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  1. API reference
  2. Welcome
    1. Component overview
    2. Get Flexmonster
    3. Quick start
    4. System requirements
    5. Troubleshooting
    6. Managing license keys
    7. Migrating from WebDataRocks to Flexmonster
  3. Integration with frameworks
    1. Available tutorials
    2. Integration with Angular
    3. Integration with React
    4. Integration with Vue
    5. Other integrations
      1. Integration with Python
        1. Integration with Django
        2. Integration with Jupyter Notebook
      2. Integration with React Native
      3. Integration with AngularJS (v1.x)
      4. Integration with TypeScript
      5. Integration with R Shiny
      6. Integration with jQuery
      7. Integration with Ionic
      8. Integration with Electron.js
      9. Integration with Webpack
      10. Integration with RequireJS
  4. Connecting to Data Source
    1. Supported data sources
    2. JSON
      1. Connecting to JSON
      2. Connecting to JSON using Flexmonster Data Server
      3. Data types in JSON
    3. CSV
      1. Connecting to CSV
      2. Connecting to CSV using Flexmonster Data Server
      3. Data types in CSV
    4. 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
    5. 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. Implementing the server filter
        5. Implementing the custom parser
        6. DLL configurations reference
        7. The controller's methods for request handling
    6. MongoDB
      1. Introduction to Flexmonster MongoDB Connector
      2. Getting started with the MongoDB Connector
      3. Embedding the MongoDB Connector into the server
    7. Microsoft Analysis Services
      1. Connecting to Microsoft Analysis Services
      2. Getting started with Flexmonster Accelerator
      3. Referring the Accelerator as a DLL
      4. Configuring the authentication process
      5. Configuring a secure HTTPS connection
      6. Troubleshooting
    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. Implement your own server
        1. Implementing the custom data source API server
        2. Implementing filters
        3. Supporting more aggregation functions
        4. Returning data for the drill-through view
    9. Elasticsearch
      1. Connecting to Elasticsearch
      2. Configuring the mapping
    10. 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
  5. 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
  6. 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
  7. Charts
    1. Available tutorials
    2. Flexmonster Pivot Charts
    3. Integration with Highcharts
    4. Integration with amCharts
    5. Integration with Google Charts
    6. Integration with FusionCharts
    7. Integration with any charting library
  8. Customizing
    1. Customizing the Toolbar
    2. Customizing appearance
    3. Customizing the context menu
    4. Customizing the grid
    5. Customizing the pivot charts
    6. 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
  10. Flexmonster CLI Reference
    1. Overview
    2. Troubleshooting the CLI
    3. flexmonster create
    4. flexmonster add
    5. flexmonster update
    6. flexmonster version
    7. flexmonster help
Table of contents

Mapping

Mapping is a process of defining how the fields contained in the data source are treated and presented within the component. For mapping in Flexmonster, you can use the Mapping Object which is a property of the Data Source.

The Mapping Object is available for all data sources but with some differences. 

For JSON, CSV, and the custom data source API, it’s possible to define field data types and captions, group fields under separate dimensions, create multi-level hierarchies and more. For SSAS and Mondrian data sources, it’s possible to set captions of dimensions and measures. For the data from Elasticsearch, it’s possible to customize hierarchies’ captions, formats, time zones, control fields’ visibility and more.

We recommend using mapping instead of defining a meta-object for JSON or adding prefixes for CSV data since the former presents a powerful way to neatly separate a data source from its representation. Moreover, mapping provides more options than an approach with prefixes for CSV data.

Mapping properties

For each field in the data source, you can set the following properties:

