Performance Benchmarks

See how CSVBox performs with different file sizes, row counts, and validation workloads.

Please see the following tables for CSVBox performance benchmarks. Benchmarks last run: 24 August 2026.

One run per size, there is no variance figure here. Treat each number as a single observation, not a median. Column count moves timing more than file size does.

Test Environment

A mid-range consumer laptop on office Wi-Fi — deliberately unexceptional hardware, so these numbers read as a realistic floor rather than a best case.

Device

Model	ASUS VivoBook X515DA
CPU	AMD Ryzen 5 3500U
Graphics	Radeon Vega Mobile
Cores	4 physical / 8 logical
Base clock	2.10 GHz
Memory	17.95 GB
OS	Windows 11 Enterprise (Build 10.0.22631, 64-bit)
Browser	Chrome 151.0.7922.173

Network

Upload	33.2 Mbps (4.15 MB/s)

Importer Sheet

Columns	3
col1	Number
col2	Email
col3	Date
Validation	Column type rules
Transform	1 function, on col2
Virtual cols	None
Source file	12 columns

File Parse

Time to read and parse the source CSV file.

Rows	Duration	Per row	Throughput
100k	406 ms	4.06 µs	246.3k rows/sec
500k	1.4 s	2.79 µs	357.9k rows/sec
1M	3.2 s	3.19 µs	313.1k rows/sec
2M	5.6 s	2.78 µs	359.3k rows/sec

Validation

Time to validate all rows against the target schema.

Rows	Duration	Per row	Throughput
100k	583 ms	5.83 µs	171.5k rows/sec
500k	7.3 s	14.55 µs	68.7k rows/sec
1M	17.7 s	17.73 µs	56.4k rows/sec
2M	38.1 s	19.04 µs	52.5k rows/sec

Transformation

Time to apply configured transforms to all rows.

Rows	Duration	Per row	Throughput
100k	188 ms	1.88 µs	531.9k rows/sec
500k	873 ms	1.75 µs	572.7k rows/sec
1M	1.6 s	1.59 µs	627.0k rows/sec
2M	6.1 s	3.05 µs	327.5k rows/sec

Destination Upload

Time to deliver the processed rows to the destination.

Rows	Duration	Per row	Throughput
100k	8.1 s	81.02 µs	12.3k rows/sec
500k	29.3 s	58.62 µs	17.1k rows/sec
1M	51.2 s	51.22 µs	19.5k rows/sec
2M	100.6 s	50.29 µs	19.9k rows/sec

End to End

Total time from upload start to completed import.

Rows	Duration	Per row	Throughput
100k	9.3 s	92.78 µs	10.8k rows/sec
500k	38.9 s	77.70 µs	12.9k rows/sec
1M	73.7 s	73.75 µs	13.6k rows/sec
2M	150.3 s	75.17 µs	13.3k rows/sec

Reproducing the Run

The sheet was configured with three columns typed Number, Email, and Date, and a single data-transform function applied to col2. The transform is intentionally trivial — one string coercion and an uppercase — so the transform timings measure the pipeline's per-row dispatch overhead, not the cost of a user's own logic. This is the exact function that produced the transformation numbers above.

Data transform function · col2

​js const helper = (value) => { return String(value).toUpperCase(); }; csvbox.row["col2"] = helper(csvbox.row["col2"]); return csvbox; ​

Source datasets

Dataset	Rows	File size (MB)	Payload sent (MB)	Bytes/row
customers-100000.csv	100,000	17.32	5.40	173
customers-500000.csv	500,000	87.03	27.85	174
customers-1000000.csv	1,000,000	174.16	55.90	174
customers-2000000.csv	2,000,000	349.42	113.92	175

Source CSVs carry 12 columns (Index, Customer Id, First Name, Last Name, Company, City, Country, Phone 1, Phone 2, Email, Subscription Date, Website), of which three were mapped into the sheet for the run. File sizes are decimal MB (10⁶ bytes).