CSFD Scraper

CSFD Scraper is a high-performance tool for extracting structured movie and TV data from CSFD pages with exceptional speed and efficiency. It solves the problem of slow, resource-heavy crawling by delivering clean, reliable film metadata at scale. Built for developers and data teams who need fast access to movie ratings, reviews, and credits.

     

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Introduction

CSFD Scraper collects detailed information from CSFD movie, series, and user pages and converts it into structured data suitable for analytics and automation. It removes the overhead of rendering and focuses on speed, consistency, and low resource usage. This project is ideal for developers, analysts, and researchers working with movie datasets.

High-Performance Movie Data Extraction

• Optimized for static HTML parsing without rendering
• Supports movies, series, episodes, users, reviews, and ratings
• Handles large-scale URL collections through sitemap discovery
• Designed for predictable memory usage and stable throughput

Features

Feature	Description
Multiple request types	Extract views, reviews, ratings, users, and sitemap URLs.
Sitemap crawling	Collects all published CSFD URLs in a single controlled process.
Header overrides	Supports global and per-request header customization.
High concurrency	Parallel request handling for faster data collection.
Structured outputs	Returns normalized JSON objects ready for processing.

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What Data This Scraper Extracts

Field Name	Field Description
header_name	Official movie or series title.
rating	Percentage rating score.
rating_votes_count	Number of user votes.
genres	List of associated genres.
origin	Country, year, and runtime information.
plot_full	Full plot description text.
creators	Directors, writers, cast, and crew details.
user_name	Username of reviewer or rater.
star_rating	User star rating value.
comment	Full review or comment text.
date	Date of review or rating submission.

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Example Output

[
      {
        "request_type": "View",
        "url": "https://www.csfd.cz/film/17592-ctyri-svatby-a-jeden-pohreb/prehled/",
        "data": {
          "header_name": "Čtyři svatby a jeden pohřeb",
          "rating": "72%",
          "rating_votes_count": 14484,
          "genres": ["Komedie", "Romantický", "Drama"],
          "origin": "Velká Británie / USA, 1994",
          "plot_full": "Snímek vypráví příběh Charlese..."
        }
      }
    ]

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Directory Structure Tree

CSFD Scraper/
├── src/
│   ├── main.rs
│   ├── parser/
│   │   ├── view.rs
│   │   ├── reviews.rs
│   │   ├── ratings.rs
│   │   └── sitemap.rs
│   ├── models/
│   │   ├── movie.rs
│   │   ├── user.rs
│   │   └── review.rs
│   └── config/
│       └── settings.json
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── Cargo.toml
└── README.md

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Use Cases

• Data analysts use it to collect CSFD ratings and reviews, so they can analyze audience sentiment.
• Developers use it to build movie recommendation systems with structured film metadata.
• Researchers use it to study trends in Czech and international cinema.
• Content platforms use it to enrich movie catalogs with ratings, genres, and cast data.

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FAQs

Does this scraper support both movies and series? Yes, it supports movies, series, episodes, and related subpages using dedicated request types.

Can I extract user reviews and ratings separately? Yes, reviews and ratings are handled as independent request types with paginated collection.

How does it handle large datasets? Results are split into manageable parts to maintain stability and consistent output delivery.

Is request order preserved in output? No, output order may differ due to concurrency, but each result includes identifiers for tracking.

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Performance Benchmarks and Results

Primary Metric: Processes static CSFD pages in milliseconds per request under normal load.

Reliability Metric: Sustains a high success rate across large URL batches with retry handling.

Efficiency Metric: Operates within low memory limits while maintaining high throughput.

Quality Metric: Extracted fields consistently match on-page content with high completeness.

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