💎 Scaling Strategies with TuskLang and Ruby
Scaling Strategies with TuskLang and Ruby
📈 Scale Your Applications to Infinity and Beyond
TuskLang enables sophisticated scaling strategies for Ruby applications, providing horizontal scaling, auto-scaling, load balancing, and performance optimization. Build applications that can handle millions of requests while maintaining performance and reliability.
🚀 Quick Start: Horizontal Scaling
Basic Scaling Configuration
config/scaling.tsk
[scaling]
strategy: @env("SCALING_STRATEGY", "horizontal") # horizontal, vertical, auto
min_instances: @env("MIN_INSTANCES", "3")
max_instances: @env("MAX_INSTANCES", "10")
target_cpu_utilization: @env("TARGET_CPU_UTILIZATION", "70")
target_memory_utilization: @env("TARGET_MEMORY_UTILIZATION", "80")[load_balancing]
algorithm: @env("LB_ALGORITHM", "round_robin") # round_robin, least_connections, ip_hash
health_check_interval: @env("LB_HEALTH_CHECK_INTERVAL", "30")
health_check_timeout: @env("LB_HEALTH_CHECK_TIMEOUT", "5")
health_check_path: @env("LB_HEALTH_CHECK_PATH", "/health")
[performance]
connection_pool_size: @env("CONNECTION_POOL_SIZE", "20")
cache_ttl: @env("CACHE_TTL", "300")
background_job_workers: @env("BACKGROUND_JOB_WORKERS", "5")
Application Load Balancer
lib/load_balancer.rb
require 'tusk'
require 'redis'
require 'json'class LoadBalancer
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
@redis = Redis.new(url: @config['redis']['url'])
@algorithm = @config['load_balancing']['algorithm']
@instances = []
@health_checks = {}
end
def add_instance(instance)
@instances << {
id: instance[:id],
host: instance[:host],
port: instance[:port],
weight: instance[:weight] || 1,
health_status: 'healthy',
last_check: Time.now,
connection_count: 0
}
end
def get_next_instance
healthy_instances = @instances.select { |instance| instance[:health_status] == 'healthy' }
return nil if healthy_instances.empty?
case @algorithm
when 'round_robin'
next_round_robin(healthy_instances)
when 'least_connections'
next_least_connections(healthy_instances)
when 'ip_hash'
next_ip_hash(healthy_instances)
when 'weighted_round_robin'
next_weighted_round_robin(healthy_instances)
else
healthy_instances.first
end
end
def record_connection(instance_id)
instance = @instances.find { |i| i[:id] == instance_id }
return unless instance
instance[:connection_count] += 1
@redis.hincrby("lb:connections", instance_id, 1)
end
def record_disconnection(instance_id)
instance = @instances.find { |i| i[:id] == instance_id }
return unless instance
instance[:connection_count] = [instance[:connection_count] - 1, 0].max
@redis.hincrby("lb:connections", instance_id, -1)
end
def start_health_checks
Thread.new do
loop do
check_all_instances
sleep @config['load_balancing']['health_check_interval'].to_i
end
end
end
private
def next_round_robin(instances)
@current_index ||= 0
instance = instances[@current_index % instances.length]
@current_index += 1
instance
end
def next_least_connections(instances)
instances.min_by { |instance| instance[:connection_count] }
end
def next_ip_hash(instances)
# This would be implemented with actual client IP
instances.first
end
def next_weighted_round_robin(instances)
total_weight = instances.sum { |instance| instance[:weight] }
random = rand(total_weight)
current_weight = 0
instances.each do |instance|
current_weight += instance[:weight]
return instance if random < current_weight
end
instances.first
end
def check_all_instances
@instances.each do |instance|
health_status = check_instance_health(instance)
instance[:health_status] = health_status
instance[:last_check] = Time.now
end
end
def check_instance_health(instance)
begin
uri = URI("http://#{instance[:host]}:#{instance[:port]}#{@config['load_balancing']['health_check_path']}")
response = Net::HTTP.get_response(uri)
response.code == '200' ? 'healthy' : 'unhealthy'
rescue => e
