Project 22: High-Frequency Real-Time Plugin

A real-time plugin (stock/crypto/system metric style) that can sustain stress at 60 updates/sec with bounded memory growth and stable UI feedback.

Quick Reference

Attribute Value
Difficulty Level 5
Time Estimate 18-30h
Main Programming Language TypeScript
Alternative Programming Languages JavaScript, Rust, C#
Coolness Level Level 5 (Performance engineering)
Business Potential Level 4 (Trading/monitoring workflows)
Prerequisites Backpressure and bounded queues, Render diffing and frame budgets, Leak detection methodology
Key Topics Event loop optimization and rendering efficiency

1. Learning Objectives

By completing this project, you will:

  1. Build a production-quality implementation of High-Frequency Real-Time Plugin.
  2. Apply concept boundaries around Backpressure and bounded queues, Render diffing and frame budgets, and Leak detection methodology.
  3. Validate behavior with explicit outcomes and failure-mode tests.
  4. Produce evidence artifacts suitable for review, support, and iteration.

2. All Theory Needed (Per-Concept Breakdown)

2.1 Backpressure and bounded queues

  • Fundamentals: This concept defines the first architectural boundary for this project. You should know the invariant conditions that must remain true during normal operation and failure operation. In Stream Deck plugin work, the most useful mindset is to treat interaction paths as explicit contracts, not ad-hoc callbacks, so behavior remains deterministic under context churn and profile switching.
  • Deep Dive into the concept: For this project, Backpressure and bounded queues is where correctness begins. Model state transitions explicitly, define allowed events, and reject illegal transitions early. Tie every side effect to context identity and traceability fields so debugging can reconstruct the full sequence. Design your test plan around race-prone paths first. Add failure classes and recovery transitions before polishing UX. This creates robust behavior under load and avoids hidden coupling across action instances.
  • How this fit on projects: This concept is the primary driver of runtime correctness in this project.
  • Definitions & key terms: invariant, transition contract, failure class, recovery path.
  • Mental model diagram:
Intent -> Validate -> Reduce -> Persist -> Render
  ^                                       |
  +--------------- Recover/Retry <--------+
  • How it works: model inputs, validate boundaries, reduce deterministic state, emit minimal side effects, then observe and recover.
  • Minimal concrete example:
PSEUDOCODE
if !isValid(event, state):
  return rejectWithHint()
next = reduce(state, event)
apply(next)
  • Common misconceptions: fast prototypes do not remove the need for explicit invariants.
  • Check-your-understanding questions: Which invalid transition causes highest user impact? Why?
  • Check-your-understanding answers: Any transition that mutates irreversible state without confirmation.
  • Real-world applications: production plugins that must survive long sessions and rapid profile switches.
  • Where you will apply it: project runtime handlers and teardown logic.
  • References: Stream Deck SDK docs + main sprint Theory Primer concepts 1/2/6.
  • Key insights: deterministic state design scales better than callback patching.
  • Summary: make invalid states unrepresentable and observable.
  • Homework/Exercises to practice the concept: draw one transition table and one failure table.
  • Solutions to the homework/exercises: each transition/failure should map to explicit UI feedback and test case.

2.2 Render diffing and frame budgets

  • Fundamentals: Render diffing and frame budgets handles data integrity and long-lived behavior. Treat user configuration, entitlement, and environment state as a schema-governed domain.
  • Deep Dive into the concept: Build validation at every boundary: PI input, backend receive, persistence write, and migration load. Use explicit versioning and conflict policy so stale updates cannot silently win. If sensitive fields exist, isolate them through secret-safe adapters and redact all diagnostics. This prevents corruption, race bugs, and support incidents that usually appear only after release.
  • How this fit on projects: ensures reliable persistence and predictable restart/recovery behavior.
  • Definitions & key terms: schema, migration, revision, redaction.
  • Mental model diagram:
Input Delta -> Merge -> Validate -> Version -> Commit -> Observe
  • How it works: merge safely, validate strictly, commit atomically, expose clear error feedback.
  • Minimal concrete example:
PSEUDOCODE
merged = merge(prev, delta)
assert schemaValid(merged)
save(merged, revision+1)
  • Common misconceptions: compile-time types are not runtime safety.
  • Check-your-understanding questions: Why must backend revalidate PI values?
  • Check-your-understanding answers: PI can be stale/malformed; backend is source of truth.
  • Real-world applications: paid plugins, sync features, and multi-account integrations.
  • Where you will apply it: persistence, entitlement checks, and API credential handling.
  • References: Stream Deck settings/secrets docs + RFC security guidance where applicable.
  • Key insights: data integrity is a user-visible feature.
  • Summary: strict boundaries prevent expensive post-release bugs.
  • Homework/Exercises to practice the concept: define v1/v2 schema and migration tests.
  • Solutions to the homework/exercises: include defaults, backward compatibility, and rollback path.

