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Formula Compatibility in DirectQuery Mode

The Data Analysis Expression language (DAX) can be used to create measures and other custom formulas that you use in tabular models and in PowerPivot workbooks. In almost every respect, the models that you create in these two environments are identical, and you can use the same measures, relationships, and KPIs, etc. However, if you build a tabular model and deploy it in DirectQuery mode, there are some restrictions on the formulas that you can use. This topic provides an overview of the differences, lists the functions that are not supported in DirectQuery mode, and lists the functions that are supported but might return different results.

Within this topic, we use the term in-memory model to refer to both PowerPivot models, which use a local cache, as well as tabular models, which are fully hosted in memory data on an Analysis Services server running in Tabular mode. We use DirectQuery models to refer to tabular models that have been deployed in DirectQuery mode. For information about DirectQuery mode, see DirectQuery Mode (SSAS Tabular).

Semantic Differences

Describes the types of differences that might arise when the same formula is used to DirectQuery mode.

Functions Supported in DirectQuery Mode

This section lists functions that can be used in DirectQuery mode, but which might return different results.

Functions Not Supported in DirectQuery Mode

The section lists functions that cannot be used in models deployed in DirectQuery mode.

Functions that are not in either of these lists are expected to behave identically regardless of the model storage or query mode.

Queries on a model deployed in DirectQuery mode can return different results than when the same model is deployed in-memory, because data is fetched directly from a relational data store and aggregations required by formulas are performed using the relevant relational engine, rather than using the xVelocity in-memory analytics engine (VertiPaq) for storage and calculation.

For example, there are differences in the way that certain relational data stores handle numeric values, dates, nulls, and so forth.

In contrast, the DAX language is intended to emulate as closely as possible the behavior of functions in Microsoft Excel. For example, when handling nulls, empty strings and zero values, Excel attempts to provide the best answer regardless of the precise data type, and therefore the xVelocity engine does the same. However, when a tabular model is deployed in DirectQuery mode and passes formulas to a relational data source for evaluation, the data must be handled according to the semantics of the relational data source, which typically require distinct handling of empty strings vs. nulls. For this reason, the same formula might return a different result when evaluated against cached data and against data fetched solely from the relational store.

Additionally, some functions cannot be used at all in DirectQuery mode because the calculation would require that the data in the current context be sent to the relational data source as a parameter. For example, measures in a PowerPivot workbook often use time intelligence functions that reference date ranges available within the workbook. Such formulas generally cannot be used in DirectQuery mode.

This section lists the types of semantic differences that you can expect, and describes any limitations that might apply to the usage of functions or to query results.

Comparisons

DAX in in-memory models supports comparisons of two expressions that resolve to scalar values of different data types. However, models that are deployed in DirectQuery mode use the data types and comparison operators of the relational engine, and therefore might return different results.

The following comparisons will always generate an error when used in a calculation on a DirectQuery data source:

  • Numeric data type compared to any string data type

  • Numeric data type compared to a Boolean value

  • Any string data type compared to a Boolean value

In general, DAX is more forgiving of data type mismatches in in-memory models, and will attempt an implicit cast of values up to two times, as described in this section. However, formulas sent to a relational data store in DirectQuery mode are evaluated more strictly, following the rules of the relational engine, and are more likely to fail.

Comparisons of strings and numbers

EXAMPLE: “2” < 3

The formula compares a text string to a number. The expression is true in both DirectQuery mode and in-memory models.

In an in-memory model, the result is true because numbers as strings are implicitly cast to a numerical data type for comparisons with other numbers. SQL also implicitly casts text numbers as numbers for comparison to numerical data types.

Note that this represents a change in behavior from the first version of PowerPivot, which would return false, because the text “2” would always be considered larger than any number.

Comparison of text with Boolean

EXAMPLE: “VERDADERO” = TRUE

This expression compares a text string with a Boolean value. In general, for DirectQuery or In-Memory models, comparing a string value to a Boolean value results in an error. The only exceptions to the rule are when the string contains the word true or the word false; if the string contains any of true or false values, a conversion to Boolean is made and the comparison takes place giving the logical result.

Comparison of nulls

EXAMPLE: EVALUATE ROW("X", BLANK() = BLANK())

This formula compares the SQL equivalent of a null to a null. It returns true in in-memory and DirectQuery models; a provision is made in DirectQuery model to guarantee similar behavior to in-memory model.

Note that in Transact-SQL, a null is never equal to a null. However, in DAX, a blank is equal to another blank. This behavior is the same for all in-memory models. It is important to note that DirectQuery mode uses, most of, the semantics of SQL Server; but, in this case it separates from it giving a new behavior to NULL comparisons.

Casts

There is no cast function as such in DAX, but implicit casts are performed in many comparison and arithmetic operations. It is the comparison or arithmetic operation that determines the data type of the result. For example,

  • Boolean values are treated as numeric in arithmetic operations, such as TRUE + 1, or the function MIN applied to a column of Boolean values. A NOT operation also returns a numeric value.

  • Boolean values are always treated as logical values in comparisons and when used with EXACT, AND, OR, &&, or ||.

Cast from string to Boolean

In in-memory and DirectQuery models, casts are permitted to Boolean values from these strings only: “” (empty string), “true”, “false”; where an empty string casts to false value.

Casts to the Boolean data type of any other string results in an error.

Cast from string to date/time

In DirectQuery mode, casts from string representations of dates and times to actual datetime values behave the same way as they do in SQL Server.

For information about the rules governing casts from string to datetime data types in PowerPivot models, see the DAX Syntax Specification for PowerPivot.

Models that use the in-memory data store support a more limited range of text formats for dates than the string formats for dates that are supported by SQL Server. However, DAX supports custom date and time formats. For more information, see Pre-Defined Date and Time formats for the FORMAT Function (DAX) and Custom Date and Time formats for the FORMAT Function (DAX).

Cast from string to other non Boolean values

When casting from strings to non-Boolean values, DirectQuery mode behaves the same as SQL Server. For more information, see CAST and CONVERT (Transact-SQL).

Cast from numbers to string not allowed

EXAMPLE: CONCATENATE(102,”,345”)

Casting from numbers to strings is not allowed in SQL Server.

This formula returns an error in tabular models and in DirectQuery mode; however, the formula produces a result in PowerPivot.

No support for two-try casts in DirectQuery

In-memory models often attempt a second cast when the first one fails. This never happens in DirectQuery mode.

EXAMPLE: TODAY() + “13:14:15”

In this expression, the first parameter has type datetime and second parameter has type string. However, the casts when combining the operands are handled differently. DAX will perform an implicit cast from string to double. In in-memory models, the formula engine attempts to cast directly to double, and if that fails, it will try to cast the string to datetime.

In DirectQuery mode, only the direct cast from string to double will be applied. If this cast fails, the formula will return an error.

Math Functions and Arithmetic Operations

Some mathematical functions will return different results in DirectQuery mode, because of differences in the underlying data type or the casts that can be applied in operations. Also, the restrictions described above on the allowed range of values might affect the outcome of arithmetic operations.

Order of addition

When you create a formula that adds a series of numbers, an in-memory model might process the numbers in a different order than a DirectQuery model. Therefore, when you have many very large positive numbers and very large negative numbers, you may get an error in one operation and results in another operation.

Use of the POWER function

EXAMPLE: POWER(-64, 1/3)

In DirectQuery mode, the POWER function cannot use negative values as the base when raised to a fractional exponent. This is the expected behavior in SQL Server.

In an in-memory model, the formula returns -4.

Numerical overflow operations

In Transact-SQL, operations that result in a numerical overflow return an overflow error; therefore, formulas that result in an overflow also raise an error in DirectQuery mode.

However, the same formula when used in an in-memory model returns an eight-byte integer. That is because the formula engine does not perform checks for numerical overflows.

LOG functions with blanks return different results

SQL Server handles nulls and blanks differently than the xVelocity engine. As a result, the following formula returns an error in DirectQuery mode, but return infinity (–inf) in in-memory mode.

EXAMPLE: LOG(blank())

The same limitations apply to the other logarithmic functions: LOG10 and LN.

For more information about the blank data type in DAX, see DAX Syntax Specification for PowerPivot.

Division by 0 and division by Blank

In DirectQuery mode, division by zero (0) or division by BLANK will always result in an error. SQL Server does not support the notion of infinity, and because the natural result of any division by 0 is infinity, the result is an error. However, SQL Server supports division by nulls, and the result must always equal null.

Rather than return different results for these operations, in DirectQuery mode, both types of operations (division by zero and division by null) return an error.

Note that, in Excel and in PowerPivot models, division by zero also returns an error. Division by a blank returns a blank.

The following expressions are all valid in in-memory models, but will fail in DirectQuery mode:

1/BLANK

1/0

0.0/BLANK

0/0

The expression BLANK/BLANK is a special case that returns BLANK in both for in-memory models, and in DirectQuery mode.

Supported Numeric and Date-Time Ranges

Formulas in PowerPivot and tabular models in memory are subject to the same limitations as Excel with regard to maximum allowed values for real numbers and dates. However, differences can arise when the maximum value is returned from a calculation or query, or when values are converted, cast, rounded, or truncated.

  • If values of types Currency and Real are multiplied, and the result is larger than the maximum possible value, in DirectQuery mode, no error is raised, and a null is returned.

  • In in-memory models, no error is raised, but the maximum value is returned.

