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ThemisDB - Entwicklungs-Roadmap

Version: 6.0
Stand: 20. Dezember 2025
Typ: Konsolidierte Gesamt-Roadmap

📌 Status Update Dezember 2025 - v1.3.0 RELEASED:

  • ✅ v1.3.0 (Dezember 2025) - LLM Integration mit llama.cpp (optional)
  • ✅ v1.2.0 (Dezember 2025) - Enterprise Features (Hypertables, Hybrid Search, FAISS)
  • ✅ v1.1.0 (Dezember 2025) - Optimization Release
  • ✅ Horizontale Skalierung (Phase 1-6) 100% abgeschlossen
  • ✅ Replication (Leader-Follower + Multi-Master) 100% abgeschlossen
  • ✅ RAID-like Redundanz (MIRROR, STRIPE, PARITY, GEO) 100% abgeschlossen
  • ✅ GPU Acceleration (10 Backends: CUDA, Vulkan, HIP, etc.) 100% abgeschlossen
  • ✅ CEP Streaming Analytics Engine 100% abgeschlossen
  • ✅ OLAP Analytics (CUBE, ROLLUP, Window Functions) 100% abgeschlossen
  • ✅ Client SDKs (7 Sprachen) 100% Feature-Parität erreicht
  • ✅ Kubernetes Operator CRDs 100% abgeschlossen
  • ✅ Content Processor Plugins (10+ Formate) 100% abgeschlossen
  • ✅ P2P Gossip Protocol 100% abgeschlossen
  • ✅ Auto-Rebalancing & Cloud Agent 100% abgeschlossen

🆕 v1.4.0 GEPLANT (Q1 2026) - Production Hardening:

  • 🔧 Fokus: Query Optimizer v2, Performance Tuning, Production Readiness
  • 🔧 Features: Advanced ML/GNN, Multi-datacenter, Real-time Materialized Views
  • 🔧 Engineering Effort: 12-16 Wochen
  • 📊 Impact: Production-grade reliability and scale

Vision & Strategie

ThemisDB ist jetzt eine vollständig verteilte, cloud-native Datenplattform mit GPU-Beschleunigung und erweiterten Analytics-Funktionen.

Erreichte Kernziele (Q4 2025):

  1. v1.3.0 - LLM Integration - Optional llama.cpp Integration ✅
  2. v1.2.0 - Enterprise Features - Hypertables, Hybrid Search, FAISS ✅
  3. v1.1.0 - Optimization - Performance improvements ✅
  4. Horizontal Scaling - Multi-Node Sharding & Replication ✅ 100%
  5. Replication - Leader-Follower + Multi-Master ✅ 100%
  6. RAID-like Redundancy - Enterprise-grade Data Protection ✅ 100%
  7. GPU Acceleration - 10 Backends (CUDA/Vulkan/HIP/etc.) ✅ 100%
  8. Streaming Analytics - CEP Engine mit EPL ✅ 100%
  9. OLAP Analytics - CUBE, ROLLUP, Window Functions ✅ 100%
  10. Enterprise Features - Multi-Tenancy, Compliance ✅ 100%
  11. Client SDKs - 7 Sprachen mit Feature-Parität ✅ 100%

🆕 Nächste Ziele (Q1 2026 - v1.4.0):

  1. Query Optimizer v2 - Advanced optimization and execution strategies
  2. Production Hardening - Stability, reliability, and scale improvements
  3. Multi-Datacenter - Cross-region replication and deployment
  4. Advanced ML/GNN - Machine learning and graph neural network features

Roadmap-Übersicht (Aktualisiert Dezember 2025)

2025 (✅ RELEASED)         2026 Q1 (🚀 v1.4.0)      2026 Q2-Q3 (🌟 v1.5.0)     2026 Q4+ (🔮 v2.0.0)
──────────────────────────────────────────────────────────────────────────────────────────────────────────
│                             │                        │                          │
│ ✅ v1.3.0, v1.2.0, v1.1.0   │ 🚀 v1.4.0 Hardening    │ 🌟 v1.5.0 Advanced       │ 🔮 v2.0 Enterprise
│   (Released Dez 2025)       │   (Q1 2026, 12-16 W)   │   (Q2-Q3 2026)           │   (Q4 2026+)
│                             │                        │                          │
│ ✅ ACID Transactions        │ • Query Optimizer v2   │ • Advanced ML/GNN        │ • Multi-DC Production
│ ✅ Multi-Model (100%)       │ • Performance Tuning   │ • Real-time Mat. Views   │ • K8s Operator Controller
│ ✅ Security Stack (100%)    │ • Production Hardening │ • Cross-Region Repl.     │ • SOC 2, HIPAA Compliance
│ ✅ Sharding (100%)          │ • Multi-Datacenter     │ • Query Optimizer v3     │ • Cloud-Native Optim.
│ ✅ Replication (100%)       │ • Monitoring++         │ • GPU Tuning++           │
│ ✅ GPU Acceleration (100%)  │ • SDK Publishing       │                          │
│ ✅ CEP Engine (100%)        │ • Documentation++      │                          │
│ ✅ OLAP Analytics (100%)    │                        │                          │
│ ✅ 7 SDKs (100%)            │ • Pen-Test Phase 1     │ • Pen-Test Phase 2       │
│ ✅ K8s CRDs (100%)          │                        │                          │
│ ✅ LLM Integration (opt)    │                        │                          │
│                             │                        │                          │
└─────────────────────────────┴────────────────────────┴──────────────────────────┴──────────────────────

✅ Released: v1.3.0 - LLM Integration (Dezember 2025)

Optional Feature: Native LLM integration mit llama.cpp

v1.3.0 Features

  • ✅ llama.cpp Integration - Optional native LLM engine (requires -DTHEMIS_ENABLE_LLM=ON)
  • ✅ GPU Acceleration - CUDA support with significant performance gains
  • ✅ PagedAttention - Memory optimization for LLM workloads
  • ✅ Continuous Batching - Concurrent request handling
  • ✅ Multi-LoRA Manager - Multiple LoRA adapter support
  • ✅ Plugin Architecture - Extensible LLM backend system

Documentation:

✅ Released: v1.2.0 - Enterprise Features (Dezember 2025)

v1.2.0 Features

  • ✅ Hypertables - TimescaleDB-compatible time-series
  • ✅ Hybrid Search - RAG-optimized BM25 + vector search
  • ✅ FAISS Advanced - IVF+PQ vector search with memory optimization
  • ✅ Embedding Cache - Cost reduction for LLM applications
  • ✅ Time-Series Aggregates - SIMD-accelerated analytics

Documentation:

✅ Released: v1.1.0 - Optimization Release (Dezember 2025)

v1.1.0 Features

  • ✅ Performance Optimizations - Improved query execution
  • ✅ Memory Improvements - Better memory management
  • ✅ Stability Enhancements - Bug fixes and reliability improvements

Documentation:

🚀 Geplant: Q1 2026 - v1.4.0 Production Hardening

Philosophie: "Production-ready reliability and scale"

v1.4.0 Kern-Features (12-16 Wochen)

1. Query Optimizer v2 (4 Wochen)

  • ✅ Advanced query optimization strategies
  • ✅ Cost-based optimization
  • ✅ Join order optimization
  • ✅ Index selection improvements

2. Production Monitoring (3 Wochen)

  • ✅ Enhanced metrics and observability
  • ✅ Performance profiling tools
  • ✅ Advanced alerting

3. Multi-Datacenter Support (4 Wochen)

  • ✅ Cross-region replication
  • ✅ Geo-distribution capabilities
  • ✅ Conflict resolution strategies

4. Performance Tuning (3 Wochen)

  • ✅ Memory optimizations
  • ✅ Query execution improvements
  • ✅ Caching enhancements

5. Documentation & Testing (2 Wochen)

  • ✅ SDK publishing
  • ✅ Comprehensive testing
  • ✅ Security hardening (Pen-Test Phase 1)

Philosophie: "Smart combination of existing libs + targeted new libs for compatibility"

v1.2.0 Enterprise Features (12-16 Wochen)

1. vLLM AI Support (8-12 Wochen, 1 neue Lib)

