Over seven months, Solson contributed to the instructure/canvas-lms repository by building and refining features that improved grading workflows, data privacy, and system performance. They enhanced the SpeedGrader UI for accessibility and analytics, implemented robust gradebook filtering, and optimized backend data retrieval across sharded databases. Using Ruby on Rails, GraphQL, and React, Solson introduced API enhancements for richer data access and batch processing, while addressing N+1 query issues and ensuring reliable error handling. Their work included comprehensive testing, documentation, and feature flagging, resulting in more maintainable, performant, and accessible tools for both end users and developers within Canvas LMS.
Monthly summary for 2025-10 for instructure/canvas-lms focusing on delivering business value and technical excellence. Highlighted work includes two new features and two reliability/performance fixes, with a clear path for safe feature rollout.
Monthly summary for 2025-10 for instructure/canvas-lms focusing on delivering business value and technical excellence. Highlighted work includes two new features and two reliability/performance fixes, with a clear path for safe feature rollout.
September 2025 (2025-09) monthly summary for instructure/canvas-lms. Focused on delivering robust gradebook filtering, improving data correctness across sharded data, and reducing latency by preloading related data to prevent N+1 queries. Delivered two primary features with targeted reliability improvements, fixed critical filtering bugs, and expanded test coverage. Business impact: improved gradebook usability and consistency, faster and more accurate data retrieval, and more reliable GraphQL behavior across multi-shard contexts. Key features delivered: - Gradebook Multiselect Filters: enabled by default in development; refactored the gradebook controller to correctly handle multiselect filters by ignoring deleted sections; moved enrollments filtering to the API by excluding soft-deleted enrollments in GraphQL; added tests to verify correct filtering. Commits: d6a9402959b9e0a680c27f9961793712d8dd5dcf; a5283fed15444d92b94669d6c16ef865f23757c0; 4ac5e59a2148966669d3e7cf11b23154d79ccb33. - Backend Performance and Data Correctness Improvements: aggregate comment bank item counts across multiple shards and enable multishard associations; optimize retrieval of context module items by preloading related data to prevent N+1 queries. Commits: 82f064da786a7ed56bbfc331c340a03172d4145b; 457cfa0778f47c7f9e1ef4c091c1a48d80437bbc. Major bugs fixed: - Fixed filtering behavior to ignore deleted sections in stored gradebook filters (Gradebook multiselect). Commit: a5283fed15444d92b94669d6c16ef865f23757c0. - Fixed enrollmentsConnection GraphQL query to exclude soft-deleted enrollments. Commit: 4ac5e59a2148966669d3e7cf11b23154d79ccb33. Overall impact and accomplishments: - Improved gradebook usability through reliable multiselect filtering and correct handling of soft-deletes. - Increased data accuracy and performance across shard boundaries, reducing latency and improving correctness of counts and module data retrieval. - Expanded test coverage to ensure filtering logic remains correct as data changes. Technologies/skills demonstrated: - Ruby on Rails, GraphQL API design and testing, multi-shard data handling, preloading associations to avoid N+1 queries, performance optimization, test-driven development.
September 2025 (2025-09) monthly summary for instructure/canvas-lms. Focused on delivering robust gradebook filtering, improving data correctness across sharded data, and reducing latency by preloading related data to prevent N+1 queries. Delivered two primary features with targeted reliability improvements, fixed critical filtering bugs, and expanded test coverage. Business impact: improved gradebook usability and consistency, faster and more accurate data retrieval, and more reliable GraphQL behavior across multi-shard contexts. Key features delivered: - Gradebook Multiselect Filters: enabled by default in development; refactored the gradebook controller to correctly handle multiselect filters by ignoring deleted sections; moved enrollments filtering to the API by excluding soft-deleted enrollments in GraphQL; added tests to verify correct filtering. Commits: d6a9402959b9e0a680c27f9961793712d8dd5dcf; a5283fed15444d92b94669d6c16ef865f23757c0; 4ac5e59a2148966669d3e7cf11b23154d79ccb33. - Backend Performance and Data Correctness Improvements: aggregate comment bank item counts across multiple shards and enable multishard associations; optimize retrieval of context module items by preloading related data to prevent N+1 queries. Commits: 82f064da786a7ed56bbfc331c340a03172d4145b; 457cfa0778f47c7f9e1ef4c091c1a48d80437bbc. Major bugs fixed: - Fixed filtering behavior to ignore deleted sections in stored gradebook filters (Gradebook multiselect). Commit: a5283fed15444d92b94669d6c16ef865f23757c0. - Fixed enrollmentsConnection GraphQL query to exclude soft-deleted enrollments. Commit: 4ac5e59a2148966669d3e7cf11b23154d79ccb33. Overall impact and accomplishments: - Improved gradebook usability through reliable multiselect filtering and correct handling of soft-deletes. - Increased data accuracy and performance across shard boundaries, reducing latency and improving correctness of counts and module data retrieval. - Expanded test coverage to ensure filtering logic remains correct as data changes. Technologies/skills demonstrated: - Ruby on Rails, GraphQL API design and testing, multi-shard data handling, preloading associations to avoid N+1 queries, performance optimization, test-driven development.