2.3 Leak detection methodology

  • Fundamentals: Leak detection methodology translates implementation quality into user trust, adoption, and maintainability.
  • Deep Dive into the concept: Build release and support workflows in parallel with features. Define observability schema, packaging checks, and non-functional budgets (latency, memory, retry behavior). Add diagnostics UX so users can self-report actionable data. If this project targets commercial outcomes, connect operational quality to listing confidence and retention. For hardware-diverse use cases, ensure adaptive behavior is explicitly tested across capability subsets.
  • How this fit on projects: provides the delivery and sustainment layer beyond core functionality.
  • Definitions & key terms: SLA mindset, supportability, release gate, degraded mode.
  • Mental model diagram:
Feature Build -> Validation Gate -> Pack/Release -> Observe -> Support -> Improve
  • How it works: define quality gates, ship artifacts, monitor signals, feed incidents back into design.
  • Minimal concrete example:
PSEUDOCHECKLIST
validate pass
smoke install pass
diagnostics export pass
rollback artifact present
  • Common misconceptions: once it works locally, release risk is low.
  • Check-your-understanding questions: Which quality gate catches packaging regressions earliest?
  • Check-your-understanding answers: deterministic CLI validate/pack + smoke install checks.
  • Real-world applications: marketplace submission, enterprise team deployment, paid support.
  • Where you will apply it: release checklist, diagnostics, and post-launch iteration.
  • References: Stream Deck CLI docs, marketplace docs, and reliability references.
  • Key insights: sustainable plugins are operated products, not one-off scripts.
  • Summary: build supportability and release discipline into the first milestone.
  • Homework/Exercises to practice the concept: create one pre-release gate matrix and one incident response runbook.
  • Solutions to the homework/exercises: each gate/runbook step must include pass/fail evidence.

3. Project Specification

3.1 What You Will Build

A real-time plugin (stock/crypto/system metric style) that can sustain stress at 60 updates/sec with bounded memory growth and stable UI feedback.

3.2 Functional Requirements

  1. Implement all user-facing behaviors listed in the source sprint project.
  2. Preserve deterministic state behavior under context churn and restart.
  3. Enforce boundary validation for configuration and external events.
  4. Expose clear feedback for success, pending, and failure modes.
  5. Provide release/support artifacts aligned with project scope.

3.3 Non-Functional Requirements

  • Performance: Remain responsive under expected event rates for this project.
  • Reliability: No orphaned timers/subscriptions after teardown paths.
  • Usability: Users can understand current state from key/PI feedback quickly.
  • Supportability: Logs and diagnostics must be actionable and redacted.

3.4 Example Usage / Output

“How do I deliver high-frequency freshness without starving the event loop or flooding the device renderer?”

3.5 Real World Outcome

Under synthetic stress, your plugin keeps p95 input-to-feedback latency below your defined threshold while processing up to 60 updates/sec. CPU and memory trend charts stabilize instead of climbing continuously. When app goes background, update cadence throttles automatically and resumes cleanly on return.

Example stress log:

$ npm run test:stress-realtime
[stress] inputRate=60/s contexts=12 duration=300s
[stress] p95_latency_ms=42 dropped_updates=0 memory_mb_delta=+7
[stress] background_mode_throttle=enabled render_rate=12/s
PASS: thresholds satisfied

4. Solution Architecture

4.1 High-Level Design

Stream Deck Events -> Runtime Reducer -> Capability/Policy Layer -> Side Effects
        ^                                                          |
        +---------------------- Diagnostics/Observability <--------+

4.2 Key Components

  • Action Runtime Layer: Handles event routing, context scoping, and state reduction.
  • Policy Layer: Applies validation, feature gates, retries, throttles, and safety rules.
  • Feedback Layer: Produces deterministic key/dial/PI feedback from canonical state.
  • Persistence/Integration Layer: Manages settings, secrets, sync, and external API boundaries.

4.3 Design Questions (From Sprint)

  1. Cadence strategy
    • What are ingest rate, processing rate, and render rate limits?
    • What policy applies in background mode?
  2. Memory safety
    • Which objects are lifecycle-owned per context?
    • How do you enforce cleanup on disappear/reload?

5. Thinking Exercise (Before Building)

Performance Budget Worksheet

Set explicit budgets for p95 latency, max memory delta/hour, max queue depth, and max redraws/second. Refuse to add features that violate the budgets.

6. Implementation Hints in Layers

Hint 1: Starting Point

  • Separate ingest queue from render scheduler.

Hint 2: Next Level

  • Keep last rendered snapshot and render only meaningful deltas.

Hint 3: Technical Details

PSEUDOFLOW
ingest sample -> queue -> reduce latest -> if diff >= threshold and frameBudgetAvailable -> render

Hint 4: Tools/Debugging

  • Add periodic metrics (queueDepth, renderRate, memoryUsed) to debug telemetry.

7. Verification and Testing Plan

  1. Unit-level: transition validity, schema validation, and policy decisions.
  2. Integration-level: PI/backend flow, persistence/restart, and dependency adapters.
  3. Failure-level: network/auth/retry/teardown behavior under injected faults.
  4. Release-level: validate/pack/smoke workflow and artifact integrity checks.

8. Interview Questions

  1. “How do you maintain responsiveness at 60 updates/sec?”
  2. “What did your memory trend look like after a 30-minute soak?”
  3. “How do you decide when to drop or coalesce updates?”
  4. “What background throttling rules did you implement?”
  5. “How did you verify no leak in context churn scenarios?”

9. Common Pitfalls and Debugging

Problem 1: “Plugin becomes laggy after 10 minutes”

  • Why: Update queue growth and redundant renders overwhelm event loop.
  • Fix: Add bounded queue + coalescing and cap render FPS.
  • Quick test: 30-minute stress run with memory and latency trend assertions.

10. Definition of Done

  • Sustains 60 updates/sec stress test within defined latency budget.
  • Memory growth remains bounded and explainable.
  • Update pipeline uses debounced/coalesced redraw strategy.
  • Background throttling preserves responsiveness and battery/CPU.
  • No context lifecycle leaks under repeated appear/disappear churn.

11. Additional Notes

  • Why this project matters: It forces deliberate performance budgets instead of accidental update storms.
  • Source sprint project file: P22-high-frequency-real-time-plugin.md
  • Traceability: Generated from ### Project 22 in the sprint guide.