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17,572 Rows/Second Peak Throughput
Last Updated: February 2026

Excel Sheet Extractor
Performance Benchmarks

17,572 rows/second single-sheet. 8,439 rows/second multi-sheet. Extract 100,000 rows in 5.7 seconds — entirely in your browser with zero uploads.

Peak Speed
17,572rows/sec
Single-sheet extraction
Multi-Sheet Speed
8,439rows/sec
5 sheets, 100K total rows
100K Rows In
5.69s
Single sheet extraction
Max File Size
2GB
Browser memory bound

Throughput by Dataset Size

Extraction Throughput (rows/second)

Time (seconds)
10K rows (single)FASTEST
3.8K
100K rows (single)
17.6K
100K rows (5 sheets)
8.4K

Lower values indicate better performance (faster processing)

Performance improves with larger files. At 10K rows, SheetJS decompression overhead is proportionally larger — that's why 10K rows shows lower throughput than 100K rows. This is expected, not a bug.

Detailed Benchmark Results

DatasetSingle-Sheet SpeedMulti-Sheet SpeedTimeNotes
10,001 rows~3,770 rows/secN/A2.65sStartup overhead visible at small sizes — SheetJS decompression dominates
100,000 rows~17,572 rows/sec~8,439 rows/sec5.69sPeak single-sheet throughput. Multi-sheet: 5 sheets, 100,005 total rows in 11.85s
100K rows (2GB file)~16,000–17,000 rows/secN/ALarge file (~2GB) — throughput stable. Memory pressure increases above 1GB.

Tested February 2026 · Chrome (stable) · Windows 11 · Intel i7-12700K · 32GB RAM · Results vary by hardware, browser, file format, and enabled transformations (±20–30%)

3 measured data points + 12 calculated extrapolations. Model: time = 2.312s startup + rows/29,605 rows/sec. Methodology in accordion below.

How Transformations Affect Speed

TransformationThroughputOverhead vs Baseline
No transformations (baseline)~17,572 rows/secBaseline
Skip first N rows~17,200 rows/sec~2% overhead
Remove empty rows~16,500 rows/sec~6% overhead
Remove empty columns~16,900 rows/sec~4% overhead
Trim whitespace~16,700 rows/sec~5% overhead
Column filtering (keep/remove)~17,000 rows/sec~3% overhead
Row filtering (regex operator)~14,800 rows/sec~16% overhead — regex is CPU-intensive
All transformations combined~13,500 rows/sec~23% overhead combined
JSON export (vs CSV)~14,200 rows/sec~19% overhead — object construction per row
Row filtering with the regex operator has the highest overhead (~16%) because regex evaluation is CPU-intensive per row. All other transformations add under 6% individually. When in doubt, enable all transformations — even at 13,500 rows/sec with everything on, you're still 240x faster than Excel's manual workflow.

ROI Calculator

Baseline: ~5 minutes per sheet via Excel manual Save As (open workbook → navigate to sheet → File > Save As → choose format → confirm dialog → rename file). SplitForge processes the entire batch in ~30 seconds regardless of sheet count.

Time Saved Per Year
34.2h
8 sheets × 52 sessions/yr
Annual Value Recovered
$1,712
At $50/hr

Benchmark Methodology

Full Test Configuration & Methodology

When Sheet Extractor Is Not the Right Tool

No tool is perfect for every use case. Here's where browser-based extraction falls short and what to use instead.

Scheduled or automated extraction in pipelines / cron jobs

Sheet Extractor has no API or CLI. It requires a browser session and human interaction to operate — it cannot run headlessly or be triggered programmatically.

Use instead: Python openpyxl + pandas for script-based automation, or AWS Glue for cloud ETL pipelines.
Password-protected workbooks

SheetJS cannot decrypt password-protected .xlsx files. The encryption is enforced at the file format level and requires the password to open.

Use instead: Remove the password in Excel first (Review → Protect Workbook → remove password), then extract.
Batch processing across multiple workbook files at once

Each session handles one workbook. There is no multi-file batch mode — files must be processed one at a time.

Use instead: Python pandas with glob.glob() to iterate workbook files, or a VBA macro for Windows-only environments.
Workbooks consistently larger than 2GB

Browser memory limits restrict processing to ~1–2GB practically. The 2GB hard upload limit is enforced before processing begins.

Use instead: Python openpyxl with chunked reading, or split the workbook first using Excel's Move/Copy Sheet feature.
Compound multi-column row filtering (AND/OR logic across columns)

Row filtering supports one condition per extraction. Multi-column compound logic (e.g., Status=Active AND Region=West) is not supported in a single pass.

Use instead: Extract the full sheet, then filter using pandas .query() or Excel AutoFilter with multiple criteria.

Frequently Asked Questions

Benchmarks last updated: February 2026. Re-tested quarterly and after major SheetJS or architecture changes.

Ready to Extract 100K Rows in 5.7 Seconds?

No installation. File contents never uploaded. Hidden sheet detection, live preview, 4 export formats. Drop your workbook and watch it run.