Expression Script Examples

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This page contains script examples written in the QPR ProcessAnalyzer expression language. See how expression scripts can be created in the Workspace. For documentation for the syntax, functions and entities can be found from the main page in the KPI Expression Language section.

Calling Expression Script from Expression

Expression scripts can be called from an expression using the Run function with the following syntax:

let result = ScriptById(123).Run(#{
  "parameter1": "value1",
  "parameter2": false,
  "parameter2": 123.45

The command waits until the run is completed, and the return value of the called script is returned by the Run function call.

Parameters can be passed to the called script, and the parameters are available as variables in the script. The parameters can contain any type of data.

Expression scripts can also be called from a dashboard. Expressions can be stored to scripts instead of dashboards, which is a way to separate complex expressions from dashboards and allow to reuse expressions across several dashboards.

Calling SQL Script from Expression

SQL script can be called from an expression using the Run function as follows (similar to calling expression scripts):

let result = ScriptById(123).Run(#{
  "parameter1": "value1",
  "parameter2": 321
let arrayOfAllReports = result.Keys;
let report1 = result.Report1;
let report2 = result.Report2;

SQL scripts can return multiple reports, which are combined to a dictionary, where the key is the name of the report ("sheet name") and value is the report data as a DataFrame. See in the above example, how the reports can be accessed by their name.


Call web service

Contact to a web service, fetch some data, and store it to a datatable.

let datatableName = "Web Service Data";
let webServiceData = CallWebService(
    #{"Address": ""}

let targetDatatable = Project.Datatables.Where(name==datatableName);
if (Count(targetDatatable) == 0) {
	targetDatatable = Project.CreateDatatable(datatableName)
	.AddColumn("Setting name", "String")
	.AddColumn("Setting value", "String")
	.AddColumn("Data read", "DateTime");
} else {
	targetDatatable = targetDatatable[0];

let currentTime = Now;
let dataAsDf = ToDataFrame(
        let key = _;
        [key, webServiceData[key], currentTime];
	["Setting name", "Setting value", "Data read"]
targetDatatable.Import(dataAsDf, #{"Append":true});
WriteLog(`${CountTop(dataAsDf.Rows)} rows written to datatable`);

Store data to datatable

Get all models in the system and store them to a datatable.

let newDatatable = Project
    .CreateDatatable("Models list " + ToString(Now, "dd.MM.yyyy HH:mm:ss"))
    .AddColumn("Model name", "String")
    .AddColumn("Project name", "String")
    .AddColumn("Created time", "DateTime")
    .AddColumn("Cases", "Integer");
let startTime = Now;
let modelsData = ToDataFrame(
    Models.([Name, Project.Name, CreatedDate, NCases]),
    ["Model name", "Project name", "Created time", "Cases"]
WriteLog(`Listing models took ${(Now - startTime).TotalSeconds.Round(2)} seconds.`);
WriteLog(`Datatable ${newDatatable.Id} created.`);

Convert datatable column data

This script can be used to convert a single column into numerical data type. To use the script, you need to setup the following in the beginning of the script:

  • Project name where the datatable is located.
  • Datatable name
  • Name of the column to be converted

Note that the conversion fails, if there is data that cannot be converted into numerical format. The conversion assumes that period (.) is used as the decimal point.

let projectName = "New Project";
let datatableName = "qpr processanalyzer events";
let columnName = "Event order in case";

let project = (Projects.Where(Name==projectName))[0];
let datatable = (project.Datatables.Where(Name==datatableName))[0];
	columnName: () => {
		let data = Column(columnName);
		if (data == null) {
		 } else {
.Persist(datatable.Name, ["ProjectId": project.Id, "Append": false]);

Instead of converting to numeric (with the ToFloat function), data can be converted into string using the ToString function.

