The context of the problem at hand is the utilization of a Tabular model which contains two tables, namely 'Base' and 'Kpis'. The 'Base' table contains all the data, while the 'Kpis' table comprises all the measures in relation to the 'Base' table. The issue being faced is the aggregation of specific measures within the 'Kpis' table, namely 'k1', 'k2', and 'k3', on the columns present in the 'Base' table, namely 'c1', 'c2', and 'c3', using the DAX function 'SUMMARIZECOLUMNS'. The primary concern is that some measures are being duplicated across rows, resulting in a deviation from the desired outcome of summing the rows to each total. The objective is to aggregate the measures based on the columns in the 'Base' table. I could I do this using a query with dax-studio?
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I'm trying to create a calculated column based on a derived measure in SSAS cube, this measure which will count the number of cases per order so for one order if it has 3 cases it will have the value 3.
Now I'm trying to create a bucket attribute which says 1caseOrder,2caseOrder,3caseOrder,3+caseOrder. I tried the below one
IF([nrofcase] = 1, "nrofcase[1]", IF([nrofcase] = 2, "nrofcase[2]",
IF([nrofcase] = 3, "nrofcase[3]", "nrofcase[>3]") )
But it doesn't work as expected, when the level of the report is changed from qtr to week it was suppose to recalculate on different level.
Please let me know if it case work.
Calculated columns are static. When the column is added and when the table is processed, the value is calculated and stored. The only way for the value to change is to reprocess the model. If the formula refers to a DAX measure, it will use the measure without any of the context from the report (eg. no row filters or slicers, etc.).
Think of it this way:
Calculated column is a fact about a row that doesn't change. It is known just by looking at a single row. An example of this is Cost = [Quantity] * [Unit Price]. Cost never changes and is known by looking at the Quantity and Unit Price columns. It doesn't matter what filters or context are in the report. Cost doesn't change.
A measure is a fact about a table. You have to look at multiple rows to calculate its value. An example is Total Cost = SUM(Sales[Cost]). You want this value to change depending on the context of time, region, product, etc., so it's value is not stored but calculated dynamically in the report.
It sounds like for your data, there are multiple rows that tell you the number of cases per order, so this is a measure. Use a measure instead of a calculated column.
I have a boolean table with ~100000 rows and 3000 columns. I know that many of these columns are redundant because they are the result of OR between some other columns in the table (even maybe up to 200 other columns). The structure is also nested - some columns may be constructed from ORing other columns and then themselves be involved in creating a new column.
My goal is to reduce this table into "base columns" and a list of OR rules which allow me to reconstruct the original data.
Is there any way to do this in a reasonable amount of run-time?
My users table doesn't have many rows... yet. 😏
Might the query plan of the same query change as the table grows?
I.e., to see how my application will scale, should I seed my users table with BILLIONS 🤑 of rows before using EXPLAIN?
Estimated row counts are probably the most important factor that influence which query plan is chosen.
Two examples that support this:
If you use a WHERE condition on an indexed column of a table, three things can happen:
If the table is very small or a high percentage of the rows match the condition, a sequential scan will be used to read the whole table and filter out the rows that match the condition.
If the table is large and a low percentage of the rows match the condition, an index scan will be used.
If the table is large and a medium percentage of rows match the condition, a bitmap index scan will be used.
If you join two tables, the estimated row counts on the tables will determine if a nested loop join is chosen or not.
I found some bottleneck of my query which select data from only single table then require time and i used non unique key index on two column and with column used in where clause.
select name ,isComplete from Student where year='2015' and isComplete='F'
Now i found some concept from internet like skewed column so what is it?
have an idea then plz help me?
and how to resolve problem of skewed column?
and how skewed column affect performance of the Query?
Skewed columns are columns in which the data is not evenly distributed among the rows.
For example, suppose:
You have a table order_lines with 100,000,000 rows
The table has a column named customer_id
You have 1,000,000 distinct customers
Some (very large) customers can have hundreds of thousands or millions of order lines.
In the above example, the data in order_lines.customer_id is skewed. On average, you'd expect each distinct customer_id to have 100 order lines (100 million rows divided by 1 million distinct customers). But some large customers have many, many more than 100 order lines.
This hurts performance because Oracle bases its execution plan on statistics. So, statistically speaking, Oracle thinks it can access order_lines based on a non-unique index on customer_id and get only 100 records back, which it might then join to another table or whatever using a NESTED LOOP operation.
But, then when it actually gets 1,000,000 order lines for a particular customer, the index access and nested loop join are hideously slow. It would have been far better for Oracle to do a full table scan and hash join to the other table.
So, when there is skewed data, the optimal access plan depends on which particular customer you are selecting!
Oracle lets you avoid this problem by optionally gathering "histograms" on columns, so Oracle knows which values have lots of rows and which have only a few. That gives the Oracle optimizer the information it needs to generate the best plan in most cases.
Full table scan and Index Scan both are depend on the Skewed column.
and Skewed column is nothing but your spread like gender column contain 60 male and 40 female.
I have a table with three different columns: FaultLevel1, FaultLevel2 and Quantity. This columns are extracted after filtering parameters from other columns, but graph uses these three columns.
So, the graph looks like a waterfall graph (Range Column graph), and in
Category Groups: FaultLevel1;
Series Groups: FaultLevel2;
Values:Sum(Quantity).
Basically, the issue is in sorting, because first I need to sort by FL1 (quantity), and then by FL2 (quantity). But when it generates, it looks like a waterfall, but the FL2 values with high to low come after each other, whereas, it should come in such an order that all quantities of FL2 on each FL1.
So, the question is how can I put sorting for FL2, that is sorts on Sum(Quantity) of FL1, not quantity of FL2.
In SSRS, the chart has same structure as matrix instead of table.
Category: Row Group
Series: Column Group
Values: Value
If your data are just the three columns in a table, they are under same level. You should not make data in tabilx without column group rendered in a chart with series. Otherwise, it will look like a waterfall as your scenario.