Rust Actor Example

This project is a boilerplate template showing how to build an Apify Actor using Rust. It provides all the necessary scaffolding — from configuration and input schema to request handling and dataset output — to help you start building Rust-based actors for web scraping or automation jobs.

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Introduction

The Rust Actor Example offers a ready-to-use framework for developers who prefer Rust and want to deploy their code as Apify Actors. With this template, you get input validation, dataset handling, and a clean project structure out of the box, making it easier to focus on custom logic and tasks.

Why It’s Useful

• Lets you build Apify Actors using Rust instead of JavaScript or Python.
• Includes input schema and dataset export support.
• Provides a standardized project layout for cleaner code and maintainability.
• Great for high-performance scraping or tasks where Rust speed and safety matter.

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Features

Feature	Description
Rust-based Actor Template	Full boilerplate for building Apify Actors in Rust.
Input Schema Validation	Automatically validate actor inputs using defined schema.
Dataset Export	Built-in support for pushing results to Apify datasets.
Modular Structure	Clean separation of config, handlers, and logic for easy extension.
Language-Level Safety & Performance	Benefit from Rust’s performance, concurrency, and compile-time checks over JS/Python.

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What This Actor Extracts (Default Template)

Field Name	Field Description
url	The URL that was processed (if implemented).
status	Placeholder for status or result metadata.

Note: This is just a boilerplate — you’ll need to add your own logic to scrape or process data as needed.

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Example Output

[
  {
    "url": "https://example.com",
    "status": "ok"
  }
]

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Directory Structure Tree

rust-actor-example/
├── src/
│   ├── main.rs
│   ├── lib.rs
│   ├── handlers.rs
│   └── config/
│       └── input_schema.json
├── Cargo.toml
├── README.md
└── Dockerfile

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Use Cases

• Developers comfortable with Rust who want to build Apify Actors with higher performance.
• Performance-critical scraping tasks where Rust’s speed and safety are beneficial.
• Automation workflows requiring compiled binaries rather than interpreted scripts.
• Teams that want typed, memory-safe data processing instead of dynamic languages like JS or Python.
• Learning — a good starting point for exploring how Rust integrates with Apify’s Actor infrastructure.

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FAQs

Does this actor do actual scraping out of the box?
No — it's a boilerplate template. You need to add request handling and parsing logic based on your target site.

Can I push JSON data to Apify datasets?
Yes — dataset export is included; just call the appropriate method in your custom logic.

Is input validation supported?
Yes — you can define an input schema that will be validated before execution.

Why use Rust instead of JS or Python?
Rust provides performance, concurrency, and memory-safety guarantees, which can be crucial for heavy or high-frequency scraping tasks.

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Performance Benchmarks and Results

Primary Metric:
Rust’s performance tends to yield faster startup and execution times compared to interpreted languages.

Reliability Metric:
Compile-time safety reduces runtime errors and prevents many classes of bugs common in dynamic languages.

Efficiency Metric:
Low-overhead concurrency and efficient memory management make it suitable for large-scale scraping.

Quality Metric:
Strong typing and explicit error handling help maintain data consistency and avoid unexpected behavior in production scraping tasks.

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