In general, because the accepted date ranges are different for Excel and SQL Server, results can be guaranteed to match only when dates are within the common date range, which is inclusive of the following dates:

  • Earliest date: March 1, 1900

  • Latest date: December 31, 9999

If any dates used in formulas fall outside this range, either the formula will result in an error, or the results will not match.

Floating point values supported by CEILING

EXAMPLE: EVALUATE ROW("x", CEILING(-4.398488E+30, 1))

The Transact-SQL equivalent of the DAX CEILING function only supports values with magnitude of 10^19 or less. A rule of thumb is that floating point values should be able to fit into bigint.

Datepart functions with dates that are out of range

Results in DirectQuery mode are guaranteed to match those in in-memory models only when the date used as the argument is in the valid date range. If these conditions are not satisfied, either an error will be raised, or the formula will return different results in DirectQuery than in in-memory mode.

EXAMPLE: MONTH(0) or YEAR(0)

In DirectQuery mode, the expressions return 12 and 1899, respectively.

In in-memory models, the expressions return 1 and 1900, respectively.

EXAMPLE: EOMONTH(0.0001, 1)

The results of this expression will match only when the data supplied as a parameter is within the valid date range.

EXAMPLE: EOMONTH(blank(), blank()) or EDATE(blank(), blank())

The results of this expression should be the same in DirectQuery mode and in-memory mode.

Truncation of time values

EXAMPLE: SECOND(1231.04097222222)

In DirectQuery mode, the result is truncated, following the rules of SQL Server, and the expression evaluates to 59.

In in-memory models, the results of each interim operation are rounded; therefore, the expression evaluates to 0.

The following example demonstrates how this value is calculated:

  1. The fraction of the input (0.04097222222) is multiplied by 24.

  2. The resulting hour value (0.98333333328) is multiplied by 60.

  3. The resulting minute value is 58.9999999968.

  4. The fraction of the minute value (0.9999999968) is multiplied by 60.

  5. The resulting second value (59.999999808) rounds up to 60.

  6. 60 is equivalent to 0.

SQL Time data type not supported

In-memory models do not support use of the new SQL Time data type. In DirectQuery mode, formulas that reference columns with this data type will return an error. Time data columns cannot be imported into an in-memory model.

However, in PowerPivot and in cached models, sometimes the engine casts the time value to an acceptable data type, and the formula returns a result.

This behavior affects all functions that use a date column as a parameter.

Currency

In DirectQuery mode, if the result of an arithmetic operation has the type Currency, the value must be within the following range:

  • Minimum: -922337203685477.5808

  • Maximum: 922337203685477.5807

Combining currency and REAL data types

EXAMPLE: Currency sample 1

If Currency and Real types are multiplied, and the result is larger than 9223372036854774784 (0x7ffffffffffffc00), DirectQuery mode will not raise an error.

In an in-memory model, an error is raised if the absolute value of the result is larger than 922337203685477.4784.

Operation results in an out-of-range value

EXAMPLE: Currency sample 2

If operations on any two currency values result in a value that is outside the specified range, an error is raised in in-memory models, but not in DirectQuery models.

Combining currency with other data types

Division of currency values by values of other numeric types can result in different results.

Aggregation Functions

Statistical functions on a table with one row return different results. Aggregation functions over empty tables also behave differently in in-memory models than they do in DirectQuery mode.

Statistical functions over a table with a single row

If the table that is used as argument contains a single row, in DirectQuery mode, statistical functions such as STDEV and VAR return null.

In an in-memory model, a formula that uses STDEV or VAR over a table with a single row returns a division by zero error.

Text Functions

Because relational data stores provide different text data types than does Excel, you may see different results when searching strings or working with substrings. The length of strings also can be different.

In general, any string manipulation functions that use fixed-size columns as arguments can have different results.

Additionally, in SQL Server, some text functions support additional arguments that are not provided in Excel. If the formula requires the missing argument you can get different results or errors in the in-memory model.

Operations that return a character using LEFT, RIGHT, etc. may return the correct character but in a different case, or no results

EXAMPLE: LEFT([“text”], 2)

In DirectQuery mode, the case of the character that is returned is always exactly the same as the letter that is stored in the database. However, the xVelocity engine uses a different algorithm for compression and indexing of values, to improve performance.

By default, the Latin1_General collation is used, which is case-insensitive but accent-sensitive. Therefore, if there are multiple instances of a text string in lower case, upper case, or mixed case, all instances are considered the same string, and only the first instance of the string is stored in the index. All text functions that operate on stored strings will retrieve the specified portion of the indexed form. Therefore, the example formula would return the same value for the entire column, using the first instance as the input.

String Storage and Collation in Tabular Models

This behavior also applies to other text functions, including RIGHT, MID, and so forth.

String length affects results

EXAMPLE: SEARCH(“within string”, “sample target text”, 1, 1)

If you search for a string using the SEARCH function, and the target string is longer than the within string, DirectQuery mode raises an error.

In an in-memory model, the searched string is returned, but with its length truncated to the length of <within text>.

EXAMPLE: EVALUATE ROW("X", REPLACE("CA", 3, 2, "California") )

If the length of the replacement string is greater than the length of the original string, in DirectQuery mode, the formula returns null.

In in-memory models, the formula follows the behavior of Excel, which concatenates the source string and the replacement string, which returns CACalifornia.

Implicit TRIM in the middle of strings

EXAMPLE: TRIM(“ A sample sentence with leading white space”)

DirectQuery mode translates the DAX TRIM function to the SQL statement LTRIM(RTRIM(<column>)). As a result, only leading and trailing white space is removed.

In contrast, the same formula in an in-memory model removes spaces within the string, following the behavior of Excel.

Implicit RTRIM with use of LEN function

EXAMPLE: LEN(‘string_column’)

Like SQL Server, DirectQuery mode automatically removes white space from the end of string columns: that is, it performs an implicit RTRIM. Therefore, formulas that use the LEN function can return different values if the string has trailing spaces.

In-memory supports additional parameters for SUBSTITUTE

EXAMPLE: SUBSTITUTE([Title],”Doctor”,”Dr.”)

EXAMPLE: SUBSTITUTE([Title],”Doctor”,”Dr.”, 2)

In DirectQuery mode, you can use only the version of this function that has three (3) parameters: a reference to a column, the old text, and the new text. If you use the second formula, an error is raised.

In in-memory models, you can use an optional fourth parameter to specify the instance number of the string to replace. For example, you can replace only the second instance, etc.

Restrictions on string lengths for REPT operations

In in-memory models, the length of a string resulting from an operation using REPT must be less than 32,767 characters.

This limitation does not apply in DirectQuery mode.

Substring operations return different results depending on character type

EXAMPLE: MID([col], 2, 5)

If the input text is varchar or nvarchar, the result of the formula is always the same.

However, if the text is a fixed-length character and the value for <num_chars> is greater than the length of the target string, in DirectQuery mode, a blank is added at the end of the result string.

In an in-memory model, the result terminates at the last string character, with no padding.

The following DAX functions can be used in DirectQuery mode, but with the qualifications as described in the preceding section.

Text functions

CONCATENATE

FIND

LEFT

LEN

MID

REPLACE

REPT

RIGHT

SUBSTITUTE

TRIM

Statistical functions

COUNT

STDEV.P

STDEV.S

STDEVX.P

STDEVX.S

VAR.P

VAR.S

VARX.P

VARX.S

Date/time functions

DATE

EDATE

EOMONTH

DATE

TIME

SECOND

Math and number functions

CEILING

LN

LOG

LOG10

POWER

DAX Table queries

There are some limitations when you evaluate formulas against a DirectQuery model by using DAX Table queries. DirectQuery does not support referring to the same column twice in an ORDER BY clause. The equivalent Transact-SQL statement cannot be created and the query fails.

In an in-memory model, repeating the ORDER by clause has no effect on the results.

Some DAX functions are not supported in models that are deployed in DirectQuery mode. The reasons that a particular function is not supported can include any or a combination of these reasons:

  • The underlying relational engine cannot perform calculations equivalent to those performed by the xVelocity engine.

  • The formula cannot be converted to en equivalent SQL expression.

  • The performance of the converted expression and the resulting calculations would be unacceptable.

The following DAX functions cannot be used in DirectQuery models.

Path functions

PATH

PATHCONTAINS

PATHITEM

PATHITEMREVERSE

PATHLENGTH

Misc functions

COUNTBLANK

FIXED

FORMAT

RAND

RANDBETWEEN

Time intelligence functions: Start and end dates

DATESQTD

DATESYTD

DATESMTD

DATESQTD

DATESINPERIOD

TOTALMTD

TOTALQTD

TOTALYTD

DATESINPERIOD

SAMEPERIODLASTYEAR

PARALLELPERIOD

Time intelligence functions: Balances

OPENINGBALANCEMONTH

OPENINGBALANCEQUARTER

OPENINGBALANCEYEAR

CLOSINGBALANCEMONTH

CLOSINGBALANCEQUARTER

CLOSINGBALANCEYEAR

Time intelligence functions: Previous and next periods

PREVIOUSDAY

PREVIOUSMONTH

PREVIOUSQUARTER

PREVIOUSYEAR

NEXTDAY

NEXTMONTH

NEXTQUARTER

NEXTYEAR

Time intelligence functions: Periods and calculations over periods

STARTOFMONTH

STARTOFQUARTER

STARTOFYEAR

ENDOFMONTH

ENDOFQUARTER

ENDOFYEAR

FIRSTDATE

LASTDATE

DATEADD

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