Ruby on Rails Performance Optimization: 5-Points Checklist To Optimize Your Work Process

Application speed is critical. A site loading in 1 second boasts a 3x higher conversion rate compared to a 5-second load time . For every second of delay, conversions can drop by 7% . Users expect highly responsive web applications, and slow performance leads to frustration and lost revenue. 

While Ruby on Rails apps are sometimes perceived as slower than other frameworks, optimization is crucial for a fast and responsive user experience, leading to higher satisfaction and engagement .

This article explores techniques and tools to optimize Ruby on Rails applications, including database optimization, efficient query design, caching, and background job processing. This guide will equip you with a comprehensive understanding of identifying and addressing performance bottlenecks, ensuring a smooth and responsive user experience.

Server Optimization

The server environment plays a crucial role in the performance of your Rails application. Here are some key areas to focus on:

• Increase RAM Capacity: As your application handles more traffic and data, increasing the RAM capacity of your server can significantly improve performance. More memory allows your application to store frequently accessed data in memory, reducing the need for disk I/O operations.
• Upgrade to SSD: Replacing traditional hard disk drives (HDDs) with solid-state drives (SSDs) can drastically improve data read and write speeds, leading to faster application performance. SSDs have much faster access times than HDDs, which can make a noticeable difference in application responsiveness.
• Use a Load Balancing Method: For applications with high traffic, load balancing is essential to distribute incoming requests across multiple servers. This prevents any single server from becoming overloaded and ensures that your application remains responsive even during peak hours.

Back-end Optimization

Optimizing your Rails application’s back-end code and database interactions is crucial for performance improvement. Here are some key strategies:

• Keep Your Code Clean: Writing clean, well-organized code is essential for performance. Avoid unnecessary complexity, use clear naming conventions, and comment your code for clarity. This not only improves performance but also makes your code easier to maintain and debug.
• Tackle N+1 Query Problems: The N+1 query problem is a common performance issue in Rails applications. It occurs when you iterate over a collection of records and then make a separate query for each record to retrieve associated data. To avoid this, use eager loading techniques like includes or preload to load associated data in advance. For example:
  Ruby
  # Inefficient: Causes N+1 issue
@posts.each do |post|
puts post.comments.count
end

# Efficient: Solves N+1 with includes
@posts = Post.includes(:comments)
@posts.each do |post|
puts post.comments.size
end
  
• Add Indexes: Database indexes can significantly speed up data retrieval. Ensure that you have indexes on columns that are frequently used in queries, especially foreign keys and columns used in WHERE clauses. For example, to add an index to the email column of a User model:
  Ruby
  add_index :users, :email, unique: true

• Use Background Job Processors: Offload time-consuming tasks to background jobs using tools like Sidekiq or Resque. This prevents these tasks from blocking the main application thread and keeps your application responsive to user requests. Here’s how to set up Sidekiq in your Rails application:

1. Add Sidekiq to your Gemfile: Add gem ‘sidekiq’ to your Gemfile and run bundle install.
2. Install Redis: Sidekiq uses Redis for job management. You can install Redis through package managers or use a hosted service.
3. Create a Sidekiq Configuration File: Create a configuration file at config/sidekiq.yml:
   YAML
   :concurrency: 5
:queues:
- default
   
4. Create a Background Worker: Generate a new worker using: rails generate sidekiq:worker HardWorker. This creates app/workers/hard_worker.rb, where you define your long-running task:
   Ruby
   class HardWorker
include Sidekiq::Worker
def perform(*args)
  # Do some long-running task here
  puts "Job done!"
end
end
   
5. Enqueue Jobs: To enqueue jobs, call the perform_async method on your worker class:
   Ruby
   HardWorker.perform_async('some_arg')


• Implement Caching: Caching frequently accessed data can drastically reduce database load and improve response times. Rails provides built-in caching mechanisms, and you can also use external caching stores like Redis or Memcached. Rails provides several caching strategies, including:

• Page Caching: Stores the entire HTML output of a page and serves it directly without invoking Rails. This is suitable for pages that rarely change.
• Action Caching: Caches the result of an action and is useful for actions that don’t require user-specific data.
• Fragment Caching: Caches a specific part of a view and is ideal for dynamic content within a mostly static page. For example:
  Code snippet
  <% cache @recent_posts do %>
<% end %>
  

