When processing very large datasets, the wrong concurrency model can hurt more than help. For one analytics workload, I had three options:
- Single sequential process (too slow)
- One goroutine per record (resource chaos)
- Fixed worker pool (best balance)
The worker pool pattern was the most stable and scalable option.
Why a fixed worker pool works
Spawning thousands of goroutines at once can overwhelm DB connections and spike CPU usage. A controlled number of workers keeps throughput high while protecting shared resources.
Core building blocks
- Job channel: queues record batches for processing
- Fixed workers: a stable number of goroutines pulling jobs continuously
- WaitGroup: ensures all workers finish before returning results
Result
Using this design, I got stable memory behavior, consistent CPU utilization, and significantly faster dashboard response times.
In Go, concurrency is not just about doing more things at once—it is about doing them efficiently and predictably.