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Prad Nukala 6406208b35 shell: fish functions -> ~/.local/bin executables + bash/zsh ports; manage ~/.agents

- 16 cross-shell utilities converted to sh/bash chezmoi executables:
  ai c2p cp-appid extract_agents fab gdc gh-sonr-org-migrate ghg-clone
  ghn gho git-summary launch_omp_livediff_overlay lc-elir mdcp mdp pj
  (ghg-clone not named ghg: unrelated cargo binary owns that name)
- cwd/history helpers stay per-shell: yy c cd clip-last + ghg/ghn/gho
  cd-wrappers in bash/functions/ and zsh/functions/, loaded by rc files
- fish keeps only shell-state functions; converted ones now hit PATH
- cp-appid ignore gating moved to .local/bin path
- dot_agents/skills: 82 skills materialized from dotstate storage so
  dot_claude skill symlinks resolve on fresh bootstrap; dotstate
  profile/symlinks.json released .agents/skills .bashrc .zshrc

2026-08-10 15:15:06 -04:00

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Cloudflare Workers Analytics Engine Reference

Expert guidance for implementing unlimited-cardinality analytics at scale using Cloudflare Workers Analytics Engine.

What is Analytics Engine?

Time-series analytics database designed for high-cardinality data (millions of unique dimensions). Write data points from Workers, query via SQL API. Use for:

Custom user-facing analytics dashboards
Usage-based billing & metering
Per-customer/per-feature monitoring
High-frequency instrumentation without performance impact

Key Capability: Track metrics with unlimited unique values (e.g., millions of user IDs, API keys) without performance degradation.

Core Concepts

Concept	Description	Example
Dataset	Logical table for related metrics	`api_requests`, `user_events`
Data Point	Single measurement with timestamp	One API request's metrics
Blobs	String dimensions (max 20)	endpoint, method, status, user_id
Doubles	Numeric values (max 20)	latency_ms, request_count, bytes
Indexes	Filtered blobs for efficient queries	customer_id, api_key

Reading Order

Task	Start Here	Then Read
First-time setup	configuration.md → api.md → patterns.md
Writing data	api.md → gotchas.md (sampling)
Querying data	api.md (SQL API) → patterns.md (examples)
Debugging	gotchas.md → api.md (limits)
Optimization	patterns.md (anti-patterns) → gotchas.md

When to Use Analytics Engine

Need to track metrics? → Yes
  ↓
Millions of unique dimension values? → Yes
    ↓
  Need real-time queries? → Yes
      ↓
    Use Analytics Engine ✓

Alternative scenarios:
- Low cardinality (<10k unique values) → Workers Analytics (free tier)
- Complex joins/relations → D1 Database
- Logs/debugging → Tail Workers (logpush)
- External tools → Send to external analytics (Datadog, etc.)

Quick Start

Add binding to wrangler.jsonc:

{
  "analytics_engine_datasets": [
    { "binding": "ANALYTICS", "dataset": "my_events" }
  ]
}

Write data points (fire-and-forget, no await):

env.ANALYTICS.writeDataPoint({
  blobs: ["/api/users", "GET", "200"],
  doubles: [145.2, 1],  // latency_ms, count
  indexes: [customerId]
});

Query via SQL API (HTTP):

SELECT blob1, SUM(double2) AS total_requests
FROM my_events
WHERE index1 = 'customer_123'
  AND timestamp >= NOW() - INTERVAL '7' DAY
GROUP BY blob1
ORDER BY total_requests DESC

In This Reference

configuration.md - Setup, bindings, TypeScript types, limits
api.md - writeDataPoint(), SQL API, query syntax
patterns.md - Use cases, examples, anti-patterns
gotchas.md - Sampling, index selection, troubleshooting

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