Update and add new Highcharts modules for use in Sisense plugins
Update and add new Highcharts modules for use in Sisense plugins The JavaScript library framework Highcharts is natively included in Sisense and is utilized in many native Sisense widgets as well as in numerous Sisense plugins. Although Sisense typically does not alter the Sisense Highcharts library version with every release, the versions of Highcharts included in Sisense may change when upgrading to a new major version release. Highcharts can load additional chart types and other types of functionality via JS module files that contain code-adding features such as additional chart types, which can be used within plugins along with additional code to create additional widget types. If a plugin utilizes a Highcharts module, you can source the module directly in the "plugin.json" file's source parameter, as shown in this example: "source": [ "HighchartModule.js", ], To determine the current Highcharts version being used in your Sisense version, you can use the "Highcharts" command in the web console while viewing any page on your Sisense server. After identifying the current Highcharts version, you can find the corresponding module hosted on the Highcharts code hosting website using the following URL format: https://code.highcharts.com/${Highcharts_Version}/modules/${module_name}.js For example: https://code.highcharts.com/6.0.4/modules/heatmap.js You can save this module and upload it to the plugin folder or replace the older module JS file simply by copying and pasting the code directly. Be sure to update the "plugin.json" file to point to the new module file if the file name has changed or if this is the first time the module is included. Simply sourcing the module file in the "plugin.json" file is sufficient to load the module into Highcharts; no further code is required to load the module.
Pivot 2.0 - Manipulating a Pivot Chart
Pivot 2.0 - Manipulating a Pivot Chart Introduction The following article discusses how to manipulate the data and styling of your Pivot 2.0 widget. Please refer to the following article first: https://sisense.dev/guides/customJs/jsApiRef/widgetClass/pivot2.html. Cell Identification To manipulate a pivot cell, we'll have to learn the different identifiers of each cell in the table. Cell Types Each cell is linked with a 'type' that represents the data it contains: A member cell refers to a Column/Row header A value cell refers to a table value cell A subtotal cell refers to a subtitle row (title + values) A grandtotal cell refers to a grand-total rows & columns (titles + values) A cell may have more than one 'type': A cell that has a subtotal and a member type represents the subtitle row title A cell that has a subtotal and a value type represents the subtitle row values (including values + column grand total values) A cell that has a grandtotal' and a member type represents the grand-total row and column titles A cell that has a grandtotal' and a value type represents the grand-total values (including row & column grand total values) A cell that has a grandtotal, a subtotal, and a value type represents the grand total values in the subtitle rows See the following pivot table and the corresponding cell types Original Table Cell Types Manipulating a Cell (Based on its Type) Here are two examples of how to manipulate a cell based on its type: widget.transformPivot( { type: ['value'] }, function (metadata, cell) { // Manuipulation code } ); widget.transformPivot( {}, function (metadata, cell) { if (metadata.type.includes('value')) { // Manuipulation code } } ); H2 - Cell Indexes Each cell is represented by three indexes: Metadata Index - Representing the logical column ID in the table (aligns with the selected rows/values/columns) Column Index - Representing the column number in the table Row Index - Representing the row number in the table Metadata Index See the following pivot table, the pivot configuration pane, and the corresponding metadata indexes: Original Table Metadata Index Here is an example of how to manipulate a cell based on its metadata index: widget.transformPivot( {}, function (metadata, cell) { if (metadata.index == 1) { // Manuipulation code } } ); Column/Row Index See the following pivot table and the corresponding row/column indexes Original Table Row/Column Indexes Here is an example of how to manipulate a cell based on its columns/row index: widget.transformPivot( {}, function (metadata, cell) { if (metadata.colIndex == 3 && metadata.rowIndex == 2) { // Manuipulation code } } ); Cell Row/Column/Measure Name Each cell may be affiliated with three metadata values: Measure - The measure calculated in this cell (name & formula) Column(s) - The column(s) this cell is under (field, title, & value) Row(s) - The rows(s) this cell belongs to (field, title, & value) Manipulating a Cell (Based on their Measure) Here is an example of how to manipulate a value cell based on the measure's name: widget.transformPivot( {}, function (metadata, cell) { if (metadata.measure.title === 'SUM') { // Manuipulation code } } ); Manipulating a Cell (Based on Their Row) Here is an example of how to manipulate a value cell based on the row's value: widget.transformPivot( {}, function (metadata, cell) { // Format based on the value of the a row's name and value metadata.rows.forEach(function(row) { if (row.title === 'Year' && row.member === '2012-01-01T00:00:00.000') { // Manuipulation code } }) } ); Manipulating a Cell (Based on Their Column) Here is an example of how to manipulate a value cell based on the column's value: widget.transformPivot( {}, function (metadata, cell) { // Format based on the value of the a row's name and value metadata.columns.forEach(function(column) { if (column.title === 'Online' && column.member === 'False') { // Manuipulation code } }) } ); Cell Manipulation The possible manipulation options of a cell include: value - Raw value of the cell from query response (manipulating this value is useless) content - The HTML contents of this cell style - The cell formatting Here is an example of how to manipulate value cells' style: widget.transformPivot( {}, function (metadata, cell) { cell.style = { backgroundColor : 'lightgray', fontSize : 14, fontWeight : 'bold', fontStyle : 'italic', textAlign : 'center', color : 'black', borderColor : 'black', borderWidth : '3px', minWidth : '150px', maxWidth : '200px' }; } ); Here is an example of how to manipulate value cells' value: widget.transformPivot( {}, function (metadata, cell) { if (cell.content == '') cell.content = '---' } ); Check out this related content
Limiting Date Range Filters in Sisense Dashboards
Wide date ranges in Sisense dashboards can lead to performance issues, especially when using live models or querying large datasets. For live data models, large queries increase costs as more data is pulled from the data warehouse. For Elasticubes, this can cause performance bottlenecks. To avoid these issues, here is a quick solution to "limit" the date range users can select, ensuring both cost-efficiency and smooth performance. Read more to find out how!
Generating a HAR file for troubleshooting Sisense
When troubleshooting complex issues in Sisense such as PDF export, widget not loading etc., it is sometimes necessary for our support team to obtain additional information about the network requests that are generated in your browser while an issue occurs. A technical solutions engineer may request you to record a .HAR file, or a log of network requests, while that issue is occurring and then provide that to them for further analysis. Below are some instructions about how you can easily generate a HAR file using different browsers. Note: Keep in mind that HAR files contain sensitive data, including content of the pages you downloaded while recording and your cookies. This allows anyone with the HAR file to impersonate your account and all the information that you submitted while recording (personal details, passwords, credit card numbers, etc.). To generate the HAR file for Chrome Open Google Chrome and go to the page where the issue is occurring. Look for the Vertical ellipsis button and select More Tools > Developer Tools. From the panel opened, select the Network tab. Look for a round Record button in the upper left corner of the tab, and make sure it is red. If it is grey, click it once to start recording. Check the box Preserve log. Click the Clear button to clear out any existing logs from the Network tab. Reproduce the issue that you were experiencing before, while the network requests are being recorded. Once you have reproduced the issue, in Chrome, click Download. Then save the file to your computer: Save as HAR with Content Upload your HAR file to your ticket or attach it to your email so that our Support team can analyze it. The instructions are the same for a MAC computer or a Windows computer. Here is a brief animation showing this process: To generate the HAR file for Firefox Open Firefox and go to