Roadmap

Trueno-DB follows a phased development approach, with each phase building on the previous foundation. Our development is guided by EXTREME TDD (Test-Driven Development) and Toyota Way principles to ensure built-in quality.

Current Release: v0.2.1 (November 2025)

Status: Phase 1 (Core Engine) and Phase 2 (Multi-GPU) complete with production-grade quality (96.3/100 TDG score, A+ grade).

Development Phases

Phase 1: Core Engine ✅ COMPLETE

Goal: Establish foundational analytics engine with GPU/SIMD backends and proven performance benchmarks.

Status: Shipped in v0.1.0 and v0.2.0

Completed Features

  • Storage Engine

    • Apache Arrow columnar format integration
    • Parquet file reader with schema validation
    • Morsel-driven iteration (128 MB chunks for out-of-core execution)
    • GPU transfer queue with bounded backpressure

  • Backend Dispatcher

    • Cost-based GPU vs SIMD selection
    • Physics-based 5x rule (compute > 5x PCIe transfer)
    • Conservative performance estimates (32 GB/s PCIe, 100 GFLOP/s GPU)

  • GPU Kernels (via wgpu)

    • MIN/MAX aggregations with parallel reduction
    • SUM aggregation with atomic operations
    • Kernel fusion via JIT WGSL compiler
    • Fused filter+sum eliminating intermediate buffers

  • SIMD Integration (via trueno v0.6.0)

    • AVX-512/AVX2/SSE2 auto-detection
    • Graceful degradation to scalar fallback
    • 10-20x speedup vs baseline implementations

  • Top-K Selection

    • O(N log K) heap-based algorithm
    • 28.75x speedup vs full sort (1M rows, K=10)
    • Support for Int32, Int64, Float32, Float64

  • SQL Query Parsing

    • SELECT, WHERE, GROUP BY, ORDER BY, LIMIT
    • Integration with sqlparser crate

Quality Metrics (v0.2.1)

  • Tests: 156/156 passing (100%)
  • Coverage: 95.24% (exceeds 90% target)
  • TDG Score: 96.3/100 (A+)
  • Critical Defects: 0 (eliminated 25 unwraps, 100% production-safe)
  • Clippy: 0 warnings in strict mode
  • Examples: 5 comprehensive demos (gaming, finance, benchmarks)

Performance Validation

  • Competitive Benchmarks: vs DuckDB, SQLite, Polars
  • PCIe Analysis: Empirical 5x rule validation
  • GPU Syscall Tracing: renacer verification of zero-copy operations
  • Property-Based Testing: 11 tests, 1,100 scenarios

Phase 2: Multi-GPU ✅ COMPLETE

Goal: Unlock multi-GPU parallelism with data partitioning and workload distribution across local GPUs.

Status: Shipped in v0.2.0

Completed Features

  • Multi-GPU Infrastructure

    • Device enumeration and capability detection
    • Multi-device initialization with error handling
    • Device selection based on workload characteristics

  • Data Partitioning

    • Range-based partitioning for sorted data
    • Hash-based partitioning for aggregations
    • Chunk-based partitioning for parallel scans

  • Query Execution

    • Parallel query execution across multiple GPUs
    • Result aggregation with reduction operators
    • Load balancing based on device capabilities

  • Benchmarks

    • 2 GPU vs 1 GPU scaling benchmarks
    • Multi-GPU aggregation performance validation
    • Near-linear scaling verification

Quality Gates Passed

  • Tests: 156/156 passing
  • Backend Equivalence: Multi-GPU == Single GPU == SIMD
  • Documentation: Architecture diagrams, usage examples
  • Benchmarks: Scaling validation across 1-4 GPUs

Phase 3: Distribution 🔄 NEXT UP

Goal: Enable distributed query execution across networked GPU clusters with fault tolerance and horizontal scaling.

