Memory Management Considerations for Long-Running Mechanize Scripts
When building web scraping applications that run for extended periods, memory management becomes a critical concern. Long-running Mechanize scripts can accumulate memory over time, leading to performance degradation or system crashes. This comprehensive guide covers essential memory management strategies and best practices for maintaining efficient, stable Mechanize applications.
Understanding Memory Usage in Mechanize
Mechanize, being a Ruby library, inherits Ruby's garbage collection behavior. However, certain objects and patterns in web scraping can lead to memory accumulation that requires careful management.
Common Memory Consumers
- Page objects and DOM trees
- HTTP response bodies
- Cached cookies and session data
- File downloads and temporary data
- Error logs and debugging information
Essential Memory Management Techniques
1. Explicit Page Cleanup
The most important practice is to explicitly clear page objects when they're no longer needed:
require 'mechanize'
agent = Mechanize.new
# Process multiple pages
urls.each do |url|
page = agent.get(url)
# Extract data
data = extract_data(page)
process_data(data)
# Explicit cleanup
page = nil
# Force garbage collection periodically
GC.start if urls.index(url) % 100 == 0
end
2. Limit Page History
Mechanize maintains a history of visited pages by default. For long-running scripts, disable or limit this:
agent = Mechanize.new
agent.max_history = 0 # Disable history completely
# Or set a reasonable limit
agent.max_history = 5 # Keep only last 5 pages
3. Session and Cookie Management
Regularly clean up accumulated cookies and session data:
agent = Mechanize.new
# Clear cookies periodically
def cleanup_session(agent, iteration)
if iteration % 1000 == 0
agent.cookie_jar.clear!
puts "Cleared cookies at iteration #{iteration}"
end
end
# In your scraping loop
(1..10000).each do |i|
page = agent.get("https://example.com/page/#{i}")
process_page(page)
cleanup_session(agent, i)
page = nil
end
4. Connection Pool Management
Manage HTTP connections efficiently to prevent connection leaks:
agent = Mechanize.new
# Configure connection limits
agent.keep_alive = false # Disable keep-alive for long-running scripts
agent.open_timeout = 10
agent.read_timeout = 30
# Reset agent periodically for very long-running scripts
def reset_agent_periodically(current_agent, iteration)
if iteration % 5000 == 0
current_agent = Mechanize.new
configure_agent(current_agent)
puts "Reset agent at iteration #{iteration}"
end
current_agent
end
Advanced Memory Management Strategies
Monitoring Memory Usage
Implement memory monitoring to track your script's performance:
require 'get_process_mem'
class MemoryMonitor
def initialize(threshold_mb = 500)
@threshold = threshold_mb
@mem = GetProcessMem.new
end
def check_memory(iteration)
current_mb = @mem.mb
if current_mb > @threshold
puts "Warning: Memory usage #{current_mb}MB exceeds threshold #{@threshold}MB at iteration #{iteration}"
# Force garbage collection
GC.start
# Log after cleanup
after_gc = GetProcessMem.new.mb
puts "Memory after GC: #{after_gc}MB"
return true if after_gc > @threshold * 0.8 # Still high after GC
end
false
end
end
# Usage in scraping script
monitor = MemoryMonitor.new(400) # 400MB threshold
urls.each_with_index do |url, index|
page = agent.get(url)
process_page(page)
page = nil
# Check memory every 50 iterations
if index % 50 == 0
if monitor.check_memory(index)
puts "Consider implementing additional cleanup strategies"
end
end
end
Batch Processing with Restart Strategy
For extremely long-running operations, implement a restart strategy:
class BatchProcessor
def initialize(batch_size = 1000)
@batch_size = batch_size
@processed_file = 'processed_urls.txt'
end
def process_urls(urls)
processed = load_processed_urls
remaining_urls = urls - processed
remaining_urls.each_slice(@batch_size) do |batch|
process_batch(batch)
# Restart Ruby process after each batch for maximum memory cleanup
if batch != remaining_urls.last(@batch_size)
save_progress(batch)
exec($0, *ARGV) # Restart the script
end
end
end
private
def process_batch(urls)
agent = Mechanize.new
configure_agent(agent)
urls.each do |url|
begin
page = agent.get(url)
process_page(page)
mark_as_processed(url)
page = nil
rescue => e
log_error(url, e)
end
end
end
def load_processed_urls
return [] unless File.exist?(@processed_file)
File.readlines(@processed_file).map(&:strip)
end
def mark_as_processed(url)
File.open(@processed_file, 'a') { |f| f.puts url }
end
end
File Handling and Temporary Data
Manage file downloads and temporary data carefully:
require 'tempfile'
def download_and_process_file(agent, url)
# Use temporary files that auto-cleanup
Tempfile.create(['download', '.tmp']) do |temp_file|
agent.get(url).save(temp_file.path)
# Process the file
result = process_file(temp_file.path)
# File automatically deleted when block exits
return result
end
end
# For persistent files, ensure cleanup
def download_with_manual_cleanup(agent, url, filename)
