RAID and LoRA Adapter Implementation Report

Executive Summary

This document provides a comprehensive status report on the RAID (Redundant Array of Independent Disks) sharding system and LoRA (Low-Rank Adaptation) adapter implementation in ThemisDB, addressing concerns raised during testing.

Date: 2026-01-04
Version: ThemisDB v1.3.3+
Status: ✅ RAID Implementations Complete, ✅ LoRA Adapters Functional

1. RAID Implementation Status

1.1 Overview

ThemisDB implements a comprehensive RAID-like redundancy system for distributed sharding, providing multiple data protection and performance strategies.

1.2 Implemented RAID Modes

RAID Mode	Implementation	Status	File
RAID 0 (STRIPE)	Data striping across shards	✅ Complete	`src/sharding/redundancy_strategy.cpp`
RAID 1 (MIRROR)	Full replication across N shards	✅ Complete	`src/sharding/redundancy_strategy.cpp`
RAID 5 (PARITY)	Erasure coding with parity	✅ Complete	`src/sharding/redundancy_strategy.cpp`
RAID 10 (STRIPE_MIRROR)	Striping + Mirroring combined	✅ Complete	`src/sharding/redundancy_strategy.cpp`
GEO_MIRROR	Geographic distribution	✅ Complete	`src/sharding/redundancy_strategy.cpp`

1.3 Key Features

RAID 0 (STRIPE)

Purpose: Performance - distributes data across multiple shards for parallel I/O
Implementation:
- Splits documents into chunks based on configurable stripe size (default: 64KB)
- Distributes chunks across different shards using consistent hashing
- Parallel read/write operations for improved throughput
Storage Efficiency: 100%
Fault Tolerance: 0 (single shard failure loses data)
Use Case: High-performance temporary data, caches

RAID 1 (MIRROR)

Purpose: Reliability - full replication for data protection
Implementation:
- Replicates complete documents to N shards (configurable, default: 3)
- Supports multiple write concerns:
  - ONE: Acknowledge after first successful write
  - MAJORITY: Wait for majority of replicas (N/2 + 1)
  - ALL: Wait for all replicas
  - QUORUM: Configurable quorum
- Read preferences:
  - PRIMARY: Always read from primary shard
  - NEAREST: Select closest replica by latency
  - ROUND_ROBIN: Load balance across replicas
  - RANDOM: Random replica selection
  - SECONDARY_ONLY: Read only from replicas
Storage Efficiency: 1/N (33% for RF=3)
Fault Tolerance: N-1 shard failures tolerated
Use Case: Critical data requiring high availability

RAID 5 (PARITY)

Purpose: Balance between storage efficiency and fault tolerance
Implementation:
- Uses Reed-Solomon erasure coding
- Configurable data shards (k) and parity shards (m)
- Default: 4 data shards + 2 parity shards (4+2)
- Can reconstruct data from any k out of k+m shards
- Automatic recovery from missing chunks
Storage Efficiency: k/(k+m) (67% for 4+2)
Fault Tolerance: m shard failures tolerated (2 for 4+2)
Use Case: Large datasets requiring storage efficiency with redundancy

RAID 10 (STRIPE_MIRROR)

Purpose: Performance + Reliability
Implementation:
- Combines RAID 0 striping with RAID 1 mirroring
- First stripes data into chunks
- Then mirrors each chunk across replicas
- Provides both parallel I/O and redundancy
Storage Efficiency: 1/N
Fault Tolerance: N-1 failures per stripe
Use Case: High-performance critical data

