Quality and performance improvements

Problem

Post-v1.0, users will surface quality and performance issues that were acceptable at launch but become pain points at scale — SVG rendering that is slow on dashboards with many charts, axis label formatting that breaks with certain locale/number formats, tooltip positioning that overlaps content on small viewports, or default color palettes that don't work well for colorblind users. These issues are individually minor but collectively determine whether the chart library feels polished or amateurish. Without tying quality and performance improvements to measurable user-facing outcomes (render time p95, accessibility audit scores, user-reported visual bugs), polish work will lack focus and prioritization.

Context

• Once visual language, chart defaults, interaction behavior, and differentiated styling 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/render/chart/, chart design docs, examples, and visualization test coverage, 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 visual language, chart defaults, interaction behavior, and differentiated styling 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

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