Dataface Tasks
    Heartbeat snapshot (JSON)

    Quality and performance improvements

    IDM5_V1_2_LAUNCH-DFT_CORE-02
    Statusnot_started
    Priorityp1
    Milestonem5-v1-2-launch
    Ownerhead-of-engineering

    Problem

    Execution adapters have accumulated performance bottlenecks and quality issues identified through production usage — slow query compilation for complex dashboards, excessive round-trips for multi-query pages, and inconsistent error handling across database backends. These issues directly impact dashboard load times and user experience, but there is no systematic effort to measure, prioritize, and ship improvements tied to user-facing outcomes like render latency and error rates.

    Context

    • Once the YAML contract, compiler/normalizer, execution adapters, and release/versioning is in regular use, quality and performance work needs to target the actual slow, flaky, or costly paths rather than generic optimization ideas.
    • The right scope here is evidence-driven: identify bottlenecks, remove the highest-friction issues, and make sure the fixes are measurable and regression-resistant.
    • Expected touchpoints include dataface/core/, schema/compiled types, docs, and core test suites, telemetry or QA evidence, and any heavy workflows where users are paying the cost today.

    Possible Solutions

    • A - Tune isolated hotspots as they are reported: useful for emergencies, but it rarely produces a coherent quality/performance program.
    • B - Recommended: prioritize measurable bottlenecks and quality gaps: couple performance work with correctness and UX validation so improvements are both faster and safer.
    • C - Rewrite broad subsystems for theoretical speedups: tempting, but usually too risky and poorly grounded for this milestone.

    Plan

    1. Identify the biggest quality and performance pain points in the YAML contract, compiler/normalizer, execution adapters, and release/versioning using real usage data, QA findings, and support feedback.
    2. Choose a small set of improvements with clear before/after measures and explicit user-facing benefit.
    3. Implement the fixes together with regression checks, docs, or operator notes wherever the change affects behavior or expectations.
    4. Review the measured outcome and turn any remaining hotspots into sequenced follow-up tasks instead of leaving them as vague future work.

    Implementation Progress

    Review Feedback

    • [ ] Review cleared