transitions during month-end billing runs or peak traffic.\n\n---\n\n### Phase 0 — Discovery\n\n**Actions**\n- Inventory every column in `tenants` and classify it as metadata, billing, both, or dead. Resolve ambiguous columns (e.g., `plan`) to a single owner.\n- Inventory every reader/writer: application code, ORMs, background jobs, cron/Lambdas, BI/ETL, admin tooling, and downstream consumers (webhooks, invoices, data warehouse) using query logs (`pg_stat_statements` or equivalent).\n- Map foreign keys, triggers, unique constraints, and indexes referencing `tenants`.\n- Measure table size (rows, GB, growth rate), write TPS, hot tenants, NULL/duplicate rates in billing columns, and whether a reliable PK + `updated_at` cursor exists for resumable backfill.\n- Check compliance: billing fields may carry stricter access control, encryption, audit, and retention requirements once isolated.\n- Rehearse the full migration in staging with production-like volume and measure DDL/backfill timing.\n\n**Invariant to verify:** Every column is assigned to exactly one destination table, and every read/write path is known and owned.\n\n**Stop signal:** Any undocumented writer (stray script, forgotten job), a column with unclear ownership, or an external consumer you can't observe or update. Do not proceed on an incomplete inventory.\n\n---\n\n### Phase 1 — Expand (create new schema, change nothing else)\n\n**Actions**\n- Create `tenant_metadata` and `tenant_billing` empty, with indexes and constraints. In Postgres use `CREATE INDEX CONCURRENTLY` and add FKs as `NOT VALID` then `VALIDATE CONSTRAINT` to avoid long locks; in MySQL require `ALGORITHM=INPLACE, LOCK=NONE` or an online-schema-change tool.\n- Optionally create a compatibility view so old code can keep reading a joined shape later.\n- Deploy no application changes yet; old code runs untouched against `tenants`.\n\n**Invariant to verify:** Production behavior is unchanged — old application works exactly as before, new tables are empty, and DDL caused no lock waits or replica lag beyond baseline.\n\n**Stop signal:** Lock wait > a few seconds, replica lag spiking during DDL, or any regression in the existing test suite against the new schema. Drop the new tables and retry off-peak.\n\n---\n\n### Phase 2 — Dual-Write\n\n**Actions**\n- Deploy application code (flag off, then ramp 1% → 100%) that writes to both `tenants` (still primary) and the new tables. Because both live in the same database, write them **in the same transaction** so they can't diverge on partial failure.\n- Make writes idempotent (`INSERT ... ON CONFLICT (tenant_id) DO UPDATE`) so retries are safe.\n- Reads still come only from `tenants`. Add metrics/alerts on dual-write failures.\n- Add a safety net: a reconciliation job (Phase 4) that catches anything dual-write misses.\n\n**Invariant to verify:** For every tenant created or updated since the flag was enabled, a corresponding row exists in both new tables, field-for-field identical to the mapped columns in `tenants`.\n\n**Stop signal:** Dual-write failure rate > ~0.05–0.1%, any duplicate-key or constraint errors, p99 write latency up >20% over baseline for 10 minutes, or rising mismatch counts. Turn the flag off — reads are unaffected, so this rollback is trivial.\n\n---\n\n### Phase 3 — Backfill (historical data)\n\n**Actions**\n- Copy existing rows in small keyset-paginated chunks (`WHERE id > :last_id ORDER BY id LIMIT 1000`), checkpointing `last_id` so the job is resumable.\n- Use idempotent upserts; only overwrite when the source `updated_at` is newer, so backfill never clobbers fresher dual-written data.\n- Throttle: pause automatically when replica lag, CPU, or I/O crosses thresholds; run heavier chunks off-peak. Estimate total duration from a staging dry run.\n\n**Invariant to verify:** For every completed ID range, destination row counts and per-range checksums match the source, and the job can restart from its checkpoint without loss or duplication.\n\n**Stop signal:** Replica lag > 5–10s sustained, DB CPU/IO saturation impacting production latency, or any checksum mismatch in a completed chunk. The job should self-pause; investigate before resuming.\n\n---\n\n### Phase 4 — Verification\n\n**Actions**\n- Continuous comparison job: row counts, aggregate/per-tenant hashes (e.g., hash of concatenated mapped columns), orphan checks (`tenant_billing` rows with no `tenant_metadata` row and vice versa), and random sampling of ~1% of tenants plus all recently written tenants.\n- **Shadow reads:** for a sample of live requests, read from the new tables and compare against the `tenants` result without serving it. Log mismatches.\n- Reconcile any diffs by replaying from `tenants` (still the source of truth), then re-verify.\n- Verify in-flight correctness: counts may differ momentarily due to writes in progress, so compare on a quiesced watermark (e.g., only rows with `updated_at` older than 5 minutes).\n\n**Invariant to verify:** Zero unexplained differences in billing fields, zero orphans, and shadow-read parity at 100% (billing data tolerates no \"close enough\") over at least several days including a peak period.\n\n**Stop signal:** Any persistent billing-field mismatch after replay, orphan rows, or shadow-read mismatch rate above zero for billing / >0.001% for metadata. Cutover is blocked until diffs are explained and fixed at the root cause.\n\n---\n\n### Phase 5 — Cutover\n\n**Actions**\n- Ramp reads to the new tables by flag: internal tenants → 1% → 5% → 25% → 50% → 100%, holding each step long enough to watch error rate, p99, and shadow-diff metrics.\n- Then flip write primacy: new tables become the source of truth, but **keep reverse dual-writing back to `tenants`** for the entire bake period (recommend 2–4 weeks, spanning at least one full billing cycle).\n- Announce a change freeze on billing logic during cutover; staff support/on-call for billing anomalies.\n\n**Invariant to verify:** User-visible responses and billing operations (invoice generation, subscription changes) are identical to pre-cutover behavior, with no latency or error-budget regression at each ramp step.\n\n**Stop signal:** 5xx/error rate > baseline +0.1%, p99 regression >20%, any confirmed billing discrepancy or relevant support tickets, or mismatch alerts firing. Execute rollback (below) — it must take <5 minutes via flag flip.\n\n---\n\n### Phase 6 — Rollback (standing capability, rehearsed before cutover)\n\n**Actions**\n- Before c
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Examples

