I am new to SSAS and I am trying to write a query in ssas caluculation tab that will show all customers (customername) and items ordered (ItemName) in last 40 days. I am not sure is calculation tab the best option to do this.
I have got only one measure VOLUME and dimension attributes (customername,itemname,officename,shipdate ...) I have got also Date Hierarchy with Year->Q->Month->Date.
I have got this statement that gives me netvolume for specific itemname in last 40 days, but what i am looking for is all customers and items ordered in last 40 days. I do not need measure. ( this 40 days might change to 20 or 60 days)
SELECT
{ [Measures].[NET VOLUME]} ON COLUMNS,
FILTER
(
{[CUBENAME].[SHIPDATE].&[2018-03-23T00:00:00]:[CUBENAME].[SHIPDATE].&[2018-05-01T00:00:00]},
[CUBENAME].[ITEMNAME].&[11# RPC 6411]
) ON ROWS
FROM[CUBENAME]
Try using named query in the DSV and filter based on the date field.
Related
Ok, I have watched many videos and read all sorts and I think I am nearly there, but must be missing something. In the data model I am trying to add the ytd calc to my product_table. I don't have unique dates in the product_table in column a and also they are weekly dates. I have all data for 2018 for each week of this year in set rows of 20, incrementing by one week every 20 rows. E.g. rows 1-20 are 01/01/2018, rows 21-40 are 07/01/2018, and so on.
Whilst I say they are in set rows of 20, this is an example. Some weeks there are more or less than 20 so I can't use the row count function-
Between columns c and h I have a bunch of other categories such as customer age, country etc. so there isn't a unique identifier. Do I need one for this to work? Column i is the sales column with the numbers. What I would like is a new column which gives me a ytd number for each row of data which all has unique criteria between a and h. Week 1 ytd is not going to be any different. For the next 20 rows I want it to add week1 sales to week2 sales, effectively giving me the ytd.
I could sumproduct this easily in the data set but I don't want do that. I want to use dax to save space etc..
I have a date_table which does have unique dates in the main_date column. All my date columns are formatted as date in the data model.
I have tried:
=calculate(products[sales],datesytd(date_table[main_date]))
This simply replicates the numbers in the sales column, not giving me an ytd as required. I also tried
=calculate(sum(products[sales]) ,datesytd(date_table[main_date]))
I don't know if what I am trying to do is possible. All the youtube clips don't seem to have the same issues I am having but I think they have unique dates in their data sets.
Id love to upload the data but its work stuff on a work computer so cant really. Hope I've painted the picture quite clearly.
Resolved, after googling sumif dax, mike honey had a response that i have adapted to get what i need. I needed to add the filter and earlier functions to my equarion and it ended up like this
Calculate (sum(products[sales]),
filter (sales, sales[we_date] <=earlier(sales[we_date]),
filter (sales, sales[year] =earlier(sales[year]),
filter (sales, sales[customer] =earlier(sales[customer]))
There are three other filter sections i had to add, but this now gives me the ytd i needed.
Hope this helps anyone else
I want to calculate sales for promotion using it's date. I need 3 measures, avg sales from 21 days before promotion start date, sales in between of promotion's start and end date, and sales from 21 days after promotion's end date.
Why Visual Studio highlights avg in code below?
CREATE MEMBER CURRENTCUBE.[Measures].[Sales in promotion]
AS Avg(Existing([Promotion].[Promotion name].[Promotion name]),[Measures].[Sales]), ...
Same in here:
CREATE MEMBER CURRENTCUBE.[Measures].[Sales before promotion]
AS (EXISTING([Promotion].[Promotion name].[Promotion name]), AVG(strtomember("[Date].[Date].&["+ [Promotion].[Date].currentmember.member_key+"]").lag(21) : strtomember("[Date].[Date].&["+ [Promotion].[Date From].currentmember.member_key+"]"),
[Measures].[Sales])) ...
If I do sum(existing()) in first measure, the sum is calculated correctly, but it doesn't allow me to get average.
EXISTING will only help if [Promotion] is part of your query in either the WHERE or SELECT clause. If it is not included in either of these clause then EXISTING will be finding 1 member - the All member.
You could try NonEmpty and maybe move the period logic into a custom set?
WITH
SET [PERIOD] AS
STRTOSET(
"[Date].[Date].&["+ [Promotion].[Date].currentmember.member_key+"].lag(21)
:
[Date].[Date].&["+ [Promotion].[Date From].currentmember.member_key+"]"
)
From the code you posted I cannot tell if you want a daily average or and average per promotion ? Say there were 2 promotions over the 21 days does this mean you want (Total/2/21) ?
I want to model a fact table for our users to help us calculate DAU (Daily active Users), WAU (Weekly active users) and MAU (Monthly active users).
The definitions of these measures are as follows:
1. DAU are users who is active every day during last 28 days.
2. WAU are users who are active at least on one day in each 7 days period during last 28 days
3. MAU are users who are active at least 20 days during last 28 days
I have built a SSAS cube with my fact table and user dimension table as follows
Fact : { date, user_id, activity_name}
Dimension: { date, user_id, gender, age, country }
Now I want to build a cube over this data so that we can see all the measures in any given day for last 28 days.
I thought of initially storing 28 days of data for all users in the SQL server and then do count distinct on date to see which measures they fall into.. but this proved very expensive since the data per day is huge..almost 10 millions rows.
