Ruby on Rails is a popular web application framework that enables developers to build web apps quickly and efficiently. However, as Rails applications grow in size and complexity, performance can start to degrade. In this comprehensive guide, we will explore various ways to optimize Rails application performance through caching, database optimizations, background processing, web server tuning, and profiling.
How to Benchmark Ruby on Rails Performance?
The first step towards optimizing performance is to benchmark your application to establish a baseline. Some useful tools for benchmarking Rails app performance include:
- Rails Benchmark gem easily benchmark Ruby code snippets
- Rack Mini Profiler displays a speed badge for each page
- New Relic performance monitoring SaaS with advanced analytics
- rpm system tool for logging requests per minute
- ab Apache benchmarking tool for stress testing
Run these tools in development and production environments to identify bottlenecks. Look out for slow database queries, high memory usage, and request queuing up under load.
How to Optimize Ruby on Rails Database?
The database is often the biggest bottleneck for Rails performance. Optimizing database interactions can significantly improve application speed. While it is advisable to hire Ruby on Rails developers from a reliable web development company to handle the intricacies of database optimization, here are a few things you should look out for –
Database Indexes
Carefully index columns that are used for lookups in WHERE clauses, joins, and sorts to avoid full table scans. Use indexes sparingly, though, as they also incur overheads for writes.
Caching
Use memory caches like Redis or Memcached to reduce database load. Rails provides built-in caching to use page, action and fragment caching appropriately.
Lazy Loading
Use gems like Bullet or request_store to easily identify and fix N+1 queries. Enable eager loading and lazy loading associations to reduce queries.
Connection Pooling
Tune the connection pools to ensure sufficient dedicated connections up to the server limit. Monitor pool waits and timeouts to increase pool size if waits occur often.
Alternative Databases
Consider NoSQL databases like MongoDB or Cassandra for high write throughput. For better scalability, shard databases use Postgres extensions like Citus.
How do you optimize cache in Ruby on Rails applications?
Caching avoids unnecessary database lookups and repetitions of the same work. Well-implemented caches can tremendously improve Rails performance.
Low-Level Caching
Enable layered caching at the database query level, object level and HTTP level using memcache or Redis. Use the request store to minimize duplicates.
Fragment Caching
Cache fragments of view templates to avoid running complex rendering logic over and over. Watch out for caching too aggressively, though.
Russian Doll Caching
Cache across actions, pages and entire site using cache layers. This effectively avoids n+1 queries for read-heavy applications.
How do you optimize Ruby on Rails Server Performance?
Optimizing the performance of your Ruby on Rails (RoR) application is crucial for delivering a responsive and efficient user experience. Here are several strategies to enhance the performance of your RoR server:
CDNs
Use the Content Delivery Network and cache static assets, such as JavaScript, stylesheets, and images, on geographically distributed edge servers to reduce load times.
Background Jobs
For I/O intensive workloads like sending emails, image processing, CSV exports, etc, delegate the work to a background job using ActiveJob workers. This prevents requests from getting tied up.
Job Queues & Workers
Set up robust job queues like Sidekiq, Resque or DelayedJob. Scale dedicated worker processes horizontally across multiple servers.
Mailer Delivery
Use async delivery for ActionMailer instead of wait_until_sent. Configure enough SMTP connections to handle queues.
Cron Jobs
Schedule recurrent tasks using cron job workers rather than blocking web requests for batch imports, clean-ups, aggregations, etc.
Application Server Tuning
Tune the web servers like Puma, Unicorn or Passenger for optimal resource utilization under load for higher throughput.
Threads vs Processes
Processes provide better isolation while threads share resources efficiently. Choose process-based servers for apps using a lot of memory.
Concurrency and Timeouts
Scale the number of web workers to meet load but keep concurrency in check to avoid resource contention. Increase timeouts during deployment.
Memory & CPU Optimization
Monitor system resources versus throughput. Tune thread count, worker processes, timeouts, and garbage collection are based on available cores and RAM.
