How Can I Optimize CSV Reading and Writing in Go for Maximum Efficiency?

Efficient Reading and Writing CSV in Go

While the Go code provided is functional, it may not be the most efficient when reading and writing CSV files. This article will explore potential inefficiencies and propose solutions to optimize the process.

The code reads a CSV file containing times and float values, performs operations on the data, and then writes the original values along with an additional score column to a new CSV. However, the process is slow, indicating potential areas for improvement.

One key issue is that the code loads the entire CSV file into memory before processing. This can be inefficient, especially for large files. A better approach is to process the file one line at a time using a channel-based approach. The processCSV function provided in the solution uses a channel to loop through the CSV file and process each line as it becomes available.

The code also uses strconv.ParseFloat excessively to convert strings to float values and back again. This can be sped up by using math/big.Float directly, which effortlessly handles decimal arithmetic.

By utilizing these optimizations, you can increase the efficiency of your CSV reading and writing operations, allowing for faster processing of large data files.

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