Rails.logger.error "Health check failed for #{instance[:id]}: #{e.message}"
'unhealthy'
end
end
end
🔄 Auto-Scaling Implementation
Auto-Scaling Manager
lib/auto_scaling_manager.rb
require 'tusk'
require 'redis'
require 'json'class AutoScalingManager
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
@redis = Redis.new(url: @config['redis']['url'])
@min_instances = @config['scaling']['min_instances'].to_i
@max_instances = @config['scaling']['max_instances'].to_i
@target_cpu = @config['scaling']['target_cpu_utilization'].to_i
@target_memory = @config['scaling']['target_memory_utilization'].to_i
@scaling_cooldown = @config['scaling']['cooldown_period'].to_i || 300
@last_scale_time = Time.now
end
def start_monitoring
Thread.new do
loop do
evaluate_scaling
sleep @config['scaling']['evaluation_interval'].to_i || 60
end
end
end
def evaluate_scaling
return if Time.now - @last_scale_time < @scaling_cooldown
current_metrics = get_current_metrics
scaling_decision = determine_scaling_decision(current_metrics)
case scaling_decision
when :scale_up
scale_up
when :scale_down
scale_down
end
end
def get_current_metrics
{
cpu_utilization: get_cpu_utilization,
memory_utilization: get_memory_utilization,
request_rate: get_request_rate,
response_time: get_average_response_time,
error_rate: get_error_rate
}
end
def determine_scaling_decision(metrics)
# Scale up conditions
if should_scale_up?(metrics)
return :scale_up
end
# Scale down conditions
if should_scale_down?(metrics)
return :scale_down
end
:no_action
end
def should_scale_up?(metrics)
metrics[:cpu_utilization] > @target_cpu ||
metrics[:memory_utilization] > @target_memory ||
metrics[:response_time] > @config['scaling']['max_response_time'].to_i ||
metrics[:error_rate] > @config['scaling']['max_error_rate'].to_f
end
def should_scale_down?(metrics)
current_instances = get_current_instance_count
return false if current_instances <= @min_instances
metrics[:cpu_utilization] < (@target_cpu * 0.5) &&
metrics[:memory_utilization] < (@target_memory * 0.5) &&
metrics[:request_rate] < @config['scaling']['min_request_rate'].to_i
end
def scale_up
current_count = get_current_instance_count
return if current_count >= @max_instances
new_count = [current_count + 1, @max_instances].min
create_new_instances(new_count - current_count)
@last_scale_time = Time.now
Rails.logger.info "Scaling up from #{current_count} to #{new_count} instances"
end
def scale_down
current_count = get_current_instance_count
return if current_count <= @min_instances
new_count = [current_count - 1, @min_instances].max
remove_instances(current_count - new_count)
@last_scale_time = Time.now
Rails.logger.info "Scaling down from #{current_count} to #{new_count} instances"
end
private
def get_cpu_utilization
# Implement CPU utilization monitoring
# This could use system metrics, Kubernetes API, or cloud provider APIs
@redis.get('metrics:cpu_utilization').to_f || 0.0
end
def get_memory_utilization
# Implement memory utilization monitoring
@redis.get('metrics:memory_utilization').to_f || 0.0
end
def get_request_rate
# Get requests per second
@redis.get('metrics:request_rate').to_i || 0
end
def get_average_response_time
# Get average response time in milliseconds
@redis.get('metrics:response_time').to_i || 0
end
def get_error_rate
# Get error rate percentage
@redis.get('metrics:error_rate').to_f || 0.0
end
def get_current_instance_count
# Get current number of running instances
@redis.get('scaling:instance_count').to_i || @min_instances
end
def create_new_instances(count)
# Implement instance creation logic
# This could use Kubernetes API, Docker API, or cloud provider APIs
count.times do
instance_id = SecureRandom.uuid
# Create new instance
@redis.hset("instances", instance_id, {
id: instance_id,
status: 'starting',
created_at: Time.now.iso8601
}.to_json)
end
@redis.incrby('scaling:instance_count', count)
