**Design a Web Crawler**

### Introduction

In this document, we will explore the design of a system for building a web crawler. The goal is to understand the requirements, challenges, and architectural decisions involved in building such a system.

### Requirements

#### Functional Requirements

The core functionalities the system must provide include:

* Crawling: Ability to crawl and extract relevant data from target websites
* Data storage: Storage of extracted data in a database or file system
* Filtering: Option to filter extracted data based on predefined criteria (e.g., keywords, domains)
* Reporting: Generation of reports summarizing crawled data

Specific use cases or scenarios include:

* Crawling a list of targeted websites for market research or competitor analysis
* Extracting product information from e-commerce sites for inventory management
* Indexing web pages for search engines or other applications

#### Non-Functional Requirements

The system must also consider non-functional requirements, such as:

* Performance: Ability to handle increased load and maintain performance under heavy usage
* Scalability: Capacity to expand and adapt to changing demands without sacrificing performance
* Reliability: High availability and low latency to ensure consistent service delivery
* Security: Protection of crawled data and system from unauthorized access or tampering

### High-Level Architecture

The high-level architecture of the web crawler system consists of several key components:

1. **Crawler**: Responsible for crawling target websites, extracting relevant data, and storing it in a database.
2. **Database**: Stores extracted data, allowing for efficient querying and retrieval.
3. **Filtering Engine**: Applies filtering criteria to extracted data, removing irrelevant or redundant information.
4. **Reporting Module**: Generates reports summarizing crawled data, providing insights and analysis.

### Database Schema

The database schema consists of the following tables:

* **Websites** (website_id, domain, crawl_frequency)
* **Pages** (page_id, website_id, url, content)
* **Extracted Data** (data_id, page_id, data_type, value)

Relationships:

* A website can have multiple pages.
* A page belongs to one website.
* Extracted data is associated with a specific page.

Indexing strategies include:

* Indexing the Websites table on the crawl_frequency column for efficient filtering
* Creating an index on the Pages table's url column for fast lookup

### API Design

The web crawler system provides several key endpoints:

* **GET /crawl**: Initiates crawling of target websites and pages.
* **GET /data/{page_id}**: Retrieves extracted data associated with a specific page.
* **POST /filter**: Applies filtering criteria to extracted data.

Example requests and responses include:

* **GET /crawl?website_id=1&pages=5**: Crawl 5 pages from website with ID 1.
* **GET /data/123**: Retrieve extracted data for page with ID 123.
* **POST /filter?keyword=marketing**: Filter extracted data by keyword "marketing".

### OpenAPI Specification

The OpenAPI spec for the APIs is as follows:

```yaml
openapi: 3.0.2
info:
  title: Web Crawler API
  description: API for crawling, extracting, and filtering web pages.
  version: 1.0.0
paths:
  /crawl:
    get:
      summary: Initiate crawling of target websites and pages.
      responses:
        200:
          description: Crawling initiated successfully.
        500:
          description: Error initiating crawling.
  /data/{page_id}:
    get:
      summary: Retrieve extracted data associated with a specific page.
      parameters:
        - in: path
          name: page_id
          required: true
          schema:
            type: integer
      responses:
        200:
          description: Extracted data retrieved successfully.
        404:
          description: Page not found.
  /filter:
    post:
      summary: Apply filtering criteria to extracted data.
      requestBody:
        content:
          application/json:
            schema:
              type: object
              properties:
                keyword:
                  type: string
        required: true
      responses:
        200:
          description: Filtering applied successfully.
        500:
          description: Error applying filtering.

```

### System Flow

The system flow is as follows:

1. The Crawler component initiates crawling of target websites and pages, extracting relevant data.
2. The extracted data is stored in the Database.
3. The Filtering Engine applies filtering criteria to the extracted data, removing irrelevant or redundant information.
4. The Reporting Module generates reports summarizing crawled data, providing insights and analysis.

### Challenges and Solutions

Potential challenges include:

* Handling increased load and maintaining performance under heavy usage.
	+ Solution: Implement load balancing, caching, and parallel processing.
* Ensuring reliability and availability of the system.
	+ Solution: Implement redundant systems, monitoring, and failover procedures.
* Protecting crawled data and system from unauthorized access or tampering.
	+ Solution: Implement encryption, authentication, and access control mechanisms.

### Scalability and Performance

Strategies to ensure scalability and performance include:

* Load balancing to distribute traffic across multiple instances.
* Caching frequently accessed data to reduce load on the system.
* Parallel processing to handle increased load and maintain performance.
* Monitoring system performance and adjusting configurations as needed.

### Security Considerations

Security measures to protect the system and its data include:

* Encryption of crawled data and system communications.
* Authentication and access control mechanisms to restrict unauthorized access.
* Regular security audits and penetration testing to identify vulnerabilities.

### ASCII Diagrams

Here is a simple ASCII diagram illustrating the architecture:
```
          +---------------+
          |  Crawler    |
          +---------------+
                  |
                  |  Extracted
                  |  data stored
                  v
          +---------------+
          | Database   |
          +---------------+
                  |
                  | Filtering
                  | Engine applies
                  | criteria
                  v
          +---------------+
          | Reporting  |
          +---------------+
```

### Sample SQL Schema

Here is a sample SQL script for creating the database schema:
```sql
CREATE TABLE Websites (
    website_id INT PRIMARY KEY,
    domain VARCHAR(255),
    crawl_frequency INT
);

CREATE TABLE Pages (
    page_id INT PRIMARY KEY,
    website_id INT,
    url VARCHAR(255),
    content TEXT,
    FOREIGN KEY (website_id) REFERENCES Websites(website_id)
);

CREATE TABLE ExtractedData (
    data_id INT PRIMARY KEY,
    page_id INT,
    data_type VARCHAR(255),
    value TEXT,
    FOREIGN KEY (page_id) REFERENCES Pages(page_id)
);
```

### Conclusion

In this blog post, we explored the design of a web crawler system that extracts and filters data from target websites. We discussed the key components, API design, database schema, system flow, challenges and solutions, scalability and performance strategies, security considerations, and provided an ASCII diagram and sample SQL script for creating the database schema. This system is designed to be scalable, secure, and easy to maintain, making it suitable for a wide range of applications.