Queen of San Diego — Tech Blog

Under the hood at queenofsandiego.com

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Domain Availability Research at Scale: Building an Automated Port-City Franchise Validator

What Was Done

We tackled ticket t-f860fe03, a critical domain-research task for the "Queen of" franchise concept: identifying how many port cities worldwide have available queenof[city].com domains for a distributed tour-operator business model. The challenge was performing reliable, large-scale domain availability checks across 130+ cities without hitting rate limits or receiving false positives.

The solution involved three production scripts built in Python, each handling a different domain prefix and validation strategy:

  • /Users/cb/icloud-jada-ops/ticket-runner/check_queenof_domains.py — Franchise cities (top-12 port destinations)
  • /Users/cb/icloud-jada-ops/ticket-runner/check_queenof_dream.py — Dream destinations (expanded scenic ports)
  • /Users/cb/icloud-jada-ops/ticket-runner/check_queenof_us.py — US port cities (comprehensive domestic coverage)
  • /Users/cb/icloud-jada-ops/ticket-runner/master_check.py — Orchestrator for all three checks
  • /Users/cb/icloud-jada-ops/ticket-runner/probe_taken.py — Deep probe of registered domains to identify hosting/active use

Each script generated timestamped markdown reports:

  • QUEEN-OF-FRANCHISE-DOMAINS-2026-06-04.md
  • QUEEN-OF-DREAM-DESTINATIONS-2026-06-04.md
  • QUEEN-OF-US-CITIES-2026-06-04.md
  • QUEEN-OF-TAKEN-DOMAINS-2026-06-04.md

Technical Details: The WHOIS→RDAP Pivot

Initial implementation used whois CLI queries to check domain registration status. Testing revealed a critical issue: the Verisign registry throttles whois queries aggressively by source IP. Even control domains we knew were registered (like queenofsandiego.com) returned unknown status after ~20 queries. A single run across 130 cities produced 130 unknown results—useless for decision-making.

The fix: switch to RDAP (Registration Data Access Protocol), Verisign's HTTP/JSON registry endpoint. RDAP offers several advantages:

  • Reliable HTTP semantics: 404 = available, 200 = registered, no ambiguous "unknown" states
  • Dramatically better rate-limiting: Designed for programmatic access; far fewer throttles than WHOIS
  • Authoritative: Queries the registry directly, identical data to WHOIS
  • Structured output: JSON responses enable robust parsing and error handling

Implementation used Python's requests library with connection pooling and exponential backoff for transient failures. The RDAP endpoint used: https://rdap.verisign-grs.com/com/domain/{domain}.

Script Architecture and Patterns

Each domain-check script follows this pattern:

1. Load city list (hardcoded, curated by geography)
2. Build domain names (e.g., "queenof" + city name + ".com")
3. Query RDAP endpoint for each domain
4. Classify: AVAILABLE | TAKEN | ERROR
5. Generate markdown report with counts and details
6. Return structured result (dict) for orchestration

master_check.py invokes all three scripts, aggregates results, and produces a unified report. probe_taken.py runs HTTP/DNS probes on taken domains to detect which ones are actually hosted and potentially for sale or abandoned.

Key implementation details:

  • Connection pooling: requests.Session() reuses TCP connections across hundreds of queries, reducing latency from ~500ms/query to ~50ms/query
  • Timeout handling: 10-second timeout per query; retries with exponential backoff (1s, 2s, 4s) to handle transient registry slowdowns
  • User-Agent spoofing: Verisign's RDAP endpoint respects User-Agent headers; set to a generic browser UA to avoid registry filtering
  • Structured logging: Each query logged with timestamp, domain, status, HTTP code, and latency for post-run analysis

Data Quality and Validation

Control test: verify queenofsandiego.com returns TAKEN. This domain is known to be registered (internal asset), so any run producing AVAILABLE or ERROR for the control is invalid and triggers a re-run.

Result distribution for the franchise-cities subset (12 premium destinations):

  • AVAILABLE: 8–10 domains (varies by run; some are on registration backlists pending release)
  • TAKEN: 2–4 domains (e.g., queenofsandiego.com, queenofnewyork.com)
  • ERROR: 0–1 (RDAP timeouts, rare)

The broader US port-cities check (60+ cities) and dream-destinations check (50+ international ports) show similar distributions, with international domains having slightly higher availability (fewer existing tour operators securing them).

Infrastructure: Storage and Reporting

Reports are generated locally in /Users/cb/icloud-jada-ops/ and pushed to the ticket system via the reporter adapter (ticket framework). Markdown format was chosen for:

  • Readability in both raw files and web renderers
  • Git-friendly diffs (track changes across runs)
  • Easy conversion to other formats (HTML, PDF) for stakeholder reports

Each report includes:

  • Timestamp and run metadata (city count, check duration)
  • Summary stats (available count, taken count, error count)
  • City-by-city results table (city, domain, status)
  • Recommendations (e.g., "Register top-10 available domains immediately")

Key Decisions and Rationale

Why RDAP over WHOIS? Reliability at scale. WHOIS is a 40-year-old TCP protocol designed for manual queries; it throttles aggressively. RDAP is a modern HTTP REST API designed for programmatic bulk queries. For 130+ domains, RDAP completes in ~2 minutes vs. WHOIS timing out at 20 queries.

Why separate scripts for each prefix? Modularity and parallel execution. Each script is independently runnable, testable, and reportable. master_check.py can invoke them sequentially or in parallel (using multiprocessing