end
def remove_instances(count)
# Implement instance removal logic
instances = @redis.hgetall("instances")
instances_to_remove = instances.select { |_, data| JSON.parse(data)['status'] == 'idle' }.first(count)
instances_to_remove.each do |instance_id, _|
@redis.hdel("instances", instance_id)
end
@redis.decrby('scaling:instance_count', instances_to_remove.length)
end
end
📊 Performance Monitoring
Metrics Collector
lib/metrics_collector.rb
require 'tusk'
require 'redis'
require 'json'
require 'prometheus/client'class MetricsCollector
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
@redis = Redis.new(url: @config['redis']['url'])
setup_prometheus_metrics
end
def record_request(method, path, status, duration, instance_id = nil)
# Record request metrics
@request_counter.increment(
method: method,
path: path,
status: status.to_s,
instance: instance_id
)
@request_duration.observe(
duration,
method: method,
path: path,
instance: instance_id
)
# Store in Redis for auto-scaling
store_request_metrics(method, path, status, duration)
end
def record_error(error_type, error_message, instance_id = nil)
@error_counter.increment(
error_type: error_type,
instance: instance_id
)
store_error_metrics(error_type, error_message)
end
def record_system_metrics(cpu_utilization, memory_utilization, instance_id = nil)
@cpu_gauge.set(cpu_utilization, instance: instance_id)
@memory_gauge.set(memory_utilization, instance: instance_id)
store_system_metrics(cpu_utilization, memory_utilization)
end
def get_metrics_summary
{
request_rate: calculate_request_rate,
average_response_time: calculate_average_response_time,
error_rate: calculate_error_rate,
cpu_utilization: get_current_cpu_utilization,
memory_utilization: get_current_memory_utilization
}
end
private
def setup_prometheus_metrics
@request_counter = Prometheus::Client::Counter.new(
:http_requests_total,
docstring: 'Total number of HTTP requests',
labels: [:method, :path, :status, :instance]
)
@request_duration = Prometheus::Client::Histogram.new(
:http_request_duration_seconds,
docstring: 'HTTP request duration in seconds',
labels: [:method, :path, :instance]
)
@error_counter = Prometheus::Client::Counter.new(
:http_errors_total,
docstring: 'Total number of HTTP errors',
labels: [:error_type, :instance]
)
@cpu_gauge = Prometheus::Client::Gauge.new(
:cpu_utilization_percent,
docstring: 'CPU utilization percentage',
labels: [:instance]
)
@memory_gauge = Prometheus::Client::Gauge.new(
:memory_utilization_percent,
docstring: 'Memory utilization percentage',
labels: [:instance]
)
end
def store_request_metrics(method, path, status, duration)
now = Time.now.to_i
pipeline = @redis.pipeline
# Store request count
pipeline.incr("metrics:requests:#{now}")
# Store response time
pipeline.lpush("metrics:response_times:#{now}", duration)
pipeline.expire("metrics:response_times:#{now}", 3600)
# Store status codes
pipeline.hincrby("metrics:status_codes:#{now}", status.to_s, 1)
pipeline.expire("metrics:status_codes:#{now}", 3600)
pipeline.exec
end
def store_error_metrics(error_type, error_message)
now = Time.now.to_i
@redis.hincrby("metrics:errors:#{now}", error_type, 1)
@redis.expire("metrics:errors:#{now}", 3600)
end
def store_system_metrics(cpu_utilization, memory_utilization)
now = Time.now.to_i
@redis.set("metrics:cpu_utilization", cpu_utilization)
@redis.set("metrics:memory_utilization", memory_utilization)
@redis.set("metrics:last_update", now)
end
def calculate_request_rate
now = Time.now.to_i
requests_last_minute = 0
60.times do |i|
requests_last_minute += @redis.get("metrics:requests:#{now - i}").to_i
end
requests_last_minute / 60.0
end
def calculate_average_response_time
now = Time.now.to_i
response_times = []
60.times do |i|
times = @redis.lrange("metrics:response_times:#{now - i}", 0, -1)
response_times.concat(times.map(&:to_f))
end
return 0 if response_times.empty?