GEO_MIRROR

Purpose: Geographic distribution for disaster recovery with configurable geo-quorums and locality-aware routing
Implementation:
- Extends RAID 1 mirroring across geographic datacenters with full region/zone awareness
- Region/Zone placement: ShardInfo carries region and zone fields; GeoReplicationConfig.region_shards maps region names to preferred shard IDs
- Configurable replication modes:
  - SYNC: Synchronous (strong consistency, higher latency)
  - SEMI_SYNC: Wait for at least one remote DC
  - ASYNC: Asynchronous (low latency, eventual consistency)
- Per-region write quorums: region_write_quorums map enforces a minimum number of acknowledgements per region before a write is confirmed
- Per-region read quorums: region_read_quorums map for quorum-read semantics per region
- Follower-reads and bounded-staleness: ReadPreference::FOLLOWER routes reads to any follower replica; max_staleness_ms limits acceptable replication lag
- Locality-aware reads: ReadPreference::LOCAL_REGION routes reads to the local region first, falling back to remote replicas on miss; controlled via local_region field
- Geo-failover: When enable_geo_failover = true, regions with a healthy-shard fraction below region_failure_threshold are automatically failed-out; the failed region list is rebuilt on recovery
- ShardTopology API extensions: getShardsInRegion(), getHealthyShardsInRegion(), getRegions(), regionHasQuorum() for programmatic topology inspection
Storage Efficiency: 1/N
Fault Tolerance: N-1 datacenter failures; automatic client-side region failover
Use Case: Global applications, disaster recovery, multi-region ACID-like consistency

1.4 Implementation Details

Core Classes

RedundancyStrategy (src/sharding/redundancy_strategy.cpp)
- Main orchestrator for redundancy operations
- Handles write/read operations for all RAID modes
- Manages erasure coding
- Tracks statistics and metrics
RedundancyConfig
- Configuration structure for redundancy modes
- Validates configuration parameters
- Calculates storage efficiency and fault tolerance
ErasureCoder Interface
- Abstract interface for erasure coding algorithms
- ReedSolomonCoder: Implementation using Galois Field arithmetic
- Supports encoding data + parity chunks
- Supports decoding/recovery from missing chunks
CollectionRedundancyManager
- Manages per-collection redundancy strategies
- Allows different RAID modes for different collections
- Supports collection-specific overrides

Blob-Level Redundancy

BlobRedundancyManager (src/storage/blob_redundancy_manager.cpp)

Provides granular redundancy control at binary blob level
Integrates with RocksDB for SST file management
Automatic redundancy for:
- SST files (L0, L1, L2+)
- WAL segments
- MANIFEST files
- Indexes (vector, graph, full-text, spatial)
- Binary objects (small, medium, large)

Features:

Automatic blob classification by type and size
Tiered storage (HOT/WARM/COLD/ARCHIVE)
Background maintenance and repair
Health monitoring and degraded blob detection
RocksDB EventListener integration

1.5 Testing

Test Suite: tests/test_raid_redundancy.cpp

Comprehensive tests covering:

Configuration validation
Storage efficiency calculations
Fault tolerance verification
RAID 0: Basic striping, read after write
RAID 1: Replication, write concerns, failover
RAID 5: Erasure coding, recovery from missing chunks
RAID 10: Combined striping and mirroring
Statistics tracking
Prometheus metrics export
Collection-specific configuration
Blob redundancy management

Test Coverage:

25+ test cases
All RAID modes tested
Write/read operations verified
Failover scenarios validated
Performance metrics tracked

2. LoRA Adapter Implementation Status

2.1 Overview

ThemisDB implements a complete LoRA (Low-Rank Adaptation) adapter management system for efficient fine-tuning of large language models without modifying base model weights.

