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MicroStrategy Functions Reference
Understanding Functions in MicroStrategy
1
Additional examples of functions in expressions
87
To perform the analysis, build a set of metrics that calculate each of the
components in the analysis, then use those metrics together to create the
final Forecasting metric.
1 The metric that calculates sales revenue at the Year level is defined as
follows:
Revenue {Year} = Sum(Revenue) {~, Year}
2 The metric that expresses the Year elements as a series of integers, to
simplify the analysis, is defined as follows:
YearNumber = RunningSum <SortBy= (Year@ID)> (Sum(1)
{~, Year})
3 The metric that calculates the slope of the line produced by the Revenue
{Year} metric is defined as follows:
SlopeMetric = Slope([Revenue {Year}], YearNumber) {~,
Year}
4 The metric that calculates the Y-intercept of the line produced by the
Revenue {Year} metric is defined as follows:
InterceptMetric = Intercept([Revenue {Year}],
YearNumber) {~, Year}
5 The final metric synthesizes the three preceding metrics into a forecast
value. This formula follows the standard formula for a line: y = mx + b.
The metric is defined as follows:
Forecast = ((SlopeMetric * YearNumber) +
InterceptMetric)
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Understanding Functions in MicroStrategy
MicroStrategy Functions Reference
1
88
Additional examples of functions in expressions
Place the Forecast metric on a report with Year. A sales forecast is generated
for all available years. The Forecast (Graph) report displays as follows:
The sales for 2000 and 2001 are actual recorded values, and the sales for
2002 and 2003 are a linear extrapolation of the existing data into the future.
There is another report in the Forecasting folder, Forecast (Grid). This
report places the Forecast metric alongside the Revenue metric for
comparison. However, it requires the use of outer joins to work
properly. The MicroStrategy Tutorial includes Microsoft Access as its
default database; in order for the Forecast report to work, the Tutorial
warehouse must be moved into a database platform that fully
supports outer joins.
The following SQL is generated for the Forecast (Graph) report.
Pass0 - Duration: 0:00:00.06
create table ZZT1Y0100BWMD000 (
YEAR_ID SHORT,
CATEGORY_ID BYTE,
WJXBFS1 DOUBLE)
Pass1 - Duration: 0:00:00.02
insert into ZZT1Y0100BWMD000
select a11.[YEAR_ID] AS YEAR_ID,
a11.[CATEGORY_ID] AS CATEGORY_ID,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [YR_CATEGORY_SLS] a11
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MicroStrategy Functions Reference
Understanding Functions in MicroStrategy
1
Additional examples of functions in expressions
89
Pass2 - Duration: 0:00:00.04
select a12.[YEAR_ID] AS YEAR_ID,
a11.[CATEGORY_ID] AS CATEGORY_ID,
sum(1.0) AS WJXBFS1
from [LU_CATEGORY] a11,
[LU_YEAR] a12
group by a12.[YEAR_ID],
a11.[CATEGORY_ID]
Pass3 - Duration: 0:00:00.03
create table ZZT1Y0100BWMD001 (
YEAR_ID SHORT,
CATEGORY_ID BYTE,
WJXBFS1 DOUBLE,
WJXBFS2 DOUBLE,
WJXBFS3 DOUBLE)
Pass4 - Duration: 0:00:00.00
[An Analytical SQL]
Pass5 - Duration: 0:00:00.11
insert into ZZT1Y0100BWMD001 values (2000, 1, 1, 1, 1)
Pass6 - Duration: 0:00:00.01
select pa1.[YEAR_ID] AS YEAR_ID,
pa1.[CATEGORY_ID] AS CATEGORY_ID,
pa1.[WJXBFS1] AS WJXBFS1,
pa2.[WJXBFS1] AS WJXBFS2,
pa1.[WJXBFS1] AS WJXBFS3,
pa2.[WJXBFS3] AS WJXBFS4
from [ZZT1Y0100BWMD000] pa1,
[ZZT1Y0100BWMD001] pa2
where pa1.[CATEGORY_ID] = pa2.[CATEGORY_ID] and
pa1.[YEAR_ID] = pa2.[YEAR_ID]
Pass7 - Duration: 0:00:00.03
create table ZZT1Y0100BWMD002 (
CATEGORY_ID BYTE,
WJXBFS1 DOUBLE,
WJXBFS2 DOUBLE)
Pass8 - Duration: 0:00:00.00
[An Analytical SQL]
Pass9 - Duration: 0:00:00.02
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Understanding Functions in MicroStrategy
MicroStrategy Functions Reference
1
90
Additional examples of functions in expressions
insert into ZZT1Y0100BWMD002 values (1, -559.25,
33788.25)
Pass10 - Duration: 0:00:00.01
select pa2.[CATEGORY_ID] AS CATEGORY_ID,
a11.[CATEGORY_DESC] AS CATEGORY_DESC,
pa2.[YEAR_ID] AS YEAR_ID,
(IIF(ISNULL((pa3.[WJXBFS1] * pa2.[WJXBFS2])), 0,
(pa3.[WJXBFS1] * pa2.[WJXBFS2])) +
IIF(ISNULL(pa3.[WJXBFS2]), 0, pa3.[WJXBFS2])) AS
WJXBFS1
from [ZZT1Y0100BWMD001] pa2,
[ZZT1Y0100BWMD002] pa3,
[LU_CATEGORY] a11
where pa2.[CATEGORY_ID] = pa3.[CATEGORY_ID] and
pa2.[CATEGORY_ID] = a11.[CATEGORY_ID]
Pass11 - Duration: 0:00:00.01
drop table ZZT1Y0100BWMD000
Pass12 - Duration: 0:00:00.00
drop table ZZT1Y0100BWMD001
Pass13 - Duration: 0:00:00.01
drop table ZZT1Y0100BWMD002
• Pass0 and Pass1 calculate the Revenue {Year} metric.
• Pass2 calculates the inner portion of the YearNumber metric, assigning
the integer 1 to each year available in the LU_YEAR lookup table. Note
that this metric was defined by adding “Year” dimensionality with
filtering “None”; this forces the metric into its own pass of SQL (instead
of sharing Pass #02 with the Revenue {Year} metric). Consequently, all
years in the lookup table get numbered, not just the years with data in the
fact table.
• Passes 3 to 9 calculate the YearNumber, SlopeMetric, and
InterceptMetrics. The Intelligence Server performs the calculations based
on the data in the temporary table from passes 0 to 2. Values are
calculated and inserted back into the temporary tables.
• In Pass10, the Forecast metric is calculated based on the information
from all of the previous calculations. The mx+b calculation can be seen in
the SQL.
• The remaining passes drop the temporary tables.
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91
2
2.
S
TANDARD
F
UNCTIONS
Introduction
The standard functions in this chapter range from simple operations like Sum
and Product to internal functions that allow you to define your own
metrics, facts, and filters. Taken as a set, they provide a powerful toolset for
performing common mathematical calculations, grouping data, examining
correlation, validating data, and formatting report information for display.
The following categories of functions are covered:
• Basic functions, page 92
• Date and time functions, page 118
• Internal functions, page 131
• NULL/Zero functions, page 148
• OLAP functions, page 150
• Rank and NTile functions, page 201
• String functions, page 221
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Standard Functions
MicroStrategy Functions Reference
2
92
Basic functions
Each section in this chapter describes a function category and presents a list
of functions within each category. Each section also presents the data
necessary to understand and implement each individual function. The
information provided for each function includes
• An explanation of the data returned by the function
• The syntax of the function including function name, available
parameters, the parameter setting defaults, and the types of data that can
be used with the function
• The mathematical expression illustrating exactly how the calculation is
defined in MicroStrategy (if applicable)
• Usage notes describing any error conditions, invalid data types, or key
information to know before using the function (if applicable)
• An example of the function in use; this may be either a report example or
a simple text description of the data returned based on the specified input
Note the following:
• The report examples in this section use objects and data found in
the MicroStrategy Tutorial.
• See Appendix A, MicroStrategy and Database Support for
Functions, for a list of the databases and the functions they
support.
Basic functions
These are basic mathematical functions like Avg, Greatest, Least, Max, Min,
and so on, and are among the most commonly used functions.
Returns the sum of two or more values. The Add function differs from the
Sum function (see Sum, page 114), which returns the sum of values in a single
value list. Add can take multiple metrics as inputs and add the values of the
metrics.
You can also construct these types of metrics using the plus operator (see
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MicroStrategy Functions Reference
Standard Functions
2
Basic functions
93
Syntax
Where
The arguments must be metrics or constants.
Example
You can use the Add function to return the sum of related metrics. In the
report shown below, the Total Paid Compensation metric uses the formula
Add([Paid Bonus],[Paid Salary]) to return the total compensation
paid to each employee. This report was created in the MicroStrategy Human
Resources Analysis Module.
Average
Average performs a sum of a set of values and divides this total by the
number of values in the set. This returns the average (also known as
arithmetic mean) for the set of values.
The Average function differs from the Avg function (see Avg (average),
page 94), which returns the average of values in a single value list. The
Average function can take multiple metrics as inputs and average the values
of the metrics. You can use this function to compute and display the average
of two or more metrics on a report.
Standard Functions
MicroStrategy Functions Reference
2
94
Basic functions
Syntax
Average(Argument1, Argument2,..., ArgumentN)
Where:
The arguments must be metrics or constants.
Avg (average)
Avg calculates the sum of a single value list and divides the result by the
number of values in the list. This returns the average (arithmetic mean) of
the listed values.
Avg is often used to create subtotals and metrics based on fact data. This is a
group-value function.
Syntax
Avg<Distinct, FactID, UseLookupForAttributes>(Argument)
Where:
• Argument is an attribute, fact, or metric representing a list of numbers.
• Distinct is a TRUE/FALSE parameter that allows you to use all values
in the calculation or to calculate using only the unique values.
• FactID is a parameter that forces a calculation to take place on a fact
table that contains the selected fact.
• UseLookupForAttributes is a TRUE/FALSE parameter that can be
used when performing an aggregation of an attribute. The Count
function is most commonly used to aggregate attributes. For information
on this parameter, including an example of using it with the Count
function, see Count, page 95.
Usage notes
The Avg function ignores NULL values but uses zero values in its calculation.
Example
Example 1: In this simple example the average of a value list containing the
values (4,9,2,9) is calculated as follows: