Connecting Bold BI to Google Cloud SQL data source

The Bold BI Dashboard Designer supports connecting to multiple databases via Google Cloud SQL using the SQL Live Query (C# API).

Choose Google Cloud SQL data source

To configure the Google Cloud SQL data source, follow these steps:

1. Click Data Sources in the configuration panel to add a new data connection.

   

2. Click CREATE NEW to launch a new connection from the connection panel.

3. Select the Google Cloud SQL connection in the connection panel.

   

NOTE: You can also create a data source from the home page by clicking on the Data Sources menu on the left menu panel and selecting Create Data Source from the data sources page.

Connect to Google Cloud SQL

Create Google Cloud SQL data source

After clicking on the data source, the NEW DATA SOURCE configuration panel opens. Follow the steps below to create a Google Cloud SQL data source:

1. Enter a name and description (optional) for the data source.
2. Select the database engine you want to use with the given Google Cloud SQL Server from the database engine drop-down box
3. Enter a valid Google Cloud SQL server or host name in the Server Name text box.
4. Enter a valid Google Cloud SQL user name in the User Name text box.
5. Enter a valid Google Cloud SQL password in the Password text box.
6. Enter a valid Google Cloud SQL Database in the Database text box or select in the combo box.

NOTE: To connect the data source with SSH, enable the SSH checkbox in the New Data Source configuration panel and enter the necessary credentials.

Google Cloud SQL supported database engine in Bold Bi:

* MySQL
* PostgreSQL

There are two connection types available in a data source:

* Live mode
* Extract mode

Live mode connection

In this connection type, the data source is directly fetched from the source. Choose the Live mode option for this connection.

Data Preview

1. Click Connect to establish a connection with the Google Cloud SQL server using the configured details. The schema represents the collection of lists retrieved from the Google Cloud SQL server. This dialog displays a list of schemas in a tree view along with their corresponding values.

   

2. Now, the data design view page with the selected table schema opens. Drag the table.

   

   You can use the Code View option for passing a query to display data.

   

3. Click Save to save the data source with a relevant name.

Extract mode connection

In this connection type, a data source is fetched from source periodically. Choose the Extract mode option for this connection.

NOTE: Initially, data will be extracted based on the Max Rows selected in order to proceed with data model creation. The remaining records (there is no limit) will be extracted during the next refresh.

Refresh Settings

Steps to configure the data source refresh settings:

1. Click Refresh Settings in the configuration panel.

   

2. Select the recurrence type, recurrence start, and end dates in the Refresh Setting dialog box.

   • Data refresh can be scheduled hourly, daily, weekly, and monthly.
   • Application Time Zone is displayed below the date picker. Start time of the schedule is converted to the client Time Zone and shown at the right-side for users convenience. After selecting, click Schedule.

   

Preview and data import

1. Click Connect to connect the Google Cloud SQL server with the configured details.
2. The Extract data dialog opens. Which has two modes of connection either via Table or Custom query.Under custom query option, write the required query and click Connect.

Under Table option, The dialog displays list of tables and views in treeview. Select the required table(s) or view(s) from treeview to use in the designer. The option is available for configuring incremental refresh column (The table must have a primary key column and date column to configure this option) for the selected items in the right-side panel. If you configure it, then the data source will work on Incremental update, otherwise it will work on Full load concept. Finally, click Connect.

3. Now, the data design view page with the selected table schema opens. Drag the table.

   

   You can use the Code View option to pass a query and display data.

   

4. Click Save to save the data source with a relevant name.

Connect using custom attribute and dashboard parameter

We have added support for custom attributes and dashboard parameters in the data source connection. You can now connect to the data source using custom attributes or dashboard parameters.

Custom Attribute

Dashboard Parameter

Note: Refer to the Dashboard Parameter Documentation and Custom Attributes Documentation for more details.

NOTE: In future, you can edit the connection information for both live and extract mode connections using the Edit Connection option.

Connecting Bold BI to Google Cloud SQL Data Source via REST API

Prerequisites

Type while creating the data source needs to be googlecloudmysql or googlecloudpostgresql

Rest API - v4.0

Modes

Through the REST API, only the live mode data source can be created and edited.

