Data Analysis Expressions のデータ型

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This section describes the data types that can be used in Data Analysis Expressions (DAX) formulas, and discusses the implicit conversion of data types when data is calculated or used in a DAX formula.

Data Types used in DAX Formulas

The following data types are supported for use in DAX formulas. When you use a value in a formula, even if the original data source contains a different data type, the data is cast to one of the following data types. Values that result from formulas also use these data types.

In general, these data types are implemented to enable accurate calculations in calculated columns, and the same restrictions apply to the rest of the client for consistency.

DAX formulas do not support data types that are smaller than those listed in the table. Any attempt to define a calculated column as being of a smaller type, such as I4, will result in an error.

New Table Data Type

In addition, DAX uses a table data type. This data type is used by DAX in many functions, such as aggregations and time intelligence calculations. Some functions require a reference to a table; other functions return a table that can then be used as input to other functions.

Implicit and Explicit Data Type Conversion

Each DAX function has specific requirements as to the types of data that are used as inputs and outputs. For example, some functions require integers for some arguments and dates for others; other functions require text or tables.

If the data in the column that you specify as an argument is incompatible with the data type required by the function, DAX in many cases will return an error. However, wherever possible DAX will attempt to implicitly convert the data to the required data type.次に例を示します。

• You can type a date as a string, and DAX will parse the string and attempt to cast it as one of the Windows date and time formats.
• You can add TRUE + 1 and get the result 2, because TRUE is implicitly converted to the number 1 and the operation 1+1 is performed.
• If you add values in two columns, and one value happens to be represented as text ('12') and the other as a number 12), DAX implicitly converts the string to a number and then does the addition for a numeric result.
• If you attempt to concatenate two numbers, the code name Gemini add-in will present them as strings and then concatenate.

The following table summarizes the implicit data type conversions that are performed in formulas. In general, Gemini behaves like Microsoft Excel, and performs implicit conversions whenever possible when required by the specified operation.

Conversions are also performed implicitly when comparing the values in columns. When comparing column values, DAX first converts values to a common type. Therefore, comparisons such as ="12" = 12 always return TRUE.

Table of Implicit Data Conversions

The type of conversion that is performed is determined by the operator, which casts the values it requires before performing the requested operation. These tables list the operators, and indicate the conversion that is performed on each data type in the column when it is paired with the data type in the intersecting row.

Addition (+)

For example, if a real number (R8) is used in an addition operation in combination with currency data (CY), both values are converted to R8, and the result is returned as R8.

Subtraction (-)

For example, if a date is used in a subtraction operation with any other data type, both values are converted to dates, and the return value is also a date.

Multiplication (*)

For example, if an integer (I8) is combined with a real number (R8) in a multiplication operation, both numbers are converted to real numbers (R8), and the return value is also R8.

Division (/)

For example, if an integer is combined with a currency value in a division operation, both values are converted to currency (CY), and the result is also a currency value.

Handling of Blanks, Empty Strings, and Zero Values

The way that DAX handles zero values, nulls, and empty strings is different from both Microsoft Excel and SQL Server. This section describes the differences, and describes how these data types are handled.

The important thing to remember is that, in Gemini, a blank value, an empty cell, or a missing value are all represented by the same new value type, a BLANK. How blanks are handled in operations, such as addition or concatenation, depends on the individual function. You can also generate blanks by using the BLANK function, or test for blanks by using the ISBLANK function.

Database nulls are not supported within a Gemini workbook, and nulls are implicitly converted to blanks when a column that contains a null value is referenced in a DAX formula.

Defining Blanks, Nulls and Empty Strings

The following table summarizes the differences between DAX and in Microsoft Excel, in the way that blanks are handled.

For details on how a particular function or operator handles blanks, see the individual topics for each DAX function, in the section, 関数リファレンス (DAX).

参照

その他の技術情報

DAX の主な概念
Creating Expressions

ヘルプおよび情報

SQL Server 2008 R2 の参考資料の入手