Efficiently Creating Large CSV Files: A Comparative Analysis and more

In the realm of data handling, creating large CSV files efficiently is crucial. This article explores four distinct methods to generate a CSV file with 10 million records, comparing their performance in terms of execution time and memory increment.

1. FmtPut Method:

   • Time: 44 seconds
   • Memory Increase: 1276 Mb
   • This traditional approach uses format-based output to write data to a CSV file. While reliable, it’s not the most efficient in terms of memory usage.
     See FormatString

2. PurePut Method:

   • Time: 36 seconds
   • Memory Increase: 2533 Mb
   • A method that writes data directly, PurePut is faster than FmtPut but requires significantly more memory, making it less ideal for memory-constrained environments.
     See PUT statement

3. DexCSV Method:

   • Time: 16 seconds
   • Memory Increase: 253 Mb
   • Using the AimmsDEX repository library to write CSV stands out as a high-performance method, striking a balance between speed and memory usage, making it a suitable choice for large-scale data processing.

4. DexParquet Method:

   • Time: 9 seconds
   • Memory Increase: 253 Mb
   • The fastest and equally memory-efficient method is DEX Parquet. It leverages the Parquet format, known for its high compression and performance, ideal for massive datasets.

Conclusion: The DexParquet method emerges as the clear winner for creating large CSV files swiftly while maintaining low memory overhead. It’s an excellent choice for those prioritizing speed and efficiency in data operations.

This comparative analysis demonstrates the importance of selecting the right method for data tasks, especially when dealing with extensive records. The right choice can lead to significant improvements in performance and resource utilization.

PS. Find the AIMMS Project used to test attached.