  • ✅ LoRA Manager - Multi-Tenant LoRA Serving (HuggingFace PEFT, 6-8 Wochen)
    • LoRA Weight Storage in RocksDB mit ZSTD Compression
    • TBB Parallel Loading für Multi-Tenant Performance
    • gRPC Integration zu vLLM (optional)
  • ✅ FAISS Advanced - IVF+PQ Vector Search (3-4 Wochen, keine neue Lib!)
    • 10-100x Memory Reduction vs. Flat Index
    • GPU Acceleration via CUDA (Kernbestand!)
  • ✅ Hybrid Search - BM25 + Vector Fusion (2-3 Wochen, keine neue Lib!)
    • Reciprocal Rank Fusion (RRF)
    • RAG Performance Optimization
  • ✅ Embedding Cache - Semantic Caching (2-3 Wochen, keine neue Lib!)
    • 70-90% Cost Reduction für vLLM API Calls
    • Fuzzy Match via Vector Similarity

2. Geo-Spatial PostGIS Compatibility (6-9 Wochen, 2 neue Libs)

  • ✅ GEOS Integration - PostGIS-kompatible Topology Operations (4-6 Wochen)
    • ST_Buffer, ST_Union, ST_Intersection
    • 3D Geometries Support
  • ✅ PROJ Transforms - Coordinate Transformations (2-3 Wochen)
    • WGS84 ↔ UTM ↔ Web Mercator
    • Geography Support (Spherical Distances)
  • ✅ cuSpatial GPU Ops - GPU-beschleunigte Geo Operations (6-8 Wochen, optional)
    • 10-100x Speedup für Spatial Joins
    • Arrow Zero-Copy Integration

3. IoT/Timescale Compatibility (5-7 Wochen, 0 neue Libs!)

  • ✅ Hypertables - RocksDB Column Families (3-4 Wochen, nur Code!)
    • Automatische Partitionierung (1 Chunk pro Tag)
    • TTL via RocksDB (v1.1.0 Feature!)
  • ✅ Arrow Aggregates - Time-Series Analytics (2-3 Wochen, keine neue Lib!)
    • SIMD Performance mit Arrow Compute
    • 5-10x Speedup bei Aggregationen
  • ✅ Parquet Archive - Cold Storage (bereits in v1.1.0!)
    • 90% Storage Reduction vs. RocksDB
    • DuckDB Parquet Queries (v1.2.0)

v1.2.0 Build-Varianten

Enterprise AI+Geo: 19 deps (+3 auf v1.1.0 Basis)

  • GEOS, PROJ, HuggingFace PEFT
  • Fokus: PostGIS + LoRA + TimescaleDB Compatibility

Enterprise AI (nur vLLM): 17 deps (+1 auf v1.1.0 Basis)

  • HuggingFace PEFT
  • Fokus: Multi-Tenant LoRA Serving

Enterprise Geo (nur PostGIS): 18 deps (+2 auf v1.1.0 Basis)

  • GEOS, PROJ
  • Fokus: PostGIS Drop-in Replacement

v1.2.0 Ressourcen

Engineering Effort: 12-16 Wochen (parallelisierbar auf ~8-10 Wochen mit Team) Neue Dependencies: 3 (GEOS, PROJ, HuggingFace PEFT) Dependency Overhead: +18% (3 neue Libs auf 16 bestehende)

Erwartete Performance:

  • vLLM AI: 10-100x Vector Search (IVF+PQ), 70-90% Cost Reduction (Embedding Cache)
  • Geo-Spatial: PostGIS Compatibility, 10-100x GPU Geo Ops
  • IoT/Timescale: TimescaleDB-kompatible Hypertables, 5-10x Aggregation Performance

Dokumentation:

Wechselwirkungen:

  • LoRA Manager: RocksDB (Storage) + TBB (Parallel Loading) + vLLM (gRPC)
  • FAISS Advanced: CUDA (GPU Acceleration, Kernbestand!)
  • GEOS/PROJ: Boost.Geometry (Hybrid Strategy)
  • cuSpatial: Arrow (Zero-Copy) + CUDA (Kernbestand!)
  • Hypertables: RocksDB Column Families + TTL (v1.1.0!)

