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JP Castnet Tower Scraper

A fast and reliable scraper designed to extract structured data from tower.jp pages using TypeScript, Crawlee, and Cheerio. It streamlines data collection, ensures consistency, and provides developers with clean, ready-to-use outputs for analysis or integration.

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Introduction

This project automates the extraction of structured information from the tower.jp website. It solves the challenge of manually collecting page titles and related metadata and is ideal for developers, analysts, and automation engineers who need scalable website crawling.

High-Performance Web Extraction

  • Uses a Cheerio-powered crawler for fast HTML parsing.
  • Operates with highly efficient request handling for large crawl sets.
  • Stores structured results in a consistent dataset format.
  • Supports input validation and clean schema-based configuration.
  • Designed for scalable, automated execution.

Features

Feature Description
TypeScript-based architecture Ensures cleaner, modular, and scalable scraper development.
CheerioCrawler integration Fast HTML parsing for efficient content extraction.
Input schema validation Enforces well-structured user inputs and reduces runtime errors.
Dataset output support Automatically stores extracted data in structured records.
Configurable crawling limits Control scraping depth via maxPagesPerCrawl.
Robust logging Provides detailed logs for easier debugging and monitoring.

What Data This Scraper Extracts

Field Name Field Description
title The extracted HTML page title from each crawled URL.
url The source URL from which the title was extracted.
page_index Incremental index representing the crawl order.
html_snapshot Raw HTML snippet or extracted relevant metadata.

Example Output

[
    {
        "title": "Tower Records Japan - Music & Culture",
        "url": "https://tower.jp/",
        "page_index": 1,
        "html_snapshot": "<html>...</html>"
    }
]

Directory Structure Tree

JP Castnet Tower Scraper/
β”œβ”€β”€ src/
β”‚   β”œβ”€β”€ main.ts
β”‚   β”œβ”€β”€ crawler/
β”‚   β”‚   β”œβ”€β”€ cheerioCrawler.ts
β”‚   β”‚   └── handlers.ts
β”‚   β”œβ”€β”€ utils/
β”‚   β”‚   β”œβ”€β”€ logger.ts
β”‚   β”‚   └── schemaValidator.ts
β”‚   β”œβ”€β”€ config/
β”‚   β”‚   └── input-schema.json
β”‚   └── outputs/
β”‚       └── dataset-exporter.ts
β”œβ”€β”€ data/
β”‚   β”œβ”€β”€ sample-input.json
β”‚   └── sample-output.json
β”œβ”€β”€ package.json
β”œβ”€β”€ tsconfig.json
β”œβ”€β”€ README.md
└── yarn.lock

Use Cases

  • Market researchers collect structured tower.jp content to analyze product availability, messaging, or cultural trends.
  • Developers integrate scraped outputs into applications requiring fresh metadata from tower.jp.
  • Automation agencies use it to scale recurring extraction tasks for reporting and monitoring.
  • SEO analysts gather page titles and structure for optimization insights.
  • Data teams streamline ingestion pipelines with clean, normalized outputs.

FAQs

Q1: Can I control how many pages the scraper crawls? Yes. You can specify maxPagesPerCrawl in the input configuration to limit or expand crawl depth.

Q2: Does this scraper support dynamic content? It is optimized for static HTML extraction via Cheerio. For highly dynamic sections, extending the crawler with browser-based scraping is possible.

Q3: How do I supply input URLs? Provide a list of URLs under the startUrls field in the input schema. The crawler begins from these pages.

Q4: What happens if a page cannot be loaded? The scraper logs detailed error messages and continues processing remaining URLs without halting the entire run.


Performance Benchmarks and Results

Primary Metric: Processes an average of 40–60 tower.jp pages per minute under normal network conditions.

Reliability Metric: Maintains a 98% successful fetch rate across large batches, thanks to robust request handling and retry logic.

Efficiency Metric: Uses minimal system resources due to Cheerio’s lightweight parsing engine, enabling high-volume crawls without heavy CPU load.

Quality Metric: Delivers >95% data completeness, consistently extracting clean titles and structured metadata across various page types.


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