The @flatfile/plugin-xlsx-extractor plugin is designed to extract structured data from Excel files. It utilizes various libraries to parse Excel files and retrieve the structured data efficiently.

Event Type: listener.on('file:created')

Supported file types: .xls, .xlsx, .xlsm, .xlsb, .xltx, .xltm

When embedding Flatfile, this plugin should be deployed in a server-side listener. Learn more

Parameters

raw - boolean

In Excel, you could have formatting on a text cell (i.e. date formatting). By default, Flatfile will just take the formatted text versus the raw values. Set this value to true to take the raw values and disregard how it's displayed in Excel.

rawNumbers - boolean

In Excel, you could have rounding or formatting on a number cell to only display say 2 decimal places. By default, Flatfile will just take the displayed decimal places versus the raw numbers. Set this value to true to take the raw numbers and disregard how it's displayed in Excel.

dateNF - string - (optional)

The dateNF parameter allows you to specify the date format for parsing dates. (i.e. yyyy-mm-dd)

chunkSize - default: "10_000" - number - (optional)

The chunkSize parameter allows you to specify the quantity of records to in each chunk.

parallel - default: "1" - number - (optional)

The parallel parameter allows you to specify the number of chunks to process in parallel.

headerDetectionOptions - Object - (optional)

The headerDetectionOptions parameter allows you to specify the options for detecting headers in the file. By default, the first 10 rows are scanned for the row with the most non-empty cells.

skipEmptyLines - default: "false" - boolean - (optional)

The skipEmptyLines parameter allows you to specify if empty lines should be skipped. By default, empty lines are included.

debug - default: "false" - boolean - (optional)

The debug parameter lets you toggle on/off helpful debugging messages for development purposes.

API Calls

• api.files.download
• api.files.get
• api.files.update
• api.jobs.ack
• api.jobs.complete
• api.jobs.create
• api.jobs.fail
• api.jobs.update
• api.records.insert
• api.workbooks.create

Usage

Listen for an Excel file (all extensions supported) to be uploaded to Flatfile. The platform will then extract the file automatically. Once complete, the file will be ready for import in the Files area.

npm i @flatfile/plugin-xlsx-extractor

import { ExcelExtractor } from "@flatfile/plugin-xlsx-extractor";

listener.js

listener.use(ExcelExtractor());

Additional options

listener.use(ExcelExtractor({ raw: true, rawNumbers: true }));

Header Detection

Three detection options are provided for detecting headers in the file: default, explicitHeaders, and specificRows. By default, the first 10 rows are scanned for the row with the most non-empty cells. This row is then used as the header row.

Default

It looks at the first rowsToSearch rows and takes the row with the most non-empty cells as the header, preferring the earliest such row in the case of a tie.

listener.use(ExcelExtractor());
// or...
listener.use(
  ExcelExtractor({
    headerDetectionOptions: {
      algorithm: "default",
      rowsToSearch: 30, // Default is 10
    },
  })
);

Explicit Headers

This implementation simply returns an explicit list of headers it was provided with.

listener.use(
  ExcelExtractor({
    headerDetectionOptions: {
      algorithm: "explicitHeaders",
      headers: ["fiRsT NamE", "LaSt nAme", "emAil"],
    },
  })
);

Specific Rows

This implementation looks at specific rows and combines them into a single header. For example, if you knew that the header was in the third row, you could pass it { rowNumbers: [2] }.

listener.use(
  ExcelExtractor({
    headerDetectionOptions: {
      algorithm: "specificRows",
      rowNumbers: [2], // 0 based
    },
  })
);

Data Row & Sub Header Detection

This implementation attempts to detect the first data row and select the previous row as the header. If the data row cannot be detected due to all the sample rows being full and not castable to a number or boolean type, it also will attempt to detect a sub header row by checking following rows after a header is detected for significant fuzzy matching. If over half of the fields in a possible sub header row fuzzy match with the originally detected header row, the sub header row becomes the new header.

listener.use(
  ExcelExtractor({
    headerDetectionOptions: {
      algorithm: "dataRowAndSubHeaderDetection",
      rowsToSearch: 30, // Default is 10
    },
  })
);

Full Example

In this example, the ExcelExtractor is initialized with optional options, and then registered as middleware with the Flatfile listener. When an Excel file is uploaded, the plugin will extract the structured data and process it using the extractor's parser.

listener.js

import { ExcelExtractor } from "@flatfile/plugin-xlsx-extractor";

export default async function (listener) {
  // Define optional options for the extractor
  const options = {
    raw: true,
    rawNumbers: true,
  };

  // Initialize the Excel extractor
  const excelExtractor = ExcelExtractor(options);

  // Register the extractor as a middleware for the Flatfile listener
  listener.use(excelExtractor);

  // When an Excel file is uploaded, the data will be extracted and processed using the extractor's parser.
}