✅ Abgeschlossen: 2025 (November-Dezember)

Horizontale Skalierung (95% Complete)

Phase Komponente Status Dateien
1 VCC-URN Schema ✅ ERLEDIGT src/sharding/urn_resolver.cpp
2 PKI/mTLS Infrastructure ✅ ERLEDIGT src/sharding/mtls_client.cpp
3 Request Routing ✅ ERLEDIGT src/sharding/shard_router.cpp
4 Data Migration ✅ ERLEDIGT src/sharding/data_migrator.cpp
P2P Gossip-Protokoll ✅ ERLEDIGT src/sharding/gossip_protocol.cpp
P2 Cross-Shard Joins ✅ ERLEDIGT shard_router.cpp::executeCrossShardJoin()
P2 Scatter-Gather ✅ ERLEDIGT shard_router.cpp::scatterGather()
Infra etcd Integration ✅ ERLEDIGT shard_topology.cpp::loadFromMetadataStore()
Infra Health Checks ✅ ERLEDIGT health_check.cpp
Infra Cloud Agent Multi-DC ✅ ERLEDIGT cloud_agent.cpp

Kubernetes Operator

Komponente Status Dateien
CRD Definition ✅ ERLEDIGT deploy/kubernetes/crds/themisdb.vcc.io_themisdbs.yaml
Cluster Example ✅ ERLEDIGT deploy/kubernetes/examples/themisdb-cluster.yaml
Single-Node Example ✅ ERLEDIGT deploy/kubernetes/examples/themisdb-single.yaml
README ✅ ERLEDIGT deploy/kubernetes/README.md

Content Processor Plugin-Architektur

Komponente Status Dateien
Plugin Interface ✅ ERLEDIGT include/content/content_plugin_interface.h
PDF Processor ✅ ERLEDIGT include/content/pdf_processor.h, src/content/pdf_processor.cpp
Office Processor ✅ ERLEDIGT include/content/office_processor.h, src/content/office_processor.cpp
YAML Configs ✅ ERLEDIGT config/content_processors.yaml, config/processors/*.yaml
Architecture Doc ✅ ERLEDIGT docs/content/CONTENT_PROCESSOR_PLUGINS.md

Plugin-Konfigurationen:

  • config/processors/pdf.yaml - PDF (poppler backend)
  • config/processors/office.yaml - DOCX, XLSX, PPTX, ODF
  • config/processors/video.yaml - MP4, MKV, WebM (FFmpeg)
  • config/processors/audio.yaml - MP3, WAV, FLAC (FFmpeg)
  • config/processors/geo.yaml - GeoJSON, GPX, Shapefile (GDAL)
  • config/processors/image.yaml - JPEG, PNG, TIFF (libvips)
  • config/processors/cad.yaml - STEP, IGES, STL (OpenCASCADE)
  • config/processors/text.yaml - TXT, JSON, XML, Markdown

Security & Compliance

Komponente Status Dateien
Penetration Test Guide ✅ ERLEDIGT docs/security/PENETRATION_TEST_GUIDE.md
Attack Vectors Analysis ✅ ERLEDIGT 50+ Vektoren dokumentiert
Comprehensive Audit ✅ ERLEDIGT docs/COMPREHENSIVE_AUDIT_TODO.md

Tests & Benchmarks

Komponente Status Dateien
Integration Tests ✅ ERLEDIGT tests/test_sharding_integration.cpp (~17 Tests)
E2E Tests ✅ ERLEDIGT tests/test_sharding_e2e.cpp (~15 Tests)
Chaos Tests ✅ ERLEDIGT tests/test_sharding_chaos.cpp (~18 Tests)
Performance Benchmarks ✅ ERLEDIGT benchmarks/bench_sharding_performance.cpp

Dokumentation

Dokument Status Beschreibung
SCALING_TODO.md ✅ ERLEDIGT Vollständige TODO-Liste
SHARDING_UNIFIED_DOCUMENTATION.md ✅ ERLEDIGT Autoritative Sharding-Docs
FEATURES.md ✅ AKTUALISIERT Status-Korrekturen
README.md ✅ AKTUALISIERT Sharding + GPU Abschnitte

Kurzfristig: Q1 2026 (0-3 Monate)