the page where you are experiencing trouble. Select the Firefox menu (three horizontal parallel lines) at the top-right of your browser window, then select Web Developer > Network. The Developer Network Tools opens as a docked panel at the side or bottom of Firefox. Click the Network tab. The recording autostarts when you start performing actions in the browser. Once you have reproduced the issue and you see that all of the actions have been generated in the Developer Network Panel (should just take a few seconds), right-click anywhere under the File column, and click on Save all as Har. Save the HAR file somewhere convenient. Upload your HAR file to your ticket or attach it to your email so that we may analyze it. The instructions are the same for a MAC computer or a Windows computer. To generate the HAR file for Edge Edge natively produces HAR files. For more instructions, see the instructions from the Microsoft website. Open the Network tool in F12 developer tools. Reproduce the issue. Export captured traffic as a HAR (CTRL + S). To generate the HAR file for Internet Explorer Open Internet Explorer and go to the page where the issue is occurring. Press F12 on your keyboard (or click the gear icon > F12 Developer Tools) Click the Network tab. Reproduce the issue that you were experiencing before, while the network requests are being recorded. Once done click the Save button. Give the trace a filename and click the Save button which will save it as a .har file or .xml file. Upload your HAR file to your ticket or attach it to your email so that we may analyze it. The instructions are the same for a MAC computer or a Windows computer. Please NOTE: You need to be aware that HAR files contain sensitive data, including the content of the pages you downloaded while recording and your cookies. Examples of information contained in the HAR file include personal details, user ID, passwords, and credit card numbers. You can always when the file is downloaded, replace the sensitive data.UserReplaceTool - Automating Dashboard Ownership Transfers - Useful for Deleting User Accounts
Managing and deleting user accounts in Sisense can create manual processes when users leave an organization or change roles. A frequent issue is the reassignment of dashboard ownership to prevent losing Sisense dashboards when a given user account is deleted, as deleting a Sisense user will delete all dashboards owned by that user. The UserReplaceTool addresses this task by automating the transfer of dashboard ownership of all dashboards owned by a given user, ensuring continuity and data integrity. UserReplaceTool is a Python-based, API-based Tool solution designed to seamlessly transfer the ownership of dashboards and data models from one user to another in Sisense. This tool simplifies and automates this process, allowing organizations to reassign dashboard ownership without manual processes or the risk of losing dashboards and widgets. All components are accomplished by using Sisense API endpoint requests.Advanced Pivot Widget Scripting - Combining Custom JAQL and the Pivot 2.0 API
While the Pivot Table Widget Type is a highly customizable and flexible Sisense widget for representing data in tabular form, certain use cases may be best achieved through custom code and scripting. The Pivot 2.0 JavaScript API facilitates the modification of existing pivot table cells, including updating cell values and adding data to cells not present in the initial results.Restoring missing connector
Sometimes connectors can go missing for some reason, leading to a breakdown in data connectivity. Restoring these connectors is crucial to maintaining a smooth data flow within your Sisense environment. This article describes the process of restoring a missing connector in Sisense; additionally, the added tutorial describes how to restore the missing connectors in the Sisense Linux environment.Alert on a M2M Risk
