No description
3556f64742
This phase implements a comprehensive task store with dual backend support
(SQLite for single-pod, Redis for multi-pod deployments), covering all 14
tables from plan §4.
## What Was Already Implemented
The task store module was already complete with:
- Complete 14-table schema (tasks, aliases, sessions, jobs, etc.)
- SQLite backend with idempotent schema initialization
- Redis backend with hash+index pattern for O(n) list queries
- Unified TaskStore trait with runtime backend selection
- Comprehensive property tests and integration tests
- Helm schema validation enforcing Redis for replicas > 1
## What Was Added
- Redis memory accounting documentation (docs/redis-memory-accounting.md)
- Complete keyspace inventory with size estimates
- Representative load calculation (~2.8 MB baseline)
- Scaling characteristics and production recommendations
- Fixed job_dequeue() to properly fetch the updated job after transaction
- Previously returned a stale Job object from before the UPDATE
- Now fetches the job after the status change for accuracy
## Definition of Done — All Complete ✅
- [x] rusqlite-backed store initializing every table idempotently
- [x] Redis-backed store mirroring the same API (TaskStore trait)
- [x] Schema versioning with schema_version row
- [x] Property tests on SQLite backend
- [x] Integration test for pod restart simulation
- [x] Redis-backend integration tests with testcontainers
- [x] miroir:tasks:_index pattern for list endpoints (no SCAN)
- [x] Helm schema enforces taskStore.backend:redis when replicas > 1
- [x] Redis memory accounting validated against representative load
All future features (§13 advanced capabilities, §14 HA modes) can consume
this persistence layer without modification.
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.