Pathik

A high-performance web crawler implemented in Go with Python and JavaScript bindings. It converts web pages to both HTML and Markdown formats.

Features

• Fast crawling with Go's concurrency model
• Clean content extraction
• Markdown conversion
• Parallel URL processing
• Cloudflare R2 integration
• Kafka streaming support with configurable buffer sizes
• Enhanced security with URL and IP validation
• Memory-efficient (uses ~10x less memory than browser automation tools)
• Automatic binary version management
• Customizable compression options (Gzip and Snappy support)
• Session-based message tracking for multi-user environments
• Type-safe API with validation using satya
• Improved error handling and structured outputs

Performance Benchmarks

Memory Usage Comparison

Pathik is significantly more memory-efficient than browser automation tools like Playwright:

Parallel Crawling Performance

Parallel crawling significantly improves performance when processing multiple URLs. Our benchmarks show:

Python Performance

Testing with 5 URLs:
- Parallel crawling completed in 7.78 seconds
- Sequential crawling completed in 18.52 seconds
- Performance improvement: 2.38x faster with parallel crawling

JavaScript Performance

Testing with 5 URLs:
- Parallel crawling completed in 6.96 seconds
- Sequential crawling completed in 21.07 seconds
- Performance improvement: 3.03x faster with parallel crawling

Parallel crawling is enabled by default when processing multiple URLs, but you can explicitly control it with the parallel parameter.

Installation

pip install pathik

The package will automatically download the correct binary for your platform from GitHub releases on first use.

Binary Version Management

Pathik now automatically handles binary version checking and updates:

• When you install or upgrade the Python package, it will check if the binary matches the package version

• If the versions don't match, it will automatically download the correct binary

• You can manually check and update the binary with:

  # Force binary update
  import pathik
  from pathik.crawler import get_binary_path
  binary_path = get_binary_path(force_download=True)
  

• Command line options:

  # Check if binary is up to date
  pathik --check-binary
  
  # Force update of the binary
  pathik --force-update-binary
  

This ensures you always have the correct binary version with all the latest features, especially when using new functionality like Kafka streaming with session IDs.

Usage

Python API

Standard API

import pathik
from pathik.crawler import CrawlerError

try:
    # Crawl a single URL with named parameters
    result = pathik.crawl(urls="https://example.com")
    
    # Check result
    if "https://example.com" in result:
        url_result = result["https://example.com"]
        if url_result.get("success", False):
            print(f"HTML saved to: {url_result.get('html')}")
            print(f"Markdown saved to: {url_result.get('markdown')}")
        else:
            print(f"Error: {url_result.get('error')}")
    
    # Crawl multiple URLs in parallel
    results = pathik.crawl(
        urls=["https://example.com", "https://httpbin.org/html"],
        parallel=True  # This is the default
    )
    
    # To disable parallel crawling
    results = pathik.crawl(
        urls=["https://example.com", "https://httpbin.org/html"],
        parallel=False
    )
    
    # To specify output directory
    results = pathik.crawl(
        urls="https://example.com",
        output_dir="./output"
    )
    
except CrawlerError as e:
    print(f"Crawler error: {e}")

Type-Safe API (Recommended)

The type-safe API provides enhanced validation, better error messages, and structured outputs:

import pathik
from pathik import safe_crawl
from pathik.schema import CrawlParams

# Crawl a single URL with validation
try:
    # Create structured parameters
    params = CrawlParams(
        urls="https://example.com",
        output_dir="./output"
    )
    
    # Use the safe API
    result = safe_crawl(
        urls="https://example.com",
        output_dir="./output"
    )
    
    # Access the validated results
    for url, data in result.items():
        print(f"URL: {url}")
        print(f"HTML file: {data.get('html')}")
        print(f"Markdown file: {data.get('markdown')}")
        
except ValueError as e:
    print(f"Validation error: {e}")
    
# Alternatively, use keyword arguments directly
try:
    result = safe_crawl(
        urls=["https://example.com", "https://httpbin.org/html"],
        parallel=True,
        output_dir="./output"
    )
    
    # Access results
    for url, data in result.items():
        print(f"URL: {url}")
        print(f"Success: {data.get('success', False)}")
        if data.get('success', False):
            print(f"Files: {data.get('html')}, {data.get('markdown')}")
        else:
            print(f"Error: {data.get('error')}")
            
except ValueError as e:
    print(f"Validation error: {e}")

R2 Upload

import pathik
import uuid
from pathik.crawler import CrawlerError

try:
    # Generate a UUID or use your own
    my_uuid = str(uuid.uuid4())
    
    # Crawl and upload to R2
    results = pathik.crawl_to_r2(
        urls="https://example.com",
        uuid_str=my_uuid
    )
    
    if "https://example.com" in results:
        r2_result = results["https://example.com"]
        print(f"UUID: {r2_result.get('uuid')}")
        print(f"R2 HTML key: {r2_result.get('r2_html_key')}")
        print(f"R2 Markdown key: {r2_result.get('r2_markdown_key')}")
    
    # Upload multiple URLs
    results = pathik.crawl_to_r2(
        urls=["https://example.com", "https://httpbin.org/html"],
        uuid_str=my_uuid
    )
    
except CrawlerError as e:
    print(f"Crawler error: {e}")

Kafka Streaming

Standard API

import pathik
import uuid

# Generate a session ID for tracking
session_id = str(uuid.uuid4())

# Stream a single URL to Kafka
result = pathik.stream_to_kafka(
    urls="https://example.com",
    session=session_id
)

if "https://example.com" in result:
    if result["https://example.com"].get("success", False):
        print(f"Successfully streamed")

# Stream multiple URLs
results = pathik.stream_to_kafka(
    urls=["https://example.com", "https://httpbin.org/html"],
    content_type="both",     # Options: "html", "markdown", or "both"
    topic="pathik_data",     # Optional custom topic
    session=session_id,      # Optional session ID
    parallel=True            # Process URLs in parallel (default)
)

# Check results
for url, status in results.items():
    if status.get("success", False):
        print(f"Successfully streamed {url}")
        if "details" in status:
            details = status["details"]
            print(f"  Topic: {details.get('topic')}")
            print(f"  HTML Content: {details.get('html_file')}")
            print(f"  Markdown Content: {details.get('markdown_file')}")
    else:
        print(f"Failed to stream {url}: {status.get('error')}")

Type-Safe API for Kafka Streaming (Recommended)

For applications requiring robust input validation and error handling:

import pathik
from pathik.safe_api import safe_stream_to_kafka
from pathik.schema import KafkaStreamParams
import uuid

try:
    # Create structured parameters
    params = KafkaStreamParams(
        urls=["https://example.com", "https://httpbin.org/html"],
        content_type="both",
        topic="pathik_data",
        session_id=str(uuid.uuid4()),
        parallel=True,
        # Compression options
        compression_type="gzip",       # Options: gzip, snappy, lz4, zstd
        max_message_size=15728640,     # 15MB message size limit (default: 10MB)
        buffer_memory=157286400        # 150MB buffer memory (default: 100MB)
    )
    
    # Stream content with validation
    result = safe_stream_to_kafka(params)
    
    # Process validated results
    if result.success:
        print(f"Successfully streamed {len(result.results)} URLs")
        print(f"Session ID: {result.session_id}")
        
        for url, stream_info in result.results.items():
            if stream_info.success:
                print(f"✅ {url}: Success")
                print(f"  Topic: {stream_info.topic}")
                print(f"  Content types: {stream_info.content_types}")
            else:
                print(f"❌ {url}: Failed - {stream_info.error}")
    else:
        print(f"Streaming operation failed: {result.error}")
        
except ValueError as e:
    print(f"Validation error: {e}")

Compression Options for Kafka Streaming

When streaming large web pages or handling high volumes of content, you can optimize performance by configuring compression:

# Optimize for best compression ratio (slower)
params = KafkaStreamParams(
    urls=urls,
    compression_type="gzip",
    max_message_size=15728640,    # 15MB
    buffer_memory=157286400       # 150MB
)

# Optimize for speed (moderate compression)
params = KafkaStreamParams(
    urls=urls,
    compression_type="snappy",
    max_message_size=10485760,    # 10MB (default)
    buffer_memory=104857600       # 100MB (default)
)

# Optimize for throughput (fastest)
params = KafkaStreamParams(
    urls=urls,
    compression_type="lz4",
    max_message_size=10485760,
    buffer_memory=104857600
)

# Best balance of speed and compression
params = KafkaStreamParams(
    urls=urls,
    compression_type="zstd",
    max_message_size=10485760,
    buffer_memory=104857600
)

Choose the appropriate configuration based on your specific requirements:

• For large pages with a lot of text, use gzip or zstd
• For high-volume crawling, use lz4 or snappy
• Adjust message size and buffer memory based on content size and available resources

Command Line

# Crawl a single URL
pathik crawl https://example.com

# Crawl multiple URLs
pathik crawl https://example.com https://httpbin.org/html

# Specify output directory
pathik crawl -o ./output https://example.com

# Use sequential (non-parallel) mode
pathik crawl -s https://example.com https://httpbin.org/html

# Upload to R2 (Cloudflare)
pathik r2 https://example.com

# Stream crawled content to Kafka
pathik kafka https://example.com

# Stream only HTML content to Kafka
pathik kafka -c html https://example.com

# Stream only Markdown content to Kafka
pathik kafka -c markdown https://example.com

# Stream to a specific Kafka topic
pathik kafka -t user1_crawl_data https://example.com

# Add a session ID for multi-user environments
pathik kafka --session user123 https://example.com

# Combine options
pathik kafka -c html -t user1_data --session user123 https://example.com

Sample Kafka Streaming Examples

We provide two example scripts in the examples directory to demonstrate Kafka streaming:

Basic Example (native_kafka_demo.py)

from pathik import stream_to_kafka

# Stream a URL to Kafka
results = stream_to_kafka(
    urls="https://example.com",
    content_type="both",
    topic="pathik_crawl_data",
    session="demo-session-123",
    parallel=True
)

# Check results
for url, result in results.items():
    print(f"URL: {url}")
    print(f"Success: {result.get('success', False)}")
    if result.get('success', False) and 'details' in result:
        details = result['details']
        print(f"Topic: {details.get('topic')}")
        print(f"HTML file: {details.get('html_file')}")
        print(f"Markdown file: {details.get('markdown_file')}")

Type-Safe Example (safe_kafka_demo.py)

from pathik import safe_stream_to_kafka
from pathik.schema import KafkaStreamParams

# Create validated parameters
params = KafkaStreamParams(
    urls=["https://example.com"],
    content_type="both",
    topic="pathik_crawl_data",
    session_id="safe-demo-123",
    parallel=True
)

try:
    # Stream with validation
    result = safe_stream_to_kafka(params)
    
    if result.success:
        print(f"Successfully streamed {len(result.results)} URLs")
        for url, info in result.results.items():
            if info.success:
                print(f"✅ {url}")
                print(f"  Topic: {info.topic}")
                print(f"  Files: {info.html_file}, {info.markdown_file}")
            else:
                print(f"❌ {url}: {info.error}")
    else:
        print(f"Operation failed: {result.error}")
        
except ValueError as e:
    print(f"Validation error: {e}")