response_times.sum / response_times.length
end
def calculate_error_rate
now = Time.now.to_i
total_requests = 0
total_errors = 0
60.times do |i|
total_requests += @redis.get("metrics:requests:#{now - i}").to_i
error_counts = @redis.hgetall("metrics:errors:#{now - i}")
total_errors += error_counts.values.sum(&:to_i)
end
return 0 if total_requests == 0
(total_errors.to_f / total_requests) * 100
end
def get_current_cpu_utilization
@redis.get('metrics:cpu_utilization').to_f || 0.0
end
def get_current_memory_utilization
@redis.get('metrics:memory_utilization').to_f || 0.0
end
end
🔄 Connection Pooling and Resource Management
Advanced Connection Pool
lib/advanced_connection_pool.rb
require 'connection_pool'
require 'tusk'
require 'redis'class AdvancedConnectionPool
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
setup_pools
end
def with_database(&block)
@database_pool.with(&block)
end
def with_redis(&block)
@redis_pool.with(&block)
end
def with_http_client(&block)
@http_pool.with(&block)
end
def pool_status
{
database: {
size: @database_pool.size,
available: @database_pool.available,
busy: @database_pool.busy
},
redis: {
size: @redis_pool.size,
available: @redis_pool.available,
busy: @redis_pool.busy
},
http: {
size: @http_pool.size,
available: @http_pool.available,
busy: @http_pool.busy
}
}
end
private
def setup_pools
@database_pool = ConnectionPool.new(
size: @config['performance']['connection_pool_size'].to_i,
timeout: @config['performance']['connection_timeout'].to_i || 5
) do
ActiveRecord::Base.connection_pool.checkout
end
@redis_pool = ConnectionPool.new(
size: @config['performance']['redis_pool_size'].to_i || 10,
timeout: @config['performance']['connection_timeout'].to_i || 5
) do
Redis.new(url: @config['redis']['url'])
end
@http_pool = ConnectionPool.new(
size: @config['performance']['http_pool_size'].to_i || 20,
timeout: @config['performance']['connection_timeout'].to_i || 5
) do
Net::HTTP.new(@config['http']['host'], @config['http']['port'])
end
end
end
🚀 Caching Strategies
Multi-Level Cache
lib/multi_level_cache.rb
require 'tusk'
require 'redis'
require 'dalli'class MultiLevelCache
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
setup_cache_layers
end
def get(key, options = {})
# Try L1 cache (memory)
value = get_from_l1(key)
return value if value
# Try L2 cache (Redis)
value = get_from_l2(key)
if value
set_l1(key, value, options[:ttl])
return value
end
# Try L3 cache (Memcached)
value = get_from_l3(key)
if value
set_l2(key, value, options[:ttl])
set_l1(key, value, options[:ttl])
return value
end
nil
end
def set(key, value, options = {})
ttl = options[:ttl] || @config['caching']['default_ttl'].to_i
# Set in all cache layers
set_l1(key, value, ttl)
set_l2(key, value, ttl)
set_l3(key, value, ttl)
end
def delete(key)
delete_from_l1(key)
delete_from_l2(key)
delete_from_l3(key)
end
def clear_pattern(pattern)
clear_l1_pattern(pattern)
clear_l2_pattern(pattern)
clear_l3_pattern(pattern)
end
private
def setup_cache_layers
# L1: Memory cache (fastest, smallest)
@l1_cache = {}
@l1_ttl = {}
# L2: Redis cache (fast, medium size)
@l2_cache = Redis.new(url: @config['redis']['url'])
# L3: Memcached cache (slower, largest)
@l3_cache = Dalli::Client.new(@config['memcached']['servers'])
end
def get_from_l1(key)
return nil unless @l1_cache[key]
return nil if @l1_ttl[key] && Time.now.to_i > @l1_ttl[key]
@l1_cache[key]
end
def set_l1(key, value, ttl)
@l1_cache[key] = value
@l1_ttl[key] = Time.now.to_i + ttl if ttl > 0
end
def delete_from_l1(key)
@l1_cache.delete(key)
@l1_ttl.delete(key)
end
def clear_l1_pattern(pattern)
@l1_cache.keys.grep(Regexp.new(pattern)).each do |key|
delete_from_l1(key)
end
end
def get_from_l2(key)
@l2_cache.get(key)
end
def set_l2(key, value, ttl)
if ttl > 0
@l2_cache.setex(key, ttl, value)
else
@l2_cache.set(key, value)
end
end
def delete_from_l2(key)
@l2_cache.del(key)
end
def clear_l2_pattern(pattern)
keys = @l2_cache.keys(pattern)
@l2_cache.del(*keys) unless keys.empty?
