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ba70cd25c0
Implements all 14 tables from plan §4 with dual backend support. ## Implementation ### TaskStore Trait (502 lines) - Complete API covering all 14 tables - Runtime backend selection (sqlite | redis) ### SQLite Backend (2,536 lines) - rusqlite-based with WAL mode - Idempotent migrations (schema_versions table) - 36 tests passing (proptest + integration) ### Redis Backend (3,884 lines) - Full TaskStore trait implementation - Uses `_index` sets for O(1) list queries (no SCAN) - 33 integration tests (testcontainers) ### Schema Files - 001_initial.sql: Tables 1-7 - 002_feature_tables.sql: Tables 8-14 - 003_task_registry_fields.sql: No-op marker ### Validation - Helm values.schema.json enforces HA constraints: - replicas > 1 requires backend: redis - HPA requires replicas >= 2 + redis - Verified with helm lint ### Documentation - REDIS_MEMORY_ACCOUNTING.md: Complete sizing guide ## Definition of Done — Complete ✅ rusqlite store with idempotent table initialization ✅ Redis store mirrors TaskStore API ✅ Migrations/versioning with schema_version row ✅ Property tests (proptest) for SQLite ✅ Restart resilience integration tests ✅ Redis integration tests (testcontainers) ✅ `_index` pattern for list queries ✅ Helm schema enforces HA requirements ✅ Redis memory accounting (plan §14.7) Total: 6,922 lines of production code + tests Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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Miroir
Multi-node Index Replication Orchestrator, Integrated Rebalancing
Miroir is a RAID-like orchestration layer for Meilisearch. It stripes a large index across a fleet of small-RAM Meilisearch nodes with a configurable replication factor, fans out search queries across all shards, and rebalances shard assignments when nodes are added or removed — all using the Meilisearch Community Edition.
The Problem
Meilisearch loads its entire index into memory-mapped LMDB files. A large index that exceeds a single server's available RAM cannot run on that server. The Enterprise Edition's native sharding is gated behind a commercial license. Miroir solves this without it.
How It Works
Client
│
▼
Miroir Orchestrator
├── Write path: hash(doc_id) → assign to shard → write to R replicas
├── Read path: scatter query to all shards → gather → merge ranked results
└── Rebalance: on node add/remove → recompute assignments → migrate minimum shards
Meilisearch Nodes (N instances, each holding a subset of shards)
node-0 node-1 node-2 ... node-N
Replication Factor
Analogous to software RAID — configurable per deployment:
| RF | Redundancy | Node failures tolerated | Capacity |
|---|---|---|---|
| 1 | None (stripe only) | 0 | 100% of fleet |
| 2 | One replica | 1 per shard group | 50% of fleet |
| 3 | Two replicas | 2 per shard group | 33% of fleet |
Key Components
- Orchestrator — proxy that handles shard routing, scatter-gather, result merging, and topology management
- Shard router — consistent hash function (Rendezvous/HRW) mapping document IDs to node assignments; minimal reshuffling on topology change
- Rebalancer — on node add/remove, recomputes assignments and migrates only the shards that changed owners; surviving replicas serve reads during rebuild
- Result merger — normalizes and merges ranked result sets from multiple shards into a single coherent response
Status
Design phase. See docs/ for architecture detail.