<details>

<summary>Distributed Job Queue Redesign — Redesign a distributed job queue while making concurrency and failure semantics explicit.</summary>

const response = await env.AI.run(
  'meta/muse-wonder-1.0',
  {
    input:
      'Design a distributed job queue for a multi-tenant SaaS platform processing billing and notification jobs. It must preserve per-tenant ordering, support at-least-once delivery, tolerate worker crashes, allow replay for seven days, enforce tenant-specific concurrency limits, and prevent one tenant from starving others. Compare two architectures, recommend one, define the job state machine and idempotency contract, and list the metrics and alerts needed for rollout.',
    max_output_tokens: 4096,
  },
)
console.log(response)

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  "max_output_tokens": 4096
}'

## Architecture Comparison

| Dimension | A: Partitioned Log (e.g., Kafka) | B: Virtual Per-Tenant Queues + Fair Scheduler |
|---|---|---|
| **Design** | Hash `tenant_id` to a partition; workers consume partitions. Ordering guaranteed per partition. | Jobs stored in a durable DB (e.g., Postgres, partitioned by tenant). A dispatcher leases jobs to stateless workers using weighted fair queuing. |
| **Ordering** | Strong per partition, but a slow/poison job blocks unrelated tenants sharing that partition. Retries/delays break ordering. | Strong per tenant lane via monotonic sequence numbers; retries block only that lane. |
| **Fairness & concurrency** | Hard: partition assignment is coarse, hot tenants skew partitions, per-tenant caps require extra machinery. | Native: dispatcher enforces per-tenant in-flight caps and round-robin/weighted scheduling. |
| **Replay (7 days)** | Excellent: built-in retention and offset rewind. | Requires retaining terminal jobs for 7 days and a replay API (manageable). |
| **Crash tolerance** | Offset committed only after success gives at-least-once. | Lease/visibility timeout with expiry and requeue gives at-least-once. |
| **Scale / ops** | Very high throughput, mature ecosystem; but rebalancing pain and limited per-job control (no per-job delay/priority easily). | Throughput bounded by DB; needs sharding at high scale. Simpler per-job control, queries, and auditing. |

## Recommendation: Architecture B

Billing and notification workloads are moderate-throughput but correctness- and fairness-sensitive, so fine-grained control beats raw log throughput.