Show DataFrame as HTML table

This script defines a function to show dataframe as a HTML table, and uses the function for a literal dataframe.

function dataframeToHtmlTable(df) {
		${StringJoin("\r\n\t\t",  + df.columns.`<th>${_}</th>`)}
	${StringJoin("", df.Rows.(
		"\r\n\t<tr>" + StringJoin("", _.`\r\n\t\t<td>${ToString(_)}</td>`) + "\r\n\t</tr>"

let data = ToDataFrame(
		["one", "two", "three"],
		["four", "five", "six"],
		["seven", "eight", "nine"]
	["Column 1", "Column 2", "Column 3"]

return dataframeToHtmlTable(data);

Copy local datatables to Snowflake

// Copies all datatables in a project to another project including datatable contents.
// Usage instructions:
// 1. Create expression script in the project from where you want to copy the datatables.
// 2. Create a new project named as "<name of the project to be moved> - Snowflake". New datatables will be created here. E.g., when moving project named "SAP_OrderToCash", the target project should be named as "SAP_OrderToCash - Snowflake".
// 3. Run the script.
// NOTE: Columns of type "Any" will be created as "String"-columns in Snowflake, thus it is recommended that actual data types are set for the tables prior to the move.

let sourceProject = Project;
let sourceProjectName = Project.Name;
let targetProjectName = `${sourceProjectName} - Snowflake`;
let targetProject = First(Projects.Where(Name == targetProjectName));
if (IsNull(targetProject)) {
  WriteLog(`Unable to find target project named "${targetProjectName}". Aborting operation.`);
let dts = sourceProject.DataTables;
WriteLog(`Copying all ${CountTop(dts)} data tables found in project "${sourceProject.Name}" (id: ${sourceProject.Id}) to Snowflake in project "${targetProject.Name}" (id: ${targetProject.Id})`);
  let sourceDt = _;
  WriteLog(`Starting to copy data table "${Name}" (id: ${Id}) having ${NRows} rows and ${NColumns} columns.`);
  let targetDt;
  targetDt = targetProject.DatatableByName(sourceDt.Name);
  if (targetDt == null) {
    targetDt = targetProject.CreateDataTable(sourceDt.Name, #{"Connection": CreateSnowflakeConnection(#{"ProjectId": targetProject.Id})});
    WriteLog(`Finished copying data table "${Name}" (id: ${Id}) to table "${targetDt.Name}" (id: ${targetDt.Id})`);
  } else {
    WriteLog(`Datatable already exist "${Name}" (id: ${Id}) to table "${targetDt.Name}" (id: ${targetDt.Id})`);
WriteLog(`Finished copying all the data tables found in project "${sourceProject.Name}" (id: ${sourceProject.Id}) to Snowflake in project "${targetProject.Name}" (id: ${targetProject.Id})`);

If you don't need to copy the data but only create the Snowflake datatables with columns, you can change the line 22 to


Copy single datatable to Snowflake

This script creates a copy of a single datatable to Snowflake. Replace the <tableId1> with the id of the source datatable.

function CopyDataTableToSnowflake(dataTableId)
  let sourceDt = DataTableById(dataTableId);
  sourceDt.SqlDataFrame.Persist(`${sourceDt.Name} - Snowflake`, #{"Append": false, "Connection": CreateSnowflakeConnection(#{"ProjectId": sourceDt.Project.Id})});

Create a copy of a data table that has all Any-type columns changed to String-type columns

function ConvertAnyDataTypesToStringsToNewTable(dataTableId)
  let dt = DataTableById(dataTableId);
  let sdf = dt.SqlDataFrame;
  let cts = dt.ColumnTypes;
    let ct = _;
    if (ct.DataType == "Any") {
      let n = ct.Name;
      sdf = sdf.WithColumn(ct.Name, #sql{Cast(Column(Variable("n")), "ShortString")});
  sdf.Persist(`${dt.Name} - Converted`, #{"Append": false, "ProjectId": dt.Project.Id});

Query number of rows in given data table having a datetime value in given year grouped by month and return resulting table as CSV

SqlDataFrame is used in order to prevent loading the whole datatable into memory first. Filtering is performed as first operation in order to minimize the amount of required work for the data source of the data table.

