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Roadmap

See GitHub Project: Profilis – v0 Roadmap.

Version Status

v0.1.0 — Core + Flask + SQLAlchemy + UI (COMPLETED)

Released: September 2025

Delivered Features: - Core Profiling Engine - AsyncCollector with configurable batching and backpressure handling - Emitter for high-performance event creation (≤15µs per event) - Runtime context management with trace/span ID support - Non-blocking architecture with configurable queue sizes

  • Flask Integration

    • Automatic request/response profiling with hooks
    • Configurable sampling and route exclusion
    • Exception tracking and error reporting
    • Bytes in/out monitoring (best-effort)

  • SQLAlchemy Instrumentation

    • Automatic query profiling with microsecond precision
    • Query redaction for security
    • Row count tracking
    • Async engine support

  • Built-in Dashboard

    • Real-time metrics visualization
    • Error tracking and display
    • Performance trend analysis
    • 15-minute rolling window statistics

  • Exporters

    • JSONL exporter with automatic rotation
    • Console exporter for development
    • Configurable file retention and naming

  • Function Profiling

    • @profile_function decorator for sync/async functions
    • Exception tracking and re-raising
    • Nested span support

Performance Metrics: - Event creation: ≤15µs per event - Memory overhead: ~100 bytes per event - Throughput: 100K+ events/second - Latency: Sub-millisecond collection overhead

v0.2.0 — Additional Database Support (COMPLETED)

Released: September 2025

Delivered Features: - MongoDB Support - PyMongo and Motor integration - Command monitoring with comprehensive metrics extraction - Query execution time tracking - Collection and operation profiling - Error tracking and failure analysis

  • Neo4j Integration

    • Cypher query profiling with session and transaction monitoring
    • Graph traversal metrics and result statistics
    • Both sync and async operation support
    • Query analysis with parameter redaction

  • pyodbc Integration

    • Raw cursor wrapper for execute/executemany operations
    • SQL monitoring with parameter redaction
    • Multi-vendor database support (SQL Server, PostgreSQL, MySQL, etc.)
    • Non-invasive instrumentation preserving cursor semantics

  • Enhanced Runtime Context

    • Improved tracing support with parent span ID tracking
    • Better integration with external tracing systems

Performance Metrics: - MongoDB command profiling: ≤15µs overhead - Neo4j query profiling: ≤15µs overhead - pyodbc cursor profiling: ≤15µs overhead - Memory overhead: ~100 bytes per database event - Throughput: 100K+ database events/second

v0.3.0 — ASGI Framework Support (COMPLETED)

Released: March 2026

Delivered Features: - ASGI Middleware - Generic ProfilisASGIMiddleware for Starlette and any ASGI framework - Configurable sampling, route exclusions, and always-sample-errors - Route template extraction from scope (e.g. path_format)

  • FastAPI Integration

    • instrument_fastapi() registers ASGI middleware with FastAPI apps
    • Automatic request/response profiling and route detection
    • make_ui_router() for built-in dashboard (metrics.json, errors.json, HTML)

  • Sanic Integration

    • instrument_sanic_app() with native request/response/exception middleware
    • make_ui_blueprint() for built-in dashboard
    • Optional ASGI app mounting (best-effort by Sanic version)

Enhancements: - Improved async performance and error handling in ASGI/Sanic contexts

v0.4.0 — Sampling, Prometheus & Resilience (COMPLETED)

Released: March 2026

Delivered Features: - Sampling Policies - Global sample_rate (0.0–1.0) for ASGI and Sanic - Per-route overrides and route excludes (prefix or regex, e.g. re:^/static/) - Always sample 5xx responses and exceptions - Seedable RNG / custom rng for deterministic tests

  • Prometheus Exporter

    • HTTP: profilis_http_requests_total, profilis_http_request_duration_seconds (histogram)
    • Functions: profilis_function_calls_total, profilis_function_duration_seconds (histogram)
    • DB: profilis_db_queries_total, profilis_db_query_duration_seconds (histogram)
    • Labels: service, instance, worker, route, status, function, db_vendor
    • /metrics endpoint for Flask (make_metrics_blueprint) and ASGI (make_asgi_app)
    • Configurable histogram buckets

  • Reliability

    • Graceful shutdown: best-effort flush with timeout; never block exit
    • JSONL exporter: disk-full fallback (no-op writer + warn once)
    • Health metrics: profilis_events_dropped_total, profilis_queue_depth via register_collector_health_metrics()

v1.0.0 — Production Ready (COMPLETED)

Target: March 2026

Planned Features: - Comprehensive Benchmarks - Performance regression testing - Load testing scenarios - Comparison with alternatives

  • Production Documentation
    • Deployment guides
    • Monitoring best practices
    • Troubleshooting guides

Enhancements: - Production validation - Community feedback integration - Long-term support commitment

Contributing to the Roadmap

How to Contribute

  1. Feature Requests: Open GitHub issues for new features
  2. Implementation: Submit pull requests for planned features
  3. Testing: Help test and validate new functionality
  4. Documentation: Improve and expand documentation
  5. Feedback: Share your experience and use cases

Development Guidelines

  • Follow the established code patterns
  • Include comprehensive tests
  • Update documentation for new features
  • Consider backward compatibility
  • Focus on performance and reliability

Community Input

  • GitHub Discussions: Share ideas and feedback
  • Issue Tracking: Report bugs and request features
  • Pull Requests: Contribute code improvements
  • Documentation: Help improve guides and examples

Release Schedule

Release Cadence

  • Minor Releases: Every 3-4 months
  • Patch Releases: As needed for bug fixes
  • Major Releases: Annual (v1.0.0)

Release Process

  1. Feature Freeze: 2 weeks before release
  2. Testing Phase: 1 week of intensive testing
  3. Release Candidate: 1 week before final release
  4. Production Release: Tagged and documented

Support Policy

  • Current Release: Full support and bug fixes
  • Previous Release: Security fixes only
  • Older Releases: Community support only

Success Metrics

Technical Metrics

  • Performance: Maintain ≤15µs event creation overhead
  • Reliability: 99.9% uptime for profiling systems
  • Scalability: Support 1M+ events/second
  • Memory: <1MB overhead per 10K events

Adoption Metrics

  • Downloads: Track PyPI download statistics
  • GitHub Stars: Monitor community interest
  • Issues & PRs: Measure community engagement
  • Documentation: Track documentation usage

Quality Metrics

  • Test Coverage: Maintain >90% test coverage
  • Performance Regression: Zero performance regressions
  • Security: Regular security audits
  • Documentation: Comprehensive and up-to-date guides