


How to Avoid Deadlock When Using Goroutines to Process Values and Gather Results into a Slice?
Using Goroutines to Efficiently Process Values and Gather Results into a Slice
The utilization of goroutines can be a transformative element in Go programming, enabling concurrent task execution and efficient processing. However, proper implementation is crucial to avoid potential pitfalls.
Problem:
When attempting to employ goroutines in a codebase, a "fatal error: all goroutines are asleep - deadlock!" arises. The aim is to process values in a list concurrently, subsequently accumulating the processed results into a new list. Difficulty, however, emerges in the accumulation phase.
Code Decomposition:
The provided code snippet includes several essential elements:
// Process each item with a goroutine and send output to sampleChan go newSample(line, *replicatePtr, *timePtr, sampleChan, &wg)
- This goroutine concurrently processes the data and sends the outcome to a channel named sampleChan.
// Read from sampleChan and put into a slice for s := range sampleChan { sampleList = append(sampleList, s) } close(sampleChan)
- The code attempts to gather results from the channel and incorporate them into a slice.
Solution:
The errors are caused by two issues: premature waiting for worker completion and improper channel closure timing. The following modifications resolve these issues:
go func() { wg.Wait() close(sampleChan) }()
- Postpone channel closure until after workers have finished by initiating a separate goroutine.
Additionally, for stylistic cohesion, consider restructuring newSample as a synchronous function for result production, resulting in the following code:
for i, line := range contents { wg.Add(1) go func(line string) { defer wg.Done() sampleChan <- newSample(line, *replicatePtr, *timePtr) }(line) }
This revised approach improves code readability, eases testing, and simplifies concurrency management, enabling clear identification of critical operations like wg.Done().
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