DataTableById(<data table id>)
  .Where(#sql{2014 == Year(Column("Start Time"))})
  .WithColumn("Month", #sql{Month(Column("Start Time"))})
  .GroupBy(["Month"]).Aggregate(["Count"], ["Count"])
  .OrderByColumns(["Month"], [true])

Function for filtering SqlDataFrame by removing rows having, or replacing, the most infrequently occurring column values

 * @name ColumnWithMinUsage
 * @descripion
 * Generic function that can be used to filter out the most infrequently occurring attribute values or replace their Values
 * with given common value.
 * @param df:
 * DataFrame to operate on.
 * @param columnName:
 * Name of the column to be filtered.
 * @param newColumnName:
 * Name of the column that will contain the new value of the original column after filtering (if includeOthers was applied).
 * @param maxNumUniqueValues:
 * Maximum number of unique values to include into the comparison for each attribute column. If the amount of unique values for any attribute exceeds this value, only given number of attributes are included that have the highest usage.
 * @param minValueUsage:
 * Minimum total usage of a value included into the comparison. The number of cases having every returned value should be at least given percentage (a float value between 0.0 and 1.0) of all the compared cases.
 * @param includeOthers:
 * Should the rest of the attribute values not included due to MinValueUsage or MaxNumUniqueValues filtering be included as an aggregated "Others" value?
 * If not empty/null, defines the name used for these other-values.
function ColumnWithMinUsage(df, columnName, newColumnName, maxNumUniqueValues, minValueUsage, includeOthers)
  let all = df
	.Aggregate(["NAllTotal"], ["Count"])
	.WithColumn("__Join2", #sql{1});
  let minValueUsageEnabled = !IsNullTop(minValueUsage);
  let maxNumUniqueValuesEnabled = !IsNullTop(maxNumUniqueValues);
  if (minValueUsageEnabled || maxNumUniqueValuesEnabled) {
	// Perform column value-based filtering if minValueUsageEnabled or maxNumUniqueValuesEnabled is defined.
    let valueColumnName = "__ValueNew";
	let filteredValuesColumns = [valueColumnName: columnName];
	let filteredValues = df
	  .GroupBy([columnName]).Aggregate(["Count"], ["Count"]);
	if (minValueUsageEnabled) {
	  filteredValues = filteredValues
		.WithColumn("__Join", #sql{1})
		.Join(all, ["__Join": "__Join2"], "leftouter")
        .WithColumn("Usage", #sql{Column("Count") / Column("NAllTotal")});
	  filteredValuesColumns = Concat(filteredValuesColumns, ["Usage"]);
	if (maxNumUniqueValuesEnabled) {
	  filteredValues = filteredValues
		.WithRowNumberColumn("RowNumber", ["Count"], null, [false]);
	  filteredValuesColumns = Concat(filteredValuesColumns, ["RowNumber"]);

	filteredValues = filteredValues

	// Generate select returning all the accepted values.
	let allValues = filteredValues
	  .(minValueUsageEnabled ? Where(#sql{Column("Usage") >= #expr{minValueUsage}}) : _)
	  .(maxNumUniqueValuesEnabled ? Where(#sql{Column("RowNumber") <= #expr{maxNumUniqueValues}}) : _)
	  .Select([valueColumnName, newColumnName: valueColumnName]);

	if (!IsNullTop(includeOthers)) {
	  // If includeOthers is defined, replace original values with the variable defined in includeOthers.
	  let otherValues = filteredValues
		.(minValueUsageEnabled ? Where(#sql{Column("Usage") < #expr{minValueUsage}}) : _)
		.(maxNumUniqueValuesEnabled ? Where(#sql{Column("RowNumber") > #expr{maxNumUniqueValues}}) : _)
		.WithColumn(newColumnName, #sql{#expr{includeOthers}})
		.Select([valueColumnName, newColumnName]);
	  allValues = allValues.Append(otherValues)
	df.Join(allValues, [columnName: valueColumnName], "inner")

// The following example will return only rows containing two of the most common values for Region-column.
//let df = DataTableById(<data table id>).SqlDataFrame;
//df = ColumnWithMinUsage(df, "Region", "_Filtered", 2, null, null);

// The following example will return all input rows, but will replace the values of rows whose Region-column
// has a value used by less than 15% of all the rows with a new value: "_Others".
//let df = DataTableById(<data table id>).SqlDataFrame;
//df = ColumnWithMinUsage(df, "Region", "_Filtered", null, 0.15, "_Others");