**Components:**

1. **Enqueue API + transactional outbox** — the job row is written in the same transaction as the business change, so jobs are never lost or phantom-created.
2. **Ordering** — ordering key is `tenant_id + lane` (billing, notifications are separate lanes so notifications can't block charges). Each lane gets a monotonic sequence; the dispatcher only leases the lowest unacknowledged sequences. Within a lane, execution is effectively sequential; tenant concurrency applies *across* lanes/keys.
3. **Fair dispatcher** — deficit round-robin / weighted fair queuing across tenants (weights by tier), with a hard per-tenant in-flight semaphore. Scheduling is work-conserving: idle tenants' capacity is usable by others, but a backlogged tenant always gets its guaranteed share within one scheduling round. Aging prevents low-weight tenants from waiting indefinitely. Separate worker pools for billing vs. notifications add a second isolation layer.
4. **Crash tolerance** — workers hold a lease (e.g., 30–60s visibility timeout) extended by heartbeat. Ack is a compare-and-swap on a fencing `lease_token`, so a zombie worker whose lease expired cannot ack. On expiry, the job returns to READY with `attempts++`.
5. **Replay** — terminal jobs (payload, result, attempt history) retained 7 days. Replay of a dead-lettered job resumes it at its original sequence position (the lane stays paused until it's replayed or explicitly skipped by an operator, preserving strict ordering). All replays are audit-logged.

## Job State Machine

```
QUEUED ──lease──▶ IN_FLIGHT ──ack──▶ SUCCEEDED (terminal)
  ▲                  │
  │                  ├─ failure, attempts < max ─▶ RETRY_WAIT ──backoff──▶ QUEUED
  │                  ├─ failure, attempts = max ─▶ DEAD_LETTERED ──replay──▶ QUEUED
  └── lease expiry ──┘
QUEUED/RETRY_WAIT ──cancel──▶ CANCELLED (terminal)
DEAD_LETTERED ──operator skip──▶ SKIPPED (terminal)
```

Retries use exponential backoff with jitter. `SUCCEEDED`, `CANCELLED`, `SKIPPED` are terminal; jobs are purged after 7 days.

## Idempotency Contract

**Producer side:**
- Every enqueue carries an `Idempotency-Key`. A unique constraint on `(tenant_id, idempotency_key)` means duplicate submissions return the original `job_id` instead of creating a second job. Same key with a different payload hash returns `409 Conflict`.

**Worker side:**
- Delivery is at-least-once, so handlers **must** be idempotent on the stable `job_id`/business key — never on `attempt_id`, which changes per delivery.
- External side effects must propagate a stable idempotency key downstream (e.g., to the payment processor or email provider), or record the effect and the processed `job_id` in one local transaction.
- Never ack before the effect is durably committed. Duplicate delivery of an already-succeeded job must be detected and acked as a no-op.
- Replaying a *succeeded* job requires explicit operator override, since it intentionally re-executes the effect under a new job ID.

## Metrics and Alerts for Rollout

**Metrics (tagged by tenant, lane, job type):**
- Queue depth and **age of oldest QUEUED job** per tenant/lane
- Dispatch latency, execution duration (p50/p95/p99), success/retry/DLQ rates
- Lease expirations (crash indicator), duplicate-delivery rate, in-flight vs. per-tenant cap (saturation)
- Fairness lag: difference between a tenant's expected and actual share of dispatches while backlogged
- Scheduler loop latency and DB lock contention

**Alerts:**
- Oldest billing job waiting > 5 min (notifications > 15 min) — SLO breach / starvation signal
- Any billing job entering DLQ; DLQ rate spike for notifications
- Lease-expiration rate spike — worker crashes or systemic slowness
- A tenant backlogged but receiving near-zero dispatches over 10 min — fairness bug
- Per-tenant saturation pinned at cap with growing depth — capacity or tenant abuse
- Zero throughput with non-empty queues — dispatcher outage

**Rollout:** ship behind a feature flag, canary with internal tenants, then low-risk tenants, with per-tenant kill switches and dashboards reviewed at each stage before ramping.