• Optimize Garbage Collection: Ruby’s garbage collector manages memory allocation. Tuning the garbage collector settings can improve performance, especially during testing.
• HTTP Caching and ETags: Implement HTTP caching using expires_in, fresh_when, or stale? to cache responses on the client-side and reduce server load. This allows browsers to store a copy of the response and reuse it for subsequent requests, reducing the need to contact the server.
• Upgrades: Upgrading to the latest stable versions of Ruby and Rails can often provide performance improvements due to bug fixes and optimizations in newer releases.
• Memory Allocation: Memory issues can arise in Rails applications, leading to performance degradation. Consider switching to the Jemalloc memory allocator, which has been reported to reduce memory usage in many cases.

Front-end Optimization

While back-end optimization is crucial, don’t neglect the front-end. Here’s how to optimize your Rails application’s front-end performance:

• Use a Content Delivery Network (CDN): A CDN can significantly speed up content delivery, especially for users located far from your server. CDNs cache your static assets (images, CSS, JavaScript) on servers around the world, allowing users to download them from the closest server.
• Serve Static Files: For files like CSS and images, use static file serving instead of generating them dynamically. This reduces the load on your Rails application.
• Compress Files: Compress your images and other assets to reduce their size and improve load times⁴. Tools like gzip or Brotli can significantly reduce file sizes without noticeable quality loss.
• Embrace AJAX: AJAX (Asynchronous JavaScript and XML) allows you to update parts of a web page without requiring a full page reload. This can make your application feel more responsive and interactive.
• Asynchronous Javascript Loading: Load JavaScript asynchronously using the async or defer attributes in your <script> tags. This allows the browser to continue rendering the page while the JavaScript files are downloaded, improving perceived load times.
• Removing Unused JS/CSS: Regularly review your JavaScript and CSS files and remove any unused code⁷. This can reduce file sizes and improve load times.

Database Optimization

Efficient database interaction is critical for Rails performance. Here are some key database optimization techniques:

Query Optimization

• Optimize Database Queries: Write efficient SQL queries and avoid unnecessary data retrieval. Use tools like explain to analyze query performance and identify potential bottlenecks.
• Select Only Required Columns: When querying data, select only the necessary columns instead of retrieving the entire row. This reduces the amount of data transferred from the database and improves performance. For example:
  Ruby
  # Fetching all columns
@users = User.all

# Fetching specific columns
@users = User.select(:id, :name)
  
• Use pluck for Simple Queries: For simple queries that only require specific columns, use the pluck method, which is more efficient than select in these cases. For example:
  Ruby
  # Fetching ActiveRecord objects
  @usernames = User.where(active: true).map(&:username)
  
  # Fetching specific attributes as an array
  @usernames = User.where(active: true).pluck(:username)
  
• Eager Loading: Eager loading is a technique to preload associated data, preventing the N+1 query problem. Use includes or preload to load associated records in advance. For example:
  Ruby
  # Avoid N+1 queries
posts = Post.all
posts.each do |post|
puts post.user.name
end

# Use eager loading
posts = Post.includes(:user).all
posts.each do |post|
puts post.user.name
end
  
• Conditional Eager Loading: You can use lambda conditions with the includes method to filter associated data. For example, to load only published books for each author:
  Ruby
  authors = Author.includes(:books).where(books: { published: true })

• Optimizing Conditions: When constructing queries, consider using database-specific features for conditions to avoid unnecessary data retrieval. For example:
  Ruby
  # Unoptimized query
users = User.all
users.select { |user| user.age > 18 && user.age < 25 }

# Optimized query
users = User.where(age: 19..24).all
  
• Preferring Query Methods: When checking for the existence of records, use the exists? method, which is more efficient than present? or any?. When retrieving a single record, use find_by, which stops searching after finding the first match, instead of where, which retrieves all matching records. For example:
  Ruby
  # Prefer exists? over present? or any?
if User.exists?(email: 'test@example.com')
# ...
end

