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Fastest Approach to Read Massive Files with Limited RAM in Go

When handling large data files with restricted memory capacity, choosing the most efficient approach becomes crucial. Among the various options available, two fundamental methodologies emerge: document parsing and stream parsing.

• Document Parsing: This approach reads the entire file and converts it into a structured object model. The advantage lies in having all data readily accessible, simplifying queries. However, it requires substantial memory allocation to store the entire document.
• Stream Parsing: Alternatively, this approach reads data incrementally, presenting elements one at a time for immediate processing. It offers memory efficiency by avoiding holding the entire file in memory at once.

Go provides libraries to facilitate parsing common file formats with ease. For instance, handling CSV files involves importing the "encoding/csv" package. To process the file, you can either:

1. Read the Entire File: Use the ReadAll() method to store the entire file as a nested string slice.
2. Stream the File: Employ the Read() method to iteratively process individual rows, saving memory.

For more complex structures like JSON or XML, streaming is recommended to manage large, hierarchical data without overwhelming the system.

Concurrency Considerations:

If your code demands parallelism, consider using channels and goroutines. Create a channel to send records concurrently to a separate function for processing, freeing up the main thread for other tasks.

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