end
def get_from_l3(key)
@l3_cache.get(key)
end
def set_l3(key, value, ttl)
@l3_cache.set(key, value, ttl)
end
def delete_from_l3(key)
@l3_cache.delete(key)
end
def clear_l3_pattern(pattern)
# Memcached doesn't support pattern deletion
# This would need to be implemented differently
end
end
🔄 Background Job Scaling
Scalable Job Queue
lib/scalable_job_queue.rb
require 'sidekiq'
require 'tusk'
require 'redis'class ScalableJobQueue
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
setup_sidekiq
end
def enqueue_job(job_class, *args, options = {})
priority = options[:priority] || 'default'
queue = options[:queue] || 'default'
retry_count = options[:retry_count] || 3
job_class.set(
queue: queue,
priority: priority,
retry: retry_count
).perform_async(*args)
end
def enqueue_batch_jobs(job_class, batch_data, options = {})
batch = Sidekiq::Batch.new
batch.on(:success, BatchCallback, job_class: job_class)
batch.jobs do
batch_data.each do |data|
enqueue_job(job_class, data, options)
end
end
end
def get_queue_stats
stats = {}
Sidekiq::Queue.all.each do |queue|
stats[queue.name] = {
size: queue.size,
latency: queue.latency,
paused: queue.paused?
}
end
stats
end
def scale_workers(target_count)
current_workers = get_current_worker_count
return if current_workers == target_count
if current_workers < target_count
scale_up_workers(target_count - current_workers)
else
scale_down_workers(current_workers - target_count)
end
end
private
def setup_sidekiq
Sidekiq.configure_server do |config|
config.redis = { url: @config['redis']['url'] }
config.concurrency = @config['background_jobs']['worker_concurrency'].to_i || 10
config.queues = @config['background_jobs']['queues'] || ['default']
end
Sidekiq.configure_client do |config|
config.redis = { url: @config['redis']['url'] }
end
end
def get_current_worker_count
# This would need to be implemented based on your deployment strategy
# Could use Kubernetes API, Docker API, or process monitoring
@redis.get('workers:count').to_i || 1
end
def scale_up_workers(count)
# Implement worker scaling logic
count.times do
# Start new worker process
spawn_worker_process
end
end
def scale_down_workers(count)
# Implement worker scaling down logic
count.times do
# Gracefully stop worker process
stop_worker_process
end
end
def spawn_worker_process
# Implementation depends on deployment strategy
# Could use system commands, Kubernetes API, etc.
end
def stop_worker_process
# Implementation depends on deployment strategy
# Could use system signals, Kubernetes API, etc.
end
end
class BatchCallback
def on_success(status, options)
job_class = options['job_class']
Rails.logger.info "Batch completed for #{job_class}"
end
end
🔄 Database Scaling
Read Replica Load Balancer
lib/read_replica_balancer.rb
require 'tusk'
require 'active_record'class ReadReplicaBalancer
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
@replicas = []
@current_index = 0
setup_replicas
end
def with_read_replica(&block)
replica = get_next_replica
replica.connection_pool.with_connection do
block.call
end
end
def add_replica(connection_config)
@replicas << create_replica_connection(connection_config)
end
def remove_replica(replica_id)
@replicas.reject! { |replica| replica.id == replica_id }
end
def replica_health_check
@replicas.each do |replica|
begin
replica.connection.execute('SELECT 1')
replica.healthy = true
rescue => e
replica.healthy = false
Rails.logger.error "Replica health check failed: #{e.message}"
end
end
end
private
def setup_replicas
@config['database']['read_replicas'].each do |replica_config|
add_replica(replica_config)
end
end
def create_replica_connection(config)
connection = ActiveRecord::Base.establish_connection(config)
connection.define_singleton_method(:healthy) { @healthy = true }
connection.define_singleton_method(:healthy=) { |value| @healthy = value }
connection
end
def get_next_replica
healthy_replicas = @replicas.select(&:healthy)
return @replicas.first if healthy_replicas.empty?