# Prefer find_by over where
user = User.find_by(email: 'test@example.com')
  

Caching

• Caching: Cache frequently accessed data to reduce database load and improve response times. Rails provides built-in caching mechanisms, and you can also use external caching stores like Redis or Memcached.
• Russian Doll Caching: As an alternative to eager loading, consider Russian Doll Caching, which involves caching hierarchical data structures and their associations. This can be particularly useful for complex data relationships. For example:
  Code snippet
  <% cache @posts do %>
<% @posts.each do |post| %>
  <% cache post do %>
    <%= post.title %>
    <% post.comments.each do |comment| %>
      <%= comment.content %>
    <% end %>
  <% end %>
<% end %>
<% end %>
  

Indexing

• Indexing: Create indexes on frequently queried columns to speed up data retrieval. Indexes help the database quickly locate the required data, reducing query execution time.

Advanced Techniques

• Batch Processing: When working with large datasets, process records in batches to avoid memory overload. Rails provides methods like find_in_batches to load and process records in chunks. For example:
  Ruby
  User.find_each(batch_size: 1000) do |user|
# Process each user
end

• Bulk Operations: Use bulk operation methods like insert_all, update_all, and delete_all to improve performance when dealing with multiple records. For example:
  Ruby
  # Inserting multiple records
User.insert_all([
{ name: 'John Doe', email: 'john@example.com' },
{ name: 'Jane Doe', email: 'jane@example.com' }
])

# Updating multiple records
User.where(active: false).update_all(active: true)

# Deleting multiple records
User.where(created_at: ..1.week.ago).delete_all
  
• Database Views: Database views can simplify complex queries and improve performance. A view is a virtual table based on the result-set of an SQL statement.
• Database Sharding and Partitioning: For large-scale applications, consider database sharding and partitioning to improve scalability and performance by distributing data across multiple databases or servers.

• Sharding: Involves distributing data across multiple database servers.
• Partitioning: Divides a large table into smaller, more manageable segments within a single database.

• CAP Theorem: The CAP theorem states that a distributed data store can only guarantee two out of three properties: consistency, availability, and partition tolerance. Understanding this theorem can help you make informed decisions about your database architecture.
• Database Connection Pooling: Connection pooling can improve performance by reducing the overhead of establishing new database connections. Optimize the database connection pool size to handle concurrent requests efficiently. Monitor usage, adjust during traffic spikes, and use health checks to ensure connections are valid.
• Minimizing Database Hits: Avoid unnecessary database calls and optimize data retrieval by using techniques like caching and eager loading.
• Lazy Loading: Lazy loading can be used to fetch records initially and then load their associated data only when necessary. This can reduce the initial database load, but be mindful of the N+1 query problem. For example:
  Ruby
  users = User.all
users.each do |user|
puts user.posts.count # Loads posts only when needed
end
  
• Retrieving Specific Columns: Use methods like select and pluck to retrieve only the necessary data, minimizing memory usage and processing time. For example:
  Ruby
  # Using select to retrieve specific columns as ActiveRecord objects
  users = User.select(:id, :name, :email)
  
  # Using pluck to retrieve specific columns as an array of values
  emails = User.pluck(:email)

• Avoiding Unnecessary Data Retrieval: Use limit, offset, and where to optimize data loading and prevent excessive memory usage. For example:
  Ruby
  # Loading only the first 100 users
  users = User.limit(100)

  # Loading users with an offset
  users = User.limit(100).offset(100)

  # Loading users with a specific condition
  users = User.where(active: true)

• Batch Processing when Loading Records: When loading large datasets, use find_each to process records in batches, preventing memory overload. For example:
  Ruby
  User.find_each do |user|
# Process each user
end
  

Using MongoDB with Rails

While Rails is typically used with relational databases like PostgreSQL or MySQL, you can also use it with MongoDB, a NoSQL document database. Rails, like many web frameworks, uses an MVC architecture to structure your application. Mongoid is an Object-Document Mapper (ODM) framework that allows you to use MongoDB with Rails.