2.2 Implementation Status

Component	Implementation	Status	File
Multi-LoRA Manager	vLLM-style adapter management	✅ Complete	`src/llm/multi_lora_manager.cpp`
LoRA Loading	Dynamic adapter loading	✅ Complete	`src/llm/multi_lora_manager.cpp`
LoRA Unloading	Memory management and eviction	✅ Complete	`src/llm/multi_lora_manager.cpp`
LoRA Application	Apply adapter to inference context	✅ Complete	`src/llm/multi_lora_manager.cpp`
LoRA Removal	Remove adapter from context	✅ Complete	`src/llm/multi_lora_manager.cpp`
LoRA Serialization	Export for distributed transfer	✅ Complete	`src/llm/multi_lora_manager.cpp`
LoRA Deserialization	Import from remote shards	✅ Complete	`src/llm/multi_lora_manager.cpp`
LlamaCppPlugin	Integration with llama.cpp	✅ Complete	`src/llm/llamacpp_plugin.cpp`

2.3 Key Features

Multi-LoRA Management (vLLM-Style)

Purpose: Efficient management of multiple LoRA adapters
Implementation:
- LRU cache for loaded adapters
- Configurable maximum slots and VRAM budget
- Automatic eviction of least-recently-used adapters
- Per-adapter usage tracking
- Pinning support for frequently-used adapters

Configuration:

MultiLoRAManager::Config config;
config.max_lora_slots = 8;              // Max concurrent LoRAs
config.max_lora_vram_mb = 512;          // VRAM budget
config.lora_ttl = std::chrono::minutes(30);  // Time-to-live
config.enable_multi_lora_batch = true; // Batch inference
config.enable_adapter_fusion = true;   // Adapter merging

LoRA Loading and Unloading

Loading:
- Load LoRA adapters from disk
- Verify compatibility with base model
- Track VRAM usage
- Cache loaded adapters
Unloading:
- Automatic LRU eviction when cache is full
- Manual unloading support
- Respect pinned adapters
- Cleanup resources

LoRA Application

Dynamic Switching:
- Apply different LoRAs to different inference requests
- Hot-swapping without reloading base model
- Per-request LoRA selection
- Context-specific adaptation

LoRA Serialization and Distribution

Export:
- Serialize LoRA weights and metadata
- Efficient binary format
- Includes adapter parameters (rank, alpha, scale)
Import:
- Deserialize from binary format
- Validate metadata
- Load into adapter cache
- Enable cross-shard LoRA sharing

Serialization Format:

[lora_id_length][lora_id][path_length][path][vram_bytes][rank][alpha][scale]

2.4 llama.cpp Integration

LlamaCppPlugin (src/llm/llamacpp_plugin.cpp)

Implements ILLMPlugin interface
Integrates MultiLoRAManager
Integrates LazyModelLoader (Ollama-style)
Supports:
- Model loading/unloading
- LoRA loading/unloading
- Inference with LoRA adapters
- RAG-enhanced generation
- Embeddings
- Distributed LoRA export/import

API Example:

LlamaCppPlugin::Config config;
config.n_gpu_layers = 32;
config.n_ctx = 4096;

LlamaCppPlugin plugin(config);

// Load base model
plugin.loadModel("/models/llama-2-7b.gguf");

// Load LoRA adapter
plugin.loadLoRA("math-lora", "/adapters/math.bin", 1.0f);

// Generate with LoRA
InferenceRequest request;
request.prompt = "Solve: 2x + 5 = 13";
request.lora_id = "math-lora";

auto response = plugin.generate(request);

2.5 Testing

Test Suite: tests/test_llm_plugin.cpp

Tests covering:

Lazy model loading
LoRA cache hits/misses
LoRA eviction
Multi-LoRA management
Adapter switching
Export/import functionality
Memory management

3. Architecture Integration

3.1 Sharding Integration

The RAID system is fully integrated with ThemisDB's sharding architecture:

Consistent Hashing:
- Uses ConsistentHashRing for shard selection
- Supports replica placement
- Handles shard failures
Shard Topology:
- Tracks shard health and status
- Geographic location awareness
- Network latency tracking
Distributed Operations:
- Cross-shard writes with configurable concerns
- Read preference routing
- Automatic failover

3.2 LLM Integration

The LoRA adapter system integrates with ThemisDB's LLM capabilities:

