Working with Memory and Performance

By default XlsxWriter holds all cell data in memory. This is to allow future features when formatting is applied separately from the data.

The effect of this is that XlsxWriter can consume a lot of memory and it is possible to run out of memory when creating large files.

Fortunately, this memory usage can be reduced almost completely by using the Workbook() 'constant_memory' property:

workbook = xlsxwriter.Workbook(filename, {'constant_memory': True})

The optimization works by flushing each row after a subsequent row is written. In this way the largest amount of data held in memory for a worksheet is the amount of data required to hold a single row of data.

Since each new row flushes the previous row, data must be written in sequential row order when 'constant_memory' mode is on:

# With 'constant_memory' you must write data in row by column order.
for row in range(0, row_max):
    for col in range(0, col_max):
        worksheet.write(row, col, some_data)

# With 'constant_memory' this would only write the first column of data.
for col in range(0, col_max):
    for row in range(0, row_max):
        worksheet.write(row, col, some_data)

Another optimization that is used to reduce memory usage is that cell strings aren’t stored in an Excel structure call “shared strings” and instead are written “in-line”. This is a documented Excel feature that is supported by most spreadsheet applications. One known exception is Apple Numbers for Mac where the string data isn’t displayed.

The trade-off when using 'constant_memory' mode is that you won’t be able to take advantage of any new features that manipulate cell data after it is written. Currently the add_table() method doesn’t work in this mode and merge_range() and set_row() only work for the current row.

For larger files 'constant_memory' mode also gives an increase in execution speed, see below.

Performance Figures

The performance figures below show execution time and memory usage for worksheets of size N rows x 50 columns with a 50/50 mixture of strings and numbers. The figures are taken from an arbitrary, mid-range, machine. Specific figures will vary from machine to machine but the trends should be the same.

XlsxWriter in normal operation mode: the execution time and memory usage increase more of less linearly with the number of rows:

Rows Columns Time (s) Memory (bytes)
200 50 0.43 2346728
400 50 0.84 4670904
800 50 1.68 8325928
1600 50 3.39 17855192
3200 50 6.82 32279672
6400 50 13.66 64862232
12800 50 27.60 128851880

XlsxWriter in constant_memory mode: the execution time still increases linearly with the number of rows but the memory usage remains small and constant:

Rows Columns Time (s) Memory (bytes)
200 50 0.37 62208
400 50 0.74 62208
800 50 1.46 62208
1600 50 2.93 62208
3200 50 5.90 62208
6400 50 11.84 62208
12800 50 23.63 62208

In the constant_memory mode the performance is also increased slightly.

These figures were generated using programs in the dev/performance directory of the XlsxWriter repo.

Benchmark of Python Excel Writers

If you wish to compare the performance of different Python Excel writing modules there is a program called in the dev/performance directory of the XlsxWriter repo.

And here is the output for 10,000 rows x 50 columns using the latest version of the modules at the time of writing:

    python      : 2.7.2
    openpyxl    : 2.2.1
    pyexcelerate: 0.6.6
    xlsxwriter  : 0.7.2
    xlwt        : 1.0.0

    Rows = 10000
    Cols = 50

    pyexcelerate          :  10.63
    xlwt                  :  16.93
    xlsxwriter (optimized):  20.37
    xlsxwriter            :  24.24
    openpyxl   (optimized):  26.63
    openpyxl              :  35.75

As with any benchmark the results will depend on Python/module versions, CPU, RAM and Disk I/O and on the benchmark itself. So make sure to verify these results for your own setup.