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outline-load

概述系统或组件的负载概况 —— 绘制随时间变化的流量模式、峰值使用情况、数据增长和资源消耗。

person作者: jakexiaohubgithub

Outline Load Profile

Map the load characteristics of a component or system to understand its scaling behavior.

Arguments: $ARGUMENTS

Workflow

Step 1: Identify the Component

Read the target component from $ARGUMENTS. Explore its:

  • Entry points (API endpoints, CLI commands, event handlers, scheduled jobs)
  • Data stores it reads from and writes to
  • External service calls

Step 2: Map Request Flow

For each entry point, trace the request path:

  1. Ingress: How does work arrive? (HTTP, queue, cron, user action)
  2. Processing: What computation happens? (CPU-bound, I/O-bound, memory-bound)
  3. Egress: Where do results go? (response, database write, file output, notification)

Step 3: Estimate Load Dimensions

For each flow, estimate:

| Dimension | Current | 10x | 100x | |-----------|---------|-----|------| | Requests/sec | | | | | Data per request | | | | | Total storage | | | | | Concurrent connections | | | | | Memory per request | | | |

Fill in what can be determined from code. Mark unknowns as ?.

Step 4: Identify Bottlenecks

Look for:

  • Single-threaded paths: Synchronous processing that blocks
  • Unbounded collections: Arrays/lists that grow with input size
  • N+1 queries: Database/API calls inside loops
  • Global state: Shared mutable state across requests
  • Missing pagination: Queries without LIMIT/OFFSET
  • Large file reads: Reading entire files into memory

Step 5: Output the Load Profile

## Load Profile: [Component]

**Entry Points**: [count] ([list])
**Processing Type**: [CPU-bound | I/O-bound | Memory-bound | Mixed]

**Current Load Estimate**:
[table from Step 3]

**Bottlenecks Identified**:
1. [file:line] — [description]
2. [file:line] — [description]

**Scaling Risks**:
- [risk 1]
- [risk 2]

**Recommendation**: [scale-ready | needs-work | redesign-required]

This feeds into /scale-review:evaluate for detailed analysis.