⚡ Performance Metrics
Benchmarked on 100GB+ financial time-series data
🏗️ System Overview
- Lazy evaluation (Polars-powered)
- Query optimizer with pushdown predicates
- Streaming joins (100M+ rows)
- Arrow-native zero-copy transfers
- Full SQL support via DuckDB
🔧 Core Components
DuckDB
Analytics Database
- ✓ Embedded columnar database
- ✓ Full SQL support
- ✓ Direct Parquet queries
- ✓ Low-latency analytics (~45ms)
- ✓ Zero operational overhead
Streamlit
Interactive Dashboards
- ✓ Real-time visualizations
- ✓ Python-native components
- ✓ No JavaScript required
- ✓ Rapid prototyping
- ✓ WebSocket updates
🔄 Data Flow Pipeline
- Arrow batches for zero-copy transfers
- Polars lazy evaluation (query optimization)
- Automatic tier routing based on data age
- Streaming aggregations for large datasets
🏗️ HFThot at Scale
End-to-end data flow — from market sources through stream processing to client delivery
Binance
Spot, futures and order-book feeds
Alpaca
Broker APIs and execution endpoints
Finnhub
Market signals, news and reference data
Polymarket
Prediction-market events and microstructure
Central ingestion and hybrid storage layer for HFThot: it normalizes feeds, keeps hot market state in memory and exposes analytics-ready data through Arrow, Polars and Delta Lake semantics.
DuckDB
Columnar analytics and time-series querying layer fed by Polarway for fast local research, snapshots and lakehouse-style SQL exploration.
Optimiz-rs
Rust optimization engine connected to Polarway for portfolio optimization, stochastic control and mean-field computations through low-overhead data exchange.
🗄️ Polarway Lakehouse
Infrastructure d'authentification et de gestion de données de nouvelle génération, basée sur Delta Lake pour ACID compliance, time-travel, et traçabilité totale.
Time-Travel
Accédez à n'importe quelle version de vos données utilisateurs, sessions ou API keys. Chaque modification est versionnée et récupérable.
- ✓
read_version(table, version_id) - ✓ Audit trail complet automatique
- ✓ Rollback instantané en cas d'erreur
- ✓ Conformité réglementaire (FINMA, MiFID II)
RGPD Compliant
Respect total du RGPD avec export, suppression et portabilité des données utilisateurs garanties par Delta Lake.
- ✓ Export JSON/CSV de toutes vos données
- ✓ Suppression définitive avec tombstones
- ✓ Droit à l'oubli (VACUUM)
- ✓ Traçabilité des accès (audit_log/)
Sécurité & Intégrité
Architecture ACID-compliant garantissant l'intégrité des données même en cas de crash ou d'erreur réseau.
- ✓ Transactions atomiques (Delta Lake)
- ✓ Chiffrement Argon2 pour passwords
- ✓ JWT avec expiration configurable
- ✓ PyArrow zero-copy pour performance
🐍 LakehouseAuthBackend (Python)
- register(username, email, password)
- login(username, password) → JWT token
- verify_persistent_token(token) → User
- save_api_keys(user_id, provider_keys, consent)
- get_api_keys(user_id) → dict[provider: queries_remaining]
- update_query_count(user_id, provider, increment)
⚙️ LakehouseClient (Rust bindings via PyO3)
- Zero-copy Arrow transfers
- PyArrow compute for filtering
- Lazy evaluation (scan → filter → collect)
📁 Delta Lake Tables (Parquet + _delta_log/)
- ACID transactions (write_deltalake atomic commits)
- Time-travel queries (restore any version)
- Snappy compression (10:1 ratio)
- S3/GCS compatible (future cloud deployment)
📊 Transparence API
Tracking en temps réel de vos queries_done et queries_remaining pour chaque provider (Finnhub, Alpha Vantage). Alertes automatiques avant limite.
🔄 Data Sharing
Programme communautaire: partagez vos données de marché (anonymes) = accès GRATUIT aux APIs premium. 100% RGPD compliant avec opt-out instantané.
⏱️ Audit Logs
Chaque action utilisateur loggée dans audit_log/ avec timestamp, IP, action type. Conformité MiFID II/FINMA garantie.
📚 Documentation Technique
Le module Polarway Lakehouse est documenté sur ReadTheDocs avec exemples d'utilisation, API reference, et best practices.
Exemple d'utilisation:
from python.lakehouse.client import LakehouseClient
# Initialize client with Delta Lake storage
client = LakehouseClient("/app/data/lakehouse")
# API Key Management
keys = client.get_api_keys(user_id="...")
print(f"Finnhub: {keys['provider_keys']['finnhub']['queries_remaining']}/3600")
# Time-Travel: Access historical data
users_v1 = client.read_version("users", version=1)
# RGPD Export: Get all user data
audit_trail = client.billing_summary(user_id, "2026-01-01", "2026-12-31")
💡 Architecture Benefits
Performance
- Zero-copy data transfers via Arrow
- SIMD-accelerated operations
- Parallel execution (multi-core)
- Lazy evaluation & query optimization
- 85% cache hit rate reduces I/O
Cost Efficiency
- 90% cost reduction vs traditional TSDB
- No database licensing fees
- Embedded DuckDB (no separate server)
- Parquet compression (10:1 ratio)
- Single server deployment
Reliability
- Rust's memory safety (no crashes)
- Railway-oriented error handling
- Type-safe gRPC contracts
- Immutable Parquet files
- Transactional DuckDB writes
📚 Resources
Documentation
Complete reference docs, API guide, and lakehouse technical specs