Target: v0.3.0 (Q1 2026)

Planned Features

  • gRPC Worker Protocol

    • Worker discovery and heartbeat protocol
    • Query dispatch and result collection
    • Network topology-aware query planning

  • Distributed Query Execution

    • Query fragmentation and distribution
    • Shuffle operations for distributed GROUP BY
    • Distributed JOIN algorithms (broadcast, shuffle)

  • Fault Tolerance

    • Query retry logic with exponential backoff
    • Worker failure detection and failover
    • Checkpoint/restart for long-running queries

  • Resource Management

    • Cluster-wide GPU memory tracking
    • Dynamic workload rebalancing
    • Priority-based query scheduling

Success Criteria

  • 4+ node distributed query benchmarks
  • Fault injection testing (worker failures, network partitions)
  • Scalability tests up to 16 GPUs across 8 nodes
  • Performance: 90% efficiency vs ideal linear scaling

Phase 4: WASM 🔮 FUTURE

Goal: Deploy Trueno-DB analytics to browsers via WebAssembly and WebGPU for client-side analytics dashboards.

Target: v0.4.0 (Q2 2026)

Planned Features

  • WASM Build Target

    • wasm32-unknown-unknown build configuration
    • wasm-bindgen integration for JavaScript interop
    • WASM-optimized binary size (<2 MB gzipped)

  • WebGPU Backend

    • Browser GPU access via WebGPU API
    • Graceful fallback to SIMD128 (Wasm SIMD)
    • SharedArrayBuffer for zero-copy operations

  • Browser Integration

    • JavaScript/TypeScript SDK
    • React/Vue component examples
    • Real-time dashboard demos

  • Client-Side Use Cases

    • Interactive dashboards (no backend required)
    • Privacy-preserving analytics (data stays local)
    • Offline analytics applications

Success Criteria

  • Browser compatibility: Chrome, Firefox, Safari, Edge
  • Performance: <100ms query latency for 1M row dataset
  • Bundle size: <2 MB total (WASM + JS glue)
  • Example dashboards deployed to GitHub Pages

Contributing to the Roadmap

We welcome community input on roadmap priorities! Here's how to get involved:

Feature Requests

  1. Check existing issues tagged roadmap or enhancement
  2. Open a discussion describing your use case
  3. Propose implementation with architecture sketch
  4. Align with quality standards (EXTREME TDD, Toyota Way)

Current Priorities

Based on user feedback and project goals, current priorities are:

  1. Phase 3 gRPC Protocol (High Priority)

    • Foundation for distributed execution
    • Enables horizontal scaling

  2. Query Optimizer (Medium Priority)

    • Cost-based plan selection
    • Predicate pushdown
    • Join reordering

  3. Window Functions (Medium Priority)

    • ROW_NUMBER, RANK, LAG, LEAD
    • GPU-accelerated implementation

  4. Production Hardening (Ongoing)

    • Additional error handling improvements
    • Performance profiling and optimization
    • Memory leak detection

Quality Gates for All Phases

Every phase must pass these gates before release:

  • ✅ Tests: 100% passing, >90% code coverage
  • ✅ TDG Score: ≥85/100 (B+ minimum)
  • ✅ Benchmarks: Performance claims validated
  • ✅ Documentation: Complete API docs + examples
  • ✅ CI/CD: All GitHub Actions workflows passing
  • ✅ Red Team Audit: Adversarial verification complete

Historical Milestones

DateVersionMilestone
2025-11-21v0.2.1Quality improvements (96.3/100 TDG, 0 critical defects)
2025-11-21v0.2.0Phase 2 complete (Multi-GPU, JIT compiler, 95.24% coverage)
2025-11-19v0.1.0Phase 1 MVP (Top-K, Storage, Backend dispatcher)
2025-11-01-Project inception

Toyota Way Applied

Our roadmap reflects Toyota Production System principles:

  • Jidoka (Built-in Quality): EXTREME TDD at every phase
  • Kaizen (Continuous Improvement): Incremental feature delivery
  • Genchi Genbutsu (Go and See): Benchmarks validate all claims
  • Muda (Waste Elimination): Feature-gating prevents bloat
  • Heijunka (Level Loading): Balanced workload across phases

Next Steps

Want to contribute to the roadmap? Start here:

  1. Review CLAUDE.md - Understand project architecture
  2. Run quality gates - make quality-gate to ensure environment setup
  3. Pick a Phase 3 task - Check GitHub issues tagged phase-3
  4. Follow EXTREME TDD - RED → GREEN → REFACTOR with mutation testing
  5. Submit PR - With benchmarks, tests, and documentation

See Contributing Guide for detailed guidelines.