如何检测Heroku环境中导致高内存占用的Sidekiq任务？

Hey there! Let's tackle this Sidekiq memory issue on Heroku together. I've dealt with similar head-scratchers before, so here are some practical ways to track down those memory-hungry jobs:

If you suspect specific jobs might be the culprit, you can instrument them to log memory usage before and after execution. This gives you granular data for individual tasks:

class YourProblematicJobCandidate
  include Sidekiq::Job

  def perform(job_args)
    # Capture memory before job runs (convert RSS to MB)
    before_memory = `ps -o rss= -p #{Process.pid}`.to_i / 1024
    Rails.logger.info "[Sidekiq Memory] Starting #{self.class.name}: #{before_memory} MB"

    # Your actual job logic goes here

    # Capture memory after job completes
    after_memory = `ps -o rss= -p #{Process.pid}`.to_i / 1024
    memory_delta = after_memory - before_memory
    Rails.logger.info "[Sidekiq Memory] Finished #{self.class.name}: #{after_memory} MB (used #{memory_delta} MB total)"
  end
end

Check your Heroku logs (heroku logs --tail --ps worker) while jobs run—you’ll spot which tasks cause the biggest memory jumps.

Instead of modifying every job, build a middleware that automatically logs memory stats for all Sidekiq tasks. This is great if you don’t have a suspect job in mind:

First, create the middleware file:

# app/middleware/sidekiq_memory_monitor.rb
class SidekiqMemoryMonitor
  def call(worker, job, queue)
    before_memory = `ps -o rss= -p #{Process.pid}`.to_i / 1024
    Rails.logger.info "[Sidekiq Memory] Job #{job['class']} (ID: #{job['jid']}) starting on #{queue}: #{before_memory} MB"

    # Execute the job
    yield

    after_memory = `ps -o rss= -p #{Process.pid}`.to_i / 1024
    delta = after_memory - before_memory
    Rails.logger.info "[Sidekiq Memory] Job #{job['class']} (ID: #{job['jid']}) finished: #{after_memory} MB (delta: #{delta} MB)"
  end
end

Then register it in your Sidekiq initializer:

# config/initializers/sidekiq.rb
Sidekiq.configure_server do |config|
  config.server_middleware do |chain|
    chain.add SidekiqMemoryMonitor
  end
end

Now every job will log memory data, making it easy to filter logs for jobs with large positive deltas (those that don’t release memory or consume a lot upfront).

Heroku logs include dyno memory usage metrics (look for source=heroku.xxx.dyno entries). You can cross-reference these with your Sidekiq job logs to see exactly when memory spikes occur and which job was running at that time.

Run this command to tail worker logs with dyno metrics:

heroku logs --tail --ps worker

Look for memory spikes tagged with dyno=worker.xxx and match the timestamp to the nearest Sidekiq job start/finish logs.

For jobs you suspect are problematic, use tools like derailed_benchmarks to analyze memory usage in a local environment (safe to run without affecting production):

First, add the gem to your Gemfile (grouped under development/test):

group :development, :test do
  gem 'derailed_benchmarks'
end

Then run a memory profile for your job:

bundle exec derailed exec perf:memory -e production --sidekiq-job YourSuspectedJobClass

This will show you which objects are consuming the most memory, helping you identify leaks (like unclosed database connections, large ActiveRecord result sets, or retained objects).

Since you’re already using New Relic, make sure you have the Sidekiq integration fully enabled. Check these spots:

• Go to APM > Services > Your Worker Service > Transactions and filter for Sidekiq jobs. Look for jobs with high "Memory Usage" metrics (you may need to enable memory tracking in your New Relic config).
• Create a custom dashboard that plots dyno memory usage alongside Sidekiq job throughput—this can help you visualize which job types correlate with memory spikes.

If you don’t see memory data for jobs, double-check your newrelic.yml to ensure sidekiq is enabled under instrumentation and that memory tracking isn’t disabled.

内容的提问来源于stack exchange，提问作者felipeecst