Export model events and cases

function ExportModelEvents(m) {
  let attrs = m.EventAttributes;
      [Case.Name, Type.Name, ToString(TimeStamp, "yyyy-MM-dd HH:mm:ss.fff")], 
      {let evt = _; attrs.{let att = _; evt.Attribute(att)}}
      ["CaseId", "EventType", "TimeStamp"], 


function ExportModelCases(m) {
  let attrs = m.CaseAttributes;
      {let cas = _; attrs.{let att = _; cas.Attribute(att)}}

First(Models.Where(Name=="SAP OtC Extended")).EventsDataTable.DataFrame.ToCsv(true)

First(Models.Where(Name=="SAP OtC Extended")).CasesDataTable.DataFrame.ToCsv(true)

Calculate all the value usages of a single column for each event in event data table

This query could be used, e.g., to find out the maximum resource usage for every resource found in the event data table.

function WithUsageColumns(resourceColumn)
  function WithTotalUsageColumnOfSingleResource(resourceColumn, resourceValue)
      .WithColumn("_Prev", #sql{Lag(Column(resourceColumn), [TimeStamp, EventType], [true, true], [CaseId], 1, null)})
      .WithColumn("_UsageDiff", #sql{
          Column(resourceColumn) == Column("_Prev"), 0, 
          Column("_Prev") == #expr{resourceValue}, -1,
          Column(resourceColumn) == #expr{resourceValue}, 1,
      .WithColumn(`${resourceValue}_Usage`, #sql{Sum(Column("_UsageDiff"), [TimeStamp, EventType])})
      .RemoveColumns(["_Prev", "_UsageDiff"])

  let sdf = _;
  let allValues = sdf.SelectDistinct([resourceColumn]).OrderByColumns([resourceColumn], [true]).Collect().Column(resourceColumn);
    let v = _;
    sdf = sdf.WithTotalUsageColumnOfSingleResource(resourceColumn, v)

let dt = ModelById(<model id>).EventsDataTable;
  .WithUsageColumns(<resource column name>)
  .OrderByColumns([dt.ColumnMappings["TimeStamp"]], [true])


  • <model id> is the id of the model containing event data to be examined.
  • <resource column name> is the name of the column in the event data table of the specified model containing the resource being used by that event.

NOTE: This expression uses functionalities that are only supported in Snowflake-based data tables.

Create new Snowflake model from filter

This script creates a new Snowflake model (and two datatables for cases and events) containing filtered event log from given filter id. The script also works if the model doesn't have a cases datatable.

let filter = FilterById(1); // filter id
let model = filter.model;
let project = model.project;
let nameSuffix = " - " + + " - " + ToString(Now, "dd-MM-yyyy HH:mm:ss");
let eventsDatatableName = model.EventsDataTable.Name + nameSuffix;
if (eventsDatatableName.length > 440) {
  eventsDatatableName = eventsDatatableName.Substring(eventsDatatableName.length - 440);
let eventsData = model
  .CreateDatatable(eventsDatatableName, #{"Connection": CreateSnowflakeConnection()})
let modelConfiguration = model.Configuration;
modelConfiguration.DataSource.Events.Set("DataTableName", eventsDatatableName);
if (model.CasesDataTable != null) {
  let eventsDataCaseIdColumn = "CaseId_" + ToString(Random());
  let casesDatatableName = model.CasesDataTable.Name + nameSuffix;
  if (casesDatatableName.length > 440) {
    casesDatatableName = casesDatatableName.Substring(casesDatatableName.length - 440);
  let casesData = model
	  eventsData.SelectDistinct([eventsDataCaseIdColumn: modelConfiguration.DataSource.Events.Columns.CaseId]),
      [modelConfiguration.DataSource.Cases.Columns.CaseId: eventsDataCaseIdColumn]
    .CreateDatatable(casesDatatableName, #{"Connection": CreateSnowflakeConnection()})
  modelConfiguration.DataSource.Cases.Set("DataTableName", casesDatatableName);
let modelName = model.Name + nameSuffix;
if (modelName > 440) {
  modelName = modelName.Substring(modelName.length - 440);
    "Name": modelName,
    "Description": model.Description,
    "Configuration": modelConfiguration
return modelName;