Local parsing processing using the Office Open XML standard and binary structure analysis
The Office Open XML format, which is the standard for Excel files, has a container structure that is essentially a ZIP-compressed set of multiple XML files.
The conversion engine that runs on the browser first expands the binary data read through the File API into memory and restores the directory structure by applying the ZIP decompression algorithm.
Inside this container, there are files that store metadata and files that hold style information, but the core of data analysis is the worksheet XML that records the cell arrangement of each sheet and the shared string XML that centrally manages all strings in the document in a dictionary.
The string is not written directly in the cell, but the index number in the shared string XML is embedded as a reference value.
The parser performs a two-step reference resolution process that scans the worksheet and looks up the corresponding index from the shared string XML when the cell type attribute indicates a string and joins the actual text data.
The entire process from reading this series of files to XML parsing and reference resolution is completed only within the client machine's browser, so there is no process of transmitting data over the network.
Therefore, secure local processing is guaranteed, completely eliminating the risk of external leakage, even for highly confidential internal management data such as financial ledgers and payroll sheets.
Cell type determination algorithm and Excel-specific serial value analysis mechanism
In addition to just text, cells contain a mixture of numbers, boolean values, formulas, and date data. In particular, the handling of date data relies on Excel's unique serial value specifications, so advanced conversion algorithms are required.
As a general rule, serial values in Excel are expressed as the number of days that have passed since January 1, 1900, but because the initial specification that misidentified 1900 as a leap year remains in place to maintain compatibility, it is not possible to derive accurate calendar days by simply adding the number of days.
The conversion engine applies a historical offset calculation to convert the serial value to an accurate UNIX timestamp and then reformats it to the specified format.
Also, for cells where a formula is entered, it is necessary to retrieve the cache value calculated at the time of saving, not the formula string itself.
It has a built-in filtering process that distinguishes between formula tags and value tags in XML elements and extracts only the final calculation result value to be output.
As a result, only the final numbers and text are accurately reflected on the CSV even if the business template has complex nested functions.
Projection and escape specifications from multiple worksheet structure to flat CSV
An Excel workbook has a three-dimensional data structure that contains multiple worksheets, but a CSV file has a two-dimensional flat structure that represents a single table.
Therefore, the mutual conversion process requires the user to select a specific target worksheet or perform a routing process to extract all sheets as individual CSV files.
After the two-dimensional array data of a particular worksheet has been extracted, we move on to the phase of serializing the string into a comma-separated text stream.
The absolute rules that must be followed here are the RFC standards that define CSV standard specifications. If the data in a cell contains commas, newline characters, or double quotes, if you output them as is, they will be confused with column delimiters or the end of a record, causing a parsing error.
The output engine scans the entire string in each cell, and when it detects these special characters, it encloses the entire string in double quotes, and then doubles and escapes the double quotes that exist inside, performing a strict standard-compliant encoding process.
Character code encoding specifications and preview rendering
Most CSV files are used for importing into other software or databases, but the character encoding specifications required by the receiving system vary widely.
In particular, older domestic core systems and some spreadsheet software often require encoding based on Shift_JIS, known as CP932.
On the other hand, UTF-8 is the standard for modern web applications and global database systems. This tool uses an architecture that allows you to specify any encoding method when outputting, and uses JavaScript's binary array operations to encode Unicode strings into the desired byte string.
When outputting UTF-8, control is also performed to add a BOM according to the system specifications. Before downloading, a function is activated that simulates the conversion to these byte arrays and previews the parsed results as an HTML table against the DOM tree on the browser.
This allows users to visually check for misaligned columns or missing special characters before issuing a command to write to a file.
Optimization of import requirements for core system linkage
CSV files are the most popular data exchange format for business system data migration and bulk registration interfaces, but pre-processing is essential to prevent validation errors during import.
Data entered in Excel often contains unintended leading or trailing spaces, invisible control characters, or empty columns due to cell merging.
The conversion algorithm is designed to selectively apply trimming processing using regular expressions and empty line purging processing to the extracted data matrix.
In addition, to maintain consistency between the header row required by the system and the actual data row, it is possible to specify an offset for the output range and include logic to skip unnecessary columns.
The CSV data generated through such a data cleansing mechanism is refined into pure structured data that eliminates impurities and reaches a quality that can smoothly pass strict type checks on database schema definitions.
Memory space management during large-scale spreadsheet processing and future developments
The biggest hurdle faced when processing spreadsheets containing tens to hundreds of thousands of rows of huge business transaction records and sales performance data on a browser is memory consumption.
If you use a DOM parser to expand the entire XML as a tree structure in memory, it will consume heap space several tens of times the file size and cause the browser to crash.
To avoid this, a memory-efficient SAX-based parsing method is used internally that sequentially interprets XML tags on a stream-by-stream basis and garbage-collects nodes that are no longer needed.
Currently, with the evolution of web technology, the introduction of binary execution environments that provide parallel processing in the background that does not block the main thread and performance similar to assembly language is progressing.
This moves us toward a next-generation data pipeline that seamlessly converts even gigabyte workbooks into CSV streams at speeds comparable to desktop native applications, with no noticeable delays for users.