SQL Expressions: Difference between revisions
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* '''Count''': Number of items. Can be used for any type of data. | * '''Count''': Number of items. Can be used for any type of data. | ||
* '''CountDistinct''': Number of unique items (i.e., duplicates are removed from calculation). Can be used for any type of data. | * '''CountDistinct''': Number of unique items (i.e., duplicates are removed from calculation). Can be used for any type of data. | ||
* '''List''': Joins texts together. There are parameters '''Ordering''' (array of columns defining how the items are ordered) and '''Separator''' (characters separating the combined texts) which can be used when the aggregation is defined as object as follows: #{"Function": "List", "Ordering": [EventType], "Separator": ", "} | |||
* '''Median''': Median, i.e. the middle value of the sorted data. If there are even number of items, the average of the two middle items. Can be used for numbers. | * '''Median''': Median, i.e. the middle value of the sorted data. If there are even number of items, the average of the two middle items. Can be used for numbers. | ||
* '''Min''': Lowest/first value in the data. Can be used for numbers, text and dates. | * '''Min''': Lowest/first value in the data. Can be used for numbers, text and dates. | ||
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* '''Stdevp''': Population standard deviation. Can be calculated for numbers. | * '''Stdevp''': Population standard deviation. Can be calculated for numbers. | ||
* '''Sum''': Sum of the items. Can be used for numbers and Timespans. | * '''Sum''': Sum of the items. Can be used for numbers and Timespans. | ||
* '''Var''': Sample variance. Can be used for numbers. | * '''Var''': Sample variance. Can be used for numbers. | ||
* '''Varp''': Population variance. Can be used for numbers. | * '''Varp''': Population variance. Can be used for numbers. |
Revision as of 16:03, 14 February 2023
SQL expressions are special expressions, which are run in Snowflake by converting them into SQL. Note that SQL expressions are different than the in-memory expressions. In addition to the Big data chart, SQL expressions are used in the Where and WithColumn functions in SQLDataFrames.
It's possible to run in-memory expressions that are embedded into SQL expressions using #expr{} syntax. For examples, see the DurationBetweenDates function.
Operators
Following operators are supported by by the SQL expressions:
- Arithmetic operators: +, -, *, /, %
- Comparison operators: ==, <, <=, >, >=, !=.
- Logical operators: &&, ||, !
- Data types: string ("this is a string"), integer (123), decimal number (123.45), boolean (true, false), null value (null)
Expressions resulting in boolean value need to be enclosed to the CaseWhen function. For example, expression Column("MyColumn") < 5 doesn't work, but following does:
CaseWhen(Column("MyColumn") < 5, true, false)
Mathematical functions
Function | Description |
---|---|
Abs | Returns absolute value of given number. Example:
Abs(-5) Returns: 5 |
Ceiling |
Returns given value rounded to the nearest equal or larger integer. The data type should be one of the numeric data types. If the value is null, then the result is also null. |
Floor |
Returns given value rounded to the nearest equal or smaller integer. The data type should be one of the numeric data types. If the value is null, then the result is also null. |
Log | Returns given logarithm of given number. The first parameter is the base number and the second parameter is number to take the logarithm from. Example:
Log(10, 1000) Returns: 3 Log(2, 16) Returns: 4 |
Mod | Returns remainder of division operation of given numbers. The first parameter is the dividend and the second parameter is the divisor. Example:
Mod(10, 4) Returns: 2 |
Power | Returns given power of given number. First parameter is the base number and second is the exponent: Example:
Power(2, 4) Returns: 16 |
Rand | Returns random number between 0 and 1, where 0 is included and 1 is excluded. It's possible that when getting random for multiple columns, the same row may have the same random number. Example:
Rand() Returns: 0.78452600 (for example) |
Round |
Rounds given number to given precision. Parameters:
Examples: Round(123.456) Returns 123 Round(123.456, 2) Returns 123.46 Round(123.456, -1) Returns 120 Round(123.456, 0) Returns 123 |
Sign | Return sign of given number, i.e., returns 1 (when number is a positive number) or 0 (when number is a negative number or zero). Example:
Sign(-2) Returns: -1 |
Sqrt | Return square root of given number. Example:
Sqrt(9) Returns: 3 |
Date functions
Function | Description |
---|---|
DateAdd |
Adds a duration to a date, i.e., moves the date back or forth in time. The duration is specified as integer and the unit can be chosen from variety of options. The duration can also be a negative number for moving to an earlier time. Parameters:
DateAdd("month", 1, "01/01/2022") Returns 1st of February 2022 at midnight. DateAdd("year", -2, "01/01/2022") Returns 1st of January 2020 at midnight. DateAdd("hour", 12, "01/01/2022") Returns 1st of January 2022 at 12 o'clock. |
DateDiff |
Calculates how many of the specified date part boundaries there are between the specified dates. Parameters:
|
Day |
Returns the days of the month (1-31) of given timestamp. Day(Column("DateColumn")) |
DurationBetweenDates | Calculates duration between the specified dates and returns the duration as float. This function gives more precise result than the DateDiff function that complies with the traditional logic in the SQL language. Parameters:
Example: Calculated duration between two dates: DurationBetweenDates("hour", Column("date1"), Column("date2")) Example: Calculated duration between two dates using model's business calendar: DurationBetweenDates("hour", Column("date1"), Column("date2"), #expr{_model.DefaultCalendar()}) |
Hour | Returns the hours part (0-59) of given timestamp. |
Millisecond | Returns the milliseconds part (0-999) of given timestamp. |
Minute | Returns the minutes part (0-59) of given timestamp. |
Month | Returns the months part (1-12) of given timestamp. |
Second | Returns the seconds part (0-59) of given timestamp. |
TruncateDate |
Truncates given date to given time unit. The truncation gives the timestamp of the beginning of the period defined by the time unit (logic thus resembles the floor function for numbers). Parameters:
Examples: TruncateDate(Column("MyDateColumn"), "year") Returns timestamp representing beginning of a year, e.g. 2022-01-01 00:00:00. TruncateDate(Column("MyDateColumn"), "day") Returns timestamp representing beginning of a day, e.g. 2022-04-16 00:00:00. |
Year | Returns the year of given timestamp. |
String functions
Function | Description |
---|---|
CharIndex | Returns the starting position of the first occurrence of the first argument in the second argument. The first character has the starting position 1. Returns 0 if the search doesn't find any results.
Parameters:
Examples: CharIndex("c", "abcdefg") Returns: 3 CharIndex("ef", "abcdefg") Returns: 5 CharIndex("fe", "abcdefg") Returns: 0 CharIndex("c", "abcdefgc", 4) Returns: 8 |
Concat |
Return the concatenated string value of given values. Concat("part 1", "part 2") Returns "part 1part 2" Concat(Column("column1"), " ", Column("column2")) Returns column1 and column2 value concatenated separated by space. |
Length |
Returns length of a given string, i.e., the number of characters in a string. Examples: Length("test") Returns: 4 Length("long text ending to space ") Returns: 26 Length(Column("MyData")) |
Like |
Determines whether given string matches given pattern, and returns either true (for match) or false (for no match). Following special characters can be used in the pattern:
Parameters:
When the percentage and underscore characters need to be used as normal characters in the search pattern (not as special characters), escaping for them is needed. By default, there is no escape character available, so it needs to be defined using the 3rd parameter. Any character can be used as the escape character, but it might be a good practice to use the backslash (\). See below an example of using the escape character. The comparison is case-sensitive for Snowflake, and for SQL Server it is dependent on the server collation. The Like function can be used with Where and CaseWhen functions which accept boolean values. Examples: Like(Column("TextData"), "a%") Returns true for all rows starting with "a". Like(Column("TextData"), "%aa%") Returns true for all rows containing "aa". Like(Column("TextData"), "abc_") Returns true for all rows starting with "abc" and having four characters. Like(Column("TextData"), "__a") Returns true for all rows ending to "a" and having three characters. dataFrame.Where(Like(Column("Region"), "t%")) Filters dataframe rows where column Region starts with "t". Like("a%b", "%\\%%", "\\") Searches for a percentage character. |
Substring |
Returns substring of given string, based on given start index and length. Function has following parameters:
Substring("123456789", 5) Returns: 56789 Substring("123456789", 5, 3) Returns: 567 Substring(Column("MyColumn"), 1, 4) Returns the first 4 characters of the MyColumn data. |
ToLower | Return string where all the characters of the input string have been converted into lower case characters.
Examples: ToLower("Test") Returns: test |
ToUpper | Return string where all the characters of the input string have been converted into upper case characters.
Example: ToUpper("Test") Returns: TEST |
Trim |
Trims a string, i.e., removes spaces from the beginning and end of a string. Only characters with code 32 are removed, which is the most common space character. Example: Trim(Column("MyColumn")) Returns column "MyColumn" where leading and trailing spaces have been removed. Trim(" test test ") Returns "test test". |
AggregateFrom function
Aggregates a value from related objects, where there can be several of them a source object (e.g., when going from cases to events). See the type of relations between objects in the process mining concepts. The diagram shows that when going to direction where the target objects has count of N, AggregateFrom function needs to be used.
Parameters:
- Aggregation level: Aggregation level to aggregate from. Also an expression can be used to produce the dataset.
- Aggregation function: Aggregation function or object definition (see more).
- Expression: Expression to generate the values to be aggregated. Default value is null.
- Filter: Optional filter to apply prior to performing the aggregation. Filter is given as JSON like syntax using dictionaries, arrays and scalar values.
Example for EventTypes:
AggregateFrom(Events, "Count") Returns the number of events having each event type.
Example for Cases:
AggregateFrom(Events, #{ "Function": "List", "Ordering": ["TimeStamp"], "Separator": "#,#" }, Column("EventType")) Returns variation/event type path string for all the cases. GetValueFrom(Variations, AggregateFrom(Cases, "Count")) Returns the number of cases having the same variation for every case. DateDiff("Seconds", AggregateFrom(Events.Where(Column("EventType") == "Sales Order"), "Min", Column("TimeStamp")), AggregateFrom(Events.Where(Column("EventType") == "Invoice"), "Max", Column("Timestamp"))) Returns the duration in seconds between the first occurrence of "Sales Order"-event type and the last occurrence of "Invoice" event type for each case.
Example for Model:
AggregateFrom(Cases, "Count", null, #{"Items":[#{"Type":"IncludeCases","Items":[#{"Type":"CaseAttributeValue","Values":["Dallas"], "Attribute":"Region"}]}]}) Returns the total number of cases in the model having "Dallas" as the value of "Region" case attribute.
GetValueFrom function
Retrieves a value from a related object that there may be only one for a source object (e.g., when going from events to cases). See the type of relations between objects in the process mining concepts. The diagram shows that when going to direction where the target objects has count of 1, GetValueFrom function needs to be used.
Parameters:
- Aggregation level: Aggregation level to aggregate from. This includes possible additional data frame expressions to prepare the aggregation level.
- Expression: Expression to evaluate in given aggregation level to get the returned value.
- Filter: Optional filter to apply prior to performing expression evaluation. Filter is given as dictionary following the JSON filter syntax.
Examples as measure expression for events:
GetValueFrom(Cases, Column("Account Manager\")) Returns for each event the value of Account Manager case attribute. GetValueFrom(Variations, Column("Variation")) Returns for each event variation/event type path string of its case.
Examples as measure expression for events:
GetValueFrom(Variations, AggregateFrom(Cases, "Count")) Returns the number of cases having the same variation for every case. GetValueFrom(Cases, Column("Variation"), #{"Items":[#{"Type":"IncludeEventTypes","Items":[#{"Type":"EventType","Values":["Shipment","Invoice"]}]}]}) Returns cases with their variations where only "Shipment" and "Invoice" event types are taken into account.
Other functions
Function | Description |
---|---|
CaseWhen |
Goes through conditions and returns a value when the first condition is true, similar to an if-then-else structure. Once a condition is true, it will stop reading and return the result. If no conditions are true, it returns the value in the else expression. If the else expression is not defined (i.e. there are even number of parameters), null value is returned. Consists of any number of pairs of condition and value expressions followed by an optional else expression. The odd parameters are the conditions and the even parameters are the return values. CaseWhen(Column("a") == null, 1, Column("a") < 1.0, 2, 3) Returns 1 if the value of column "a" is null. Returns 2 if the value of column "a" is less than 1.0. Returns 3 otherwise. Returns given value rounded to the nearest equal or larger integer. The data type should be one of the numeric data types. If the value is null, then the result is also null. |
Coalesce |
Returns the first non-null parameter. There can be any number of parameters. If all parameters are null, returns null. Coalesce(null, 3, 2) Returns 3. Coalesce(Column("column1"), "N/A") Returns column "column1" value, except replaces nulls with "N/A". |
Column |
Return the value of given column. Column("column1") Column("My Column 2") |
In |
Returns true, if the test expression (given as the first parameter) matches with any of the other expressions given as parameters (starting from the seconds parameter). Examples: In(Column("Country"), "Germany", "France", "UK") Return true if column "Country" is Germany, France or UK. In("Germany", Column("SourceCountry"), Column("DestinationCountry")) Returns true, if column SourceCountry or DestinationCountry is Germany (or both columns). |
Lag | Similar to the Lead function, except refers to the previous row(s) instead of the next row(s).
Example: For events list, get the MyAttribute attribute value from the two steps previous event: Lag(Column("MyAttribute"), [TimeStamp, EventType], [true, true], [CaseId], 2, "N/A")) |
Lead |
Returns value that is evaluated by accessing data in the next row. If a column with given columnId already exists in the DataFrame, the new column will overwrite the existing column. Parameters:
Example: For events list, get the next event timestamp: Lead(TimeStamp, [TimeStamp, EventType], [true, true], [CaseId])) |
Variable |
Returns value of given variable available in the context where the SQL expression is run. Supports string, number, boolean and date values. In addition, the variable can contain SQL expression which is added as such to the contained SQL expression. Examples: let myRegion = "Dallas"; DatatableById(123).SqlDataFrame.Where(Column("Region") == Variable("myRegion")).Collect() Filters datatable by Region is Dallas. |
Process mining objects
The following variable names are supported in the beginning of a root expression and in the AggregateFrom and GetValueFrom functions:
- Cases: Returns SqlDataFrame for cases. There are following properties:
- CaseId: Case id.
- All case attributes (can be referred using Column("<AttributeName>")
- Events: Returns SqlDataFrame for events with following properties:
- CaseId: Case id.
- EventType: Event type name.
- Timestamp: Event timestamp.
- All event attributes (can be referred using Column("<AttributeName>")
- EventTypes: Returns SqlDataFrame for event types. There are the following properties:
- EventType: Event type name.
- Variations: Returns SqlDataFrame for variations. There the following properties:
- Variation: Variation identifier, which is concatenated event type names separated by separator "#,#".
- Flows: Returns SqlDataFrame for flows. There are the following properties:
- FromEventType: Event type name of the flow start.
- ToEventType: Event type name of the flow end.
- FlowOccurrences: Returns SqlDataFrame for flow occurrences. There are the following properties:
- CaseId: Case id.
- FromEventType: Event type name of the flow start.
- FromTimeStamp: Time stamp of the flow start event.
- All starting event attributes when adding text From to the beginning of the attribute name (can be referred using Column("From<AttributeName>")
- ToEventType: Event type name of the flow end.
- ToTimeStamp: Time stamp of the flow end event.
- All ending event attributes when adding text To to the beginning of the attribute name (can be referred using Column("To<AttributeName>")
- Model: Returns SqlDataFrame containing one row representing the model. There are the following properties:
- ModelId: Model id.
After these variables, all functions supported by the SqlDataFrame can be used.
Examples: For cases (and also events), the case id can be referred using CaseId:
Cases.Where(CaseId == "Case_123")
Assuming that there is an Order Id column that is mapped to the CaseId, also the original column name can be used:
Cases.Where(Column("Order Id") == "Case_123")
For events, the event type can be referred using EventType:
Events.Where(EventType == "Order created")
Assuming that there is an Process step column that is mapped to the EventType, also the original column name can be used:
Cases.Where(Column("Process step") == "Order created")
Aggregation operations
Aggregations are used when multiple items are combined into one value. The following aggregations are available:
- Average: Average of the items (sum of items divided by count of items). Can be used for numbers.
- Count: Number of items. Can be used for any type of data.
- CountDistinct: Number of unique items (i.e., duplicates are removed from calculation). Can be used for any type of data.
- List: Joins texts together. There are parameters Ordering (array of columns defining how the items are ordered) and Separator (characters separating the combined texts) which can be used when the aggregation is defined as object as follows: #{"Function": "List", "Ordering": [EventType], "Separator": ", "}
- Median: Median, i.e. the middle value of the sorted data. If there are even number of items, the average of the two middle items. Can be used for numbers.
- Min: Lowest/first value in the data. Can be used for numbers, text and dates.
- Max: Highest/last value in the data. Can be used for numbers, text and dates.
- Percentile: Percentile value. Can be used for numbers. There are parameter Percentile (between 0 and 1) which can be used when the aggregation is defined as object as follows: #{"Function": "Percentile", "Percentile": 0.75 }
- Stdev: Sample standard deviation. Can be calculated for numbers.
- Stdevp: Population standard deviation. Can be calculated for numbers.
- Sum: Sum of the items. Can be used for numbers and Timespans.
- Var: Sample variance. Can be used for numbers.
- Varp: Population variance. Can be used for numbers.