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Unexpected Memory Bloat in a Ruby 3.1 Background Job using Sidekiq with Large Payloads

👀 Views: 427 đŸ’Ŧ Answers: 1 📅 Created: 2025-06-21
ruby sidekiq memory-management Ruby

I'm wondering if anyone has experience with This might be a silly question, but I tried several approaches but none seem to work. I'm sure I'm missing something obvious here, but I'm working with an scenario with memory consumption when processing jobs in Sidekiq for a Ruby on Rails application. The jobs are processing large JSON payloads, and I've noticed that the memory usage increases significantly over time, leading to potential out-of-memory errors. This is particularly concerning in production where we have limited resources. For example, when processing a job that handles around 5MB of JSON data, the memory usage for the Sidekiq worker either remains high or gradually increases even after the job completes. Here is a simplified version of my Sidekiq worker: ```ruby class JsonProcessorWorker include Sidekiq::Worker def perform(json_data) data = JSON.parse(json_data) # Processing logic here, e.g., saving records to the database data['records'].each do |record| MyModel.create(record) end end end ``` I am serializing the data to JSON before sending it to Sidekiq and deserializing it in the worker. I've tried using `ObjectSpace.each_object` to identify any lingering objects, but I haven't pinpointed any obvious memory leaks. Additionally, I've enabled garbage collection logging and verified that garbage collection is occurring as expected during the job processing. When I run `MemoryProfiler.report` around the job execution, it doesn't seem to show any unusually retained objects, but I suspect that some objects might not be released properly due to the large size of the payload. I've set the concurrency to 5 in my `sidekiq.yml`, and while monitoring memory with tools like `NewRelic`, I've confirmed that the memory spikes continue. Is there a best practice for handling large payloads in Sidekiq? Should I consider breaking down the payload into smaller chunks or is there a more efficient way to manage memory during processing? Any pointers or suggestions would be greatly appreciated. Am I missing something obvious? For reference, this is a production CLI tool. Hoping someone can shed some light on this.