bf-1p4v: Verify compile error already fixed

The borrow-of-moved-value error for `state` was already fixed in the codebase.
Line 568 uses `.with_state(state.clone())` and `UnifiedState` derives Clone.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jedarden 2026-05-20 06:48:20 -04:00
parent 360378bde2
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## Status
Design phase. See [`docs/`](docs/) for architecture detail.
## Quick Start
Get Miroir running locally in 5 minutes with Docker Compose:
```bash
# Clone the repository
git clone https://github.com/jedarden/miroir.git
cd miroir
# Start the development stack (3 Meilisearch nodes + 1 Miroir orchestrator)
docker compose -f examples/docker-compose-dev.yml up -d
# Verify health
curl http://localhost:7700/health
# Expected: {"status":"available"}
# Index documents (Meilisearch-compatible API)
curl -X POST http://localhost:7700/indexes/movies/documents \
-H "Authorization: Bearer dev-key" \
-H "Content-Type: application/json" \
-d '[{"id": 1, "title": "Inception"}, {"id": 2, "title": "Interstellar"}]'
# Search
curl -X POST http://localhost:7700/indexes/movies/search \
-H "Authorization: Bearer dev-key" \
-H "Content-Type: application/json" \
-d '{"q": "inception"}'
# Teardown (removes containers and volumes)
docker compose -f examples/docker-compose-dev.yml down -v
```
See [`examples/README.md`](examples/README.md) for more details on the development stack, configuration options, and troubleshooting.
## Production deployment
For production deployments, see the [Deployment Sizing Guide](docs/horizontal-scaling/sizing.md) to determine orchestrator pod count and task store configuration based on your corpus size and query throughput.
### When to use
- **Multi-pod with Redis** — Recommended for production. Horizontal scaling with 2+ orchestrator pods delivers fault tolerance (zero-downtime rollouts, pod-loss survival) and scales query throughput via HPA. See [Deployment Sizing Guide](docs/horizontal-scaling/sizing.md).
- **Single oversized pod** — Supported for dev clusters, very small deployments, or constrained environments. A single pod at 4 vCPU / 8 GB is validated but loses HA benefits (no zero-downtime rollouts, no pod-loss survival). See [Single-Pod Mode](docs/horizontal-scaling/single-pod.md).
- **Large index sharding** — When a single Meilisearch node cannot fit your corpus in RAM, Miroir stripes it across multiple nodes with configurable replication factor.
Additional production resources:
- [Production Deployment Guide](docs/onboarding/production.md) — Operational considerations, monitoring, and troubleshooting
- [Versioning Policy](docs/versioning-policy.md) — Backward compatibility commitments and upgrade guidance

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# Deployment Sizing Guide
This guide provides a sizing matrix for provisioning Miroir orchestrator pods based on corpus size and query throughput. Meilisearch node sizing follows the guidance in [plan.md §6](../../plan/plan.md) independently.
## Sizing matrix
| Corpus | Peak QPS | Orchestrator pods | Task store |
|--------|----------|-------------------|------------|
| ≤ 10 GB | ≤ 500 | 2 (HA) | Redis (or SQLite if replicas=1) |
| ≤ 50 GB | ≤ 2 k | 24 (HPA) | Redis |
| ≤ 200 GB | ≤ 5 k | 48 (HPA) | Redis |
| ≤ 1 TB | ≤ 20 k | 812 (HPA) | Redis |
| ≤ 5 TB | ≤ 100 k | 1224 (HPA) | Redis (clustered or Sentinel) |
**Orchestrator pod specification:** 2 vCPU / 3.75 GB per pod.
**Key insight:** Orchestrator count scales with *query throughput*, while Meilisearch node count scales with *corpus size* and replication factor. These are orthogonal axes.
## Task-store memory accounting
When Redis is the task store, it backs shared state for multiple subsystems:
- **Idempotency replay keys** — request deduplication cache
- **Session pinning rows** — per-session sticky routing state
- **Alias cache** — atomic index alias mappings
- **Background job queue** — chunked dump import, reshard backfill jobs
- **Leader lease** — HA coordinator election state
- **CDC overflow buffer** — change-data-capture spill-over (when configured)
- **Search UI rate-limit buckets** — IP-based rate limiter state
Add **~20 MB for search UI rate-limit buckets per 10k active IPs** on top of the task-store baseline when `search_ui.rate_limit.backend: redis`. Bucket rows auto-expire per `redis_ttl_s`, keeping the footprint bounded even under IP-scan or spray attacks.
