I'm trying to replicate the Qlikview Aggr function in microstrategy. I have the Qlikview code below, and what I think is the equivalent sql translation. The fields would be attributes in my data set. The idea is if you select a value from a filter in the dashboard it filters "where" clause in the metric. Is there a way to build parameters in to metrics in microstrategy? Or does anyone have a suggestion how to build an equivalent metric? I'm really scratching my head on the subquery part, I don't know if I could build that as a fact, and then build a metric from it?
Qlikview:
Max({<Field1=>} Aggr(Sum({<Field2={'Value1'},Field3={'Value2'}, Field1=>} ThingCount), Field1))
SQL Equivalent:
select
Max(Value)
from(
select Sum(case when Field2 in ('Value1')
and Field3 in ('Value2')
and Field1 is not NULL
then ThingCount end) as Value,
Field1
from Table
where Field1 = "FilterValue"
group by Field1
)a
To obtain the SQL that you want you should build two metrics in MSTR:
one that calculates the SUM, with a case (actually I would put the case in the fact definition).
a second metric (a level metric) will calculate the MAX ignoring the Field1 attribute.
The where condition is just a selector in a MSTR document/dashboard.
Related
I'm using PostgreSQL 10 and trying to run this query. I started with a CTE which I am referencing as 'query.'
SELECT
ROW_NUMBER()OVER() AS my_new_id,
query.geom AS geom,
query.pop AS pop,
query.name,
query.distance AS dist,
query.amenity_size,
((amenity_size)/(distance)^2) AS attract_score,
SUM((amenity_size)/(distance)^2) AS tot_attract_score,
((amenity_size)/(distance)^2) / SUM((amenity_size)/(distance)^2) as marketshare
INTO table_mktshare
FROM query
WHERE
distance > 0
GROUP BY
query.name,
query.amenity_size,
query.geom,
query.pop,
query.distance
The query runs but the problem lies in the 'markeshare' column. It returns the same answer with or without the SUM operator and returns one, which appears to make both the attract_score and the tot_attract_score the same. Why is the SUM operator read the same as the expression above it?
This is occurring specifically because each combination of columns in the group by clause uniquely identifies one row in the table. I don't know if this is intentional, but more normally, one would expect something like this:
SELECT ROW_NUMBER() OVER() AS my_new_id,
query.geom AS geom, query.pop AS pop, query.name,
SUM((amenity_size)/(distance)^2) AS tot_attract_score,
INTO table_mktshare
FROM query
WHERE distance > 0
GROUP BY query.name, query.geom, query.pop;
This is not your intention, but it does give a flavor of what's expected.
I have to aggregate in my query SUM of AMUNT field according to WERKS, DATUM and UZEIT
I try to make a group by without any success
I have an error like that:
What is the problem in my code?
That is my ABAP code:
DATA: gt_compr TYPE TABLE OF yrt_h_sales
SELECT werks, extnb, datum, uzeit, sumvt, deprt, dpext, SUM( amunt ) AS amunt
INTO CORRESPONDING FIELDS OF TABLE #gt_compr
FROM yrt_h_sales
WHERE werks IN #so_werks
AND datum IN #so_datum
GROUP BY werks, datum, uzeit.
After I corrected it and I did this, the code looks as follows:
SELECT werks, datum, uzeit, extnb, deprt, dpext, SUM( amunt ) AS amunt
INTO CORRESPONDING FIELDS OF TABLE #gt_compr
FROM yrt_h_sales
WHERE werks IN #so_werks
AND datum IN #so_datum
GROUP BY werks, datum, uzeit, extnb, deprt, dpext.
So I don't have the compilation error anymore but the aggregation is still not working! I have a 43 line result without sum on the AMUNT column
P.S. this is the structure of my table:
Your observation is consistent with the documentation (and what I have so far seen in any other RDBMS I've worked with):
If aggregate expressions are used, any column identifiers that are not
included as arguments of an aggregate function must be included after
the addition GROUP BY.
Take for example the time field UZEIT: You can tell the system to aggregate (in your case, sum up) all amounts for the same point in time by adding it to the GROUP BY clause, or you can apply an aggregate function as well (SUM would not make any sense here, but MIN might), or you could omit the field altogether. You can not leave it dangling around without further specification - the field either needs to be part of the new key set created by GROUP BY or has to have an aggregate function applied to it so that the system knows what to do with multiple datasets that might occur in the group.
(This is basic SQL btw and not ABAP-specific knowledge.)
remove the CORRESPONDING FIELD OF and just place results INTO TABLE
I am trying to get the max value of column A ("original_list_price") over windows defined by 2 columns (namely - a unique identifier, called "address_token", and a date field, called "list_date"). I.e. I would like to know the max "original_list_price" of rows with both the same address_token AND list_date.