P0 - Kritische Priorität

1.1 Client SDK Publishing

Status: 🔧 In Arbeit
Aufwand: 2 Wochen
Owner: TBD

JavaScript SDK:

  • ✅ Basic CRUD, URN Routing, Transactions
  • ⚠️ Graph Traversal API hinzufügen
  • ⚠️ Connection Pooling
  • ⚠️ NPM Package veröffentlichen

Python SDK:

  • ✅ Basic CRUD, URN Routing, Transactions
  • ⚠️ Async/Await Support hinzufügen
  • ⚠️ PyPI Package veröffentlichen

1.2 Penetration Testing

Status: 📋 Vorbereitet
Aufwand: 4-6 Wochen
Owner: Externer Dienstleister

Scope:

  • ✅ Attack Vectors dokumentiert (docs/security/PENETRATION_TEST_GUIDE.md)
  • ⚠️ Externen Pen-Tester beauftragen
  • ⚠️ Test durchführen
  • ⚠️ Findings beheben
  • ⚠️ Re-Test

1.3 Content Processor DLL Plugins

Status: ✅ Architektur implementiert, DLL-Build ausstehend
Aufwand: 2-3 Wochen
Owner: TBD

Implementiert:

  • ✅ Plugin Interface (content_plugin_interface.h)
  • ✅ YAML-Konfigurationen für alle Prozessoren
  • ✅ PDF Processor Header + Implementierung
  • ✅ Office Processor Header + Implementierung

Ausstehend:

  • ⚠️ CMake für Plugin-Build (separate DLLs)
  • ⚠️ Video/Audio Plugin mit FFmpeg
  • ⚠️ Geo Plugin mit GDAL
  • ⚠️ Image Plugin mit libvips
  • ⚠️ CAD Plugin mit OpenCASCADE

P1 - Hohe Priorität

1.4 Go & Rust SDK

Status: 📋 Geplant
Aufwand: 6-8 Wochen
Owner: TBD

Go SDK:

  • Idiomatic Go API
  • Context cancellation
  • Connection pooling
  • Comprehensive tests

Rust SDK:

  • Safe wrapper
  • Async/await
  • Type-safe query builder

1.5 Window Functions (AQL Analytics)

Status: 📋 Design
Aufwand: 2-3 Wochen
Owner: TBD

Features:

  • OVER clause
  • PARTITION BY
  • ROW_NUMBER, RANK, DENSE_RANK
  • LAG, LEAD
  • Running totals

Mittelfristig: Q2-Q3 2026 (3-9 Monate)

P0 - Kritische Priorität

2.1 Replication (Aufbauend auf Sharding) 🚀

Status: 📋 Geplant
Aufwand: 3-4 Monate
Owner: TBD

Hinweis: Sharding Phase 1-4 ist bereits zu 95% implementiert. Die Replication baut darauf auf.

Phase 1: Leader-Follower (Q2 2026)

  • WAL-basierte Replikation
  • Async mit konfigurierbarem Lag
  • Automatic Failover
  • Read Replicas

Phase 2: Multi-Master (Q3 2026)

  • CRDT-basierte Konfliktlösung
  • Vector Clocks für Kausalität
  • Last-Write-Wins als Fallback
  • Quorum-basierte Konsistenz

Bereits implementiert (Dezember 2025):

  • ✅ Shard Routing Layer
  • ✅ Cross-Shard Transactions
  • ✅ P2P Gossip Protocol
  • ✅ Health Checks & Failover Detection

2.2 GPU Acceleration (CUDA/DirectX) 🎮

Status: 📋 Geplant
Aufwand: 2-3 Monate
Owner: TBD

2.2.1 Vector Search GPU (CUDA) Priorität: P0
Aufwand: 6-8 Wochen

Implementierung:

  • Faiss GPU Integration
  • CUDA Kernels für Distance Computation
  • GPU Memory Management (VRAM)
  • Batch Processing Optimization
  • Hybrid CPU/GPU Strategy

Hardware Requirements:

  • CUDA Toolkit 11.0+
  • GPU: Compute Capability 7.0+ (Volta/Turing/Ampere/Hopper)
  • VRAM: Mindestens 8GB (empfohlen 16GB+)

Erwartete Performance:

  • 10-50x Speedup für Batch Queries
  • Sub-millisecond latency für k=100
  • Durchsatz: 50.000-100.000 queries/s

Dokumentation:

  • docs/performance/gpu_vector_search.md
  • docs/performance/cuda_setup.md
  • Benchmarks & Tuning Guide

2.2.2 Geo Operations GPU Priorität: P1
Aufwand: 4-6 Wochen

Implementierung:

  • Spatial Index GPU Queries
  • Parallel Distance Computations
  • GPU-accelerated R-Tree
  • GeoJSON processing on GPU

Erwarteter Speedup: 5-20x für komplexe Spatial Queries

2.2.3 DirectX Compute Shaders (Windows) Priorität: P2
Aufwand: 4-6 Wochen

Use Cases:

  • Windows-native GPU acceleration
  • Fallback wenn CUDA nicht verfügbar
  • DirectML für ML Workloads

Technologie:

  • DirectX 12 Compute Shaders
  • DirectML API
  • Windows 10/11 optimiert

2.3 Advanced OLAP Features

Status: Design
Aufwand: 2-3 Monate
Owner: TBD

Features:

  • CUBE operator (all combinations)
  • ROLLUP operator (hierarchical aggregation)
  • GROUPING SETS
  • Recursive CTEs
  • Materialized Views

Optimization:

  • Columnar storage optimization
  • Apache Arrow acceleration
  • Parallel aggregation
  • Query result caching

P1 - Hohe Priorität

2.4 Client SDKs Erweiterung

Status: Planung
Aufwand: 8-12 Wochen
Owner: TBD

Go SDK:

  • Idiomatic Go API
  • Connection pooling
  • Transaction support
  • Context cancellation
  • Comprehensive tests

Rust SDK:

  • Safe wrapper
  • Async/await
  • Zero-copy where possible
  • Type-safe query builder

Dokumentation:

  • SDK Quick Start Guides
  • API Reference
  • Best Practices

2.5 Query Optimizer Verbesserungen

Status: Planung
Aufwand: 4-6 Wochen
Owner: TBD

Features:

  • Join optimizations (Hash Join, Merge Join)
  • Statistics & Histograms
  • Cost model refinement
  • Cardinality estimation
  • Adaptive query execution

2.6 Multi-Tenancy

Status: Design
Aufwand: 6-8 Wochen
Owner: TBD

Features:

  • Tenant isolation
  • Resource quotas (CPU, Memory, Storage)
  • Rate limiting per tenant
  • Billing integration
  • Tenant-level encryption keys

Langfristig: Q4 2026+ (9+ Monate)

Vision: Cloud-Native Distributed Platform

3.1 Multi-Datacenter Replication

Status: Research
Aufwand: 4-6 Monate
Owner: TBD

Features:

  • Cross-DC replication
  • Geo-distributed queries
  • Conflict resolution strategies
  • WAN-optimized protocols
  • Disaster recovery

Challenges:

  • Latency management
  • Consistency models (Eventual, Strong, Causal)
  • Network partitions
  • Data sovereignty (GDPR)

3.2 Kubernetes Operator

Status: Research
Aufwand: 3-4 Monate
Owner: TBD

Features:

  • Automated deployment
  • Scaling (horizontal/vertical)
  • Rolling updates
  • Backup/restore automation
  • Monitoring integration

Technologies:

  • Operator SDK
  • Custom Resource Definitions (CRDs)
  • Helm Charts

3.3 In-Database Machine Learning

Status: Research
Aufwand: 6-8 Monate
Owner: TBD

Features:

  • Graph Neural Networks (GNNs)
  • Embedding generation
  • Model training in-database
  • Inference API
  • Feature store integration

Technologies:

  • TensorFlow/PyTorch integration
  • ONNX Runtime
  • GPU acceleration (CUDA)

3.4 Real-Time Streaming Analytics

Status: Research
Aufwand: 4-6 Monate
Owner: TBD

Features:

  • Stream processing engine
  • Window operations (Tumbling, Sliding, Session)
  • Complex Event Processing (CEP)
  • Apache Kafka integration
  • Low-latency aggregations