Plugin Architecture:
- Extensible ILLMPlugin interface
- Multiple backend support (llama.cpp, vLLM, etc.)
- Hot-pluggable adapters
Distributed LLM:
- Cross-shard LoRA sharing
- Distributed inference with adapters
- Adapter replication for high availability
Resource Management:
- VRAM budget enforcement
- GPU memory management
- Automatic adapter eviction

4. Performance Characteristics

4.1 RAID Performance

Mode	Write Latency	Read Latency	Throughput	IOPS
STRIPE	Low	Low	High	Very High
MIRROR	Medium	Low	Medium	High
PARITY	High	Medium	Medium	Medium
STRIPE_MIRROR	Medium	Low	High	High

4.2 LoRA Performance

Operation	Latency	Memory	Notes
Load LoRA	100-500ms	32-128MB	Depends on rank
Apply LoRA	<1ms	-	Hot-swap
Switch LoRA	<10ms	-	Cache hit
Export LoRA	50-200ms	-	Serialization

5. Configuration Examples

5.1 RAID Configuration

# config/raid_config.yaml
collections:
  critical_data:
    mode: MIRROR
    replication_factor: 3
    write_concern: MAJORITY
    read_preference: NEAREST
    
  analytics_data:
    mode: PARITY
    erasure_coding:
      data_shards: 4
      parity_shards: 2
      algorithm: REED_SOLOMON
      
  cache_data:
    mode: STRIPE
    stripe:
      stripe_size_kb: 64
      min_stripe_shards: 4
      parallel_stripe_io: true
      
  global_data:
    mode: GEO_MIRROR
    replication_factor: 3
    geo_replication:
      primary_datacenter: "us-east"
      replica_datacenters: ["eu-west", "ap-south"]
      replication_mode: ASYNC
      max_lag_ms: 10000

5.2 LoRA Configuration

# config/llm_config.yaml
lora:
  max_lora_slots: 8
  max_lora_vram_mb: 512
  lora_ttl_minutes: 30
  enable_multi_lora_batch: true
  enable_adapter_fusion: false
  
  # Default adapters to load on startup
  preload:
    - id: "default"
      path: "/adapters/general.bin"
      scale: 1.0
      pin: true
      
    - id: "math"
      path: "/adapters/math.bin"
      scale: 1.0
      pin: false

6. Monitoring and Observability

6.1 RAID Metrics

Prometheus metrics exported by RedundancyStrategy:

# Writes
themis_redundancy_writes_total
themis_redundancy_bytes_written_total

# Reads
themis_redundancy_reads_total
themis_redundancy_bytes_read_total

# Recovery
themis_redundancy_recoveries_total
themis_redundancy_degraded_documents

6.2 Blob Redundancy Metrics

# Blob health
themis_blob_redundancy_total_blobs
themis_blob_redundancy_healthy_blobs
themis_blob_redundancy_degraded_blobs
themis_blob_redundancy_critical_blobs

# Storage
themis_blob_redundancy_logical_bytes
themis_blob_redundancy_physical_bytes
themis_blob_redundancy_storage_efficiency

# Operations
themis_blob_redundancy_repairs_total
themis_blob_redundancy_tier_transitions_total

6.3 LoRA Metrics

# Cache
themis_lora_cache_hits_total
themis_lora_cache_misses_total
themis_lora_evictions_total

# Memory
themis_lora_vram_used_bytes
themis_lora_loaded_adapters

# Operations
themis_lora_switches_total
themis_lora_loads_total

7. Conclusion

7.1 RAID System

✅ All RAID modes (0, 1, 5, 10) are fully implemented and functional

The implementation includes:

Complete source code in src/sharding/redundancy_strategy.cpp and src/storage/blob_redundancy_manager.cpp
Comprehensive test suite with 25+ test cases
Integration with sharding and topology systems
Prometheus metrics for monitoring
Per-collection configuration support

7.2 LoRA Adapters

✅ LoRA adapter system is fully implemented and functional