Question 1

How to process large CSV files without running out of memory in Ruby?

Accepted Answer

Use SmarterCSV's chunked processing with the chunk_size option or the Enumerator API via Reader#each, which streams rows in batches to minimize memory usage, ideal for database imports or parallel jobs.

Question 2

SmarterCSV vs Ruby CSV: which should I use for a Rails app?

Accepted Answer

Choose SmarterCSV for production data imports due to its speed, robustness, and Rails-ready hashes; Ruby CSV is fine for simple, controlled files but risks silent data corruption and requires more post-processing code.

Question 3

How to skip or quarantine bad rows in a CSV import with SmarterCSV?

Accepted Answer

Set the on_bad_row: :collect option when creating a Reader, which accumulates errors in reader.errors[:bad_rows] without halting, allowing you to report or retry problematic lines separately.

Question 4

Does SmarterCSV work with JRuby or other Ruby implementations?

Accepted Answer

Yes, but it falls back to a pure Ruby implementation without the C extension, so performance is slower; it's fully functional but misses the acceleration benefits on MRI Ruby.

Question 5

How to convert CSV strings to hashes with automatic number parsing in SmarterCSV?

Accepted Answer

Use SmarterCSV.parse(csv_string), which by default converts numeric values, strips whitespace, and returns symbol-keyed hashes, similar to CSV.parse with headers but more robust and faster.

Question 6

What are the real-world performance gains of SmarterCSV compared to other libraries?

Accepted Answer

Benchmarks show it's 7–129× faster than CSV.table for equivalent hash output, and often outperforms ZSV when including full pipeline work like hash construction and conversions, not just tokenization.

smarter_csv

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