E.g.:
SELECT
address_token, list_date, original_list_price,
max(original_list_price) OVER (PARTITION BY address_token, list_date) as max_list_price
FROM table1
The query already takes >10 minutes when I use just 1 expression in the PARTITION (e.g. using address_token only, nothing after that). Sometimes the query times out. (I use Mode Analytics and get this error: An I/O error occurred while sending to the backend) So my questions are:
1) Will the Window function with multiple PARTITION BY expressions work?
2) Any other way to achieve my desired result?
3) Any way to make Windows functions, especially the Partition part run faster? e.g. use certain data types over others, try to avoid long alphanumeric string identifiers?
Thank you!
The complexity of the window functions partitioning clause should not have a big impact on performance. Do realize that your query is returning all the rows in the table, so there might be a very large result set.
Window functions should be able to take advantage of indexes. For this query:
SELECT address_token, list_date, original_list_price,
max(original_list_price) OVER (PARTITION BY address_token, list_date) as max_list_price
FROM table1;
You want an index on table1(address_token, list_date, original_list_price).
You could try writing the query as:
select t1.*,
(select max(t2.original_list_price)
from table1 t2
where t2.address_token = t1.address_token and t2.list_date = t1.list_date
) as max_list_price
from table1 t1;
This should return results more quickly, because it doesn't have to calculate the window function value first (for all rows) before returning values.
How to write this expression in PowerBI
select distinct([date]),Temperature from Device47A8F where Temperature>25
Totally new to PowerBI. Is there any tool that can change the query from sql to PowerBI expression?
I have tried so many type of different type of expressions but getting error, Most of the time I am getting this:
The expression refers to multiple columns. Multiple columns cannot be converted to a scalar value.
Need help, Thanks.
After I posted my answer, wondered if your expected result is get only one date by temperature, In other words, without repeated dates in your result set.
A side note: select distinct([date]),Temperature from Device47A8F where Temperature>25 returns repeated dates since DISTINCT keyword evaluate distinct columns values specified in the SELECT statement, it doesn't return distinct values in a specific column even if you surround it with parenthesis.
Now what brings us here. What I can see in your error is that you are trying to use a table-valued (produces a table with multiple columns) expression in a measure which only accepts scalar-valued (calculate only one value).
Supposing you have a table like this:
Running your SQL query you will get the highlighted in yellow rows:
You can see 01/09/2016 date is repeated. If you want to create a measure you have to define what calculation you want to show for temperature. i.e, average, max or min etc.
In the below expression is being calculated the maximum temperature greater than 25 per date:
MaxTempGreaterThan25 =
CALCULATE ( MAX ( Device47A8F[Temperature] ), Device47A8F[Temperature] > 25 )
In this case the measure MaxTempGreaterThan25 is calculated per date.
If you don't want to produce a measure but a table. In the Power BI Tool bar select Modeling tab and click New Table icon.
Use this expression:
MyTemperatureTable =
FILTER ( Device47A8F, Device47A8F[Temperature] > 25 )
It should produce a new table named MyTemperatureTable like this:
I recommend you learn some basics about DAX, it is pretty different from SQL / T-SQL and there are things you can't do depending on your model and data.
Let me know if this helps.
You probably don't need to write any code if your objective is to show the result in a Power BI visual e.g. a table. Power BI naturally aggregates data if the datatype is numeric (e.g. Temperature).
I would just add a Table visual on a Report page and add the Date and Temperature columns to it. Then in Visualizations / Fields / Values I would click the little down-arrow on the Temperature field and set the Aggregation e.g. Maximum. Then in Visualizations / Fields / Filters I would click the little down-arrow on the Temperature field and set the Filter e.g. is greater than: 25
Hard-coded solutions are unlikely to survive the next question from your users e.g. "but what if I want to see Temperature > 24? Or 20? Or 30?"
I have following problem - I'm computing weighted average in SQL, as following: SUM(Value * Weight) / SUM(Weight). However, there can be issue that rows are empty => SUM(Weight) == 0), and in this case the query fails. Is it somehow possible to return '0' as result in this case?
I have tried CASE SUM(Weight) WHEN 0 THEN 0 ELSE SUM(Value * Weight) / SUM(Weight) END, but I'm afraid that it evaluates SUM(Weight) twice, and that can be fairly expensive in my case.
Use NULLIF and ISNULL:
ISNULL(SUM(Value * Weight) / NULLIF(SUM(Weight),0),0)
The SQL engine doesn't compute sum(Weight) twice, just once. The conceptual process is:
compute the full cartesian join of all tables in the from clause
apply the join criteria to filter the results
apply the where clause criteria to filter the results
partition this result set into groups as defined by the group by clause
collapse each such group into one row, computing any aggregate functions that have been specified, keeping only those columns listed in the result set (aggregrate functions and grouping columns),
apply the criteria in the having clause to filter the grouped results,
drop all columns but those specified in the queries result columns, creating those that are computed expressions.
apply the ordering specified in the order by statement.
No actual SQL engine does this, but it must behave as if that is what happened. Your aggregate function is computed just once, along with any other aggregate functions, in a single pass.