Connection Pool Tuning
Size connection pools correctly; pools that are too large cause memory issues, while small ones lead to blocked threads. Tune pool timeouts as well.
Profiling and Auditing
Profile code execution to zoom in on optimizations using tools like rack-mini-profiler, ruby-prof, memory_profiler, and stackprof.
N+1 Detection
As mentioned earlier, install Bullet Gem and keep an eye out for alerts to fix N+1 lazy loading issues causing repeated queries.
Memory Leaks
Monitor memory usage over time to detect abnormal heap growth indicating leaks. Inspect object growth and retention using memory profiling.
Performance Regression
When deploying enhancements, run benchmark tests to check for performance regressions versus baseline. Roll back regressive code and address separately.
Advanced Performance Techniques
Now that we have covered caching, database and server optimizations, here are some additional advanced techniques for pushing Rails performance further:
Russian Doll Caching
Russian doll caching refers to nesting caches across various layers of page, action, and fragment caches together to efficiently handle read-heavy workloads.
Cache Stampede Protection
Implement mutex locks or memcache add instead of set to avoid thundering herd problems with dogpile cache expiration.
Streaming
For heavy reporting or file downloads, use streaming to push partial responses continuously instead of buffering the entire response.
Assets Pipeline
Enable asset pipeline cache digests, compile assets locally and gzip assets. Consider third-party CDNs to offload asset delivery.
Caching POST Requests
Default Rails caching works only for GET requests. To cache POST API responses, use action caching with request headers as the key.
Request Collapsing
For requests hitting external services, collapse multiple identical requests into one to avoid hitting rate limits or authentication overheads.
Ruby GC Tuning
Tune Ruby garbage collector using environment variables to reduce GC pressure. Upgrade to jRuby for better native threading and GC.
Horizontal Scaling
For large-scale production workloads, scale Ruby web workers across multiple servers. Optimize load balancing strategy and shared cache stores.
Load Balancing
Set up a load balancer like HAProxy or use cloud-based LB services. Configure multiple web server nodes to scale horizontally.
Read Replicas
Offload read queries to read-only replicas. Scale-out across read replicas using database extensions like PostGIS, Octopus or Sequoia.
Master-Master Replication
Set up bi-directional master-to-master database replication for high availability and read/write sharding across geographies.
Polyglot Persistence
Store different data types across specialized data stores based on access patterns for ideal performance instead of forcing everything into one database.
Service Oriented Architecture
Break up Rails monolith into separate microservices by domains with message queues and APIs facilitating communication.
Performance Monitoring
Here are some tips for monitoring application performance continuously in production:
Error Monitoring
Use error monitoring tools like Sentry or Honeybadger to get alerted about errors impacting user experience.
APM and Tracing
Implement mature application performance monitoring like New Relic or Scouts to visualize bottlenecks. Integrate distributed tracing for microservices.
Synthetic Monitoring
Set up synthetic user journeys to simulate production usage paths and get early warnings on performance regressions.
Request Logging
Log key meta-data like response times, database times, cache hits, etc., per request to analyze usage patterns, detect outliers and improve slow paths.
Release Tracking
Tag performance metrics across releases to analyze trends periodically. Compare with baseline benchmarks to prevent optimizations from going unnoticed.
Final Words
Ruby on Rails makes it ideal for building custom web applications rapidly. It gives developers full control to optimize and scale performance from prototype to production system capable of handling enterprise workloads.
Using the combination of database enhancements, liberal caching, background workers, server tuning, and continuous monitoring allows even the most complex rails applications to stay responsive under increasing customer demands. Build your Ruby on Rails team with a credible and reliable IT staff augmentation company, or get dedicated developers from an offshore development centre to build your remote Rails team who can help you develop, optimize and deploy your Ruby on Rails project.
Hope this guide gives you insights into available knobs for fine-tuning rails application performance till one hits the infrastructure limit necessitating an architectural redesign.