So my next thought was to model the fact table (before moving it to SQL) such that it has a new column called "active_status" which is a 32 bit binary type column.
Basically, I'll store a binary number (or decimal equivalent) like 11000001101111011111111111111 which has a bit set on the days the user is active and off on the days user is not active.
This way I can compress 28 days worth of data in a single day before loading into data mart
Now the problem is , I think MDX doesn't support bitwise operations on columns in the expressions for calculated members like regular SQL does. I was hoping to create calculated measures daily_active_users, weekly_active_users and monthly_active_users using MDX that looks at this active_status bit for the user and does bitwise operation to determine the status.
Any suggestions on how to solve this problem? if MDX doesn't allow bitwise, what else can I do SSAS to achieve this.
thanks for the help
Additonal notes:
#Frank
Interesting thought about using a view to do the conversion from bitset to a dimension category..but I'm afraid it won't work. Because I have few dimensions connected to this fact table that have many-many relationships..for ex: I have a dimension called DimLanguage and another dimension called DimCountry and they have many-many relationship. And what ultimately I would like to do in the cube is to calculate the DAU/WAU/MAU which are COUNT(DISTINCT UserId) based on the combination of dimensions. So for ex; If a user is not MAU for dimension country US because he is only active 15 days out of 28 ....but he will be considered
You do not want to show the bitmap data to the users of the cube, but just the categories DAU, WAU, MAU, you should do the conversion from bitmap to category on data loading time. Just create a dimension table containing e. g. the following data:
id category
-- --------
1 DAU
2 WAU
3 MAU
Then define a view on your fact table that evaluates the bitmap data, and for each user and each date just calculates the id value of the category the user is in. This is then conceptually a foreign key to the dimension table. Use this view instead of the fact table in your cube.
All the bitmap evaluations are thus done on the relational side, where you have the bit operators available.
EDIT
As your requirement is that you need to aggregate the bitmap data in Analysis Services using bitwise OR as the aggregation method, I see no simple way to do that.
What you could do, however, would be to have 28 single columns, say Day1 to Day28, which would be either 0 or 1. These could be of type byte to save some space. You would use Maximum as aggregation method, which is equivalent to binary OR on a single bit.
Then, it would not be really complex to calculate the final measure, as we know the values are either zero or one, and thus we can just sum across the days:
CASE
WHEN Measures.[Day1] + ... + Measures.[Day28] = 28 THEN 'DAU'
WHEN Measures.[Day1] + ... + Measures.[Day7] >= 1 AND
Measures.[Day8] + ... + Measures.[Day14] >= 1 AND
Measures.[Day15] + ... + Measures.[Day21] >= 1 AND
Measures.[Day22] + ... + Measures.[Day28] >= 1 THEN 'WAU'
WHEN Measures.[Day1] + ... + Measures.[Day28] >= 20 THEN 'MAU'
ELSE 'Other'
END
The order of the clauses in the CASE is relevant, as the first condition matching is taken, and your definitions of WAU and MAU have some intersection.
If you have finally tested everything, you would make the measures Day1 to Day28 invisible in order not to confuse the users of the cube.
I'm trying to add a new measure to my OLAP cube, in SSAS.
The fact table is at the order detail level, so each row in the fact table represents an order and a product, like:
I'd like to add a measure that indicates the maximum number of days overdue per order number. So that measure should say:
OrderNo 1 -> 5 days overdue
OrderNo 2 -> 1 day overdue
OrderNo 3 -> 0 days overdue
I have tried using the MAX operator, with no success.
By the way, this is a multidimensional SSAS schema. I'm using SQL Server 2008.
Thanks in advance!
Just define a measure "max days overdue" or how ever you want to call it in the cube designer, tab "cube structure". In the properties of the measure, use "Max" as the aggregate function. That's it.
Im trying to create a daily calculation in my Cube or an MDX statement that will do a calculation daily and roll up to any dimension. I've been able to successfully get the values back, however the performance is not what I think it should be.
My fact table will have 4 dimensions 1 of which being daily date (time). I have a formula that uses 4 other measures in this fact table and those need to be calculated daily and then geometrically linked across the time dimension.
The following MDX statement works great and produces the correct value but it is very slow. I have tried using exp(sum(log+1))-1 and multiply seems to perform a little better but not good enough. Is there another approach to this solution or is there something wrong with my MDX statement?
I have tried defining aggregations For [Calendar_Date] and [Dim_Y].[Y ID], but it does not seem to use these aggregations.
WITH
MEMBER Measures.MyCustomCalc AS (
(
Measures.x -Measures.y
) -
(
Measures.z - Measures.j
)
)
/
Measures.x
MEMBER Measures.LinkedCalc AS ASSP.MULTIPLY(
[Dim_Date].[Calendar Date].Members,
Measures.MyCustomCalc + 1
) - 1
SELECT
Measures.LinkedCalc ON Columns,
[Dim_Y].[Y ID].Members ON Rows
FROM
[My DB]
The above query takes 7 seconds to run w/ the following number of records:
Measure: 98,160 records
Dim_Date: 5,479 records
Dim_Y: 42 records
We have assumed that by defining an aggregation that the amount of calculations we'd be performing would only be 42 * number of days, in this case a maximum of 5479 records.
Any help or suggestions would be greatly appreciated!