Introduction This article describes how to enable an alert* when there is a risk of Many-To-Many relationship (M2M) between two or more tables. * This implementation involve the use of Pulse and it's supported in Sisense v6.5 and above. Motivation Almost every Data Model designer that dealt with a bit more advanced modeling at some point ran into data\performance issue and after a long troubleshoot process he\her found out that the root cause came from M2M. M2M can occur due to the following**: Connecting 2 tables with a pure M2M relationship such as two Fact tables with a non-unique key. A unique key became a non-unique due to changes or data issue within the source system. A business need to have M2M between two entities. From the described scenario you can assume that the M2M can be identified not only during the EC design but also during its ongoing use. Therefore, having a proactive alert for the EC designer can prevent from the user experiencing poor dashboard performances, wrong result sets and save many hours of troubleshooting, panicking users and general feeling of frustration. ** There can be other more complex cases of M2M which we won't describe here as the mechanism of the M2M alert is less relevant for them. Please refer to this article for a more detailed description of M2M relationship Implementation As an Data Model designer, during the design and after the final version of my schema is completed, I want to track on my connection and make sure that my assumptions of uniqueness on one side of the relationship (to get 1:M relationship) is being captured. Step 1 - Build a M2M Detection Table We'll create a table that will reflect the uniqueness of each table within the final schema that has to be unique according to its relevant primary or surrogate key. Basically we'll count the number of total rows within a table, the number of unique values of the table key and compare between them. if they are equal then the key is unique within the table, but if not there is a risk for M2M and so we'll raise a flag as you can see here: We can learn from the output of the table that for our case we have only one table that is not unique (Cost). Here's the query I used in order to create this table: SELECT 'DimDates' AS Table, 'Date' AS Key, count(t.[Date]) [Total Count], DISTINCT_count(t.[Date]) [Distinct Count], count(t.[Date])-DISTINCT_count(t.[Date]) AS [Duplicated Records], ifint(count(t.[Date])-DISTINCT_count(t.[Date])>0,1,0) AS M2M_Flag FROM DimDates t UNION ALL SELECT 'Customer' AS Table, 'CustomerID' AS Key, count(t.CustomerID) [Total Count], DISTINCT_count(t.CustomerID) [Distinct Count], count(t.CustomerID)-DISTINCT_count(t.CustomerID) AS [Duplicated Records], ifint(count(t.CustomerID)-DISTINCT_count(t.CustomerID)>0,1,0) AS M2M_Flag FROM Customer t UNION ALL SELECT 'SalesPerson' AS Table, 'BusinessEntityID' AS Key, count(t.BusinessEntityID) [Total Count], DISTINCT_count(t.BusinessEntityID) [Distinct Count], count(t.BusinessEntityID)-DISTINCT_count(t.BusinessEntityID) AS [Duplicated Records], ifint(count(t.BusinessEntityID)-DISTINCT_count(t.BusinessEntityID)>0,1,0) AS M2M_Flag FROM SalesPerson t UNION ALL SELECT 'Product' AS Table, 'ProductID' AS Key, count(t.ProductID) [Total Count], DISTINCT_count(t.ProductID) [Distinct Count], count(t.ProductID)-DISTINCT_count(t.ProductID) AS [Duplicated Records], ifint(count(t.ProductID)-DISTINCT_count(t.ProductID)>0,1,0) AS M2M_Flag FROM Product t UNION ALL SELECT 'SalesOrderHeader' AS Table, 'SalesOrderID' AS Key, count(t.SalesOrderID) [Total Count], DISTINCT_count(t.SalesOrderID) [Distinct Count], count(t.SalesOrderID)-DISTINCT_count(t.SalesOrderID) AS [Duplicated Records], ifint(count(t.SalesOrderID)-DISTINCT_count(t.SalesOrderID)>0,1,0) AS M2M_Flag FROM SalesOrderHeader t --Example for uniqueness by surrogate key UNION ALL SELECT 'Cost' AS Table, 'Size + Weight' AS Key, count(t2.Key) [Total Count], DISTINCT_count(t2.Key) [Distinct Count], count(t2.Key)-DISTINCT_count(t2.Key) AS [Duplicated Records], ifint(count(t2.Key)-DISTINCT_count(t2.Key)>0,1,0) AS M2M_Flag FROM ( SELECT t.Size + '|' + tostring(t.Weight) AS Key FROM Cost t) t2 --Example for checking uniqueness for a table which not in the final schema but effects another table (as a source of a lookup custom field) UNION ALL SELECT 'Store' AS Table, 'BusinessEntityID' AS Key, count(t.BusinessEntityID) [Total Count], DISTINCT_count(t.BusinessEntityID) [Distinct Count], count(t.BusinessEntityID)-DISTINCT_count(t.BusinessEntityID) AS [Duplicated Records], ifint(count(t.BusinessEntityID)-DISTINCT_count(t.BusinessEntityID)>0,1,0) AS M2M_Flag FROM Store t Step 2 - Create relevant KPI within a widget We'll create an indicator with the following formula: if([Total M2M_Flag]>0,1,0) This widget can be temporary as it only will serve us for alerting when the total of M2M flags is bigger than 0. Step 3 - Add the KPI to Pulse After Adding the widget to the Pulse, we'll mark the condition according the threshold 'Greater than' 0. Feel free to adjust the notification mode and other preferences according your requirements.