Here’s how to get started with MongoDB and Rails:

1. Add Mongoid to your Gemfile: Add gem ‘mongoid’ to your Gemfile and run bundle install.
2. Generate Mongoid Configuration: Run rails g mongoid:config to generate the mongoid.yml configuration file.
3. Configure MongoDB Connection: Update the mongoid.yml file with your MongoDB connection details.
4. Create Models: Define your models using Mongoid instead of ActiveRecord.

Optimizing MongoDB Performance

If you’re using MongoDB with Rails, here are some tips to optimize its performance:

• Create Relevant Indexes: Create indexes on fields that are frequently queried to improve query performance.
• Avoid Over-Indexing: Too many indexes can negatively impact write performance.
• Optimize Query Patterns: Use projection to retrieve only the necessary fields and leverage the aggregation framework for complex queries.
• Hardware Considerations: Ensure your server has sufficient RAM and uses SSDs for storage to maximize MongoDB performance.
• Replication and Sharding: For large-scale applications, consider using replication and sharding to improve availability and scalability.
• MongoDB Schema Design: When designing your MongoDB schema, consider the query patterns of your application. This can help you structure your data in a way that optimizes query performance.
• MongoDB Memory Sizing: Determine the appropriate memory size for your MongoDB deployment to minimize disk activity and reduce latency.
• MongoDB Indexing Sparingly: Avoid over-indexing in MongoDB, as indexes can consume resources and impact write performance.
• MongoDB ESR Rule: When creating compound indexes, follow the ESR (Equality, Sort, Range) rule to optimize index usage.
• MongoDB Bulk Writes: For write-intensive workloads, use bulk writes to improve performance.
• MongoDB Connection Management: Use connection pools to achieve optimal parallelism in your MongoDB deployment.
• MongoDB Scaling Strategies: Plan for scaling your MongoDB deployment by considering different high-level strategies.
• MongoDB Shard Key Selection: Choose the right shard key for your application’s needs to optimize sharding performance.
• MongoDB Cost-Performance Trade-off: Balance performance and cost when choosing your MongoDB deployment strategy.

Rails Performance Tuning Tools

Several tools can help you identify and address performance bottlenecks in your Rails applications:

• New Relic: A comprehensive application performance monitoring (APM) tool that provides detailed insights into your application’s performance.
• Scout: A user-friendly monitoring tool that offers real-time performance data and alerts.
• Bullet: A gem that helps identify N+1 query problems in your application.
• Rack-mini-profiler: A profiling tool that provides performance information during development, helping you identify bottlenecks and optimize your code.
• Rails Performance: A self-hosted tool to monitor the performance of your Rails application.
• Tools for Preventing N+1 Queries: In addition to Bullet, tools like Rack Mini Profiler and Prosopite can help identify and prevent N+1 query problems.
• Performance Indicators: When using performance monitoring tools, pay attention to key indicators like high response times, error rates, and throughput to identify areas for optimization.

Case Studies and Examples

Several case studies and examples demonstrate successful Rails performance optimization:

• Sloboda Studio: Improved the performance of a property rental marketplace by 90% by focusing on server upgrades, image optimization, and back-end optimization. They achieved this by updating the server, reducing and compressing images, and optimizing the back-end code.
• Snapsheet: Optimized ActiveRecord query performance by using techniques like eager loading, selective column retrieval, and batch processing. They also minimized database hits and upgraded to newer versions of Ruby and Rails.
• GitHub: Scaled their Rails application to handle massive traffic by implementing database sharding, caching, and a GraphQL API. They also optimized their database schema, used faster storage, and implemented efficient scaling strategies.

Conclusion

Optimizing the performance of your Ruby on Rails application is crucial for providing a positive user experience and achieving your business goals. By understanding common performance bottlenecks and applying the techniques and tools discussed in this article, you can significantly improve the speed and responsiveness of your application. Remember to prioritize key optimization techniques like addressing N+1 queries, implementing caching strategies, and optimizing database interactions.

To maintain optimal performance as your application grows and evolves, continuous performance monitoring and regular optimization are essential. Utilize monitoring tools to identify performance regressions and inform your optimization efforts. By staying proactive and continuously improving your application’s performance, you can ensure its success and provide a seamless experience for your users.