Is it possible to make asynchronous requests with HTTParty?

No, HTTParty does not support asynchronous requests natively. HTTParty is designed for simplicity and readability, providing a clean interface for synchronous HTTP requests. When you make a request using HTTParty, the call is blocking—it waits for the response before continuing to the next line of code.

Why HTTParty is Synchronous

HTTParty prioritizes ease of use over performance optimization. Its API is built around blocking operations:

require 'httparty'

# This blocks until the response is received
response = HTTParty.get('https://api.example.com/data')
puts response.body

Best Alternatives for Asynchronous HTTP Requests

1. Typhoeus with Hydra

Typhoeus provides true asynchronous HTTP requests through its Hydra interface:

require 'typhoeus'

# Create multiple requests
request1 = Typhoeus::Request.new('https://api.example.com/endpoint1')
request2 = Typhoeus::Request.new('https://api.example.com/endpoint2')

# Set up callbacks
request1.on_complete do |response|
  puts "Request 1 completed: #{response.code}"
end

request2.on_complete do |response|
  puts "Request 2 completed: #{response.code}"
end

# Execute requests concurrently
hydra = Typhoeus::Hydra.hydra
hydra.queue(request1)
hydra.queue(request2)
hydra.run # Runs both requests simultaneously

2. Concurrent Ruby with HTTParty

Use concurrent-ruby to make HTTParty requests in parallel:

require 'httparty'
require 'concurrent'

urls = ['https://api.example.com/1', 'https://api.example.com/2', 'https://api.example.com/3']

# Create futures for concurrent execution
futures = urls.map do |url|
  Concurrent::Future.execute do
    HTTParty.get(url)
  end
end

# Wait for all requests to complete
responses = futures.map(&:value)
responses.each_with_index do |response, index|
  puts "Response #{index + 1}: #{response.code}"
end

3. Async Gem (Modern Ruby Approach)

The async gem provides a modern approach to asynchronous programming:

require 'async'
require 'async/http'

Async do
  internet = Async::HTTP::Internet.new

  # Make multiple concurrent requests
  tasks = [
    async { internet.get('https://api.example.com/1') },
    async { internet.get('https://api.example.com/2') },
    async { internet.get('https://api.example.com/3') }
  ]

  # Wait for all to complete
  responses = tasks.map(&:wait)
  responses.each_with_index do |response, index|
    puts "Response #{index + 1}: #{response.status}"
  end
ensure
  internet&.close
end

4. Thread Pool Approach

For simpler cases, use Ruby's built-in thread pool:

require 'httparty'

urls = ['https://api.example.com/1', 'https://api.example.com/2']
threads = []

urls.each do |url|
  threads << Thread.new do
    response = HTTParty.get(url)
    puts "#{url}: #{response.code}"
  end
end

# Wait for all threads to complete
threads.each(&:join)

Rails-Specific Solutions

Background Jobs

For Rails applications, consider background jobs for HTTP requests:

# app/jobs/http_request_job.rb
class HttpRequestJob < ApplicationJob
  def perform(url, callback_method = nil)
    response = HTTParty.get(url)

    # Process response or call callback
    send(callback_method, response) if callback_method
  end
end

# Usage
HttpRequestJob.perform_later('https://api.example.com/data')

Action Cable for Real-time Updates

Combine background jobs with Action Cable for real-time updates:

class ApiRequestJob < ApplicationJob
  def perform(url, user_id)
    response = HTTParty.get(url)

    ActionCable.server.broadcast(
      "user_#{user_id}",
      { data: response.parsed_response, status: response.code }
    )
  end
end

Performance Considerations

• Typhoeus: Best for high-volume concurrent requests
• Concurrent-ruby: Good balance of simplicity and performance
• Async gem: Modern approach with excellent memory efficiency
• Background jobs: Best for long-running or non-critical requests

Thread Safety Notes

When using concurrent approaches:

1. Ensure shared resources are thread-safe
2. Use proper synchronization mechanisms (Mutex, Queue)
3. Be aware of Ruby's Global Interpreter Lock (GIL) limitations
4. Consider using JRuby or TruffleRuby for true parallelism

Choose the approach that best fits your application's architecture and performance requirements.