Evidence-Driven Lucid Performance Tuning

Overview

Tune one measured workflow at a time while preserving document fidelity, data reconciliation, authorization, and documented limits.

Prerequisites

• A reproducible scenario, representative sanitized fixture, baseline, and service objective
• Component classification: import, export, REST operation, editor extension, or connector
• Owners for data correctness and any live environment

Tool Discipline

Use Read, Glob, and Grep for code, fixtures, and receipts, WebFetch for current limits/contracts, and Write or Edit only for local benchmarks, scoped improvements, and reports.

Current Contract

Each Lucid surface has different constraints. Standard Import performance depends on archive structure, uncompressed assets, pages, objects, and data; extension/connector performance depends on the installed SDK, transforms, network behavior, and UI work. Rate limits are endpoint-specific.

Authentication

Benchmark offline first. Use dedicated test credentials and synthetic data for live measurements. Never log tokens or expose restricted document data in traces.

Instructions

1. Define the user-visible objective and correctness invariants before measuring.
2. Record versions, fixture digest, cold/warm state, network assumptions, concurrency, and timing method.
3. Establish at least three comparable baseline samples with latency distribution and resource counts.
4. Profile the dominant phase: archive generation/upload/render, export polling/download, extension transform/UI, or connector fetch/reconcile.
5. Propose one reversible change such as bounded batching, deduplication, incremental reconciliation, asset reduction, caching with invalidation, or deferred UI work.
6. Run the same samples and compare latency, memory, requests, payload, rejects, document fidelity, and reconciliation.
7. Present any live load increase or concurrency change for approval; honor endpoint-specific limits and backpressure.
8. Keep only improvements that meet both performance and correctness thresholds.

Approval Boundaries

Do not load-test Lucid or a source system, increase concurrency, reduce validation, or alter production documents without approval.

Output

Return scenario, versions, baseline and candidate distributions, bottleneck evidence, correctness checks, limits consulted, decision, and rollback.

Error Handling

Example

scenario=fixture-import; n=5; p50-before=4.8s; p50-after=3.6s; fidelity=pass; requests-delta=0

Resources

• Official documentation map

Next Steps

Add the representative benchmark and correctness assertions to the release regression suite.