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All documentation
  • Introduction
  • Connecting to data source
    1. Supported data sources
    2. Connecting to other data sources
  • Browser compatibility
  • Documentation for older versions
  • Connecting to CSV data

    This guide illustrates how to connect Flexmonster to a CSV data source.

    You can connect to your CSV data using the client-side or the server-side approach. To connect to a CSV file smaller than 100 MB, use the client-side approach, which is described in this guide.
    To connect to a CSV file larger than 100 MB, we recommend using Flexmonster Data Server — our server-side solution for processing large datasets. For more details, refer to the Connecting to CSV using Flexmonster Data Server guide.

    Prerequisites

    Your CSV data should be specified in the following format:

    • The first record contains field names.
    • Each record is located on a separate line.
    • Fields in records are separated by , or ;.
      To specify another character that separates fields in your CSV file, use the dataSource.fieldSeparator property.
    • Fields can be enclosed in double quotation marks.
    • Fields containing line breaks or field separators (e.g., commas) must be enclosed in double quotation marks.
      Note If your CSV file has fields with line breaks, set the dataSource.ignoreQuotedLineBreaks property to false.
    • If a field member is enclosed in double quotation marks, the double quotes inside the field member must be represented by two double quotation marks.  For instance, Speciality "Bike" Shop must be specified as "Speciality ""Bike"" Shop".
    Example of a valid CSV file
    Category,Color,Country,Price
    Accessories,red,Australia,174
    Components,blue,France,768
    Clothing,green,Canada,512

    Other CSV formats aren’t officially supported and may have unexpected results.

    Note Ensure that dates are also specified in a supported format.

    Step 1. Embed Flexmonster into your webpage

    If Flexmonster is not yet embedded, set up an empty component in your webpage:

    In pure JavaScript

    Complete the Integrating Flexmonster guide. Your code should look similar to the following example:

    let pivot = new Flexmonster({
      container: "pivotContainer",
      componentFolder: "node_modules/flexmonster/",
      toolbar: true
    });

    In React

    Complete the Integration with React guide. Your code should look similar to the following example:

    <FlexmonsterReact.Pivot
     toolbar={true}
    />

    In Angular

    Complete the Integration with Angular guide. Your code should look similar to the following example:

    <fm-pivot
     [toolbar]="true">
    </fm-pivot>

    In Vue

    Complete the Integration with Vue guide. Your code should look similar to the following example:

    <Pivot
     toolbar
    />

    Step 2. Connect to your CSV data

    You can connect Flexmonster to remote or local CSV data.

    Connect to remote CSV data (from a file or a server-side script)

    Remote CSV data can be a remote CSV file or data generated by a server-side script. Flexmonster can be connected to remote CSV data in one of the following ways:

    • Via UI
    • In the report
    • Using API calls

    Via UI

    To connect to remote СSV data via UI, use the Toolbar:

    Step 1. On the Toolbar, select Connect > To remote CSV. As a result, the Open remote CSV pop-up window will appear.

    Step 2. Enter the URL to your CSV data in the input field and click Open.

    In the report

    To connect to remote CSV data in the report, use the dataSource.filename property:

    report: {
      dataSource: {
        filename: "<url-to-remote-csv-data>"
      }
    }

    Live example

    Using API calls

    To connect to remote CSV data at runtime, use the connectTo() or updateData() API call with the DataSourceObject input parameter. For details on data source configurations, go to the In the report tab:

    • To load the data and clear the report, use the connectTo() API call:
      pivot.connectTo({
      filename: "<url-to-remote-csv-data>"
      });
      Live example
    • To load the data without clearing the report, use the updateData() API call:
      pivot.updateData({
      filename: "<url-to-remote-csv-data>"
      });
      Live example

    Connect to a CSV file from your computer

    The pivot table can be connected to a CSV file from your computer in one of the following ways:

    • Via UI
    • In the report
    • Using API calls

    Via UI

    To connect to a local СSV file via UI, use the Toolbar:

    Step 1. On the Toolbar, select Connect > To local CSV. As a result, the file manager will appear.

    Step 2. Select the file via the file manager.

    In the report

    To connect to a local CSV file in the report, use the dataSource.browseForFile property:

    report: {
      dataSource: {
        type: "csv",
        browseForFile: true
      }
    }

    Note The type property must be defined explicitly.

    Live example

    Using API calls

    To connect to a local CSV file at runtime, use the connectTo() or updateData() API call with the DataSourceObject input parameter. For details on data source configurations, go to the In the report tab:

    • To load the file and clear the report, use the connectTo() API call:
      pivot.connectTo({
      type: "csv",
      browseForFile: true
      });

      Note The type property must be defined explicitly.

      Live example
    • To load the file without clearing the report, use the updateData() API call:
      pivot.updateData({
      type: "csv",
      browseForFile: true
      });

      Note The type property must be defined explicitly.

      Live example

    Configure the data source

    You can define how fields from the data source are treated and presented within the component using the mapping. For example, you can:

    • Specify a caption for a field.
    • Hide a field.
    • Define available aggregations for a field.
    • Configure multilevel hierarchies.

    Note If you are using column prefixes to set data types in CSV, we recommend migrating from CSV prefixes to the mapping.

    Migrate from CSV prefixes to the MappingObject

    For easy migration from CSV prefixes to the mapping, see the table below.

    Migration table
    CSV prefixMapping typeDescription
    +"string"The field is a dimension.
    -"number"The field is a value.
    m+"month"The field stores months.
    w+"weekday"The field stores days of the week.
    d+"date"The field stores a date. The field of this type is split into 3 different fields: Year, Month, and Day.
    D+"year/month/day"The field stores a date. It’s displayed as a multilevel hierarchy with the following levels: Year > Month > Day.
    D4+"year/quarter/month/day"The field is a date. It’s displayed as a multilevel hierarchy with the following levels: Year > Quarter > Month > Day.
    ds+"date string"The field stores a date. It can be formatted using the datePattern option (default is "dd/MM/yyyy").
    t+"time"The field stores time.
    dt+"datetime"The field stores a date. It can be formatted using the dateTimePattern option (default is "dd/MM/yyyy HH:mm:ss").
    id+"id"The field is an id. The field of this type can be used for editing data. It’s not shown in the Field List.

    Display non-English characters correctly

    If your data contains non-Latin characters, ensure you have set UTF-8 encoding for your data and page so the data is displayed correctly in the component.

    Troubleshooting

    If you run into any issues, visit our troubleshooting page.

    What's next?

    You may be interested in the following articles: