QPR ProcessAnalyzer Objects in Expression Language: Difference between revisions
| Line 520: | Line 520: | ||
3;Done: three=31;three=3;three=31;1234 | 3;Done: three=31;three=3;three=31;1234 | ||
|- | |- | ||
||Tail | ||Tail (DataFrame) | ||
|| | || | ||
* Number of rows | |||
|| | || | ||
Creates a new DataFrame that has only the bottom number of rows of this DataFrame. If the DataFrame has less than n rows, all its rows are returned. | |||
Example: | |||
<pre> | |||
ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Tail(2).ToCsv() | ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Tail(2).ToCsv() | ||
Results string: | Results string: | ||
| Line 535: | Line 533: | ||
2;two | 2;two | ||
3;three | 3;three | ||
</pre> | |||
|- | |- | ||
||ToCsv (String) | ||ToCsv (String) | ||
|| | ||(none) | ||
|| | || | ||
Converts a DataFrame into a CSV data. The CSV data has the following formatting: | Converts a DataFrame into a CSV data. The CSV data has the following formatting: | ||
* | * Column separator: semicolon | ||
* | * Decimal separator in numeric fields: period | ||
* | * Quotation character for text fields: double quotes (used when the textual value contains semicolon, double quotes, linebreak or tabulator) | ||
* | * Escape character: Double quotes in textual fields are escaped with two double quotes. | ||
* | * Date format for date fields: yyyy-MM-dd HH:mm:ss,fff | ||
* | * First line: contains column headers | ||
Example: | Example: | ||
Revision as of 16:13, 17 December 2019
DataFrame
| DataFrame properties | Description |
|---|---|
| Columns (String*) | DataFrame columns names as an array in the order the columns are in the DataFrame. |
| Rows (Object**) | Returns the data content of the DataFrame as a two-dimensional array (matrix). The column names are not part of the data content.
Examples: DatatableById(5).DataFrame.Rows[0][0] Returns: the value in the first row and first column in a datatable with id 5. |
| <column name> (Object*) |
Returns an array of values of given column in the datatable. If the column name contains spaces, the Column function needs to be used to refer to a column. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).right Returns: [zero, two, three] ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).right[2] Returns: three |
| DataFrame functions | Parameters | Description |
|---|---|---|
| Append (DataFrame) |
|
Creates a new DataFrame that has the contents of given DataFrame added to the end of this DataFrame. When the data is combined, the order of columns matters, not the names of the columns. The resulting DataFrame gets column names from this DataFrame. If the number of columns is different between this DataFrame and the other DataFrame, an exception is thrown. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).Append( ToDataFrame([[1, "one"], [4, "four"]], ["id", "text"]) ); Returns string: id;text 0;zero 2;two 3;three 1;one 4;four |
| ColumnIndexes (Integer*) |
|
Convert column names into column indexes. The indexes are starting from zero. If a column is not found, an exception is given. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).ColumnIndexes(["right", "id"]) Returns: [1, 0] |
| Column (Object*) |
|
Returns an array of values of given column in the order rows are in the datatable. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Column("right")
Returns: [zero, two, three]
|
| Columns (DataFrame) |
|
Creates a new DataFrame having only the defined columns of the original DataFrame. Note that Columns function is different than Columns property - difference is that the function has parameters. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Columns([1]).ToCsv() Returns string: right zero two three |
| Head |
|
Creates a new DataFrame that only contains top number of rows of this DataFrame. If the DataFrame has less than n rows, all its rows are returned. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Head(2).ToCsv() Results string: id;right 0;zero 2;two |
| Join |
1. Performs Join operation between two DataFrames by given columns. 2. Parameters: 2.1. other: Other DataFrame to join this DataFrame with. 2.2. keys: Identifies columns to be matched with each other. Value can be either: 2.2.1. Single column index (numerical) or name. 2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2. An array of any number of: 2.2.2.1. Column identifiers or names 2.2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2.2. Hierarchical arrays (#29290#) containing identifiers or names both in their context object and value object. 2.2.2.1.1. In this case, the column identified with the context value is used to identify the column in this DataFrame, whereas the column identified with the value of the context is used to identify the column in the other DataFrame to match the rows. 2.3. joinType: Optional parameter defining the join type. Supported values are: 2.3.1. "Inner": 2.3.1.1. Inner join (row is generated only if both context and other have the key value. 2.3.1.2. This is the default value. 2.3.2. "LeftOuter": 2.3.1.1. Left outer join (at least one row is generated for each context row, even if there is no matching other row, in which case null is given as value for the other columns) 3. Returns a new DataFrame that the result of the join operation. Note that the key-column of the other -DataFrame is not included. Examples: Let("left", ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"])); Let("right", ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"])); left.join(right, "id", "inner").ToCsv() Returns string: id;left;right 0;zero;zero Let("left", ToDataFrame([[0, "zero"], [1, "one"]], ["id", "left"])); Let("right", ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"])); left.join(right, "id", "leftouter").ToCsv() Returns string: id;left;right 0;zero;zero 1;one; Let("left", ToDataFrame([[0, 0, "zerozeroleft"], [0, 1, "zeroleft"], [1, 2, "oneleft"]], ["idleft1", "idleft2", "left"])); Let("right", ToDataFrame([[0, 0, "zerozeroright"], [0, 1, "zeroright"], [2, 3, "tworight"], [3, 4, "threeright"]], ["idright1", "idright2", "right"])); left.join(right, ["idleft1": "idright1"], "inner"); Returns string: idleft1;idleft2;left;idright2;right 0;0;zerozeroleft;0;zerozeroright 0;0;zerozeroleft;1;zeroright 0;1;zeroleft;0;zerozeroright 0;1;zeroleft;1;zeroright Let(""left"", ToDataFrame([[0, 0, ""zerozeroleft""], [0, 1, ""zeroleft""], [1, 2, ""oneleft""]], [""idleft1"", ""idleft2"", ""left""])); Let(""right"", ToDataFrame([[0, 0, ""zerozeroright""], [0, 1, ""zeroright""], [2, 3, ""tworight""], [3, 4, ""threeright""]], [""idright1"", ""idright2"", ""right""])); left.join(right, [""idleft1"": ""idright1"", ""idleft2"": ""idright2""], ""inner""); Returns string: idleft1;idleft2;left;right 0;0;zerozeroleft;zerozeroright 0;1;zeroleft;zeroright | |
| GroupBy |
1. Create a new DataFrame based on the current DataFrame having rows grouped by given columns and values aggregated using given functions. 2. Parameters: 2.1. columns: Columns to group the rows by identified by column indexes or column labels. 2.2. aggregationExpressions: A hierarchical array (#29290#) containing the name of the column as context object and the value as context value. The value can be either: 2.2.1. Expression object (#29355#), in which case the expression is evaluated in the context of each row in the DataFrame. 2.2.2. Otherwise the value is expected to be a constant value that is assigned as the value for all the rows. 3. Returns a new DataFrame whose rows are grouped by given keys and for group, given expressions are calculated separately. One row in the end result correspond with one group. Examples: ToDataFrame([[0, "zero"], [0, "zero2"], [2, "two"], [2, "two"], [2, "two3"], [3, "three"]], ["id", "text"]).GroupBy(["id"], [ "ids": Def("", Sum(id)),
"texts": Def("", StringJoin(",", text)),
"constant": 123
]).ToCsv() Returns string: ids;texts;constant 0;zero,zero2;123 6;two,two,two3;123 3;three;123 ToDataFrame([[0, "zero"], [0, "zero2"], [2, "two"], [2, "two"], [2, "two3"], [3, "three"]], ["id", "text"]).GroupBy(["id", "text"], [ "ids": Def("", Sum(id)),
"texts": Def("", StringJoin(",", text)),
"constant": 123
]).ToCsv() Returns string: ids;texts;constant 0;zero;123 0;zero2;123 4;two,two;123 2;two3;123 3;three;123 Analysis("OperationLog") .GroupBy( ["User Name"],
[
"User Name": Def("", Column("User Name")[0]),
"Count": Def("Rows", CountTop(Rows)),
"Avg. Duration": Def("", Average(Duration)),
"Max. Duration": Def("", Max(Duration))
]
).ToCsv() Returns string (similar to this): User Name;Count;Avg. Duration;Max. Duration
Administrator;665;16.3750631578947;4225.497 qpr;128;2.158765625;20.346 Analysis("OperationLog").OrderBy(Duration).Head(1) Results string: id;right 0;zero 2;two 3;three | |
| Merge |
1. Create a new DataFrame that has the contents of given (target) DataFrame merged with another (source) DataFrame using method described in parameters. 1.1. Principles somewhat follow how MERGE SQL command works in SQL Server. 1.2. In order for the merge to work properly, both the source and target table should have exactly the same columns in exactly the same order. 2. Parameters: 2.1. source: DataFrame to be merged with this (target) DataFrame. 2.2. matchColumns: Identifies columns to be matched with each other. Value can be either: 2.2.1. Single column index (numerical) or name. 2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2. An array of any number of: 2.2.2.1. Column identifiers or names 2.2.2.1.1. In this case, the column identified with this value is used in both the DataFrames to match the rows. 2.2.2.2. Hierarchical arrays (#29290#) containing identifiers or names both in their context object and value object. 2.2.2.1.1. In this case, the column identified with the context value is used to identify the column in this DataFrame, whereas the column identified with the value of the context is used to identify the column in the other DataFrame to match the rows. 2.3. copyOnMatch: An array similar to the one in 2.2.2 of column names or indexes of columns to copy from the source to target, if a matching row is found. 2.3.1. If the value is _remove (#27680#), then all the rows for which a match was found will be removed from the resulting table. 2.3.2. If the value is null (#27679#), then the target row will be completely replaced with all the columns in the source row that have identical name in both source and target table. 2.3.3. The default value is null. 2.3. copyIfNotMatchedInTarget: An array similar to the one in 2.3 of column names or indexes of columns to copy from the source to target, if a matching row is not found. 2.4. addIfNotMatchedInTarget: A boolean value that defines whether the source row is added to the result if there is no matching row in the target DataFrame. 2.4.1. The default value is true. 2.5. keepIfNotMatchedBySource: A boolean value that defines whether the target row is added to the result if there is no matching row in the source DataFrame. 2.5.1. The default value is true. 3. Returns a new DataFrame that has the combination of rows from both the DataFrames. 3.1. If the number of columns is different between this DataFrame and the other DataFrame, an exception is thrown. Examples: Let("target", ToDataFrame([[0, "zero", "target"], [1, "", "target"]], ["id", "text", "frame"])); Let("source", ToDataFrame([[1, "one", "source"], [2, "two", "source"], [3, "three", "source"]], ["id", "text", "frame"])); target.Merge(source, "id").ToCsv() Returns string (one key, default parameters, identical dataframe columns): id;text;frame 0;zero;target 1;one;source 2;two;source 3;three;source target.Merge(source, "id", ["text"]).ToCsv() Returns string (one key, default parameters, identical dataframe columns, copy only text column from source): id;text;frame 0;zero;target 1;one;target 2;two; 3;three; target.Merge(source, "id", ["text"], _remove).ToCsv() Returns string (one key, default parameters, identical dataframe columns, copy only text column from source, remove rows found only in source): id;text;frame 0;zero;target 1;one;target target.Merge(source, "id", ["text"], _remove, false).ToCsv() Returns string (one key, identical dataframe columns, copy only text column from source, remove rows found only in source or only in target): id;text;frame 1;one;target target.Merge(source, "id", _remove, _remove, false).ToCsv() Returns string (one key, identical dataframe columns, remove all rows): id;text;frame Let("target", ToDataFrame([[0, 0, "zerozeroleft", "target"], [0, 1, "zeroleft", "target"], [1, 2, "left", "target"], [4, 5, "fourleft", "target"]], ["idleft1", "idleft2", "textleft", "frame"])); Let("source", ToDataFrame([[0, 0, "zerozeroright", "source"], [0, 1, "zeroright", "target"], [1, 2, "oneright", "source"], [2, 3, "tworight", "source"], [3, 4, "threeright", "source"]], ["idright1", "idright2", "textright", "frame"])); target.Merge(source, ["idleft1": "idright1"]).ToCsv() Returns string (one key, default parameters, different dataframe columns, copy all matching columns): idleft1;idleft2;textleft;frame 0;0;zerozeroleft;source 0;0;zerozeroleft;target 0;1;zeroleft;source 0;1;zeroleft;target 1;2;left;source 4;5;fourleft;target 2;;;source 3;;;source target.Merge(source, ["idleft1": "idright1"], []).ToCsv() Returns string (one key, default parameters, different dataframe columns, copy only key column): idleft1;idleft2;textleft;frame 0;0;zerozeroleft;target 0;0;zerozeroleft;target 0;1;zeroleft;target 0;1;zeroleft;target 1;2;left;target 4;5;fourleft;target 2;;; 3;;; target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"]).ToCsv() Returns string (two keys, default parameters, different dataframe columns, copy all matching columns): idleft1;idleft2;textleft;frame 0;0;zerozeroleft;source 0;1;zeroleft;target 1;2;left;source 4;5;fourleft;target 2;3;;source 3;4;;source target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"]).ToCsv() Returns string (two keys, default parameters, different dataframe columns, copy only textright -column): idleft1;idleft2;textleft;frame 0;0;zerozeroright;target 0;1;zeroright;target 1;2;oneright;target 4;5;fourleft;target 2;3;tworight; 3;4;threeright; target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright", "frame"]).ToCsv() Returns string (two keys, default parameters, different dataframe columns, copy textright and frame -columns): idleft1;idleft2;textleft;frame 0;0;zerozeroright;source 0;1;zeroright;target 1;2;oneright;source 4;5;fourleft;target 2;3;tworight;source 3;4;threeright;source target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], _remove).ToCsv() Returns string (two keys, default parameters, different dataframe columns, remove all matching rows, copy only matching columns): idleft1;idleft2;textleft;frame 4;5;fourleft;target 2;3;; 3;4;; target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright"]).ToCsv() Returns string (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id columns and textright-column for rows not found in target): idleft1;idleft2;textleft;frame 0;0;zerozeroright;target 0;1;zeroright;target 1;2;oneright;target 4;5;fourleft;target 2;3;tworight; 3;4;threeright; target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright", "frame"]).ToCsv() Returns string (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id, frame and textright-column for rows not found in target): idleft1;idleft2;textleft;frame 0;0;zerozeroright;target 0;1;zeroright;target 1;2;oneright;target 4;5;fourleft;target 2;3;tworight;source 3;4;threeright;source target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], null, ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright"]).ToCsv() Returns string (two keys, default parameters, different dataframe columns, don't copy any columns from source for matching columns, copy id columns and textright-column for rows not found in target): idleft1;idleft2;textleft;frame 0;0;zerozeroleft;source 0;1;zeroleft;target 1;2;left;source 4;5;fourleft;target 2;3;tworight; 3;4;threeright; target.Merge(source, ["idleft1": "idright1", "idleft2": "idright2"], ["textleft": "textright"], ["idleft1": "idright1", "idleft2": "idright2", "textleft": "textright", "frame"], false).ToCsv() Returns string (two keys, default parameters, different dataframe columns, copy only textright-column for matching columns, copy id, frame and textright-column for rows not found in target, remove all rows not found in source): idleft1;idleft2;textleft;frame 0;0;zerozeroright;target 0;1;zeroright;target 1;2;oneright;target 2;3;tworight;source 3;4;threeright;source | |
| OrderBy |
1. Create a new DataFrame having rows ordered using the values returned by given expression evaluated on each row. 2. Parameters: 2.1. expression: Ordering expression to use. 3. Returns a new DataFrame whose rows are ordered in ascending fashion based on the results of evaluating given expression for each row. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).OrderBy(text).ToCsv() Returns string: id;right 3;three 2;two 0;zero Analysis("OperationLog").OrderBy(Duration).Head(1) Results string: id;right 0;zero 2;two 3;three | |
| OrderByDescending |
1. Create a new DataFrame having rows ordered using the values returned by given expression evaluated on each row. 2. Parameters: 2.1. expression: Ordering expression to use. 3. Returns a new DataFrame whose rows are ordered in descending fashion based on the results of evaluating given expression for each row. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).OrderByDescending(text).ToCsv() Returns string: id;right 0;zero 2;two 3;three Analysis("OperationLog").OrderByDescending(Duration).Head(1) Returns a DataFrame having one row that contains the longest operation found in the operation log analysis. | |
| Persist |
1. Persists the DataFrame into database as DataTable (#63863#) under given name. 1.1. If the DataTable with that name does not exist in the project previously, a new DataTable is created. 1.2. If the DataTable with that name already exists, the DataFrame will be stored into that table. 2. Parameters: 2.1. dataTableName: Name of the data table to use for storing the value. 2.2. parameters: Additional parameters for the persist operation #34201#. Supported parameters: 2.2.1. Append: 2.2.1.1. Can be used to determine whether to append (=true/1) or overwrite (=false, default) the existing data. 2.2.2. ProjectName: 2.2.2.1. Name of the project under which the DataTable is to be created. 2.2.3. ProjectId: 2.2.3.1. Id of the project under which the DataTable is to be created. 3. Returns DataTable object containing the persisted DataFrame. Examples: Let("right", ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"])); right.Persist("RightDataTable", ["ProjectName": "TestData"]) Results: Id of the new data table named "RightDataTable" created into project named TestData (which is created if it doesn't already exist). If the table already existed, its contents will be overwritten by the new content. Let("newData", ToDataFrame(4, "four", ["id", "right"])); newData.Persist("RightDataTable", ["ProjectName": "TestData", "Append": true]) Results: Id of the new data table named "RightDataTable" created into project named TestData (which is created if it doesn't already exist). If the table already existed, new content will be appended into the end of the table. | |
| SetColumns |
1. Create a new DataFrame having contents of this DataFrame and create a new columns and/or modify existing columns of the resulting DataFrame. 2. Parameters: 2.1. columnChanges: Name of the new column. 2.1.1. Hierarchical array (#29290#) containing the name of the column as context object and the value as context value. The value can be either: 2.1.1.1. Expression object (#29355#), in which case the expression is evaluated in the context of each row in the DataFrame. 2.1.1.2. Otherwise the value is expected to be a constant value that is assigned as the value for all the rows. 3. Returns a new DataFrame that is the copy of the original DataFrame with the following modifications: 3.1. For all the elements in the columnChanges -array: 3.1.1. If column name identifies a column that already exists, then the values in that column will be replaced with newly calculated values using the value. 3.1.2. The original column values can be used in the valueExpression, including those that were listed earlier in the list of column changes. Examples: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns(["both": Def("", text + "=" + id)]).ToCsv() Result string: id;text;both 0;zero;zero=0 2;two;two=2 3;three;three=3 ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns(["text": Def("", text + "=" + id)]).ToCsv() Result string: id;text 0;zero=0 2;two=2 3;three=3 ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "text"]).SetColumns(["both": Def("", text + "=" + id), "both+1": Def("", both + 1), "text": Def("", "Done: " + Column("both+1")), "constant": 1234]).ToCsv() Result string: id;text;both;both+1;constant 0;Done: zero=01;zero=0;zero=01;1234 2;Done: two=21;two=2;two=21;1234 3;Done: three=31;three=3;three=31;1234 | |
| Tail (DataFrame) |
|
Creates a new DataFrame that has only the bottom number of rows of this DataFrame. If the DataFrame has less than n rows, all its rows are returned. Example: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).Tail(2).ToCsv() Results string: id;right 2;two 3;three |
| ToCsv (String) | (none) |
Converts a DataFrame into a CSV data. The CSV data has the following formatting:
Example: ToDataFrame([[0, "zero"], [2, "two"], [3, "three"]], ["id", "right"]).ToCsv() Returns string: id;right 0;zero 2;two 3;three |
| Where |
1. Create a new DataFrame having only rows for which given expression returns true. 1.1. Overrides Where-function defined in the generic context #27644#. Create a new DataFrame having only top n rows of this DataFrame. 2. Parameters: 2.1. condition: Condition expression to evaluate for each DataFrame row separately. 3. Returns a new DataFrame that has only the rows that evaluate to true when the condition expression is evaluated for the row. 3.1. DataFrame's inherited functionalities (#63899#) can be used in the condition expression. Examples: Let("df", ToDataFrame([[0, "zero", 1], [2, "two", true], [3, "three", Now]], ["id", "string", "variant values"])); All the following expression return exactly the same: df.Where(id < 3); df.Where(Column("id") < 3); df.Where(_[0] < 3); Returns string: id;string;variant values 0;zero;1 2;two;True
| |
| Zip |
1. Create a new DataFrame that has the contents of given DataFrame appended as new columns into the end of this DataFrame. 2. Parameters: 2.1. other: DataFrame to be added as new columns into the end of this DataFrame. 3. Returns a new DataFrame that has the colums from both the data frames so that the columns from the other DataFrame are appended to the end of the columns of this DataFrame. 3.1. If the number of rows is different between this DataFrame and the other DataFrame, an exception is thrown. 3.2. All the column names should be unique between both the DataFrames. If they aren't, an exception is thrown. Examples: Let("df1", ToDataFrame([[0, "zero"], [1, "one"], [4, "four"]], ["id", "text"])); Let("df2", ToDataFrame([[1, "one"], [2, "two"], [3, "three"]], ["id2", "text2"])); df1.Zip(df2).ToCsv(); Returns string: id;text;id2;text2 0;zero;1;one 1;one;2;two 4;four;3;three |
Datatable
| Datatable properties | Description |
|---|---|
| CreatedDate (DateTime) | Timestamp when the datatable was created. |
| DataFrame (DataFrame) | Contents of the DataTable as a DataFrame. |
| Description (String) | Datatable description. The datatable description may contain line breaks. |
| Id (Integer) | Datatable Id. Datatable Id is generated by QPR ProcessAnalyzer when the datatable is created. |
| LastImportDate (DateTime) | Timestamp when data was the last time imported to the datatable. |
| LastModifiedDate (DateTime) | Timestamp when the datatable was modified the last time. |
| Name (String) | Name of the datatable. |
| NColumns (Integer) | Number of columns in the datatable. |
| NRows (Integer) | Number of data rows in the datatable. |
| Project (Project) | Project the datatable belongs to. |
Model
| Model properties | Description |
|---|---|
| CaseAttributes (AttributeType*) | CaseAttributes in the model returned in the alphabetical order. Using this property requires that the model is loaded in the memory. If the model is not in the memory, it's loaded when this property is used. |
| ConfigurationJson (String) | Returns an model configuration in JSON format. |
| CreatedDate (DateTime) | Timestamp when the model was created. |
| DefaultFilterId (Integer) | Default filter id of the model. |
| ConfigurationJson (String) | Returns an model configuration in JSON format. |
| Description (String) | Model description. The model description may contain line breaks. |
| EstimatedMemory (Integer) | Returns an estimation of how much memory in bytes the model requires. |
| EventAttributes (AttributeType*) | EventAttributes in the model returned in the alphabetical order. Using this property requires that the model is loaded in the memory. If the model is not in the memory, it's loaded when this property is used. |
| EventLog (EventLog) | Model EventLog containing all events in the model, i.e. no filters have been applied. Using this property requires that the model is loaded in the memory. If the model is not in the memory, it's loaded when this property is used. |
| Id (Integer) | Model Id. Model Id is generated by QPR ProcessAnalyzer when the model is created. |
| LastImportDate (DateTime) | Timestamp when data was the last time imported to the model. |
| LastModifiedDate (DateTime) | Timestamp when the model was modified the last time. |
| Name (String) | Model name. |
| NBookmarks (Integer) | Number of bookmarks in the model. |
| NCache (Integer) | Amount of objects related to the model when the model is loaded into the memory. |
| NCaseAttributes (Integer) | Number of CaseAttributes in model. |
| NCases (Integer) | Number of Cases in the model. |
| NEventAttributes (Integer) | Number of EventAttributes in model. |
| NEvents (Integer) | Number of Events in model. |
| NEventTypes (Integer) | Number of EventTypes in the model. |
| NFilters (Integer) | Number of filters in the model. |
| NOpens (Integer) | Number of times analysis has been requested from the model. |
| Project (Project) | Project the model belongs to. |
| ProjectId (Integer) | Project id the model belongs to. |
| Status (String) |
Memory availability status of the model. There are the following statuses:
|
Notes:
- Regarding properties returning object counts: For models downloaded from a datatable, ODBC or expression datasource, the numbers represent the situation in the previous data load. Null is returned for models downloaded from an ODBC datasource, if the model has never been loaded into the memory. Note also that if Case permissions are used for the model, null is returned for data security reasons.
- Using CaseAttributes, EventAttributes or Eventlog properties of the Model object requires the model to be loaded into the memory. If the model is not in the memory, it's loaded when these properties is called. Other Model object properties down require the model to be in the memory.
Project
| Project properties | Description |
|---|---|
| Id (Integer) | Id of the Project. It's generated by QPR ProcessAnalyzer when the model is loaded. |
| Name (String) | Name of the Project. |
| Models (Model*) | Models that are in the Project. |
User/Group
User objects represents users and user groups. Note that some properties can only be used for users and some for groups.
| User properties | Description |
|---|---|
| Description (String) | Description of the user. |
| Email (String) | Email address of the user. |
| FullName (String) | Full name of the user or group name. |
| GroupMemberNames (String*) | Array of names of members of a user group. This property is available for groups. |
| GroupMembers (User*) | Array of members of a user group. This property is available for groups. |
| GroupNames (String*) | Array of names of user groups the user belongs to. This property is available for users. |
| Groups (User*) | Array of user groups the user belongs to. This property is available for users. |
| Id (Integer) | Id of the user, which is unique for every user. |
| IsActive (Boolean) | Returns true only if the user is active (not disabled). |
| IsGroup (Boolean) | Returns true if the user is a user group. |
| LogonMessage (String) | Returns the logon message of the user. |
| Name (String) | Login name of the user or group. |
| Roles (String Array*) | Roles of user as string array. |