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  1. API reference
  2. Welcome
    1. Component overview
    2. Quick start
    3. System requirements
    4. Troubleshooting
    5. Managing license keys
    6. Migrating from WebDataRocks to Flexmonster
  3. Connecting to Data Source
    1. JSON
      1. Connecting to JSON
      2. Connecting to JSON using Flexmonster Data Server
      3. Data types in JSON
    2. CSV
      1. Connecting to CSV
      2. Connecting to CSV using Flexmonster Data Server
      3. Data types in CSV
    3. Database
      1. Connecting to SQL databases
      2. Connecting to a MySQL database
      3. Connecting to a Microsoft SQL Server database
      4. Connecting to a PostgreSQL database
      5. Connecting to an Oracle database
      6. Connecting to other databases
    4. MongoDB
      1. Introduction to Flexmonster MongoDB Connector
      2. Getting started with the MongoDB Connector
      3. Embedding the MongoDB Connector into the server
    5. Microsoft Analysis Services
      1. Connecting to Microsoft Analysis Services
      2. Getting started with the Accelerator
      3. Installing the Accelerator as a Windows service
      4. Referring the Accelerator as a DLL
      5. Configuring the authentication process
      6. Configuring a secure HTTPS connection
      7. Troubleshooting
    6. Pentaho Mondrian
      1. Connecting to Pentaho Mondrian
      2. Getting started with the Accelerator
      3. Configuring Mondrian roles
      4. Configuring username/password protection
      5. Configuring a secure HTTPS connection
      6. Troubleshooting
    7. Elasticsearch
      1. Connecting to Elasticsearch
      2. Configuring the mapping
    8. Custom data source API
      1. Introduction to the custom data source API
      2. A quick overview of a sample Node.js server
      3. A quick overview of a sample .NET Core server
      4. Implementing the custom data source API server
      5. Implementing filters
      6. Supporting more aggregation functions
      7. Returning data for the drill-through view
    9. Flexmonster Data Server
      1. Getting started with Flexmonster Data Server
      2. Installation guide
      3. Configurations reference
      4. Data sources guide
      5. Security and authorization guide
      6. The Data Server as a DLL
        1. Getting started with the Data Server as a DLL
        2. Referring the Data Server as a DLL
        3. Implementing the API controller
        4. DLL configurations reference
  4. Security
    1. Security in Flexmonster
    2. Security aspects of connecting to an OLAP cube
      1. Ways of connecting to an OLAP cube
      2. The data transfer process
      3. Data security
      4. Data access management
  5. Configuring report
    1. What is a report
    2. Data source
    3. Slice
    4. Options
    5. Mapping
    6. Number formatting
    7. Conditional formatting
    8. Set the report for the component
    9. Get the report from the component
    10. Date and time formatting
    11. Configuring global options
    12. Export and print
    13. Calculated values
    14. Custom sorting
  6. Integration with frameworks
    1. Available tutorials
    2. Integration with AngularJS (v1.x)
    3. Integration with Angular
    4. Integration with React
    5. Integration with React Native
    6. Integration with Vue
    7. Integration with Python
      1. Integration with Django
      2. Integration with Jupyter Notebook
    8. Integration with R Shiny
    9. Integration with Webpack
    10. Integration with ASP.NET
    11. Integration with jQuery
    12. Integration with JSP
    13. Integration with TypeScript
    14. Integration with Ionic
    15. Integration with RequireJS
    16. Integration with PhoneGap
  7. Charts
    1. Flexmonster Pivot Charts
    2. Integration with Highcharts
    3. Integration with Google Charts
    4. Integration with FusionCharts
    5. Integration with any charting library
  8. Customizing
    1. Customizing the Toolbar
    2. Customizing appearance
    3. Customizing the context menu
    4. Customizing the grid
    5. Localizing the component
  9. Updating to the latest version
    1. Updating to the latest version
    2. Release notes
    3. Migration guide from 2.7 to 2.8
    4. Migration guide from 2.6 to 2.7
    5. Migration guide from 2.5 to 2.6
    6. Migration guide from 2.4 to 2.5
    7. Migration guide from 2.3 to 2.4
    8. Migration guide from 2.2 to 2.3
    9. Documentation for older versions
Table of contents

Data types in CSV

Since 2.7.14 version of Flexmonster, it’s recommended to use a Mapping Object to customize the representation and structure of the CSV data source fields.  See the migration guide from CSV prefixes to the mapping.

Alternatively, you can use special prefixes for column names to indicate how data should be interpreted by Flexmonster Pivot. Note that the Mapping Object provides more options that the approach with prefixes. 

Migrating from CSV prefixes to the Mapping Object

The Mapping Object is a simple and convenient way of defining the fields’ data type, and we recommend using it instead of CSV prefixes.

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

CSV prefix Mapping type Description
+ "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 multi-level hierarchy with the following levels: Year > Month > Day.
D4+ "year/quarter/month/day" The field is a date. It’s displayed as a multi-level 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.

Supported CSV prefixes

Here is the list of supported prefixes that can be used to customize the CSV data: 

  • + – the field is a dimension.
  • - – the field is a value.
  • m+ – the field is a month. Note that if the field stores month names only (in either short or full form), the field will be recognized by Flexmonster as a field of "m+" type automatically. If the field contains custom month names, specify its type as "m+" explicitly.
  • w+ – the field is a day of the week.
  • d+ – the field is a date. Such fields will be split into 3 different fields: Year, Month, and Day. Date formats that are supported by Flexmonster Pivot are described below.
  • D+ – the field is a date. You will see these dates as a hierarchy: Year > Month > Day.
  • D4+ – the field is a date. You will see these dates as a hierarchy: Year > Quarter > Month > Day.
  • ds+ – the field is a date. Such fields will be formatted using a date pattern (default is "dd/MM/yyyy").
  • t+ – the field is a time (measure). Such fields will be formatted using "HH:mm:ss" pattern.
  • dt+ – the field is a date (measure). Such fields will be formatted using "dd/MM/yyyy HH:mm:ss" pattern.
  • id+ – the field is an id of the fact.

Here is the minimal CSV data that will treat Year as a dimension, rather than a numeric measure:

Country, +Year, Sales 
US, 2010, 200 
UK, 2010, 100

Supported date formats

To make date column be interpreted as a date, use prefixes d+, D+, and D4+ for CSV columns. Additionally, data from these columns should have a special date format to be understood properly. The pivot table component supports the ISO 8601 date format, for example: "2016-03-20" (just date) or "2016-03-20T14:48:00" (date and time). Other formats aren’t officially supported and may have unexpected results.

Here is an example of CSV data with date columns – Date1 and Date2:

Size, Discount, d+Date1, D+Date2
214 oz, 14, 2009-11-01, 2009-11-09
214 oz, 12, 2010-12-09, 2009-12-09
212 oz, 36, 2009-09-01, 2009-12-01
212 oz, 27, 2009-09-01, 2010-12-02
212 oz, 18, 2010-11-09, 2009-12-11
212 oz, 16, 2009-09-01, 2009-12-20

The pivot table based on this CSV will look as follows:

date data types from CSV

As you can see, the Date1 column with prefix d+ is split into three separate fields — Year, Month, and Day. In the Field List, the Date1 column will look as follows:

date data types from CSV in the Field List

The Date2 column with D+ prefix is interpreted as a hierarchy that can be drilled down to months and days.