I have three tables, one dim table called "ISO_ccy" only showing the ISO acronyms of currencies, one dim table showing the "home currency" of an entity ("entities") and another (fact) table ("trades") showing foreign exchange (FX) trades. The thing with FX trades is, they always have two currencies (ccy) involved, hence the latter table has two columns with currency ISO codes (and corresponding amounts). The two dim tables both only have one column with ISO ccy codes (the table "ISO_ccy" having distinct values only).
I now have one (active) relationship for currency 1 (ccy1) and one inactive for currency 2 (ccy2) between the "ISO_ccy" and the "trades" table. There is also an active relationship between "ISO_ccy" and "entities" tables.
I need to calculate the sum for each currency and each entity where the currency is not equal to the "home currency" of that entity.
Seems to be pretty straight forward for the ccy with an active relationship (ccy1):
Sum_Hedges_activeRelation:=
CALCULATE(
SUM([Amount_ccy1]);
FILTER(trades;trades[ccy1]>LOOKUPVALUE(entities[ccy];entities[name];trades[name]))
)
The filter expression ensures that only amounts are shown where the ccy of a trade is not equal to the "home" ccy of the entity.
Here, I'm getting the desired result.
Now I need to do the same with the inactive relation (ccy2).
This is what I tried:
Sum_Hedges_in-activeRelation:=
CALCULATE(
SUM([Amount_ccy2]);
USERELATIONSHIP(trades[ccy2];ISO_ccy[ccy]);
FILTER(trades;trades[ccy2]<>LOOKUPVALUE(entities[ccy];entities[name];trades[name]))
)
However, I only get an "empty" result.
I also tried to add "ALL(trades)" to the CALCULATE function. No results there as well.
So, I am a bit at a loss now, how I can make this work. Can you please help?
UPDATE 08 April 2019 with a solution:
I found a solution to my problem here:
sqlbi: USERELATIONSHIP in a Measure
Now my forumlar looks like this:
Sum_Hedges_in-activeRelation:=
CALCULATE(
CALCULATE(
SUM([Amount_ccy2]);
FILTER(trades;trades[ccy2]
<>LOOKUPVALUE(entities[ccy];entities[name];trades[name]))
);
USERELATIONSHIP(trades[ccy2];ISO_ccy[ccy])
)
This is slightly different from the solution provided (for a column related context) in the referenced article, as I omitted the ALL() instruction in the outer CALCULATE(). I could not explain, though...
Related
I have a table that shows the recurrence of purchasing a product, with the columns: product_id, report_date, quantity.
I need to list in a table the products that are more than 50 days unsold. The opposite I managed to do (list those that were sold in the last 50 days) but the opposite logic has not yet been able to implement.
Does anyone have any tips?
An example of the table:
product_id,date,report_date,quantity
329,2019-01-02 08:19:17,2019-01-02 14:34:12,6
243,2019-01-03 09:19:17,2019-01-03 15:34:12,6
238,2019-02-02 08:19:17,2019-03-02 14:34:12,84
170,2019-04-02 08:19:17,2019-04-02 14:34:12,84
238,2019-04-02 08:19:17,2019-04-02 14:34:12,8
238,2019-04-02 08:19:17,2019-04-02 14:34:12,100
238,2019-08-02 08:19:17,2019-08-02 14:34:12,100
238,2019-10-02 08:19:17,2019-10-02 14:34:12,100
170,2020-01-02 08:19:17,2020-01-02 14:34:12,84
170,2020-01-02 08:19:17,2020-01-02 14:34:12,84
There are many steps to do this task. I assume the date column is the one to work with. Your example from table includes duplicated entries. Is it right that at the same time the order is there twice?
So here are the steps:
At first add an calculated field date_past to your dataset:
DATE_DIFF(CURRENT_DATE(),date)
To the dataset add a filter SO_demo with:
include date_past<30
Then blend the data with it self. Use product_id as Join key. Only the 2nd dataset has the SO_demo filter. Add to the dimension of this dataset the calculated field sold_last_30_days with the formula "yes".
In the table/chart to display add a filter on the field include sold_last_30_days is Null.
While using Power BI for a few months now, we (the user group) encountered an issue that is not really clear to us.
We use Power-BI with a remote SQL-Server data source, we access the data source through direct query.
Let's pretend we have 2 Tables as below-
Table name: Issue
Column:
ResolutionTime(Date/Time)
IssueID(Unique Numbers)
Table Name: WorkItem
Column:
start (Date/Time)
end (Date/Time)
IssueID (Unique Numbers, Foreign Key to "Issue" table)
Table WorkItem also contain a calculated column "WorkTime" which uses this DAX-expression as below-
WorkTime = WorkItem[end] - WorkItem[start]
The two tables are configured through Power-Bi having a two-way 1:n relationship that can be queried to collect all "WorkItem"(s) assigned to an "Issue" entry, using the "IssueID" as correlation column.
To be able to compute the aggregated "work-time" for each "WorkItem", we use a new/calculated table with the following DAX expression to aggregate the total amount of time invested for a single "Issue":
SumWork =
SUMMARIZE(
WorkItem, WorkItem[IssueID], "All work per item", SUM(WorkItem[WorkTime])
)
The above table computes the total invested work-time for a particular issue, grouping/summarizing results based on the "IssueID" foreign key. This new calculated table is also configured to have a relationship with the "Issue" table, this time a "1:1" relationship, using the IssueID as correlation column.
Now to compute the time that the issue was worked on + the time for Resolution should be summarized in a calculated column inside "Issue", but this does not work:
ResolutionAndWorkTime = Issue[ResolutionTime] + SumWork["All work per item"]
But the above DAX expression fails to compile, as it always reports that it returns "more than one result", thus not being a singular result. But that is suprising, as the two table ("Issue" and "SumWork" are related to each other with a "1:1" relationship).
Tables:
Issues
IssueID ResolutionTime ResolutionAndWorkTime
1 03:20:20 ???
2 01:20:20 ???
3 00:20:20 ???
WorkItem
IssueID start end WorkTime
1 1-2-2020 3:20:20 1-2-2020 3:25:20 00:05:00
1 2-2-2020 6:20:20 2-2-2020 7:20:20 01:00:00
3 1-3-2020 3:20:20 1-3-2020 3:29:20 00:09:00
Any ideas what to look for? Data-types? Table-definition? Table-relationships? We checked other Stackoverflow questions/answers, but no good ideas retrieved so far.
NOTE that a lot of join/merge features of Power BI are not available if direct-query is used and thus joining the tables is not really an option (we think).
You need this following code for your new Calculated column.
Visit HERE To know more about RELATED.
ResolutionAndWorkTime = Issues[ResolutionTime] + RELATED(SumWork[All work per item])
Based on input provided by "mkRabbani" (see other answer) we investigated why "RELATED" does not function as expected. The problem originates in the access to the database. As suspected earlier the function delivers the expected results once the database access is switched to "import" instead of "direct-query".
As a workaround we now joins the data inside the SQL server by using traditional database views. Of course this only works for scenarios where the database is under control of the data analytics team.
I'm using ssas tabular (powerpivot) and need to design a data-model and write some DAX.
I have 4 tables in my relational database-model:
Orders(order_id, order_name, order_type)
Spots (spot_id,order_id, spot_name, spot_time, spot_price)
SpotDiscount (spot_id, discount_id, discount_value)
Discounts (discount_id, discount_name)
One order can include multiple spots but one spot (spot_id 1) can only belong to one order.
One spot can include different discounts and every discount have one discount_value.
Ex:
Order_1 has spot_1 (spot_price 10), spot_2 (spot_price 20)
Spot_1 has discount_name_1(discount_value 10) and discount_name_2 (discount_value 20)
Spot_2 has discount_name_1(discount_value 15) and discount_name_3 (discount_value 30)
I need to write two measures: price(sum) and discount_value(average)
How do I correctly design a star schema with fact table (or maybe two fact tables) so that I in my powerpivot cube can get:
If i choose discount_name_1 I should get
order_1 with spot_1 and spot_2 and price on order_1 level will have value 50 and discount_value = 12,5
If I choose discount_name_3 I should get
order_1 with only spot_2 and price on order level = 20 and discount_value = 30
Fact(OrderKey, SpotKey, DiscountKey, DateKey, TimeKey Spot_Price, Discount_Value,...)
DimOrder, DimSpot, DimDiscount, etc....
TotalPrice:=
SUMX(
SUMMARIZE(
Fact
,Fact[OrderKey]
,Fact[SpotKey]
,Fact[Spot_Price]
)
,Fact[Spot_Price]
)
AverageDiscount:=
AVERAGE(Fact[Discount_Value])
Fact table is denormalized and you end up with the simplest star schema you can have.
First measure deserves some explanation. [Spot_Price] is duplicated for any spot with multiple discounts, and we would get wrong results with a simple SUM(). SUMMARIZE() does a group by on all the columns passed to it, following relationships (if necessary, we're looking at a single table here so nothing to follow).
SUMX() iterates over this table and accumulates the value of the expression in its second argument. The SUMMARIZE() has removed our duplicate [Spot_Price]s so we accumulate the unique ones (per unique combination of [OrderKey] and [SpotKey]) in a sum.
You say
One order can include multiple spots but one spot (spot_id 1) can only
belong to one order.
That's is not supported in the table definitions you give just above that statement. In the table definitions, one order has only one spot but (unless you've added a unique index to Orders on spot_id) each Spot can have multiple orders. Each Spot can also have multiple discounts.
If you want to have the relationship described in your words, the table definitions should be:
Orders(order_id, order_name, order_type)
OrderSpot(order_id, spot_id) -- with a Unique index on spot_id)
Spots (spot_id, spot_name, spot_time, price)
or:
Orders(order_id, order_name, order_type)
Spots (spot_id, spot_name, spot_time, order_id, price)
You can create the ssas cube with Order as the fact table, with one dimention in the Spot Table. If you then add the SpotDiscount and Discount tables with their relations (SpotDiscount to Spot, Discount to SpotDiscount) you have a 1 dimentional.
EDIT as per comments
Well, the Fact table would have order_id, order_name, order_type
The Dimension would be made up of the other 3 tables and have the columns you're interested in: probably spot_name, spot_time, spot_price, discount_name, discount_value.
I was wondering if there's any contradiction or futur problems against a table in a database which contains about 80 columns. There will be only VARCHARs, few INT and maybe 1 or 2 MESSAGE. I did some research on the net but there's nothing really talking about that kind of problem...In other terms, is this okay or even 'normal' to put that much of values inside a table??
Thanks in advance!
You shouldn't have any real problems if the fields are mostly integers. Most DBMSes have a limit on row length, so a bunch of long columns can cause issues...but unless the varchar columns are very long, you're probably OK.
I've honestly never even needed to think about that, though -- with a properly normalized database, it's quite rare to ever need that many columns in a table.
More columns you have, more memory server needs to process the records.
I recomend to use the "multiple to one" relation scheme in this case.
Example of tables:
customer
id
name
email
...
ins_app_form (Insurance application form)
id
customer_id (relation with customer)
date
... (here comes some other data if you need)
ins_app_item (Insurance application form items/fields)
id
ins_app_form_id (relation with Insurance application form)
question (the name of a question in application form)
answer (customer's answer)
So to show the application form with this scheme you will need to run a query:
SELECT
iaf.id AS application_id,
iaf.date AS `date`,
iai.question,
iai.answer
FROM ins_app_form AS iaf
LEFT JOIN ins_app_item AS iai ON iai.ins_app_form_id=iaf.id
WHERE iaf.customer_id=<ID of a customer>
This query will bring you something like this:
id date question answer
1 2014-03-31 "Year" "2008"
1 2014-03-31 "Car make" "Audi"
1 2014-03-31 "Car model" "Q7"
...
I have to add a coupon table to my db. There are 3 types of coupons : percentage, amount or 2 for 1.
So far I've come up with a coupon table that contains these 3 fields. If there's a percentage value not set to null then it's this kind of coupon.
I feel it's not the proper way to do it. Should I create a CouponType table and how would you see it? Where would you store these values?
Any help or cue appreciated!
Thanks,
Teebot
You're correct, I think a CouponType table would be fit for your problem.
Two tables: Coupons and CouponTypes. Store the CouponTypeId inside the Coupons table.
So for an example, you'll have a Coupon record called "50% off", if would reference the percent off CouponType record and from there you could determine the logic to take 50% off the cost of the item.
So now you can create unlimited coupons, if it's a dollar amount coupon type it will take the "amount" column and treat it as a dollar amount. If it's a percent off it will treat it as a percentage and if it's an "x for 1" deal, it will treat the value as x.
- Table Coupons
- ID
- name
- coupon_type_id # (or whatever fits your style guidelines)
- amount # Example: 10.00 (treated as $10 off for amount type, treated as
# 10% for percent type or 10 for 1 with the final type)
- expiration_date
- Table CouponTypes
- ID
- type # (amount, percent, <whatever you decided to call the 2 for 1> :))
In the future you might have much more different coupon types. You could also have different business logic associated with them - you never know. It's always useful to do the things right in this case, so yes, definitely, create a coupon type field and an associated dictionary table to go with it.
I would definitely create a CouponType lookup table. That way you avoid all the NULL's and allow for more coupon types in the future.
Coupon
coupon_id INT
name VARCHAR
coupon_type_id INT <- Foreign Key
CouponType
coupon_type_id INT
type_description VARCHAR
...
Or I suppose you could have a coupon type column in your coupon table CHAR(1)