IronXL Data-Loading Method Performance
When you populate a large worksheet in IronXL, the loading method you pick has a big effect on how long the file takes to generate. The benchmark below compares three approaches against the same dataset: 20,000 rows by 55 columns, each written to disk as an .xlsx file.
Test Overview
Every test populates the same data and writes the result to disk, recording both the duration in seconds and the resulting file size.
- Adding data cell by cell: ~24 sec, 3094 KB
- Loading from a
DataTable: ~13 sec, 3094 KB - Loading from CSV: ~9 sec, 3094 KB
The file size is identical across all three. Only the time to build the file changes.
The Methods
Option 1: Adding Data Cell by Cell
This is the slowest of the three. Each cell is assigned individually through nested loops.
string[] fruits = { "Apples", "Apricot", "Banana", "Blackberry", "Blueberry", "Boysenberry", "Canary Melon", "Cantaloupe",
"Currants", "Dates (tree dried only) Dragon Fruit", "Durian (purchase cut) Figs", "Gooseberry", "Grapes", "Grapefruit",
"Lemon", "Lime", "Loganberry", "Longan", "Loquat", "Lychee", "Mandarin", "Mango",
"Blood Orange", "Papaya", "Passion Fruit", "Peach", "Pear", "Persimmon", "Pineapple", "Plum", "Honeydew",
"Rambutan", "Starfruit", "Tamarind", "Yuzu", "Açaí", "Abiu", "Ackee", "Breadfruit", "Cempedak",
"Cherimoya", "Buddha's Hand", "Citron", "Finger Lime", "Kumquat", "Pomelo", "Tangelo", "Ugli Fruit",
"Galia Melon", "Kiwano (Horned Melon)", "Mouse Melon", "Muskmelon",
"cherry", "nectarine", "Butternut squash"};
var workbook = WorkBook.Create(ExcelFileFormat.XLSX);
var worksheet = workbook.CreateWorkSheet("Fruits");
var columnsTitles = GetExcelCellLetters(fruits.Length);
for (int col = 0; col < columnsTitles.Count; col++)
{
string columnLetter = columnsTitles[col]; // To avoid interpolation that can cause performance issues in large loops because it build string objects in every loop
for (int row = 1; row <= 20000; row++)
{
worksheet[columnLetter + row].Value = fruits[col];
//worksheet.Rows[row].Columns[columnLetter].Value.ToString();
}
}
workbook.SaveAs("C:\\Temp\\ManualCell-by-CellAssignment.xlsx");
private static List<string> GetExcelCellLetters(int iMaxNum)
{
string[] alphabet = { string.Empty, "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z" };
IEnumerable<string> lst = (from c1 in alphabet
from c2 in alphabet
from c3 in alphabet.Skip(1)
where c1 == string.Empty || c2 != string.Empty
select c1 + c2 + c3).Take(iMaxNum);
return lst.ToList();
}Note the cached columnLetter: building the column reference once per column instead of interpolating it inside the inner loop avoids allocating a new string on every iteration. Reserve this method for cases that need genuine per-cell logic or styling.
Option 2: Loading from a DataTable
A solid middle ground, and significantly faster than manual cell insertion. Build a DataTable in memory, then hand the whole thing to LoadWorkSheet.
string[] fruits = { "Apples", "Apricot", "Banana", "Blackberry", "Blueberry", "Boysenberry", "Canary Melon", "Cantaloupe",
"Currants", "Dates (tree dried only) Dragon Fruit", "Durian (purchase cut) Figs", "Gooseberry", "Grapes", "Grapefruit",
"Lemon", "Lime", "Loganberry", "Longan", "Loquat", "Lychee", "Mandarin", "Mango",
"Blood Orange", "Papaya", "Passion Fruit", "Peach", "Pear", "Persimmon", "Pineapple", "Plum", "Honeydew",
"Rambutan", "Starfruit", "Tamarind", "Yuzu", "Açaí", "Abiu", "Ackee", "Breadfruit", "Cempedak",
"Cherimoya", "Buddha's Hand", "Citron", "Finger Lime", "Kumquat", "Pomelo", "Tangelo", "Ugli Fruit",
"Galia Melon", "Kiwano (Horned Melon)", "Mouse Melon", "Muskmelon",
"cherry", "nectarine", "Butternut squash"};
var workbook = WorkBook.Create(ExcelFileFormat.XLSX);
DataTable table = new DataTable("Fruits");
foreach (string fruitName in fruits)
{
table.Columns.Add(fruitName);
}
int rowCount = 20000;
for (int i = 0; i < rowCount; i++)
{
table.Rows.Add(fruits);
}
workbook.LoadWorkSheet(table);
workbook.SaveAs("C:\\Temp\\LoadDataTableDirectly.xlsx");LoadWorkSheet ingests the entire table in one call, which is why it beats writing each cell by hand. Reach for this when your data already lives in structured memory.
Option 3: Loading from CSV
Fastest overall, and the natural choice for flat, delimited data. Write the rows to a CSV file, then read it back with LoadCSV.
string[] fruits = { "Apples", "Apricot", "Banana", "Blackberry", "Blueberry", "Boysenberry", "Canary Melon", "Cantaloupe",
"Currants", "Dates (tree dried only) Dragon Fruit", "Durian (purchase cut) Figs", "Gooseberry", "Grapes", "Grapefruit",
"Lemon", "Lime", "Loganberry", "Longan", "Loquat", "Lychee", "Mandarin", "Mango",
"Blood Orange", "Papaya", "Passion Fruit", "Peach", "Pear", "Persimmon", "Pineapple", "Plum", "Honeydew",
"Rambutan", "Starfruit", "Tamarind", "Yuzu", "Açaí", "Abiu", "Ackee", "Breadfruit", "Cempedak",
"Cherimoya", "Buddha's Hand", "Citron", "Finger Lime", "Kumquat", "Pomelo", "Tangelo", "Ugli Fruit",
"Galia Melon", "Kiwano (Horned Melon)", "Mouse Melon", "Muskmelon",
"cherry", "nectarine", "Butternut squash"};
var sb = new StringBuilder();
// Step 1: Add header row
sb.AppendLine(string.Join(",", fruits));
// Step 2: Add 20,000 identical rows
string rowData = string.Join(",", fruits); // same as headers
for (int i = 1; i < 20000; i++)
{
sb.AppendLine(rowData);
}
File.WriteAllText("C:\\Temp\\csvfile.csv", sb.ToString(), Encoding.UTF8);
var workbook = WorkBook.LoadCSV("C:\\Temp\\csvfile.csv");
workbook.SaveAs("C:\\Temp\\LoadFromCSV.xlsx");LoadCSV parses the delimited text in bulk, which keeps it ahead of the other two approaches.
Conclusion
To get the best performance from IronXL on large datasets:
- Use CSV: the fastest path for raw data imports when the source is already flat.
- Use a
DataTable: ideal when the data already exists in structured memory. - Avoid cell-by-cell writing: only worth it when you need per-cell logic or styling.
All three produce the same file size. The loading method is what dramatically changes how long the file takes to generate.
Curtis Chau holds a Bachelor’s degree in Computer Science (Carleton University) and specializes in front-end development with expertise in Node.js, TypeScript, JavaScript, and React. Passionate about crafting intuitive and aesthetically pleasing user interfaces, Curtis enjoys working with modern frameworks and creating well-structured, visually appealing manuals.