  • caption – String. The hierarchy’s caption.
  • type – String. The field’s data type. Only for "json", "csv", and "api" data source types. type can be:
    • "string" – the field stores string data. It can be aggregated only with count and distinctcount aggregations.
    • "number" – the field stores numerical data. It can be aggregated with all the available aggregations.
    • "month" – the field stores months. 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 "month" type automatically. If the field contains custom month names, specify its type as "month" explicitly.
    • "weekday" – the field stores days of the week.
    • "date" – the field stores a date. The field of this type is split into 3 different fields: Year, Month, Day.
    • "date string" – the field stores a date. It can be formatted using the datePattern option (default is "dd/MM/yyyy").
    • "year/month/day" – the field stores a date. It’s displayed as a multi-level hierarchy with the following levels: Year > Month > Day.
    • "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.
    • "time" – the field stores time. It can be formatted using the timePattern option (default is "HH:mm:ss").
    • "datetime" – the field is a date. It can be formatted using the dateTimePattern option (default is "dd/MM/yyyy HH:mm:ss"). minmaxcount, and distinctcount aggregations can be applied to it.
    • "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.
    • "property" – the field for setting member properties. This field is not shown in the Field List. For example, it can be used to associate a productID with a product. See the example.
  • hierarchy – String. The hierarchy’s name. When configuring hierarchies, specify this property to mark the field as a level of a hierarchy or as a member property of a hierarchy (in this case, the type parameter should be set to "property"). Only for "json", "csv", and "api" data source types.
  • parent – String. The unique name of the parent level. This property is necessary to specify if the field is a level of a hierarchy and has a parent level. Only for "json", "csv", and "api" data source types.
  • dimensionUniqueName – String. The dimension’s unique name. This property can be used to group several fields under one dimension. Only for "json", "csv", and "api" data source types.
  • dimensionCaption – String. The dimension’s caption. This property specifies the name of a folder in the Field List under which several fields are grouped. Only for "json", "csv", and "api" data source types.
  • aggregations (optional) — Array of strings. It represents the list of aggregation functions that can be applied to the current measure.
  • filters – Boolean. It allows enabling and disabling the UI filters for a specific hierarchy. When set to false, the UI filters are disabled. Default value: true.
  • visible (optional) – Boolean. When set as false, hides the field from the Field List. Only for "elasticsearch", "csv", and "api" data source types.
  • interval (optional) – String. Allows aggregating dates by the given interval. The interval property can be used in the following ways:
    • for date histogram in Elasticsearch. Check out supported time units. Only for "elasticsearch" data source type.
    • for "date string" and "datetime" field types. Supported date intervals are the following: "d" for days, "h" for hours, "m" for minutes, and "s" for seconds (e.g., "1d", "7h", "20m", "30s"). The maximum value for the interval is "1d". Only for "csv" and "json" data source types.
  • time_zone (optional) – String. Used for date histogram. You can specify timezones as either an ISO 8601 UTC offset (e.g. +01:00 or -08:00) or as a timezone ID as specified in the IANA timezone database, such as `America/Los_Angeles`. Only for "elasticsearch" data source type. Check out the example.
  • format (optional) – String. Used to format different types of date fields. format can be used in the following ways:
    • for date histogram in Elasticsearch. Check out the date format/pattern. Only for "elasticsearch" data source type.
    • for the "date string" field type ("date" in custom data source API), it allows overriding datePattern set in the Options Object for a certain field. The pattern string is the same as in the datePattern option (i.e. "dd/MM/yyyy"). Only for "json", "csv", and "api" data source types.
    • for the "datetime" field type, it allows overriding dateTimePattern set in the Options Object for a certain field. The pattern string is the same as in the dateTimePattern option (i.e. "dd/MM/yyyy HH:mm:ss"). Only for "json" and "csv" data source types.
    • for the "time" field type, it allows overriding timePattern set in the Options Object for a certain field. The pattern string is the same as in the timePattern option (i.e. "HH:mm:ss"). Only for "json" and "csv" data source types.
    To learn more about the formatting, see the date and time formatting tutorial.
  • min_doc_count (optional) – Number. Only for "elasticsearch" data source type. Used for date histogram. Can be used to show intervals with empty values (min_doc_count: 0). Default value: 1 (empty intervals are hidden).

Other ways to customize fields presentation

Another way to define how the fields are displayed in the report is by setting these configurations right in the data source. Please note that this approach is available only for CSV and JSON data sources. For more details, please refer to the Data types in CSV and Data types in JSON articles. It also should be noted that there are certain limitations in the case of CSV data source – not all the field properties can be customized using prefixes.

Thus, we strongly recommend preferring the Mapping Object to other types of fields’ customization.

Examples

1) Creating multi-level hierarchies in JSON:

mapping: {
"Color": {
type: "string"
},
"Country": {
type: "string",
hierarchy: "Geography",
},
"State": {
type: "string",
hierarchy: "Geography",
parent: "Country"
},
"City": {
type: "string",
hierarchy: "Geography",
parent: "State"
},
"Price": {
type: "number"
}
}

Try the live demo on JSFiddle.

2) Setting custom captions and field data types, creating multi-level hierarchies in the CSV data source:

mapping: {
"Order ID": { "type": "string" },
"Month": { "type": "month" },
"Company Name": { "type": "string" },
"Customer": { "type": "string" },
"region": {
"caption": "Region",
"type": "string",
"hierarchy": "Geography"
},
"State": {
"type": "string",
"parent": "region",
"hierarchy": "Geography"
},
"City": {
"type": "string",
"parent": "State",
"hierarchy": "Geography"
},
"Salesperson": { "type": "string" },
"Payment Method": { "type": "string" },
"Category": { "type": "string" },
"Name": { "type": "string", "caption": "Product Name" },
"price": { "type": "number", "caption": "Unit Price" },
"Quantity": { "type": "number" },
"Revenue": { "type": "number" },
"Shipping Cost": { "type": "number" }
}

Try it on JSFiddle.

3) Setting custom captions and grouping fields under separate dimensions in the JSON data source:

mapping: {
"Color": {
caption: "color"
},
"Country": {
caption: "MyCountry",
dimensionCaption: "Place",
dimensionUniqueName: "Place"
},
"State": {
caption: "MyState",
dimensionCaption: "Place",
dimensionUniqueName: "Place"
},
"City": {
caption: "MyCity",
dimensionCaption: "Place",
dimensionUniqueName: "Place"
},
"Price": {
type: "number",
caption: "MyPrice"
},
"Quantity": {
type: "number",
caption: "MyQuantity"
}
}

See the live demo on JSFiddle.

4) Specifying custom captions for hierarchies and measures for SSAS:

mapping: {
"[Geography].[Geography]": {
caption: "My Geography"
},
"[Product].[Category]": {
caption: "My Category"
}
}

See the live demo on JSFiddle.

5) Setting the custom captions for hierarchies and measures for Mondrian:

mapping: {
"[Store]": {
caption: "My Store"
},
"[Measures].[Profit]": {
caption: "My Profit"
}
}

Try it on JSFiddle.