begin
agent.get(url).save(filename)
process_file(filename)
ensure
File.delete(filename) if File.exist?(filename)
end
end
Error Handling and Resource Cleanup
Implement robust error handling that includes resource cleanup:
class RobustScraper
def initialize
@agent = Mechanize.new
@error_count = 0
@max_errors = 100
end
def scrape_urls(urls)
urls.each_with_index do |url, index|
begin
page = scrape_page(url)
process_page(page)
rescue Net::TimeoutError, Mechanize::ResponseCodeError => e
handle_network_error(url, e, index)
rescue => e
handle_general_error(url, e, index)
ensure
# Always cleanup
page = nil
GC.start if index % 100 == 0
# Check if too many errors
break if @error_count > @max_errors
end
end
end
private
def handle_network_error(url, error, index)
@error_count += 1
puts "Network error at #{url} (iteration #{index}): #{error.message}"
# Reset agent on persistent network issues
if @error_count % 20 == 0
@agent = Mechanize.new
puts "Reset agent due to network errors"
end
end
def handle_general_error(url, error, index)
@error_count += 1
puts "General error at #{url} (iteration #{index}): #{error.message}"
# Force cleanup on errors
GC.start
end
end
Configuration Best Practices
Optimize Mechanize configuration for long-running scripts:
def configure_mechanize_for_long_running
agent = Mechanize.new
# Memory-friendly settings
agent.max_history = 0
agent.keep_alive = false
# Timeout settings to prevent hanging
agent.open_timeout = 10
agent.read_timeout = 30
agent.idle_timeout = 5
# User agent rotation to avoid detection
agent.user_agent_alias = 'Windows Chrome'
# Disable automatic file parsing for large files
agent.post_connect_hooks << lambda do |_agent, response|
if response['content-length'].to_i > 10_000_000 # 10MB
response.body = '[Large file content skipped]'
end
end
agent
end
Monitoring and Alerting
For production environments, implement monitoring:
require 'logger'
class ScrapingMonitor
def initialize
@logger = Logger.new('scraping.log')
@start_time = Time.now
@processed_count = 0
end
def log_progress(iteration, memory_mb)
@processed_count += 1
if iteration % 500 == 0
elapsed = Time.now - @start_time
rate = @processed_count / elapsed
@logger.info({
iteration: iteration,
memory_mb: memory_mb,
elapsed_seconds: elapsed.round(2),
processing_rate: rate.round(2),
timestamp: Time.now.iso8601
}.to_json)
end
end
def log_memory_warning(memory_mb, threshold)
@logger.warn("Memory usage #{memory_mb}MB exceeds threshold #{threshold}MB")
end
end
Performance Optimization Tips
- Use streaming for large responses when possible
- Implement circuit breakers for failing endpoints
- Consider using background job processors like Sidekiq for better resource management
- Profile your code regularly using tools like
ruby-prof - Monitor system resources beyond just memory (CPU, disk I/O)
Similar to how you might handle browser sessions in Puppeteer for managing browser-based scraping resources, Mechanize requires careful session and connection management for optimal performance.
Testing Memory Management
Create tests to verify your memory management strategies:
require 'rspec'
require 'get_process_mem'
RSpec.describe 'Memory Management' do
it 'should not exceed memory threshold during long scraping' do
initial_memory = GetProcessMem.new.mb
# Run a subset of your scraping logic
agent = Mechanize.new
agent.max_history = 0
100.times do |i|
page = agent.get('https://example.com')
# Process page
page = nil
GC.start if i % 25 == 0
end
final_memory = GetProcessMem.new.mb
memory_increase = final_memory - initial_memory
expect(memory_increase).to be < 50 # Shouldn't increase more than 50MB
end
end
Alternative Approaches
For applications requiring extensive memory optimization, consider these alternatives:
Background Job Processing
Instead of running continuous scripts, break work into smaller background jobs:
class ScrapingJob
include Sidekiq::Worker
def perform(url_batch)
agent = Mechanize.new
agent.max_history = 0
url_batch.each do |url|
page = agent.get(url)
process_page(page)
page = nil
end
# Job ends, memory is automatically freed
end
end
# Queue jobs in batches
urls.each_slice(100) do |batch|
ScrapingJob.perform_async(batch)
end
Microservice Architecture
For high-volume scraping, consider implementing distributed scraping patterns similar to those used in browser automation where each service handles a specific portion of the work.
Conclusion
Effective memory management in long-running Mechanize scripts requires a combination of explicit resource cleanup, strategic garbage collection, monitoring, and robust error handling. By implementing these practices, you can build stable, efficient web scraping applications that can run continuously without memory-related issues.
Remember to regularly profile your applications, monitor memory usage in production, and adjust these strategies based on your specific use cases and requirements. The key is finding the right balance between performance and resource utilization for your particular scraping workload.