3.5 Cloud-Native Deployment

Status: Planning
Aufwand: 3-4 Monate
Owner: TBD

Platforms:

  • AWS (EKS, ECS, S3, RDS)
  • Azure (AKS, Blob Storage, Cosmos DB)
  • GCP (GKE, Cloud Storage, BigQuery)

Features:

  • Managed service option
  • Auto-scaling
  • Cloud storage integration
  • Serverless functions
  • Terraform/CloudFormation templates

3.6 Advanced Analytics

Status: Research
Aufwand: 6+ Monate
Owner: TBD

Features:

  • Graph algorithms library (Louvain, PageRank, etc.)
  • Time-series forecasting
  • Anomaly detection
  • Recommendation engine
  • Natural Language Processing (NLP)

Performance Targets & Benchmarks

Q1 2026 Targets (Current + Improvements)

Metric Current Q1 Target Improvement
Write Throughput 45K ops/s 60K ops/s +33%
Read Throughput 120K ops/s 150K ops/s +25%
Query Latency (p50) 0.12 ms 0.08 ms -33%
Vector Search (p50) 0.55 ms 0.40 ms -27%
Graph Traversal (p50) 0.31 ms 0.25 ms -19%

Q2-Q3 2026 Targets (With GPU)

Metric Q1 Target Q2-Q3 Target Improvement
Vector Search (Batch) 1,800 q/s 50,000 q/s +2,700%
Geo Operations 5,000 ops/s 50,000 ops/s +900%
OLAP Aggregation 1,000 q/s 10,000 q/s +900%

Q4 2026+ Targets (Distributed)

Metric Q2-Q3 Target Q4+ Target Improvement
Horizontal Scalability 1 node 10+ nodes Linear scaling
Write Throughput 60K ops/s 600K+ ops/s +900%
Read Throughput 150K ops/s 1.5M+ ops/s +900%

Abhängigkeiten & Risiken

Technische Abhängigkeiten

GPU Acceleration:

  • ⚠️ CUDA Toolkit Version Compatibility
  • ⚠️ GPU Driver Support
  • ⚠️ VRAM Requirements (8GB+ recommended)
  • ⚠️ Faiss Library Stability

Distributed System:

  • ⚠️ Consensus Algorithm Choice (Raft vs. Paxos)
  • ⚠️ Network Latency Management
  • ⚠️ CAP Theorem Trade-offs

Cloud Deployment:

  • ⚠️ Multi-cloud Compatibility
  • ⚠️ Vendor Lock-in Avoidance
  • ⚠️ Cost Optimization

Risiken & Mitigation

Risiko 1: Distributed System Complexity

Wahrscheinlichkeit: HIGH
Impact: HIGH

Mitigation:

  • Phased rollout (Sharding → Replication → Multi-DC)
  • Comprehensive testing (Jepsen-style)
  • Fallback to single-node mode
  • Expert consultation

Risiko 2: GPU Acceleration Performance

Wahrscheinlichkeit: MEDIUM
Impact: MEDIUM

Mitigation:

  • Prototype & benchmark early
  • Hybrid CPU/GPU strategy
  • Graceful degradation without GPU
  • Alternative: DirectX Compute for Windows

Risiko 3: Client SDK Adoption

Wahrscheinlichkeit: MEDIUM
Impact: HIGH

Mitigation:

  • Developer-friendly APIs
  • Comprehensive documentation
  • Code examples & tutorials
  • Community engagement

Risiko 4: Performance Regression

Wahrscheinlichkeit: MEDIUM
Impact: MEDIUM

Mitigation:

  • Automated benchmark suite
  • Performance budgets in CI
  • Regular profiling
  • Optimization sprints

Ressourcen & Team

Empfohlene Team-Struktur

Q1 2026:

  • 1-2 Core Engineers (C++)
  • 1 DevOps Engineer
  • 1 Technical Writer

Q2-Q3 2026 (Scaling Phase):

  • 2-3 Core Engineers (C++)
  • 1 GPU/CUDA Specialist
  • 1 Distributed Systems Engineer
  • 1 DevOps Engineer
  • 1 Technical Writer

Q4 2026+ (Innovation Phase):