Parameters for creating Data Source

NOTE: The ability to provide join support is only available during the creation of a new data source. Join in edit connection mode is not supported.

Parameters for edit the connection while upload the dashboard via API

Parameters for adding expressions when creating Data Source

Parameters for editing Data Source

NOTE: To edit a Data Source via API, all parameters are optional. Only the parameter that needs to be changed should be provided.

Parameters for modifying expressions when editing Data Source

Connection Sample for Table Mode

For creating connection:

"Connection":
{
"Servername": "string",
"Port": "string",
"Username": "string",
"Password": "string",
"Database": "string",
"Schemaname": "string",
"Tablename": "string",
"AdvancedSettings": "string",
"CommandTimeout": "string",
"IsSshConnection": "false",
"SshServerName": "string",
"SshPort": 0,
"SshUsername": "string",
"SshPassword": "string",
"Expressions" : [{
"Name": "Expression1",
"Expression" : "SUM(numeric expression)"
    },
    {
"Name": "Expression2",
"Expression" :  "UPPER(string expression)"
}]
}

For creating connection with multiple tables :

"Connection":
{
"Servername": "string",
"Port": "string",
"Username": "string",
"Password": "string",
"Database": "string",
"Tables": [
            {
                "Tablename": "string",
                "Schemaname": "string"
            },
            {
                "Tablename": "string",
                "Schemaname": "string"
            }
        ],
"JoinInfo": [
            {
                "JoinType": "string",
                "LeftTable": "string",
                "RightTable": "string",
                "JoinFieldInfos": [
                    {
                        "Condition": "string",
                        "LeftField": "string",
                        "Operator": "string",
                        "RightField": "string",
                        "Value": "string"
                    },
                    {
                        "Condition": "string",
                        "LeftField": "string",
                        "Operator": "string",
                        "RightField": "string",
                        "Value": "string"
                    }
                ]
            }
        ],
"AdvancedSettings": "string",
"CommandTimeout": "string",
"IsSshConnection": "false",
"SshServerName": "string",
"SshPort": 0,
"SshUsername": "string",
"SshPassword": "string",
"Expressions" : [{
"Name": "Expression1",
"Expression" : "SUM(numeric expression)"
    },
    {
"Name": "Expression2",
"Expression" :  "UPPER(string expression)"
}]
}

For editing connection:

"Connection":
{
"Servername": "string",
"Port": "string",
"Username": "string",
"Password": "string",
"Database": "string",
"Schemaname": "string",
"Tablename": "string",
"AdvancedSettings": "string",
"CommandTimeout": "string",
"IsSshConnection": "false",
"SshServerName": "string",
"SshPort": 0,
"SshUsername": "string",
"SshPassword": "string",
"Expressions" : [{
"Name": "Expression1",
"Expression" : "SUM(numeric expression)",
"NewName" : "Sum",
"Action": "edit"
    },
    {
"Name": "Expression2",
"Expression" :  "UPPER(string expression)"
"Action": "delete"
}]
}

NOTE: Through the Rest API, the data source can be created or edited with only one table. If different table is provided in edit data source, the table will be replaced. The widgets will be retained only if the schema is same as the previous table.

Connection Sample for Code View Mode

"Connection":
{
"Servername": "string",
"Port": "string",
"Username": "string",
"Password": "string",
"Database": "string",
"Query": "string",
"AdvancedSettings": "string",
"CommandTimeout": "string",
"IsSshConnection": "false",
"SshServerName": "string",
"SshPort": 0,
"SshUsername": "string",
"SshPassword": "string",
"Expressions" : [{
"Name": "Expression1",
"Expression" : "SUM(numeric expression)"
    },
    {
"Name": "Expression2",
"Expression" :  "UPPER(string expression)"
}]
}

Sample connection for editing the connection while uploading the dashboard via API.

"Connection":  {   
     "ServerName": "string", 
      "Database": "string",
      "UserName": "string",
      "Password": "string",
      "Port": "string",
      "SslMode": "string",
      "TrustServerCertificate": "string",
      "AdvancedSettings": "string",
      "CommandTimeout": "string",
      "Schema": "string"
  } 

Related links

Data Transformation

Editing a Data Connection

Dashboard Designer Walkthrough