## Worked example: ≤ 200 GB / ≤ 5 k QPS
**From the table:** 48 orchestrator pods (HPA-scaled between these limits), Redis task store.
### Memory budget validation
Per [plan.md §14.2](../plan/plan.md#142-per-pod-memory-budget), steady-state per-pod memory at default config:
| Component | Budget |
|-----------|--------|
| Baseline (runtime + pools) | ~330 MB |
| Request/response buffers (p99 concurrent) | 200 MB |
| Task registry + idempotency + sessions | 250 MB |
| Feature overhead (§13.1121 features) | ~200 MB |
| Allocator headroom | ~800 MB |
| **Steady-state total (idle background)** | **~1.2 GB** |
| **With one heavy background job active** | **~1.7 GB** |
At 48 pods, cluster-wide steady-state memory is 4.89.6 GB, which fits within a single Redis instance (allocate 48 GB for Redis at this tier). Heavy background jobs only run on the pod that claimed them from the shared queue — not all pods simultaneously — so the per-pod budget stays within envelope.
### Query throughput validation
Per [plan.md §14.3](../../plan/plan.md#143-per-pod-cpu-budget):
- **Small searches** (1 KB responses): ~3 kQPS per pod at 70% CPU
- **Large searches** (10 KB responses): ~1 kQPS per pod at 70% CPU
At 5 kQPS peak with 48 pods:
- Each pod handles 6251250 QPS
- This is within the 13 kQPS per-pod envelope
- HPA will scale up toward 8 pods during sustained peak
### Result
The ≤ 200 GB / ≤ 5 k QPS row is validated: 48 orchestrator pods with Redis task store can handle the workload within the 2 vCPU / 3.75 GB per-pod resource envelope.
## When to escalate
If your workload exceeds the ≤ 5 TB / ≤ 100 k QPS tier, or if you're hitting resource limits despite following this matrix, consider:
1. **Vertical scaling escape valve** — For constrained environments, a single larger pod (e.g., 4 vCPU / 8 GB) is supported but not recommended for production. See [plan.md §14.10](../../plan/plan.md#1410-vertical-scaling-escape-valve).
2. **Redis clustering** — At very high scale, consider Redis Cluster or Sentinel for the task store to improve availability and distribute memory pressure.
3. **Tune feature flags** — Disable unused capabilities (e.g., multi-search, vector search, shadow tee) to reclaim memory. Every feature in [plan.md §13](../../plan/plan.md#13-feature-scaling-modes) has an `enabled: true` knob that can be flipped off.
4. **Consult the metrics** — Use the resource-pressure metrics in [plan.md §14.9](../../plan/plan.md#149-resource-pressure-metrics-and-alerts) to identify the bottleneck (CPU, memory, request queue depth, or background job backlog).

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# Production Deployment Guide
This guide covers operational considerations for running Miroir in production.
## Sizing
Start with the [Deployment Sizing Guide](../horizontal-scaling/sizing.md) to determine the number of orchestrator pods and task store configuration for your workload.
## Resource envelope
Each orchestrator pod is designed for **2 vCPU / 3.75 GB RAM**. This matches common small-instance tiers across cloud providers:
- AWS: t3.medium
- GCP: e2-medium
- Hetzner: CX22
- Rackspace Spot: 2c/3.75GB
See [plan.md §14](../plan/plan.md#14-resource-envelope-and-horizontal-scaling) for the full resource budget breakdown.
## Task store selection
| Replicas | Recommended task store |
|----------|----------------------|
| 1 | SQLite (default) |
| 2+ | Redis (required) |
The Helm chart's `values.schema.json` enforces this requirement — it rejects configurations where `miroir.replicas > 1` and `taskStore.backend: sqlite`.
For Redis deployment modes:
- **Small deployments** (≤ 200 GB corpus): Single Redis instance is sufficient
- **Medium deployments** (≤ 1 TB corpus): Redis with persistence enabled
- **Large deployments** (≤ 5 TB corpus): Redis Cluster or Sentinel for HA
## Horizontal Pod Autoscaler
Enable HPA for production deployments with variable traffic:
```yaml
miroir:
hpa:
enabled: true
minReplicas: 2
maxReplicas: 24
```
HPA requires `taskStore.backend: redis` and `miroir.replicas >= 2`. The chart enforces these constraints.
See the [sizing matrix](../horizontal-scaling/sizing.md#sizing-matrix) for recommended replica ranges per workload tier.
## High availability
### Minimum HA configuration
- **2 orchestrator pods** (handles one pod failure without downtime)
- **Redis task store** (shared state across pods)
- **Replication factor ≥ 2** for Meilisearch (handles one node failure per shard group)
### Leader election
The orchestrator uses Redis-based leader election for operations that require exactly-one semantics (settings broadcast, ILM rollover). Leader lease TTL is 30s with heartbeat renewal at 10s intervals — a new leader is elected within 30s if the current leader fails.
### Pod disruption budgets
The chart includes a PodDisruptionBudget that ensures minimum availability during voluntary disruptions:
```yaml
minAvailable: 1
```
For stricter guarantees, adjust to `minAvailable: 2` or use percentage-based policies.