  • 3-4 Core Engineers
  • 1-2 ML Engineers
  • 2 Distributed Systems Engineers
  • 1-2 DevOps Engineers
  • 1 Technical Writer
  • 1 Community Manager

Budget-Schätzung

Q1 2026: $50K-$100K

  • Entwicklung (SDK, Encryption, Content)
  • Infrastructure (CI/CD, Testing)
  • Documentation

Q2-Q3 2026: $200K-$400K

  • GPU Hardware (Development & Testing)
  • Cloud Infrastructure
  • Distributed Systems Development
  • Performance Testing

Q4 2026+: $400K-$800K

  • Multi-DC Infrastructure
  • ML/Analytics Development
  • Enterprise Support
  • Marketing & Community

Erfolgskriterien

Q4 2025 (Dezember) ✅ ERREICHT

  • ✅ Horizontale Skalierung Phase 1-4 implementiert (95%)
  • ✅ P2P Gossip-Protokoll implementiert
  • ✅ Kubernetes Operator CRDs erstellt
  • ✅ Content Processor Plugin-Architektur definiert
  • ✅ Penetration Test Guide erstellt
  • ✅ Performance Benchmarks implementiert
  • ✅ Integration/E2E/Chaos Tests erstellt

Q1 2026

  • ⚠️ SDK Publishing (NPM, PyPI)
  • ⚠️ Penetration Test durchgeführt
  • ⚠️ Content Processor DLLs gebaut
  • ⚠️ Go/Rust SDK Alpha

Q2-Q3 2026

  • ⚠️ GPU acceleration operational (10x speedup)
  • ⚠️ Replication (Leader-Follower) functional
  • ⚠️ Production deployments (3+ customers)
  • ⚠️ Performance targets met

Q4 2026+

  • ⚠️ Multi-DC deployment
  • ⚠️ Kubernetes Operator Controller released
  • ⚠️ 10+ production customers
  • ⚠️ Community adoption (1000+ GitHub stars)

Feedback & Anpassungen

Diese Roadmap ist ein lebendes Dokument. Änderungen ergeben sich aus:

  • Stakeholder-Feedback
  • Technologische Entwicklungen
  • Marktanforderungen
  • Ressourcenverfügbarkeit

Review-Zyklus: Monatlich (Q1 2026), Quarterly (Q2+)

Kontakt & Zusammenarbeit

Repository: https://github.com/makr-code/ThemisDB
Issues: https://github.com/makr-code/ThemisDB/issues
Diskussionen: https://github.com/makr-code/ThemisDB/discussions

Letzte Aktualisierung: 5. Dezember 2025
Version: 4.0
Nächstes Review: Januar 2026

ThemisDB v1.3.4 | GitHub | Documentation | Discussions | License

Last synced: January 02, 2026 | Commit: 6add659

ThemisDB Dokumentation

Version: 1.3.0 | Stand: Dezember 2025

📋 Schnellstart

🏗️ Architektur

🗄️ Basismodell

💾 Storage & MVCC

📇 Indexe & Statistiken

🔍 Query & AQL

💰 Caching

📦 Content Pipeline

🔎 Suche

⚡ Performance & Benchmarks

🏢 Enterprise Features

✅ Qualitätssicherung

🧮 Vektor & GNN

🌍 Geo Features

🛡️ Sicherheit & Governance

Authentication

Schlüsselverwaltung

Verschlüsselung

TLS & Certificates

PKI & Signatures

PII Detection

Vault & HSM

Audit & Compliance

Security Audits

Gap Analysis

🚀 Deployment & Betrieb

Docker

Observability

Change Data Capture

Operations

💻 Entwicklung

API Implementations

Changefeed

Security Development

Development Overviews

📄 Publikation & Ablage

🔧 Admin-Tools

🔌 APIs

📚 Client SDKs

📊 Implementierungs-Zusammenfassungen

📅 Planung & Reports

📖 Dokumentation

📝 Release Notes

📖 Styleguide & Glossar

🗺️ Roadmap & Changelog

💾 Source Code Documentation

Main Programs

Source Code Module

🗄️ Archive

🤝 Community & Support

Vollständige Dokumentation: https://makr-code.github.io/ThemisDB/

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