replica = healthy_replicas[@current_index % healthy_replicas.length]
@current_index += 1
replica
end
end
🎯 Configuration Management
Dynamic Configuration
config/dynamic_scaling.tsk
[scaling]
enabled: @env("AUTO_SCALING_ENABLED", "true")
evaluation_interval: @env("SCALING_EVALUATION_INTERVAL", "60")
cooldown_period: @env("SCALING_COOLDOWN_PERIOD", "300")[thresholds]
cpu_high: @env("CPU_HIGH_THRESHOLD", "80")
cpu_low: @env("CPU_LOW_THRESHOLD", "30")
memory_high: @env("MEMORY_HIGH_THRESHOLD", "85")
memory_low: @env("MEMORY_LOW_THRESHOLD", "40")
response_time_high: @env("RESPONSE_TIME_HIGH_THRESHOLD", "1000")
error_rate_high: @env("ERROR_RATE_HIGH_THRESHOLD", "5")
[scaling_rules]
scale_up_factor: @env("SCALE_UP_FACTOR", "1.5")
scale_down_factor: @env("SCALE_DOWN_FACTOR", "0.8")
min_scale_up_interval: @env("MIN_SCALE_UP_INTERVAL", "60")
min_scale_down_interval: @env("MIN_SCALE_DOWN_INTERVAL", "300")
[performance]
connection_pool_size: @env("CONNECTION_POOL_SIZE", "20")
cache_ttl: @env("CACHE_TTL", "300")
background_job_workers: @env("BACKGROUND_JOB_WORKERS", "5")
read_replica_count: @env("READ_REPLICA_COUNT", "2")
🚀 Deployment Strategies
Blue-Green Deployment
lib/blue_green_deployment.rb
require 'tusk'
require 'redis'class BlueGreenDeployment
def initialize(config_path = 'config/scaling.tsk')
@config = Tusk.load(config_path)
@redis = Redis.new(url: @config['redis']['url'])
end
def deploy_new_version(version)
# Determine current active environment
current_env = get_current_environment
new_env = current_env == 'blue' ? 'green' : 'blue'
# Deploy to new environment
deploy_to_environment(new_env, version)
# Run health checks
return false unless health_check_environment(new_env)
# Switch traffic
switch_traffic(new_env)
# Clean up old environment
cleanup_environment(current_env)
true
end
def rollback
current_env = get_current_environment
previous_env = current_env == 'blue' ? 'green' : 'blue'
# Switch back to previous environment
switch_traffic(previous_env)
# Clean up failed environment
cleanup_environment(current_env)
end
private
def get_current_environment
@redis.get('deployment:current_environment') || 'blue'
end
def deploy_to_environment(environment, version)
# Implementation depends on deployment platform
# Could use Kubernetes API, Docker API, etc.
Rails.logger.info "Deploying version #{version} to #{environment} environment"
end
def health_check_environment(environment)
# Implement health checks for the environment
# Return true if healthy, false otherwise
true
end
def switch_traffic(environment)
@redis.set('deployment:current_environment', environment)
Rails.logger.info "Switched traffic to #{environment} environment"
end
def cleanup_environment(environment)
# Clean up resources for the old environment
Rails.logger.info "Cleaning up #{environment} environment"
end
end
🎯 Summary
This comprehensive guide covers scaling strategies with TuskLang and Ruby, including:
- Horizontal Scaling: Load balancers and instance management - Auto-Scaling: Dynamic scaling based on metrics and thresholds - Performance Monitoring: Comprehensive metrics collection and analysis - Connection Pooling: Advanced resource management and optimization - Caching Strategies: Multi-level caching for optimal performance - Background Jobs: Scalable job queue management - Database Scaling: Read replica load balancing - Configuration Management: Dynamic configuration updates - Deployment Strategies: Blue-green deployment and rollback
The scaling strategies with TuskLang provide a robust foundation for building applications that can handle massive scale while maintaining performance, reliability, and cost efficiency.