## Monitoring
### Resource pressure metrics
Key metrics to monitor:
| Metric | Meaning | Alert threshold |
|--------|---------|-----------------|
| `miroir_requests_in_flight` | Concurrent requests | > 80% of `server.max_concurrent_requests` |
| `miroir_background_queue_depth` | Pending background jobs | > 10 for > 5min |
| `miroir_task_store_latency_seconds` | Task store operation p99 | > 100ms |
| Container CPU | Orchestrator CPU utilization | > 80% sustained |
| Container memory | Orchestrator RAM usage | > 85% sustained |
See [plan.md §14.9](../plan/plan.md#149-resource-pressure-metrics-and-alerts) for the full alerting catalog.
### Search UI rate limiting
When `miroir.replicas > 1`, the Search UI rate limiter **must** use Redis backend:
```yaml
search_ui:
rate_limit:
backend: redis # Required for multi-pod deployments
```
With `backend: local`, the effective cluster-wide rate is `per_ip × pod_count` because each pod maintains its own bucket table.
## Vertical scaling escape valve
If your environment cannot support horizontal scaling (e.g., single-node Kubernetes), you can use a larger pod size (e.g., 4 vCPU / 8 GB). This is supported but not recommended for production — horizontal scaling provides better fault isolation and elasticity.
See [plan.md §14.10](../plan/plan.md#1410-vertical-scaling-escape-valve) for trade-offs.
## Feature flags for memory-constrained deployments
Every advanced capability has an `enabled: true` knob that can be flipped off to reclaim memory:
| Feature | Memory savings | Config path |
|---------|---------------|-------------|
| Vector search | ~30 MB per pod | `vector_search.enabled` |
| Multi-search | ~5 MB per pod | `multi_search.enabled` |
| Shadow tee | ~50 MB per pod (5% sample) | `shadow.enabled` |
| CDC publisher | ~64 MB per pod | `cdc.enabled` |
See [plan.md §13](../plan/plan.md#13-feature-scaling-modes) for the full feature list and scaling modes.
## Backup and recovery
### Meilisearch data
Meilisearch persists index data to disk at `/meilisearch-data`. Backup strategy:
1. **Snapshot volumes** — Take volume snapshots before index operations (reshard, ILM rollover)
2. **Dump exports** — Use `/_indexes/{uid}/documents/export` for logical backups
3. **RG/RF awareness** — With replication factor > 1, you can survive node loss without data loss
### Task store (Redis)
Redis task store contains:
- Idempotency keys (ephemeral, TTL 24h)
- Session pinning state (ephemeral, TTL per session)
- Background job queues (rebuildable from operations log)
- Leader lease (ephemeral, auto-recoverable)
Redis data is **rebuildable** — a fresh Redis instance is acceptable after failure. The orchestrator will reconstruct queues and lease state on startup.
For persistence:
- Enable RDB snapshots (`save 900 1`, `save 300 10`, `save 60 10000`)
- Or use AOF (`appendonly yes`) for stronger durability
### Configuration
Store your Helm values in git. The chart supports external secrets for sensitive values (API keys, JWT secrets).
## Upgrade path
See [versioning-policy.md](../versioning-policy.md) for backward compatibility commitments.
Upgrade procedure:
1. Read the changelog for breaking changes
2. Test in non-production first
3. Roll out orchestrator pods first (Meilisearch API compatibility)
4. Update Meilisearch nodes independently
## Troubleshooting
### Orchestrator pods stuck in CrashLoopBack
Check task store connectivity:
```bash
kubectl logs -n search deployment/miroir --tail 100 | grep -i task
```
If Redis is unreachable, verify `taskStore.backend` and connection settings.
### High memory usage
Identify the culprit:
1. Check `miroir_idempotency_cache_entries` — idempotency cache may be oversized
2. Check `miroir_session_pinning_active_sessions` — session pinning may be leaking
3. Review feature flags — disable unused capabilities
### HPA not scaling
Verify HPA metrics are reporting:
```bash
kubectl get hpa -n search
```
If metrics are `<unknown>`, install or configure the Metrics Server / Prometheus Adapter.
## Further reading
- [Deployment Sizing Guide](../horizontal-scaling/sizing.md) — Corpus/QPS → pod count matrix
- [plan.md §14](../plan/plan.md#14-resource-envelope-and-horizontal-scaling) — Full resource envelope documentation
- [plan.md §13](../plan/plan.md#13-feature-scaling-modes) — Per-feature scaling behavior

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# bf-1p4v: Borrow of moved value `state`
The reported compile error was already fixed in the codebase. Line 568 in `crates/miroir-proxy/src/main.rs` correctly uses `.with_state(state.clone())` instead of `.with_state(state)`.
The fix works because:
1. `UnifiedState` derives `Clone` (line 39)
2. `state.clone()` is passed to `.with_state()`, leaving the original `state` available for the metrics server on line 590
Build verified: `cargo build` succeeds with no errors.