Explore Concurrency (goroutines and channels) for Copying from Multiple Files

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When copying content from multiple files, clipper currently processes them sequentially. This might be inefficient, especially when dealing with a large number of files.

We should investigate the use of concurrency with goroutines and channels to parallelize the reading of content from multiple files. This could significantly speed up the copying process in such scenarios.

See:

• How to read multiple files...

Tasks:

1. Design and implement a concurrent solution using goroutines and channels to read content from multiple files simultaneously.
2. Consider potential synchronization issues and error handling in a concurrent environment.
3. Benchmark the performance improvement when copying content from multiple files.
4. Update documentation to explain the use of concurrency.

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Performance Comparison Review: Concurrency Improvement in ParseContent Function

Benchmark Results

With Concurrency Improvement

• Average Time per Operation: 9.03 ms
• Benchmark Runs: 3

Without Concurrency Improvement

• Average Time per Operation: 12.95 ms
• Benchmark Runs: 3

Analysis

The benchmarks demonstrate significant performance gains from the concurrency improvements introduced in PR #34. Here are the key findings:

• Performance Improvement: The updated ParseContent function, utilizing goroutines and channels for concurrent file reading, achieved an average operation time of 9.03 ms per invocation. This represents a 30% improvement over the previous non-concurrent implementation, which averaged 12.95 ms per operation.

• Task Completed: Implemented concurrency using goroutines and channels in ParseContent to handle multiple file inputs more efficiently.

Next Steps

1. Further Optimization: Explore additional optimizations to handle larger file sizes and enhance scalability.
2. Benchmark Expansion: Extend benchmarking to include scenarios with varying file sizes and concurrency levels to validate performance across different workloads.

Conclusion

PR #34 successfully enhanced the ParseContent function's performance through concurrent file reading, demonstrating tangible benefits in efficiency and responsiveness. This improvement aligns with the project's goal to explore concurrency for better resource utilization and scalability in Clipper.