August 2025: Canvas LMS backend/API improvements with targeted bug fixes and feature refinements. Delivered reliable grading behavior, robust submission/text extraction, and a smoother SpeedGrader UI, underpinned by expanded tests and clearer API semantics. Focused on business value: data integrity for grades, accurate submission metrics, and developer-friendly GraphQL/Rubrics API usage.
August 2025: Canvas LMS backend/API improvements with targeted bug fixes and feature refinements. Delivered reliable grading behavior, robust submission/text extraction, and a smoother SpeedGrader UI, underpinned by expanded tests and clearer API semantics. Focused on business value: data integrity for grades, accurate submission metrics, and developer-friendly GraphQL/Rubrics API usage.
July 2025 performance summary for instructure/canvas-lms. Delivered core SpeedGrader enhancements with a focus on accuracy, reliability, analytics, and accessibility. Highlights include moderation-aware Submission Status Sorting to ensure correct status reflection during moderation actions; GraphQL schema tightening by making connections non-nullable to simplify client logic and improve performance for SpeedGrader 2; Pendo analytics integration in Platform SpeedGrader to enable user behavior insights; and UI polish plus WCAG 2.1 reflow improvements for faster, more accessible grading workflows. These changes collectively increase grader accuracy, reduce client-side complexity, enhance product visibility through analytics, and deliver a more responsive and accessible SpeedGrader experience.
July 2025 performance summary for instructure/canvas-lms. Delivered core SpeedGrader enhancements with a focus on accuracy, reliability, analytics, and accessibility. Highlights include moderation-aware Submission Status Sorting to ensure correct status reflection during moderation actions; GraphQL schema tightening by making connections non-nullable to simplify client logic and improve performance for SpeedGrader 2; Pendo analytics integration in Platform SpeedGrader to enable user behavior insights; and UI polish plus WCAG 2.1 reflow improvements for faster, more accessible grading workflows. These changes collectively increase grader accuracy, reduce client-side complexity, enhance product visibility through analytics, and deliver a more responsive and accessible SpeedGrader experience.
June 2025 monthly review for instructure/canvas-lms focused on API enhancements that elevate data access, batch processing, and analytics capabilities. Implemented targeted changes to submission data exposure and GraphQL schema to support faster, richer data access and improved developer experience.
June 2025 monthly review for instructure/canvas-lms focused on API enhancements that elevate data access, batch processing, and analytics capabilities. Implemented targeted changes to submission data exposure and GraphQL schema to support faster, richer data access and improved developer experience.
May 2025 monthly summary for instructure/canvas-lms. Focused on delivering privacy-preserving search enhancements and robust debugging support. Key work included server-side submission search sorting options and a fix to use anonymous_id for anonymized assignments (enhancing data privacy and SpeedGrader capabilities), as well as a new HTML-based error report for FlamegraphService failures saved to user Canvas files to streamline debugging. These efforts improved data privacy, search performance, and developer troubleshooting workflows.
May 2025 monthly summary for instructure/canvas-lms. Focused on delivering privacy-preserving search enhancements and robust debugging support. Key work included server-side submission search sorting options and a fix to use anonymous_id for anonymized assignments (enhancing data privacy and SpeedGrader capabilities), as well as a new HTML-based error report for FlamegraphService failures saved to user Canvas files to streamline debugging. These efforts improved data privacy, search performance, and developer troubleshooting workflows.
April 2025: Implemented Flamegraph support for performance profiling in instructure/canvas-lms, including backend generation via flamegraph=true, storage to Canvas Files in a flamegraphs folder with optional flamename, documentation, and UI integration in the new Files UI to render flamegraphs. Authored documentation and provided admin-facing controls to generate flamegraphs. The change enhances observability and performance diagnostics across deployments.
April 2025: Implemented Flamegraph support for performance profiling in instructure/canvas-lms, including backend generation via flamegraph=true, storage to Canvas Files in a flamegraphs folder with optional flamename, documentation, and UI integration in the new Files UI to render flamegraphs. Authored documentation and provided admin-facing controls to generate flamegraphs. The change enhances observability and performance diagnostics across deployments.
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